Move person_detection_experimental to person_detection and scrap uint8 version.

Addresses https://github.com/tensorflow/tensorflow/issues/44912

PiperOrigin-RevId: 345335944
Change-Id: If46bdf590faf66d4158596030b2f41a50c9eb388

tensorflow/lite/micro/benchmarks/BUILD

@@ -62,21 +62,3 @@ cc_binary(
         "//tensorflow/lite/schema:schema_fbs",
     ],
 )
-
-cc_binary(
-    name = "person_detection_experimental_benchmark",
-    srcs = ["person_detection_experimental_benchmark.cc"],
-    deps = [
-        ":micro_benchmark",
-        "//tensorflow/lite:version",
-        "//tensorflow/lite/c:common",
-        "//tensorflow/lite/micro:micro_error_reporter",
-        "//tensorflow/lite/micro:micro_framework",
-        "//tensorflow/lite/micro:micro_utils",
-        "//tensorflow/lite/micro:op_resolvers",
-        "//tensorflow/lite/micro/examples/person_detection_experimental:model_settings",
-        "//tensorflow/lite/micro/examples/person_detection_experimental:person_detect_model_data",
-        "//tensorflow/lite/micro/examples/person_detection_experimental:simple_images_test_data",
-        "//tensorflow/lite/schema:schema_fbs",
-    ],
-)

tensorflow/lite/micro/benchmarks/Makefile.inc

@@ -7,21 +7,12 @@ tensorflow/lite/micro/benchmarks/keyword_scrambled_model_data.h
 
 PERSON_DETECTION_BENCHMARK_SRCS := \
 tensorflow/lite/micro/benchmarks/person_detection_benchmark.cc \
-$(MAKEFILE_DIR)/downloads/person_model_grayscale/no_person_image_data.cc \
-$(MAKEFILE_DIR)/downloads/person_model_grayscale/person_detect_model_data.cc \
-$(MAKEFILE_DIR)/downloads/person_model_grayscale/person_image_data.cc
-
-PERSON_DETECTION_BENCHMARK_HDRS := \
-tensorflow/lite/micro/examples/person_detection/person_detect_model_data.h
-
-PERSON_DETECTION_EXPERIMENTAL_BENCHMARK_SRCS := \
-tensorflow/lite/micro/benchmarks/person_detection_experimental_benchmark.cc \
 $(MAKEFILE_DIR)/downloads/person_model_int8/no_person_image_data.cc \
 $(MAKEFILE_DIR)/downloads/person_model_int8/person_detect_model_data.cc \
 $(MAKEFILE_DIR)/downloads/person_model_int8/person_image_data.cc
 
-PERSON_DETECTION_EXPERIMENTAL_BENCHMARK_HDRS := \
+PERSON_DETECTION_BENCHMARK_HDRS := \
-tensorflow/lite/micro/examples/person_detection_experimental/person_detect_model_data.h
+tensorflow/lite/micro/examples/person_detection/person_detect_model_data.h
 
 # Builds a standalone binary.
 $(eval $(call microlite_test,keyword_benchmark,\
@@ -30,6 +21,3 @@ $(KEYWORD_BENCHMARK_SRCS),$(KEYWORD_BENCHMARK_HDRS)))
 $(eval $(call microlite_test,person_detection_benchmark,\
 $(PERSON_DETECTION_BENCHMARK_SRCS),$(PERSON_DETECTION_BENCHMARK_HDRS)))
 
-$(eval $(call microlite_test,person_detection_experimental_benchmark,\
-$(PERSON_DETECTION_EXPERIMENTAL_BENCHMARK_SRCS),$(PERSON_DETECTION_EXPERIMENTAL_BENCHMARK_HDRS)))
-

tensorflow/lite/micro/benchmarks/person_detection_benchmark.cc

@@ -35,13 +35,11 @@ limitations under the License.
 namespace {
 
 using PersonDetectionOpResolver = tflite::AllOpsResolver;
-using PersonDetectionBenchmarkRunner = MicroBenchmarkRunner<uint8_t>;
+using PersonDetectionBenchmarkRunner = MicroBenchmarkRunner<int8_t>;
-
-constexpr int kRandomSeed = 42;
 
 // Create an area of memory to use for input, output, and intermediate arrays.
 // Align arena to 16 bytes to avoid alignment warnings on certain platforms.
-constexpr int kTensorArenaSize = 95 * 1024;
+constexpr int kTensorArenaSize = 135 * 1024;
 alignas(16) uint8_t tensor_arena[kTensorArenaSize];
 
 uint8_t op_resolver_buffer[sizeof(PersonDetectionOpResolver)];
@@ -52,9 +50,9 @@ PersonDetectionBenchmarkRunner* benchmark_runner = nullptr;
 // issues on Sparkfun. Use new since static variables within a method
 // are automatically surrounded by locking, which breaks bluepill and stm32f4.
 void CreateBenchmarkRunner() {
-  // We allocate PersonDetectionOpResolver from a global buffer because the
+  // We allocate PersonDetectionOpResolver from a global buffer
-  // object's lifetime must exceed that of the PersonDetectionBenchmarkRunner
+  // because the object's lifetime must exceed that of the
-  // object.
+  // PersonDetectionBenchmarkRunner object.
   benchmark_runner = new (benchmark_runner_buffer)
       PersonDetectionBenchmarkRunner(g_person_detect_model_data,
                                      new (op_resolver_buffer)
@@ -62,24 +60,20 @@ void CreateBenchmarkRunner() {
                                      tensor_arena, kTensorArenaSize);
 }
 
-void PersonDetectionTenIterationsWithRandomInput() {
+void InitializeBenchmarkRunner() {
-  benchmark_runner->SetRandomInput(kRandomSeed);
+  CreateBenchmarkRunner();
-  for (int i = 0; i < 10; i++) {
+  benchmark_runner->SetInput(reinterpret_cast<const int8_t*>(g_person_data));
-    benchmark_runner->RunSingleIteration();
-  }
 }
 
 void PersonDetectionTenIerationsWithPerson() {
-  // TODO(b/152644476): Add a way to run more than a single deterministic input.
+  benchmark_runner->SetInput(reinterpret_cast<const int8_t*>(g_person_data));
-  benchmark_runner->SetInput(g_person_data);
   for (int i = 0; i < 10; i++) {
     benchmark_runner->RunSingleIteration();
   }
 }
 
 void PersonDetectionTenIerationsWithoutPerson() {
-  // TODO(b/152644476): Add a way to run more than a single deterministic input.
+  benchmark_runner->SetInput(reinterpret_cast<const int8_t*>(g_no_person_data));
-  benchmark_runner->SetInput(g_no_person_data);
   for (int i = 0; i < 10; i++) {
     benchmark_runner->RunSingleIteration();
   }
@@ -89,8 +83,8 @@ void PersonDetectionTenIerationsWithoutPerson() {
 
 TF_LITE_MICRO_BENCHMARKS_BEGIN
 
-TF_LITE_MICRO_BENCHMARK(CreateBenchmarkRunner());
+TF_LITE_MICRO_BENCHMARK(InitializeBenchmarkRunner());
-TF_LITE_MICRO_BENCHMARK(PersonDetectionTenIterationsWithRandomInput());
+TF_LITE_MICRO_BENCHMARK(benchmark_runner->RunSingleIteration());
 TF_LITE_MICRO_BENCHMARK(PersonDetectionTenIerationsWithPerson());
 TF_LITE_MICRO_BENCHMARK(PersonDetectionTenIerationsWithoutPerson());
 

tensorflow/lite/micro/benchmarks/person_detection_experimental_benchmark.cc

@@ -1,92 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/micro/all_ops_resolver.h"
-#include "tensorflow/lite/micro/benchmarks/micro_benchmark.h"
-#include "tensorflow/lite/micro/examples/person_detection_experimental/model_settings.h"
-#include "tensorflow/lite/micro/examples/person_detection_experimental/no_person_image_data.h"
-#include "tensorflow/lite/micro/examples/person_detection_experimental/person_detect_model_data.h"
-#include "tensorflow/lite/micro/examples/person_detection_experimental/person_image_data.h"
-#include "tensorflow/lite/micro/micro_error_reporter.h"
-#include "tensorflow/lite/micro/micro_interpreter.h"
-#include "tensorflow/lite/micro/micro_utils.h"
-#include "tensorflow/lite/schema/schema_generated.h"
-#include "tensorflow/lite/version.h"
-
-/*
- * Person Detection benchmark.  Evaluates runtime performance of the visual
- * wakewords person detection model.  This is the same model found in
- * exmaples/person_detection.
- */
-
-namespace {
-
-using PersonDetectionExperimentalOpResolver = tflite::AllOpsResolver;
-using PersonDetectionExperimentalBenchmarkRunner = MicroBenchmarkRunner<int8_t>;
-
-// Create an area of memory to use for input, output, and intermediate arrays.
-// Align arena to 16 bytes to avoid alignment warnings on certain platforms.
-constexpr int kTensorArenaSize = 135 * 1024;
-alignas(16) uint8_t tensor_arena[kTensorArenaSize];
-
-uint8_t op_resolver_buffer[sizeof(PersonDetectionExperimentalOpResolver)];
-uint8_t
-    benchmark_runner_buffer[sizeof(PersonDetectionExperimentalBenchmarkRunner)];
-PersonDetectionExperimentalBenchmarkRunner* benchmark_runner = nullptr;
-
-// Initialize benchmark runner instance explicitly to avoid global init order
-// issues on Sparkfun. Use new since static variables within a method
-// are automatically surrounded by locking, which breaks bluepill and stm32f4.
-void CreateBenchmarkRunner() {
-  // We allocate PersonDetectionExperimentalOpResolver from a global buffer
-  // because the object's lifetime must exceed that of the
-  // PersonDetectionBenchmarkRunner object.
-  benchmark_runner =
-      new (benchmark_runner_buffer) PersonDetectionExperimentalBenchmarkRunner(
-          g_person_detect_model_data,
-          new (op_resolver_buffer) PersonDetectionExperimentalOpResolver(),
-          tensor_arena, kTensorArenaSize);
-}
-
-void InitializeBenchmarkRunner() {
-  CreateBenchmarkRunner();
-  benchmark_runner->SetInput(reinterpret_cast<const int8_t*>(g_person_data));
-}
-
-void PersonDetectionTenIerationsWithPerson() {
-  benchmark_runner->SetInput(reinterpret_cast<const int8_t*>(g_person_data));
-  for (int i = 0; i < 10; i++) {
-    benchmark_runner->RunSingleIteration();
-  }
-}
-
-void PersonDetectionTenIerationsWithoutPerson() {
-  benchmark_runner->SetInput(reinterpret_cast<const int8_t*>(g_no_person_data));
-  for (int i = 0; i < 10; i++) {
-    benchmark_runner->RunSingleIteration();
-  }
-}
-
-}  // namespace
-
-TF_LITE_MICRO_BENCHMARKS_BEGIN
-
-TF_LITE_MICRO_BENCHMARK(InitializeBenchmarkRunner());
-TF_LITE_MICRO_BENCHMARK(benchmark_runner->RunSingleIteration());
-TF_LITE_MICRO_BENCHMARK(PersonDetectionTenIerationsWithPerson());
-TF_LITE_MICRO_BENCHMARK(PersonDetectionTenIerationsWithoutPerson());
-
-TF_LITE_MICRO_BENCHMARKS_END

tensorflow/lite/micro/examples/person_detection/BUILD

@@ -135,9 +135,3 @@ cc_binary(
         "//tensorflow/lite/schema:schema_fbs",
     ],
 )
-
-sh_test(
-    name = "person_detection_binary_test",
-    srcs = ["person_detection_binary_test.sh"],
-    data = [":person_detection"],
-)

tensorflow/lite/micro/examples/person_detection/Makefile.inc

@@ -1,6 +1,8 @@
+$(eval $(call add_third_party_download,$(PERSON_MODEL_INT8_URL),$(PERSON_MODEL_INT8_MD5),person_model_int8,))
+
 person_detection_MODEL_SRCS := \
 tensorflow/lite/micro/examples/person_detection/model_settings.cc \
-$(MAKEFILE_DIR)/downloads/person_model_grayscale/person_detect_model_data.cc
+$(MAKEFILE_DIR)/downloads/person_model_int8/person_detect_model_data.cc
 
 person_detection_MODEL_HDRS := \
 tensorflow/lite/micro/examples/person_detection/model_settings.h \
@@ -8,8 +10,8 @@ tensorflow/lite/micro/examples/person_detection/person_detect_model_data.h
 
 person_detection_TEST_SRCS := \
 tensorflow/lite/micro/examples/person_detection/person_detection_test.cc \
-$(MAKEFILE_DIR)/downloads/person_model_grayscale/no_person_image_data.cc \
+$(MAKEFILE_DIR)/downloads/person_model_int8/no_person_image_data.cc \
-$(MAKEFILE_DIR)/downloads/person_model_grayscale/person_image_data.cc \
+$(MAKEFILE_DIR)/downloads/person_model_int8/person_image_data.cc \
 $(person_detection_MODEL_SRCS)
 
 person_detection_TEST_HDRS := \
@@ -50,7 +52,7 @@ $(person_detection_MODEL_HDRS)
 include $(wildcard tensorflow/lite/micro/examples/person_detection/*/Makefile.inc)
 
 # Tests loading and running a vision model.
-$(eval $(call microlite_test,person_detection_test,\
+$(eval $(call microlite_test,person_detection_test_int8,\
 $(person_detection_TEST_SRCS),$(person_detection_TEST_HDRS)))
 
 # Three conflicting issues here:
@@ -68,14 +70,14 @@ $(person_detection_TEST_SRCS),$(person_detection_TEST_HDRS)))
 # basically equivalent).
 ifneq ($(TARGET),sparkfun_edge)
 # Tests the image provider module.
-$(eval $(call microlite_test,image_provider_test,\
+$(eval $(call microlite_test,image_provider_test_int8,\
 $(IMAGE_PROVIDER_TEST_SRCS),$(IMAGE_PROVIDER_TEST_HDRS)))
 endif
 
 # Tests the detection responder module.
-$(eval $(call microlite_test,detection_responder_test,\
+$(eval $(call microlite_test,detection_responder_test_int8,\
 $(DETECTION_RESPONDER_TEST_SRCS),$(DETECTION_RESPONDER_TEST_HDRS)))
 
 # Builds a standalone object recognition binary.
-$(eval $(call microlite_test,person_detection,\
+$(eval $(call microlite_test,person_detection_int8,\
 $(person_detection_SRCS),$(person_detection_HDRS)))

tensorflow/lite/micro/examples/person_detection/README.md

@@ -1,10 +1,9 @@
-<!-- mdformat off(b/169948621#comment2) -->
-
 # Person detection example
 
 This example shows how you can use Tensorflow Lite to run a 250 kilobyte neural
 network to recognize people in images captured by a camera.  It is designed to
 run on systems with small amounts of memory such as microcontrollers and DSPs.
+This uses the experimental int8 quantized version of the person detection model.
 
 ## Table of contents
 
@@ -12,6 +11,7 @@ run on systems with small amounts of memory such as microcontrollers and DSPs.
 -   [Running on ARC EM SDP](#running-on-arc-em-sdp)
 -   [Running on Arduino](#running-on-arduino)
 -   [Running on ESP32](#running-on-esp32)
+-   [Running on HIMAX WE1 EVB](#running-on-himax-we1-evb)
 -   [Running on SparkFun Edge](#running-on-sparkfun-edge)
 -   [Run the tests on a development machine](#run-the-tests-on-a-development-machine)
 -   [Debugging image capture](#debugging-image-capture)
@@ -25,10 +25,8 @@ board. General information and instructions on using the board with TensorFlow
 Lite Micro can be found in the common
 [ARC targets description](/tensorflow/lite/micro/tools/make/targets/arc/README.md).
 
-This example is quantized with symmetric uint8 scheme. As noted in
+This example uses asymmetric int8 quantization and can therefore leverage
-[kernels/arc_mli/README.md](/tensorflow/lite/micro/kernels/arc_mli/README.md),
+optimized int8 kernels from the embARC MLI library
-embARC MLI supports optimized kernels for int8 quantization only. Therefore,
-this example will only use TFLM reference kernels.
 
 The ARC EM SDP board contains a rich set of extension interfaces. You can choose
 any compatible camera and modify
@@ -53,9 +51,14 @@ The example project for ARC EM SDP platform can be generated with the following
 command:
 
 ```
-make -f tensorflow/lite/micro/tools/make/Makefile TARGET=arc_emsdp TAGS=no_arc_mli generate_person_detection_make_project
+make -f tensorflow/lite/micro/tools/make/Makefile \
+TARGET=arc_emsdp TAGS=reduce_codesize \
+generate_person_detection_int8_make_project
 ```
 
+Note that `TAGS=reduce_codesize` applies example specific changes of code to
+reduce total size of application. It can be omitted.
+
 ### Build and Run Example
 
 For more detailed information on building and running examples see the
@@ -73,7 +76,7 @@ get it started.
 2.  Go to the generated example project director
 
     ```
-    cd tensorflow/lite/micro/tools/make/gen/arc_emsdp_arc/prj/person_detection/make
+    cd tensorflow/lite/micro/tools/make/gen/arc_emsdp_arc/prj/person_detection_int8/make
     ```
 
 3.  Build the example using
@@ -141,7 +144,7 @@ Connect the Arducam pins as follows:
 ### Install the Arduino_TensorFlowLite library
 
 Download the current nightly build of the library:
-[person_detection.zip](https://storage.googleapis.com/tensorflow-nightly/github/tensorflow/tensorflow/lite/micro/tools/make/gen/arduino_x86_64/prj/person_detection/tensorflow_lite.zip)
+[person_detection.zip](https://storage.googleapis.com/download.tensorflow.org/data/tf_lite_micro_person_data_int8_grayscale_2020_01_13.zip)
 
 This example application is included as part of the official TensorFlow Lite
 Arduino library. To install it, open the Arduino library manager in
@@ -344,6 +347,112 @@ The previous two commands can be combined:
 idf.py --port /dev/ttyUSB0 flash monitor
 ```
 
+## Running on HIMAX WE1 EVB
+
+The following instructions will help you build and deploy this example to
+[HIMAX WE1 EVB](https://github.com/HimaxWiseEyePlus/bsp_tflu/tree/master/HIMAX_WE1_EVB_board_brief)
+board. To understand more about using this board, please check
+[HIMAX WE1 EVB user guide](https://github.com/HimaxWiseEyePlus/bsp_tflu/tree/master/HIMAX_WE1_EVB_user_guide).
+
+### Initial Setup
+
+To use the HIMAX WE1 EVB, please make sure following software are installed:
+
+#### MetaWare Development Toolkit
+
+See
+[Install the Synopsys DesignWare ARC MetaWare Development Toolkit](/tensorflow/lite/micro/tools/make/targets/arc/README.md#install-the-synopsys-designware-arc-metaware-development-toolkit)
+section for instructions on toolchain installation.
+
+#### Make Tool version
+
+A `'make'` tool is required for deploying Tensorflow Lite Micro applications on
+HIMAX WE1 EVB, See
+[Check make tool version](/tensorflow/lite/micro/tools/make/targets/arc/README.md#make-tool)
+section for proper environment.
+
+#### Serial Terminal Emulation Application
+
+There are 2 main purposes for HIMAX WE1 EVB Debug UART port
+
+-   print application output
+-   burn application to flash by using xmodem send application binary
+
+You can use any terminal emulation program (like [PuTTY](https://www.putty.org/)
+or [minicom](https://linux.die.net/man/1/minicom)).
+
+### Generate Example Project
+
+The example project for HIMAX WE1 EVB platform can be generated with the
+following command:
+
+Download related third party data
+
+```
+make -f tensorflow/lite/micro/tools/make/Makefile TARGET=himax_we1_evb third_party_downloads
+```
+
+Generate person detection project
+
+```
+make -f tensorflow/lite/micro/tools/make/Makefile generate_person_detection_int8_make_project TARGET=himax_we1_evb
+```
+
+### Build and Burn Example
+
+Following the Steps to run person detection example at HIMAX WE1 EVB platform.
+
+1.  Go to the generated example project directory.
+
+    ```
+    cd tensorflow/lite/micro/tools/make/gen/himax_we1_evb_arc/prj/person_detection_int8/make
+    ```
+
+2.  Build the example using
+
+    ```
+    make app
+    ```
+
+3.  After example build finish, copy ELF file and map file to image generate
+    tool directory. \
+    image generate tool directory located at
+    `'tensorflow/lite/micro/tools/make/downloads/himax_we1_sdk/image_gen_linux_v3/'`
+
+    ```
+    cp person_detection_int8.elf himax_we1_evb.map ../../../../../downloads/himax_we1_sdk/image_gen_linux_v3/
+    ```
+
+4.  Go to flash image generate tool directory.
+
+    ```
+    cd ../../../../../downloads/himax_we1_sdk/image_gen_linux_v3/
+    ```
+
+    make sure this tool directory is in $PATH. You can permanently set it to
+    PATH by
+
+    ```
+    export PATH=$PATH:$(pwd)
+    ```
+
+5.  run image generate tool, generate flash image file.
+
+    *   Before running image generate tool, by typing `sudo chmod +x image_gen`
+        and `sudo chmod +x sign_tool` to make sure it is executable.
+
+    ```
+    image_gen -e person_detection_int8.elf -m himax_we1_evb.map -o out.img
+    ```
+
+6.  Download flash image file to HIMAX WE1 EVB by UART:
+
+    *   more detail about download image through UART can be found at
+        [HIMAX WE1 EVB update Flash image](https://github.com/HimaxWiseEyePlus/bsp_tflu/tree/master/HIMAX_WE1_EVB_user_guide#flash-image-update)
+
+After these steps, press reset button on the HIMAX WE1 EVB, you will see
+application output in the serial terminal.
+
 ## Running on SparkFun Edge
 
 The following instructions will help you build and deploy this sample on the
@@ -381,14 +490,14 @@ Enter the following command to set up some dummy cryptographic keys we can use
 for development:
 
 ```
-cp tensorflow/lite/micro/tools/make/downloads/AmbiqSuite-Rel2.2.0/tools/apollo3_scripts/keys_info0.py \
+cp tensorflow/lite/micro/tools/make/downloads/AmbiqSuite-Rel2.0.0/tools/apollo3_scripts/keys_info0.py \
-tensorflow/lite/micro/tools/make/downloads/AmbiqSuite-Rel2.2.0/tools/apollo3_scripts/keys_info.py
+tensorflow/lite/micro/tools/make/downloads/AmbiqSuite-Rel2.0.0/tools/apollo3_scripts/keys_info.py
 ```
 
 Next, run the following command to create a signed binary:
 
 ```
-python3 tensorflow/lite/micro/tools/make/downloads/AmbiqSuite-Rel2.2.0/tools/apollo3_scripts/create_cust_image_blob.py \
+python3 tensorflow/lite/micro/tools/make/downloads/AmbiqSuite-Rel2.0.0/tools/apollo3_scripts/create_cust_image_blob.py \
 --bin tensorflow/lite/micro/tools/make/gen/sparkfun_edge_cortex-m4/bin/person_detection.bin \
 --load-address 0xC000 \
 --magic-num 0xCB \
@@ -401,7 +510,7 @@ command to create a final version of the file that can be used to flash our
 device with the bootloader script we will use in the next step:
 
 ```
-python3 tensorflow/lite/micro/tools/make/downloads/AmbiqSuite-Rel2.2.0/tools/apollo3_scripts/create_cust_wireupdate_blob.py \
+python3 tensorflow/lite/micro/tools/make/downloads/AmbiqSuite-Rel2.0.0/tools/apollo3_scripts/create_cust_wireupdate_blob.py \
 --load-address 0x20000 \
 --bin main_nonsecure_ota.bin \
 -i 6 \
@@ -437,7 +546,7 @@ hit the button marked `RST`. Continue holding the button marked `14` while
 running the following command:
 
 ```
-python3 tensorflow/lite/micro/tools/make/downloads/AmbiqSuite-Rel2.2.0/tools/apollo3_scripts/uart_wired_update.py \
+python3 tensorflow/lite/micro/tools/make/downloads/AmbiqSuite-Rel2.0.0/tools/apollo3_scripts/uart_wired_update.py \
 -b ${BAUD_RATE} ${DEVICENAME} \
 -r 1 \
 -f main_nonsecure_wire.bin \

tensorflow/lite/micro/examples/person_detection/apollo3evb/image_provider.cc

@@ -147,12 +147,10 @@ TfLiteStatus InitCamera(tflite::ErrorReporter* error_reporter) {
 
   hm01b0_power_up(&s_HM01B0Cfg);
 
-  // TODO(njeff): check the delay time to just fit the spec.
   am_util_delay_ms(1);
 
   hm01b0_mclk_enable(&s_HM01B0Cfg);
 
-  // TODO(njeff): check the delay time to just fit the spec.
   am_util_delay_ms(1);
 
   hm01b0_init_if(&s_HM01B0Cfg);

tensorflow/lite/micro/examples/person_detection/arc_emsdp/Makefile.inc

@@ -1,24 +1,43 @@
 ifeq ($(TARGET), arc_emsdp)
 
-# Patch of arc make project to adjust it specifically 
+#Patch of arc make project to adjust it specifically
-# for person detection example. In particular:
+#for experimental person detection example.In particular:
 # - Use Linker command file with better usage of fast memory
-# - In case project was generated with MLI usage, reduce scratch buffers.
+#- Stripout TFLM reference code by default.
+#- Optional : replace mli switchers with specialized kernels
+#for smaller code size
 
   person_detection_HDRS += \
-  person_detection_patch.txt
+  person_detection_int8_patch.txt
   
   person_detection_TEST_HDRS += \
-  person_detection_patch.txt
+  person_detection_int8_patch.txt
   
+  ARC_MLI_BACKEND_PATH = /tensorflow/lite/micro/kernels/arc_mli
 
-%/person_detection_patch.txt: %/emsdp.lcf %/Makefile
+#Apply changes in generated project files.
-	@cp tensorflow/lite/micro/tools/make/targets/arc/emsdp/emsdp_v2.lcf $< 
+#See related comment echoed(@echo <comment>) after each change
-	@echo emsdp.lcf > $@
+#to get understanding on it's purpose.
-	@sed -E -i 's#MLI_ONLY *\?= *false#MLI_ONLY \?= false\n\
+%/person_detection_int8_patch.txt: %/emsdp.lcf %/Makefile %$(ARC_MLI_BACKEND_PATH)/conv.cc %$(ARC_MLI_BACKEND_PATH)/depthwise_conv.cc %$(ARC_MLI_BACKEND_PATH)/pooling.cc
-	CXXFLAGS += -DSCRATCH_MEM_X_SIZE=0 -DSCRATCH_MEM_Y_SIZE=0 -DSCRATCH_MEM_Z_SIZE=0\
+	@cp tensorflow/lite/micro/examples/person_detection/arc_emsdp/emsdp.lcf $< 
-	CCFLAGS += -DSCRATCH_MEM_X_SIZE=0 -DSCRATCH_MEM_Y_SIZE=0 -DSCRATCH_MEM_Z_SIZE=0#'\
+	@echo emsdp.lcf: Replace with example specific memory map  > $@
-	  $(word 2, $^)
+
-	@echo Makefile >> $@
+	@sed -E -i 's#MLI_ONLY *\?= *false#MLI_ONLY \?= true#' $(word 2, $^)
+	@echo Makefile: No Reference fallback for MLI supported functions >> $@
+
+ifneq ($(filter $(ALL_TAGS), reduce_codesize),)
+#In case 'reduce_codesize' tag is present, we replace common MLI functions with
+#specializations appropriate for this particular graph.But such changes of code
+#with high probability may not be acceptable for other graphs and will need
+#to be adjusted by the user
+
+	@sed -E -i 's#mli_krn_conv2d_nhwc_sa8_sa8_sa32#mli_krn_conv2d_nhwc_sa8_sa8_sa32_k1x1_nopad#' $(word 3, $^)
+	@sed -E -i 's#mli_krn_depthwise_conv2d_hwcn_sa8_sa8_sa32#mli_krn_depthwise_conv2d_hwcn_sa8_sa8_sa32_k3x3_krnpad#' $(word 4, $^)
+	@sed -E -i 's#mli_krn_avepool_hwc_sa8#mli_krn_avepool_hwc_sa8_k3x3_nopad#' $(word 5, $^)
+	@sed -E -i 's#mli_krn_maxpool_hwc_sa8\(in_ptr, \&cfg, out_ptr\);#return kTfLiteError;#' $(word 5, $^)
+	@echo $(word 3, $^): Use specialization >> $@
+	@echo $(word 4, $^): Use specialization >> $@
+	@echo $(word 5, $^): Use specialization and remove max pooling >> $@
+endif
 
 endif

tensorflow/lite/micro/examples/person_detection/arduino/detection_responder.cc

@@ -25,7 +25,7 @@ limitations under the License.
 
 // Flash the blue LED after each inference
 void RespondToDetection(tflite::ErrorReporter* error_reporter,
-                        uint8_t person_score, uint8_t no_person_score) {
+                        int8_t person_score, int8_t no_person_score) {
   static bool is_initialized = false;
   if (!is_initialized) {
     // Pins for the built-in RGB LEDs on the Arduino Nano 33 BLE Sense

tensorflow/lite/micro/examples/person_detection/arduino/image_provider.cc

@@ -150,7 +150,7 @@ TfLiteStatus ReadData(tflite::ErrorReporter* error_reporter) {
 // Decode the JPEG image, crop it, and convert it to greyscale
 TfLiteStatus DecodeAndProcessImage(tflite::ErrorReporter* error_reporter,
                                    int image_width, int image_height,
-                                   uint8_t* image_data) {
+                                   int8_t* image_data) {
   TF_LITE_REPORT_ERROR(error_reporter,
                        "Decoding JPEG and converting to greyscale");
   // Parse the JPEG headers. The image will be decoded as a sequence of Minimum
@@ -221,11 +221,14 @@ TfLiteStatus DecodeAndProcessImage(tflite::ErrorReporter* error_reporter,
         // See https://en.wikipedia.org/wiki/Grayscale for magic numbers
         float gray_value = (0.2126 * r) + (0.7152 * g) + (0.0722 * b);
 
+        // Convert to signed 8-bit integer by subtracting 128.
+        gray_value -= 128;
+
         // The x coordinate of this pixel in the output image
         int current_x = x_origin + mcu_col;
         // The index of this pixel in our flat output buffer
         int index = (current_y * image_width) + current_x;
-        image_data[index] = static_cast<uint8_t>(gray_value);
+        image_data[index] = static_cast<int8_t>(gray_value);
       }
     }
   }
@@ -235,7 +238,7 @@ TfLiteStatus DecodeAndProcessImage(tflite::ErrorReporter* error_reporter,
 
 // Get an image from the camera module
 TfLiteStatus GetImage(tflite::ErrorReporter* error_reporter, int image_width,
-                      int image_height, int channels, uint8_t* image_data) {
+                      int image_height, int channels, int8_t* image_data) {
   static bool g_is_camera_initialized = false;
   if (!g_is_camera_initialized) {
     TfLiteStatus init_status = InitCamera(error_reporter);

tensorflow/lite/micro/examples/person_detection/detection_responder.cc

@@ -19,7 +19,7 @@ limitations under the License.
 // console. Real applications will want to take some custom action instead, and
 // should implement their own versions of this function.
 void RespondToDetection(tflite::ErrorReporter* error_reporter,
-                        uint8_t person_score, uint8_t no_person_score) {
+                        int8_t person_score, int8_t no_person_score) {
   TF_LITE_REPORT_ERROR(error_reporter, "person score:%d no person score %d",
                        person_score, no_person_score);
 }

tensorflow/lite/micro/examples/person_detection/detection_responder.h

@@ -29,6 +29,6 @@ limitations under the License.
 // image is considered to contain a person.  This threshold may be adjusted for
 // particular applications.
 void RespondToDetection(tflite::ErrorReporter* error_reporter,
-                        uint8_t person_score, uint8_t no_person_score);
+                        int8_t person_score, int8_t no_person_score);
 
 #endif  // TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_DETECTION_RESPONDER_H_

tensorflow/lite/micro/examples/person_detection/detection_responder_test.cc

@@ -25,8 +25,8 @@ TF_LITE_MICRO_TEST(TestCallability) {
   // This will have external side-effects (like printing to the debug console
   // or lighting an LED) that are hard to observe, so the most we can do is
   // make sure the call doesn't crash.
-  RespondToDetection(&micro_error_reporter, 100, 200);
+  RespondToDetection(&micro_error_reporter, -100, 100);
-  RespondToDetection(&micro_error_reporter, 200, 100);
+  RespondToDetection(&micro_error_reporter, 100, 50);
 }
 
 TF_LITE_MICRO_TESTS_END

tensorflow/lite/micro/examples/person_detection/himax_driver/HM01B0_optimized.c

@@ -45,8 +45,9 @@ static const int kStrideShift = 1;
 //! @return Error code.
 //
 //*****************************************************************************
-uint32_t hm01b0_blocking_read_oneframe_scaled(
+uint32_t hm01b0_blocking_read_oneframe_scaled(hm01b0_cfg_t* psCfg,
-    hm01b0_cfg_t* psCfg, uint8_t* buffer, int w, int h, int channels) {
+                                              int8_t* buffer, int w, int h,
+                                              int channels) {
   hm01b0_single_frame_capture(psCfg);
 
   // Calculate the number of pixels to crop to get a centered image.
@@ -76,7 +77,9 @@ uint32_t hm01b0_blocking_read_oneframe_scaled(
       if (output_x < w && output_y < h) {
         const int output_idx = (output_y * w + output_x) * channels;
         for (int i=0; i<channels; i++) {
-          buffer[output_idx + i] = value;
+          // See the top of main_functions.cc for an explanation of and
+          // rationale for our unsigned to signed input conversion.
+          buffer[output_idx + i] = value - 128;
         }
       }
 

tensorflow/lite/micro/examples/person_detection/himax_driver/HM01B0_optimized.h

@@ -40,7 +40,7 @@ extern "C" {
 //
 //*****************************************************************************
 uint32_t hm01b0_blocking_read_oneframe_scaled(hm01b0_cfg_t* psCfg,
-                                              uint8_t* buffer, int w, int h,
+                                              int8_t* buffer, int w, int h,
                                               int channels);
 
 #ifdef __cplusplus

tensorflow/lite/micro/examples/person_detection/himax_we1_evb/detection_responder.cc

@@ -13,9 +13,15 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#include "tensorflow/lite/micro/examples/person_detection_experimental/detection_responder.h"
+#if defined(ARDUINO)
+#define ARDUINO_EXCLUDE_CODE
+#endif  // defined(ARDUINO)
 
-#include "hx_drv_tflm.h"
+#ifndef ARDUINO_EXCLUDE_CODE
+
+#include "tensorflow/lite/micro/examples/person_detection/detection_responder.h"
+
+#include "hx_drv_tflm.h"  // NOLINT
 
 // This dummy implementation writes person and no person scores to the error
 // console. Real applications will want to take some custom action instead, and
@@ -31,3 +37,5 @@ void RespondToDetection(tflite::ErrorReporter* error_reporter,
   TF_LITE_REPORT_ERROR(error_reporter, "person score:%d no person score %d",
                        person_score, no_person_score);
 }
+
+#endif  // ARDUINO_EXCLUDE_CODE

tensorflow/lite/micro/examples/person_detection/himax_we1_evb/image_provider.cc

@@ -13,10 +13,16 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#include "tensorflow/lite/micro/examples/person_detection_experimental/image_provider.h"
+#if defined(ARDUINO)
+#define ARDUINO_EXCLUDE_CODE
+#endif  // defined(ARDUINO)
 
-#include "hx_drv_tflm.h"
+#ifndef ARDUINO_EXCLUDE_CODE
-#include "tensorflow/lite/micro/examples/person_detection_experimental/model_settings.h"
+
+#include "tensorflow/lite/micro/examples/person_detection/image_provider.h"
+
+#include "hx_drv_tflm.h"  // NOLINT
+#include "tensorflow/lite/micro/examples/person_detection/model_settings.h"
 
 hx_drv_sensor_image_config_t g_pimg_config;
 
@@ -39,3 +45,5 @@ TfLiteStatus GetImage(tflite::ErrorReporter* error_reporter, int image_width,
 
   return kTfLiteOk;
 }
+
+#endif  // ARDUINO_EXCLUDE_CODE

tensorflow/lite/micro/examples/person_detection/image_provider.cc

@@ -18,7 +18,7 @@ limitations under the License.
 #include "tensorflow/lite/micro/examples/person_detection/model_settings.h"
 
 TfLiteStatus GetImage(tflite::ErrorReporter* error_reporter, int image_width,
-                      int image_height, int channels, uint8_t* image_data) {
+                      int image_height, int channels, int8_t* image_data) {
   for (int i = 0; i < image_width * image_height * channels; ++i) {
     image_data[i] = 0;
   }

tensorflow/lite/micro/examples/person_detection/image_provider.h

@@ -34,6 +34,6 @@ limitations under the License.
 // it just returns a static image. For real applications, you should
 // ensure there's a specialized implementation that accesses hardware APIs.
 TfLiteStatus GetImage(tflite::ErrorReporter* error_reporter, int image_width,
-                      int image_height, int channels, uint8_t* image_data);
+                      int image_height, int channels, int8_t* image_data);
 
 #endif  // TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_IMAGE_PROVIDER_H_

tensorflow/lite/micro/examples/person_detection/image_provider_test.cc

@@ -27,7 +27,7 @@ TF_LITE_MICRO_TESTS_BEGIN
 TF_LITE_MICRO_TEST(TestImageProvider) {
   tflite::MicroErrorReporter micro_error_reporter;
 
-  uint8_t image_data[kMaxImageSize];
+  int8_t image_data[kMaxImageSize];
   TfLiteStatus get_status = GetImage(&micro_error_reporter, kNumCols, kNumRows,
                                      kNumChannels, image_data);
   TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, get_status);

tensorflow/lite/micro/examples/person_detection/main_functions.cc

@@ -32,8 +32,15 @@ const tflite::Model* model = nullptr;
 tflite::MicroInterpreter* interpreter = nullptr;
 TfLiteTensor* input = nullptr;
 
+// In order to use optimized tensorflow lite kernels, a signed int8_t quantized
+// model is preferred over the legacy unsigned model format. This means that
+// throughout this project, input images must be converted from unisgned to
+// signed format. The easiest and quickest way to convert from unsigned to
+// signed 8-bit integers is to subtract 128 from the unsigned value to get a
+// signed value.
+
 // An area of memory to use for input, output, and intermediate arrays.
-constexpr int kTensorArenaSize = 93 * 1024;
+constexpr int kTensorArenaSize = 136 * 1024;
 static uint8_t tensor_arena[kTensorArenaSize];
 }  // namespace
 
@@ -64,12 +71,15 @@ void setup() {
   //
   // tflite::AllOpsResolver resolver;
   // NOLINTNEXTLINE(runtime-global-variables)
-  static tflite::MicroMutableOpResolver<3> micro_op_resolver;
+  static tflite::MicroMutableOpResolver<5> micro_op_resolver;
   micro_op_resolver.AddAveragePool2D();
   micro_op_resolver.AddConv2D();
   micro_op_resolver.AddDepthwiseConv2D();
+  micro_op_resolver.AddReshape();
+  micro_op_resolver.AddSoftmax();
 
   // Build an interpreter to run the model with.
+  // NOLINTNEXTLINE(runtime-global-variables)
   static tflite::MicroInterpreter static_interpreter(
       model, micro_op_resolver, tensor_arena, kTensorArenaSize, error_reporter);
   interpreter = &static_interpreter;
@@ -89,7 +99,7 @@ void setup() {
 void loop() {
   // Get image from provider.
   if (kTfLiteOk != GetImage(error_reporter, kNumCols, kNumRows, kNumChannels,
-                            input->data.uint8)) {
+                            input->data.int8)) {
     TF_LITE_REPORT_ERROR(error_reporter, "Image capture failed.");
   }
 
@@ -101,7 +111,7 @@ void loop() {
   TfLiteTensor* output = interpreter->output(0);
 
   // Process the inference results.
-  uint8_t person_score = output->data.uint8[kPersonIndex];
+  int8_t person_score = output->data.uint8[kPersonIndex];
-  uint8_t no_person_score = output->data.uint8[kNotAPersonIndex];
+  int8_t no_person_score = output->data.uint8[kNotAPersonIndex];
   RespondToDetection(error_reporter, person_score, no_person_score);
 }

tensorflow/lite/micro/examples/person_detection/model_settings.cc

@@ -16,7 +16,6 @@ limitations under the License.
 #include "tensorflow/lite/micro/examples/person_detection/model_settings.h"
 
 const char* kCategoryLabels[kCategoryCount] = {
-    "unused",
-    "person",
     "notperson",
+    "person",
 };

tensorflow/lite/micro/examples/person_detection/model_settings.h

@@ -27,9 +27,9 @@ constexpr int kNumChannels = 1;
 
 constexpr int kMaxImageSize = kNumCols * kNumRows * kNumChannels;
 
-constexpr int kCategoryCount = 3;
+constexpr int kCategoryCount = 2;
 constexpr int kPersonIndex = 1;
-constexpr int kNotAPersonIndex = 2;
+constexpr int kNotAPersonIndex = 0;
 extern const char* kCategoryLabels[kCategoryCount];
 
 #endif  // TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_MODEL_SETTINGS_H_

tensorflow/lite/micro/examples/person_detection/person_detection_test.cc

@@ -26,8 +26,7 @@ limitations under the License.
 #include "tensorflow/lite/version.h"
 
 // Create an area of memory to use for input, output, and intermediate arrays.
-constexpr int tensor_arena_size = 93 * 1024;
+constexpr int tensor_arena_size = 136 * 1024;
-__attribute__((section(".bss.NoInit"), aligned(16)))
 uint8_t tensor_arena[tensor_arena_size];
 
 TF_LITE_MICRO_TESTS_BEGIN
@@ -51,12 +50,12 @@ TF_LITE_MICRO_TEST(TestInvoke) {
   // An easier approach is to just use the AllOpsResolver, but this will
   // incur some penalty in code space for op implementations that are not
   // needed by this graph.
-  //
+  tflite::MicroMutableOpResolver<5> micro_op_resolver;
-  // tflite::AllOpsResolver resolver;
-  tflite::MicroMutableOpResolver<3> micro_op_resolver;
   micro_op_resolver.AddAveragePool2D();
   micro_op_resolver.AddConv2D();
   micro_op_resolver.AddDepthwiseConv2D();
+  micro_op_resolver.AddReshape();
+  micro_op_resolver.AddSoftmax();
 
   // Build an interpreter to run the model with.
   tflite::MicroInterpreter interpreter(model, micro_op_resolver, tensor_arena,
@@ -74,13 +73,11 @@ TF_LITE_MICRO_TEST(TestInvoke) {
   TF_LITE_MICRO_EXPECT_EQ(kNumRows, input->dims->data[1]);
   TF_LITE_MICRO_EXPECT_EQ(kNumCols, input->dims->data[2]);
   TF_LITE_MICRO_EXPECT_EQ(kNumChannels, input->dims->data[3]);
-  TF_LITE_MICRO_EXPECT_EQ(kTfLiteUInt8, input->type);
+  TF_LITE_MICRO_EXPECT_EQ(kTfLiteInt8, input->type);
 
   // Copy an image with a person into the memory area used for the input.
-  const uint8_t* person_data = g_person_data;
+  TFLITE_DCHECK_EQ(input->bytes, static_cast<size_t>(g_person_data_size));
-  for (size_t i = 0; i < input->bytes; ++i) {
+  memcpy(input->data.int8, g_person_data, input->bytes);
-    input->data.uint8[i] = person_data[i];
-  }
 
   // Run the model on this input and make sure it succeeds.
   TfLiteStatus invoke_status = interpreter.Invoke();
@@ -92,26 +89,21 @@ TF_LITE_MICRO_TEST(TestInvoke) {
   // Get the output from the model, and make sure it's the expected size and
   // type.
   TfLiteTensor* output = interpreter.output(0);
-  TF_LITE_MICRO_EXPECT_EQ(4, output->dims->size);
+  TF_LITE_MICRO_EXPECT_EQ(2, output->dims->size);
   TF_LITE_MICRO_EXPECT_EQ(1, output->dims->data[0]);
-  TF_LITE_MICRO_EXPECT_EQ(1, output->dims->data[1]);
+  TF_LITE_MICRO_EXPECT_EQ(kCategoryCount, output->dims->data[1]);
-  TF_LITE_MICRO_EXPECT_EQ(1, output->dims->data[2]);
+  TF_LITE_MICRO_EXPECT_EQ(kTfLiteInt8, output->type);
-  TF_LITE_MICRO_EXPECT_EQ(kCategoryCount, output->dims->data[3]);
-  TF_LITE_MICRO_EXPECT_EQ(kTfLiteUInt8, output->type);
 
   // Make sure that the expected "Person" score is higher than the other class.
-  uint8_t person_score = output->data.uint8[kPersonIndex];
+  int8_t person_score = output->data.int8[kPersonIndex];
-  uint8_t no_person_score = output->data.uint8[kNotAPersonIndex];
+  int8_t no_person_score = output->data.int8[kNotAPersonIndex];
   TF_LITE_REPORT_ERROR(&micro_error_reporter,
                        "person data.  person score: %d, no person score: %d\n",
                        person_score, no_person_score);
   TF_LITE_MICRO_EXPECT_GT(person_score, no_person_score);
 
-  // Now test with a different input, from an image without a person.
+  // TODO(b/161461076): Update model to make this work on real negative inputs.
-  const uint8_t* no_person_data = g_no_person_data;
+  memset(input->data.int8, 0, input->bytes);
-  for (size_t i = 0; i < input->bytes; ++i) {
-    input->data.uint8[i] = no_person_data[i];
-  }
 
   // Run the model on this "No Person" input.
   invoke_status = interpreter.Invoke();
@@ -123,16 +115,14 @@ TF_LITE_MICRO_TEST(TestInvoke) {
   // Get the output from the model, and make sure it's the expected size and
   // type.
   output = interpreter.output(0);
-  TF_LITE_MICRO_EXPECT_EQ(4, output->dims->size);
+  TF_LITE_MICRO_EXPECT_EQ(2, output->dims->size);
   TF_LITE_MICRO_EXPECT_EQ(1, output->dims->data[0]);
-  TF_LITE_MICRO_EXPECT_EQ(1, output->dims->data[1]);
+  TF_LITE_MICRO_EXPECT_EQ(kCategoryCount, output->dims->data[1]);
-  TF_LITE_MICRO_EXPECT_EQ(1, output->dims->data[2]);
+  TF_LITE_MICRO_EXPECT_EQ(kTfLiteInt8, output->type);
-  TF_LITE_MICRO_EXPECT_EQ(kCategoryCount, output->dims->data[3]);
-  TF_LITE_MICRO_EXPECT_EQ(kTfLiteUInt8, output->type);
 
   // Make sure that the expected "No Person" score is higher.
-  person_score = output->data.uint8[kPersonIndex];
+  person_score = output->data.int8[kPersonIndex];
-  no_person_score = output->data.uint8[kNotAPersonIndex];
+  no_person_score = output->data.int8[kNotAPersonIndex];
   TF_LITE_REPORT_ERROR(
       &micro_error_reporter,
       "no person data.  person score: %d, no person score: %d\n", person_score,

tensorflow/lite/micro/examples/person_detection/sparkfun_edge/detection_responder.cc

@@ -26,7 +26,7 @@ limitations under the License.
 // This implementation will light up LEDs on the board in response to the
 // inference results.
 void RespondToDetection(tflite::ErrorReporter* error_reporter,
-                        uint8_t person_score, uint8_t no_person_score) {
+                        int8_t person_score, int8_t no_person_score) {
   static bool is_initialized = false;
   if (!is_initialized) {
     // Setup LED's as outputs.  Leave red LED alone since that's an error

tensorflow/lite/micro/examples/person_detection/sparkfun_edge/image_provider.cc

@@ -190,7 +190,7 @@ TfLiteStatus InitCamera(tflite::ErrorReporter* error_reporter) {
 // Capture single frame.  Frame pointer passed in to reduce memory usage.  This
 // allows the input tensor to be used instead of requiring an extra copy.
 TfLiteStatus GetImage(tflite::ErrorReporter* error_reporter, int frame_width,
-                      int frame_height, int channels, uint8_t* frame) {
+                      int frame_height, int channels, int8_t* frame) {
   if (!g_is_camera_initialized) {
     TfLiteStatus init_status = InitCamera(error_reporter);
     if (init_status != kTfLiteOk) {

tensorflow/lite/micro/examples/person_detection/training_a_model.md

@@ -372,6 +372,9 @@ tf.lite.TFLiteConverter.from_frozen_graph('vww_96_grayscale_frozen.pb',
 ['input'], ['MobilenetV1/Predictions/Reshape_1'])
 converter.optimizations = [tf.lite.Optimize.DEFAULT]
 converter.representative_dataset = representative_dataset_gen
+converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
+converter.inference_input_type = tf.int8
+converter.inference_output_type = tf.int8
 
 tflite_quant_model = converter.convert()
 open("vww_96_grayscale_quantized.tflite", "wb").write(tflite_quant_model)

tensorflow/lite/micro/examples/person_detection_experimental/BUILD

@@ -1,138 +0,0 @@
-# Description:
-#   TensorFlow Lite for Microcontrollers Vision Example.
-
-load(
-    "//tensorflow/lite/micro/testing:micro_test.bzl",
-    "tflite_micro_cc_test",
-)
-
-package(default_visibility = ["//visibility:public"])
-
-licenses(["notice"])  # Apache 2.0
-
-cc_library(
-    name = "model_settings",
-    srcs = [
-        "model_settings.cc",
-    ],
-    hdrs = [
-        "model_settings.h",
-    ],
-)
-
-cc_library(
-    name = "person_detect_model_data",
-    srcs = [
-        "person_detect_model_data.cc",
-    ],
-    hdrs = [
-        "person_detect_model_data.h",
-    ],
-)
-
-cc_library(
-    name = "simple_images_test_data",
-    srcs = [
-        "no_person_image_data.cc",
-        "person_image_data.cc",
-    ],
-    hdrs = [
-        "no_person_image_data.h",
-        "person_image_data.h",
-    ],
-    deps = [
-        ":model_settings",
-    ],
-)
-
-tflite_micro_cc_test(
-    name = "person_detection_test",
-    srcs = ["person_detection_test.cc"],
-    deps = [
-        ":model_settings",
-        ":person_detect_model_data",
-        ":simple_images_test_data",
-        "//tensorflow/lite:schema_fbs_version",
-        "//tensorflow/lite/c:common",
-        "//tensorflow/lite/micro:micro_error_reporter",
-        "//tensorflow/lite/micro:micro_framework",
-        "//tensorflow/lite/micro:op_resolvers",
-        "//tensorflow/lite/micro/testing:micro_test",
-        "//tensorflow/lite/schema:schema_fbs",
-    ],
-)
-
-cc_library(
-    name = "image_provider",
-    srcs = [
-        "image_provider.cc",
-    ],
-    hdrs = [
-        "image_provider.h",
-    ],
-    deps = [
-        ":model_settings",
-        "//tensorflow/lite/c:common",
-        "//tensorflow/lite/micro:micro_error_reporter",
-    ],
-)
-
-tflite_micro_cc_test(
-    name = "image_provider_test",
-    srcs = [
-        "image_provider_test.cc",
-    ],
-    deps = [
-        ":image_provider",
-        ":model_settings",
-        "//tensorflow/lite/c:common",
-        "//tensorflow/lite/micro:micro_error_reporter",
-        "//tensorflow/lite/micro:micro_framework",
-        "//tensorflow/lite/micro/testing:micro_test",
-    ],
-)
-
-cc_library(
-    name = "detection_responder",
-    srcs = [
-        "detection_responder.cc",
-    ],
-    hdrs = [
-        "detection_responder.h",
-    ],
-    deps = [
-        "//tensorflow/lite/c:common",
-        "//tensorflow/lite/micro:micro_error_reporter",
-    ],
-)
-
-tflite_micro_cc_test(
-    name = "detection_responder_test",
-    srcs = [
-        "detection_responder_test.cc",
-    ],
-    deps = [
-        ":detection_responder",
-        "//tensorflow/lite/micro/testing:micro_test",
-    ],
-)
-
-cc_binary(
-    name = "person_detection",
-    srcs = [
-        "main.cc",
-        "main_functions.cc",
-        "main_functions.h",
-    ],
-    deps = [
-        ":detection_responder",
-        ":image_provider",
-        ":model_settings",
-        ":person_detect_model_data",
-        "//tensorflow/lite:schema_fbs_version",
-        "//tensorflow/lite/micro:micro_error_reporter",
-        "//tensorflow/lite/micro:micro_framework",
-        "//tensorflow/lite/micro:op_resolvers",
-        "//tensorflow/lite/schema:schema_fbs",
-    ],
-)

tensorflow/lite/micro/examples/person_detection_experimental/Makefile.inc

@@ -1,83 +0,0 @@
-$(eval $(call add_third_party_download,$(PERSON_MODEL_INT8_URL),$(PERSON_MODEL_INT8_MD5),person_model_int8,))
-
-person_detection_MODEL_SRCS := \
-tensorflow/lite/micro/examples/person_detection_experimental/model_settings.cc \
-$(MAKEFILE_DIR)/downloads/person_model_int8/person_detect_model_data.cc
-
-person_detection_MODEL_HDRS := \
-tensorflow/lite/micro/examples/person_detection_experimental/model_settings.h \
-tensorflow/lite/micro/examples/person_detection_experimental/person_detect_model_data.h
-
-person_detection_TEST_SRCS := \
-tensorflow/lite/micro/examples/person_detection_experimental/person_detection_test.cc \
-$(MAKEFILE_DIR)/downloads/person_model_int8/no_person_image_data.cc \
-$(MAKEFILE_DIR)/downloads/person_model_int8/person_image_data.cc \
-$(person_detection_MODEL_SRCS)
-
-person_detection_TEST_HDRS := \
-tensorflow/lite/micro/examples/person_detection_experimental/no_person_image_data.h \
-tensorflow/lite/micro/examples/person_detection_experimental/person_image_data.h \
-$(person_detection_MODEL_HDRS)
-
-IMAGE_PROVIDER_TEST_SRCS := \
-tensorflow/lite/micro/examples/person_detection_experimental/image_provider.cc \
-tensorflow/lite/micro/examples/person_detection_experimental/image_provider_test.cc \
-tensorflow/lite/micro/examples/person_detection_experimental/model_settings.cc
-
-IMAGE_PROVIDER_TEST_HDRS := \
-tensorflow/lite/micro/examples/person_detection_experimental/image_provider.h \
-tensorflow/lite/micro/examples/person_detection_experimental/model_settings.h
-
-DETECTION_RESPONDER_TEST_SRCS := \
-tensorflow/lite/micro/examples/person_detection_experimental/detection_responder.cc \
-tensorflow/lite/micro/examples/person_detection_experimental/detection_responder_test.cc
-
-DETECTION_RESPONDER_TEST_HDRS := \
-tensorflow/lite/micro/examples/person_detection_experimental/detection_responder.h
-
-person_detection_SRCS := \
-tensorflow/lite/micro/examples/person_detection_experimental/detection_responder.cc \
-tensorflow/lite/micro/examples/person_detection_experimental/image_provider.cc \
-tensorflow/lite/micro/examples/person_detection_experimental/main.cc \
-tensorflow/lite/micro/examples/person_detection_experimental/main_functions.cc \
-$(person_detection_MODEL_SRCS)
-
-person_detection_HDRS := \
-tensorflow/lite/micro/examples/person_detection_experimental/detection_responder.h \
-tensorflow/lite/micro/examples/person_detection_experimental/image_provider.h \
-tensorflow/lite/micro/examples/person_detection_experimental/main_functions.h \
-$(person_detection_MODEL_HDRS)
-
-#Find any platform - specific rules for this example.
-include $(wildcard tensorflow/lite/micro/examples/person_detection_experimental/*/Makefile.inc)
-
-# Tests loading and running a vision model.
-$(eval $(call microlite_test,person_detection_test_int8,\
-$(person_detection_TEST_SRCS),$(person_detection_TEST_HDRS)))
-
-# Three conflicting issues here:
-# 1. The image_provider_test fails on Sparkfun Edge we do not have a way to
-#    filter out individual tests within and example.
-# 2. We do not want to completely remove person_detection from the sparkfun_edge
-#    build.
-# 3. We do want to keep as many targets as possible be part of the sparkfun_edge
-#    CI build to avoid getting into similar situations where some parts of the
-#    code are supported on a platform while other parts are not.
-#
-# The current nasty workaround is to explicitly exclude the offending test for
-# the sparkfun_edge target. Note that we are not exluding it for
-# TARGET=apollo3evb becuase that is not part of our CI builds (and the two are
-# basically equivalent).
-ifneq ($(TARGET),sparkfun_edge)
-# Tests the image provider module.
-$(eval $(call microlite_test,image_provider_test_int8,\
-$(IMAGE_PROVIDER_TEST_SRCS),$(IMAGE_PROVIDER_TEST_HDRS)))
-endif
-
-# Tests the detection responder module.
-$(eval $(call microlite_test,detection_responder_test_int8,\
-$(DETECTION_RESPONDER_TEST_SRCS),$(DETECTION_RESPONDER_TEST_HDRS)))
-
-# Builds a standalone object recognition binary.
-$(eval $(call microlite_test,person_detection_int8,\
-$(person_detection_SRCS),$(person_detection_HDRS)))

tensorflow/lite/micro/examples/person_detection_experimental/README.md

@@ -1,570 +0,0 @@
-# Person detection example
-
-This example shows how you can use Tensorflow Lite to run a 250 kilobyte neural
-network to recognize people in images captured by a camera.  It is designed to
-run on systems with small amounts of memory such as microcontrollers and DSPs.
-This uses the experimental int8 quantized version of the person detection model.
-
-## Table of contents
-
--   [Getting started](#getting-started)
--   [Running on ARC EM SDP](#running-on-arc-em-sdp)
--   [Running on Arduino](#running-on-arduino)
--   [Running on HIMAX WE1 EVB](#running-on-himax-we1-evb)
--   [Running on SparkFun Edge](#running-on-sparkfun-edge)
--   [Run the tests on a development machine](#run-the-tests-on-a-development-machine)
--   [Debugging image capture](#debugging-image-capture)
--   [Training your own model](#training-your-own-model)
-
-## Running on ARC EM SDP
-
-The following instructions will help you to build and deploy this example to
-[ARC EM SDP](https://www.synopsys.com/dw/ipdir.php?ds=arc-em-software-development-platform)
-board. General information and instructions on using the board with TensorFlow
-Lite Micro can be found in the common
-[ARC targets description](/tensorflow/lite/micro/tools/make/targets/arc/README.md).
-
-This example uses asymmetric int8 quantization and can therefore leverage
-optimized int8 kernels from the embARC MLI library
-
-The ARC EM SDP board contains a rich set of extension interfaces. You can choose
-any compatible camera and modify
-[image_provider.cc](/tensorflow/lite/micro/examples/person_detection_experimental/image_provider.cc)
-file accordingly to use input from your specific camera. By default, results of
-running this example are printed to the console. If you would like to instead
-implement some target-specific actions, you need to modify
-[detection_responder.cc](/tensorflow/lite/micro/examples/person_detection_experimental/detection_responder.cc)
-accordingly.
-
-The reference implementations of these files are used by default on the EM SDP.
-
-### Initial setup
-
-Follow the instructions on the
-[ARC EM SDP Initial Setup](/tensorflow/lite/micro/tools/make/targets/arc/README.md#ARC-EM-Software-Development-Platform-ARC-EM-SDP)
-to get and install all required tools for work with ARC EM SDP.
-
-### Generate Example Project
-
-The example project for ARC EM SDP platform can be generated with the following
-command:
-
-```
-make -f tensorflow/lite/micro/tools/make/Makefile \
-TARGET=arc_emsdp TAGS=reduce_codesize \
-generate_person_detection_int8_make_project
-```
-
-Note that `TAGS=reduce_codesize` applies example specific changes of code to
-reduce total size of application. It can be omitted.
-
-### Build and Run Example
-
-For more detailed information on building and running examples see the
-appropriate sections of general descriptions of the
-[ARC EM SDP usage with TFLM](/tensorflow/lite/micro/tools/make/targets/arc/README.md#ARC-EM-Software-Development-Platform-ARC-EM-SDP).
-In the directory with generated project you can also find a
-*README_ARC_EMSDP.md* file with instructions and options on building and
-running. Here we only briefly mention main steps which are typically enough to
-get it started.
-
-1.  You need to
-    [connect the board](/tensorflow/lite/micro/tools/make/targets/arc/README.md#connect-the-board)
-    and open an serial connection.
-
-2.  Go to the generated example project director
-
-    ```
-    cd tensorflow/lite/micro/tools/make/gen/arc_emsdp_arc/prj/person_detection_int8/make
-    ```
-
-3.  Build the example using
-
-    ```
-    make app
-    ```
-
-4.  To generate artefacts for self-boot of example from the board use
-
-    ```
-    make flash
-    ```
-
-5.  To run application from the board using microSD card:
-
-    *   Copy the content of the created /bin folder into the root of microSD
-        card. Note that the card must be formatted as FAT32 with default cluster
-        size (but less than 32 Kbytes)
-    *   Plug in the microSD card into the J11 connector.
-    *   Push the RST button. If a red LED is lit beside RST button, push the CFG
-        button.
-    *   Type or copy next commands one-by-another into serial terminal: `setenv
-        loadaddr 0x10800000 setenv bootfile app.elf setenv bootdelay 1 setenv
-        bootcmd fatload mmc 0 \$\{loadaddr\} \$\{bootfile\} \&\& bootelf
-        saveenv`
-    *   Push the RST button.
-
-6.  If you have the MetaWare Debugger installed in your environment:
-
-    *   To run application from the console using it type `make run`.
-    *   To stop the execution type `Ctrl+C` in the console several times.
-
-In both cases (step 5 and 6) you will see the application output in the serial
-terminal.
-
-## Running on Arduino
-
-The following instructions will help you build and deploy this sample
-to [Arduino](https://www.arduino.cc/) devices.
-
-The sample has been tested with the following device:
-
-- [Arduino Nano 33 BLE Sense](https://store.arduino.cc/usa/nano-33-ble-sense-with-headers)
-
-You will also need the following camera module:
-
-- [Arducam Mini 2MP Plus](https://www.amazon.com/Arducam-Module-Megapixels-Arduino-Mega2560/dp/B012UXNDOY)
-
-### Hardware
-
-Connect the Arducam pins as follows:
-
-|Arducam pin name|Arduino pin name|
-|----------------|----------------|
-|CS|D7 (unlabelled, immediately to the right of D6)|
-|MOSI|D11|
-|MISO|D12|
-|SCK|D13|
-|GND|GND (either pin marked GND is fine)|
-|VCC|3.3 V|
-|SDA|A4|
-|SCL|A5|
-
-### Install the Arduino_TensorFlowLite library
-
-Download the current nightly build of the library:
-[person_detection.zip](https://storage.googleapis.com/download.tensorflow.org/data/tf_lite_micro_person_data_int8_grayscale_2020_01_13.zip)
-
-This example application is included as part of the official TensorFlow Lite
-Arduino library. To install it, open the Arduino library manager in
-`Tools -> Manage Libraries...` and search for `Arduino_TensorFlowLite`.
-
-### Install other libraries
-
-In addition to the TensorFlow library, you'll also need to install two
-libraries:
-
-* The Arducam library, so our code can interface with the hardware
-* The JPEGDecoder library, so we can decode JPEG-encoded images
-
-The Arducam Arduino library is available from GitHub at
-[https://github.com/ArduCAM/Arduino](https://github.com/ArduCAM/Arduino).
-To install it, download or clone the repository. Next, copy its `ArduCAM`
-subdirectory into your `Arduino/libraries` directory. To find this directory on
-your machine, check the *Sketchbook location* in the Arduino IDE's
-*Preferences* window.
-
-After downloading the library, you'll need to edit one of its files to make sure
-it is configured for the Arducam Mini 2MP Plus. To do so, open the following
-file:
-
-```
-Arduino/libraries/ArduCAM/memorysaver.h
-```
-
-You'll see a bunch of `#define` statements listed. Make sure that they are all
-commented out, except for `#define OV2640_MINI_2MP_PLUS`, as so:
-
-```
-//Step 1: select the hardware platform, only one at a time
-//#define OV2640_MINI_2MP
-//#define OV3640_MINI_3MP
-//#define OV5642_MINI_5MP
-//#define OV5642_MINI_5MP_BIT_ROTATION_FIXED
-#define OV2640_MINI_2MP_PLUS
-//#define OV5642_MINI_5MP_PLUS
-//#define OV5640_MINI_5MP_PLUS
-```
-
-Once you save the file, we're done configuring the Arducam library.
-
-Our next step is to install the JPEGDecoder library. We can do this from within
-the Arduino IDE. First, go to the *Manage Libraries...* option in the *Tools*
-menu and search for `JPEGDecoder`. You should install version _1.8.0_ of the
-library.
-
-Once the library has installed, we'll need to configure it to disable some
-optional components that are not compatible with the Arduino Nano 33 BLE Sense.
-Open the following file:
-
-```
-Arduino/libraries/JPEGDecoder/src/User_Config.h
-```
-
-Make sure that both `#define LOAD_SD_LIBRARY` and `#define LOAD_SDFAT_LIBRARY`
-are commented out, as shown in this excerpt from the file:
-
-```c++
-// Comment out the next #defines if you are not using an SD Card to store the JPEGs
-// Commenting out the line is NOT essential but will save some FLASH space if
-// SD Card access is not needed. Note: use of SdFat is currently untested!
-
-//#define LOAD_SD_LIBRARY // Default SD Card library
-//#define LOAD_SDFAT_LIBRARY // Use SdFat library instead, so SD Card SPI can be bit bashed
-```
-
-Once you've saved the file, you are done installing libraries.
-
-### Load and run the example
-
-Go to `File -> Examples`. You should see an
-example near the bottom of the list named `TensorFlowLite`. Select
-it and click `person_detection` to load the example. Connect your device, then
-build and upload the example.
-
-To test the camera, start by pointing the device's camera at something that is
-definitely not a person, or just covering it up. The next time the blue LED
-flashes, the device will capture a frame from the camera and begin to run
-inference. Since the vision model we are using for person detection is
-relatively large, it takes a long time to run inference—around 19 seconds at the
-time of writing, though it's possible TensorFlow Lite has gotten faster since
-then.
-
-After 19 seconds or so, the inference result will be translated into another LED
-being lit. Since you pointed the camera at something that isn't a person, the
-red LED should light up.
-
-Now, try pointing the device's camera at yourself! The next time the blue LED
-flashes, the device will capture another image and begin to run inference. After
-19 seconds, the green LED should light up!
-
-Remember, image data is captured as a snapshot before each inference, whenever
-the blue LED flashes. Whatever the camera is pointed at during that moment is
-what will be fed into the model. It doesn't matter where the camera is pointed
-until the next time an image is captured, when the blue LED will flash again.
-
-If you're getting seemingly incorrect results, make sure you are in an
-environment with good lighting. You should also make sure that the camera is
-oriented correctly, with the pins pointing downwards, so that the images it
-captures are the right way up—the model was not trained to recognize upside-down
-people! In addition, it's good to remember that this is a tiny model, which
-trades accuracy for small size. It works very well, but it isn't accurate 100%
-of the time.
-
-We can also see the results of inference via the Arduino Serial Monitor. To do
-this, open the *Serial Monitor* from the *Tools* menu. You'll see a detailed
-log of what is happening while our application runs. It's also interesting to
-check the *Show timestamp* box, so you can see how long each part of the process
-takes:
-
-```
-14:17:50.714 -> Starting capture
-14:17:50.714 -> Image captured
-14:17:50.784 -> Reading 3080 bytes from ArduCAM
-14:17:50.887 -> Finished reading
-14:17:50.887 -> Decoding JPEG and converting to greyscale
-14:17:51.074 -> Image decoded and processed
-14:18:09.710 -> Person score: 246 No person score: 66
-```
-
-From the log, we can see that it took around 170 ms to capture and read the
-image data from the camera module, 180 ms to decode the JPEG and convert it to
-greyscale, and 18.6 seconds to run inference.
-
-## Running on HIMAX WE1 EVB
-
-The following instructions will help you build and deploy this example to
-[HIMAX WE1 EVB](https://github.com/HimaxWiseEyePlus/bsp_tflu/tree/master/HIMAX_WE1_EVB_board_brief)
-board. To understand more about using this board, please check
-[HIMAX WE1 EVB user guide](https://github.com/HimaxWiseEyePlus/bsp_tflu/tree/master/HIMAX_WE1_EVB_user_guide).
-
-### Initial Setup
-
-To use the HIMAX WE1 EVB, please make sure following software are installed:
-
-#### MetaWare Development Toolkit
-
-See
-[Install the Synopsys DesignWare ARC MetaWare Development Toolkit](/tensorflow/lite/micro/tools/make/targets/arc/README.md#install-the-synopsys-designware-arc-metaware-development-toolkit)
-section for instructions on toolchain installation.
-
-#### Make Tool version
-
-A `'make'` tool is required for deploying Tensorflow Lite Micro applications on
-HIMAX WE1 EVB, See
-[Check make tool version](/tensorflow/lite/micro/tools/make/targets/arc/README.md#make-tool)
-section for proper environment.
-
-#### Serial Terminal Emulation Application
-
-There are 2 main purposes for HIMAX WE1 EVB Debug UART port
-
--   print application output
--   burn application to flash by using xmodem send application binary
-
-You can use any terminal emulation program (like [PuTTY](https://www.putty.org/)
-or [minicom](https://linux.die.net/man/1/minicom)).
-
-### Generate Example Project
-
-The example project for HIMAX WE1 EVB platform can be generated with the
-following command:
-
-Download related third party data
-
-```
-make -f tensorflow/lite/micro/tools/make/Makefile TARGET=himax_we1_evb third_party_downloads
-```
-
-Generate person detection project
-
-```
-make -f tensorflow/lite/micro/tools/make/Makefile generate_person_detection_int8_make_project TARGET=himax_we1_evb
-```
-
-### Build and Burn Example
-
-Following the Steps to run person detection example at HIMAX WE1 EVB platform.
-
-1.  Go to the generated example project directory.
-
-    ```
-    cd tensorflow/lite/micro/tools/make/gen/himax_we1_evb_arc/prj/person_detection_int8/make
-    ```
-
-2.  Build the example using
-
-    ```
-    make app
-    ```
-
-3.  After example build finish, copy ELF file and map file to image generate
-    tool directory. \
-    image generate tool directory located at
-    `'tensorflow/lite/micro/tools/make/downloads/himax_we1_sdk/image_gen_linux_v3/'`
-
-    ```
-    cp person_detection_int8.elf himax_we1_evb.map ../../../../../downloads/himax_we1_sdk/image_gen_linux_v3/
-    ```
-
-4.  Go to flash image generate tool directory.
-
-    ```
-    cd ../../../../../downloads/himax_we1_sdk/image_gen_linux_v3/
-    ```
-
-    make sure this tool directory is in $PATH. You can permanently set it to
-    PATH by
-
-    ```
-    export PATH=$PATH:$(pwd)
-    ```
-
-5.  run image generate tool, generate flash image file.
-
-    *   Before running image generate tool, by typing `sudo chmod +x image_gen`
-        and `sudo chmod +x sign_tool` to make sure it is executable.
-
-    ```
-    image_gen -e person_detection_int8.elf -m himax_we1_evb.map -o out.img
-    ```
-
-6.  Download flash image file to HIMAX WE1 EVB by UART:
-
-    *   more detail about download image through UART can be found at
-        [HIMAX WE1 EVB update Flash image](https://github.com/HimaxWiseEyePlus/bsp_tflu/tree/master/HIMAX_WE1_EVB_user_guide#flash-image-update)
-
-After these steps, press reset button on the HIMAX WE1 EVB, you will see
-application output in the serial terminal.
-
-## Running on SparkFun Edge
-
-The following instructions will help you build and deploy this sample on the
-[SparkFun Edge development board](https://sparkfun.com/products/15170).  This
-sample requires the Sparkfun Himax camera for the Sparkfun Edge board.  It is
-not available for purchase yet.
-
-If you're new to using this board, we recommend walking through the
-[AI on a microcontroller with TensorFlow Lite and SparkFun Edge](https://codelabs.developers.google.com/codelabs/sparkfun-tensorflow)
-codelab to get an understanding of the workflow.
-
-### Compile the binary
-
-The following command will download the required dependencies and then compile a
-binary for the SparkFun Edge:
-
-```
-make -f tensorflow/lite/micro/tools/make/Makefile TARGET=sparkfun_edge person_detection_bin
-```
-
-The binary will be created in the following location:
-
-```
-tensorflow/lite/micro/tools/make/gen/sparkfun_edge_cortex-m4/bin/person_detection.bin
-```
-
-### Sign the binary
-
-The binary must be signed with cryptographic keys to be deployed to the device.
-We'll now run some commands that will sign our binary so it can be flashed to
-the SparkFun Edge. The scripts we are using come from the Ambiq SDK, which is
-downloaded when the `Makefile` is run.
-
-Enter the following command to set up some dummy cryptographic keys we can use
-for development:
-
-```
-cp tensorflow/lite/micro/tools/make/downloads/AmbiqSuite-Rel2.0.0/tools/apollo3_scripts/keys_info0.py \
-tensorflow/lite/micro/tools/make/downloads/AmbiqSuite-Rel2.0.0/tools/apollo3_scripts/keys_info.py
-```
-
-Next, run the following command to create a signed binary:
-
-```
-python3 tensorflow/lite/micro/tools/make/downloads/AmbiqSuite-Rel2.0.0/tools/apollo3_scripts/create_cust_image_blob.py \
---bin tensorflow/lite/micro/tools/make/gen/sparkfun_edge_cortex-m4/bin/person_detection.bin \
---load-address 0xC000 \
---magic-num 0xCB \
--o main_nonsecure_ota \
---version 0x0
-```
-
-This will create the file `main_nonsecure_ota.bin`. We'll now run another
-command to create a final version of the file that can be used to flash our
-device with the bootloader script we will use in the next step:
-
-```
-python3 tensorflow/lite/micro/tools/make/downloads/AmbiqSuite-Rel2.0.0/tools/apollo3_scripts/create_cust_wireupdate_blob.py \
---load-address 0x20000 \
---bin main_nonsecure_ota.bin \
--i 6 \
--o main_nonsecure_wire \
---options 0x1
-```
-
-You should now have a file called `main_nonsecure_wire.bin` in the directory
-where you ran the commands. This is the file we'll be flashing to the device.
-
-### Flash the binary
-
-Next, attach the board to your computer via a USB-to-serial adapter.
-
-**Note:** If you're using the [SparkFun Serial Basic Breakout](https://www.sparkfun.com/products/15096),
-you should [install the latest drivers](https://learn.sparkfun.com/tutorials/sparkfun-serial-basic-ch340c-hookup-guide#drivers-if-you-need-them)
-before you continue.
-
-Once connected, assign the USB device name to an environment variable:
-
-```
-export DEVICENAME=put your device name here
-```
-
-Set another variable with the baud rate:
-
-```
-export BAUD_RATE=921600
-```
-
-Now, hold the button marked `14` on the device. While still holding the button,
-hit the button marked `RST`. Continue holding the button marked `14` while
-running the following command:
-
-```
-python3 tensorflow/lite/micro/tools/make/downloads/AmbiqSuite-Rel2.0.0/tools/apollo3_scripts/uart_wired_update.py \
--b ${BAUD_RATE} ${DEVICENAME} \
--r 1 \
--f main_nonsecure_wire.bin \
--i 6
-```
-
-You should see a long stream of output as the binary is flashed to the device.
-Once you see the following lines, flashing is complete:
-
-```
-Sending Reset Command.
-Done.
-```
-
-If you don't see these lines, flashing may have failed. Try running through the
-steps in [Flash the binary](#flash-the-binary) again (you can skip over setting
-the environment variables). If you continue to run into problems, follow the
-[AI on a microcontroller with TensorFlow Lite and SparkFun Edge](https://codelabs.developers.google.com/codelabs/sparkfun-tensorflow)
-codelab, which includes more comprehensive instructions for the flashing
-process.
-
-The binary should now be deployed to the device. Hit the button marked `RST` to
-reboot the board. You should see the device's four LEDs flashing in sequence.
-
-Debug information is logged by the board while the program is running. To view
-it, establish a serial connection to the board using a baud rate of `115200`.
-On OSX and Linux, the following command should work:
-
-```
-screen ${DEVICENAME} 115200
-```
-
-To stop viewing the debug output with `screen`, hit `Ctrl+A`, immediately
-followed by the `K` key, then hit the `Y` key.
-
-## Run the tests on a development machine
-
-To compile and test this example on a desktop Linux or MacOS machine, download
-[the TensorFlow source code](https://github.com/tensorflow/tensorflow), `cd`
-into the source directory from a terminal, and then run the following command:
-
-```
-make -f tensorflow/lite/micro/tools/make/Makefile
-```
-
-This will take a few minutes, and downloads frameworks the code uses like
-[CMSIS](https://developer.arm.com/embedded/cmsis) and
-[flatbuffers](https://google.github.io/flatbuffers/). Once that process has
-finished, run:
-
-```
-make -f tensorflow/lite/micro/tools/make/Makefile test_person_detection_test
-```
-
-You should see a series of files get compiled, followed by some logging output
-from a test, which should conclude with `~~~ALL TESTS PASSED~~~`. If you see
-this, it means that a small program has been built and run that loads a trained
-TensorFlow model, runs some example images through it, and got the expected
-outputs. This particular test runs images with a and without a person in them,
-and checks that the network correctly identifies them.
-
-To understand how TensorFlow Lite does this, you can look at the `TestInvoke()`
-function in
-[person_detection_test.cc](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/micro/examples/person_detection/person_detection_test.cc).
-It's a fairly small amount of code, creating an interpreter, getting a handle to
-a model that's been compiled into the program, and then invoking the interpreter
-with the model and sample inputs.
-
-## Debugging image capture
-When the sample is running, check the LEDs to determine whether the inference is
-running correctly.  If the red light is stuck on, it means there was an error
-communicating with the camera.  This is likely due to an incorrectly connected
-or broken camera.
-
-During inference, the blue LED will toggle every time inference is complete. The
-orange LED indicates that no person was found, and the green LED indicates a
-person was found. The red LED should never turn on, since it indicates an error.
-
-In order to view the captured image, set the DUMP_IMAGE define in main.cc.  This
-causes the board to log raw image info to the console. After the board has been
-flashed and reset, dump the log to a text file:
-
-
-```
-screen -L -Logfile <dump file> ${DEVICENAME} 115200
-```
-
-Next, run the raw to bitmap converter to view captured images:
-
-```
-python3 raw_to_bitmap.py -r GRAY -i <dump file>
-```
-
-## Training your own model
-
-You can train your own model with some easy-to-use scripts. See
-[training_a_model.md](training_a_model.md) for instructions.

tensorflow/lite/micro/examples/person_detection_experimental/apollo3evb/image_provider.cc

@@ -1,198 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/micro/examples/person_detection_experimental/image_provider.h"
-
-#include "tensorflow/lite/micro/examples/person_detection_experimental/himax_driver/HM01B0.h"
-#include "tensorflow/lite/micro/examples/person_detection_experimental/himax_driver/HM01B0_RAW8_QVGA_8bits_lsb_5fps.h"
-#include "tensorflow/lite/micro/examples/person_detection_experimental/himax_driver/HM01B0_debug.h"
-#include "tensorflow/lite/micro/examples/person_detection_experimental/himax_driver/HM01B0_optimized.h"
-#include "tensorflow/lite/micro/examples/person_detection_experimental/himax_driver/platform_Sparkfun_Edge.h"
-
-// These are headers from Ambiq's Apollo3 SDK.
-#include "am_bsp.h"         // NOLINT
-#include "am_mcu_apollo.h"  // NOLINT
-#include "am_util.h"        // NOLINT
-
-// #define DEMO_HM01B0_FRAMEBUFFER_DUMP_ENABLE
-
-// Enabling logging increases power consumption by preventing low power mode
-// from being enabled.
-#define ENABLE_LOGGING
-
-namespace {
-
-//*****************************************************************************
-//
-// HM01B0 Configuration
-//
-//*****************************************************************************
-static hm01b0_cfg_t s_HM01B0Cfg = {
-  // i2c settings
-  ui16SlvAddr : HM01B0_DEFAULT_ADDRESS,
-  eIOMMode : HM01B0_IOM_MODE,
-  ui32IOMModule : HM01B0_IOM_MODULE,
-  sIOMCfg : {
-    eInterfaceMode : HM01B0_IOM_MODE,
-    ui32ClockFreq : HM01B0_I2C_CLOCK_FREQ,
-  },
-  pIOMHandle : NULL,
-
-  // MCLK settings
-  ui32CTimerModule : HM01B0_MCLK_GENERATOR_MOD,
-  ui32CTimerSegment : HM01B0_MCLK_GENERATOR_SEG,
-  ui32CTimerOutputPin : HM01B0_PIN_MCLK,
-
-  // data interface
-  ui8PinSCL : HM01B0_PIN_SCL,
-  ui8PinSDA : HM01B0_PIN_SDA,
-  ui8PinD0 : HM01B0_PIN_D0,
-  ui8PinD1 : HM01B0_PIN_D1,
-  ui8PinD2 : HM01B0_PIN_D2,
-  ui8PinD3 : HM01B0_PIN_D3,
-  ui8PinD4 : HM01B0_PIN_D4,
-  ui8PinD5 : HM01B0_PIN_D5,
-  ui8PinD6 : HM01B0_PIN_D6,
-  ui8PinD7 : HM01B0_PIN_D7,
-  ui8PinVSYNC : HM01B0_PIN_VSYNC,
-  ui8PinHSYNC : HM01B0_PIN_HSYNC,
-  ui8PinPCLK : HM01B0_PIN_PCLK,
-
-  ui8PinTrig : HM01B0_PIN_TRIG,
-  ui8PinInt : HM01B0_PIN_INT,
-  pfnGpioIsr : NULL,
-};
-
-static constexpr int kFramesToInitialize = 4;
-
-bool g_is_camera_initialized = false;
-
-void boost_mode_enable(tflite::ErrorReporter* error_reporter, bool bEnable) {
-  am_hal_burst_avail_e eBurstModeAvailable;
-  am_hal_burst_mode_e eBurstMode;
-
-  // Check that the Burst Feature is available.
-  if (AM_HAL_STATUS_SUCCESS ==
-      am_hal_burst_mode_initialize(&eBurstModeAvailable)) {
-    if (AM_HAL_BURST_AVAIL == eBurstModeAvailable) {
-      TF_LITE_REPORT_ERROR(error_reporter, "Apollo3 Burst Mode is Available\n");
-    } else {
-      TF_LITE_REPORT_ERROR(error_reporter,
-                           "Apollo3 Burst Mode is Not Available\n");
-      return;
-    }
-  } else {
-    TF_LITE_REPORT_ERROR(error_reporter,
-                         "Failed to Initialize for Burst Mode operation\n");
-  }
-
-  // Make sure we are in "Normal" mode.
-  if (AM_HAL_STATUS_SUCCESS == am_hal_burst_mode_disable(&eBurstMode)) {
-    if (AM_HAL_NORMAL_MODE == eBurstMode) {
-      TF_LITE_REPORT_ERROR(error_reporter,
-                           "Apollo3 operating in Normal Mode (48MHz)\n");
-    }
-  } else {
-    TF_LITE_REPORT_ERROR(error_reporter,
-                         "Failed to Disable Burst Mode operation\n");
-  }
-
-  // Put the MCU into "Burst" mode.
-  if (bEnable) {
-    if (AM_HAL_STATUS_SUCCESS == am_hal_burst_mode_enable(&eBurstMode)) {
-      if (AM_HAL_BURST_MODE == eBurstMode) {
-        TF_LITE_REPORT_ERROR(error_reporter,
-                             "Apollo3 operating in Burst Mode (96MHz)\n");
-      }
-    } else {
-      TF_LITE_REPORT_ERROR(error_reporter,
-                           "Failed to Enable Burst Mode operation\n");
-    }
-  }
-}
-
-}  // namespace
-
-TfLiteStatus InitCamera(tflite::ErrorReporter* error_reporter) {
-  TF_LITE_REPORT_ERROR(error_reporter, "Initializing HM01B0...\n");
-
-  am_hal_clkgen_control(AM_HAL_CLKGEN_CONTROL_SYSCLK_MAX, 0);
-
-  // Set the default cache configuration
-  am_hal_cachectrl_config(&am_hal_cachectrl_defaults);
-  am_hal_cachectrl_enable();
-
-  // Configure the board for low power operation. This breaks logging by
-  // turning off the itm and uart interfaces.
-#ifndef ENABLE_LOGGING
-  am_bsp_low_power_init();
-#endif
-
-  // Enable interrupts so we can receive messages from the boot host.
-  am_hal_interrupt_master_enable();
-
-  boost_mode_enable(error_reporter, true);
-
-  hm01b0_power_up(&s_HM01B0Cfg);
-
-  am_util_delay_ms(1);
-
-  hm01b0_mclk_enable(&s_HM01B0Cfg);
-
-  am_util_delay_ms(1);
-
-  hm01b0_init_if(&s_HM01B0Cfg);
-
-  hm01b0_init_system(&s_HM01B0Cfg, (hm_script_t*)sHM01B0InitScript,
-                     sizeof(sHM01B0InitScript) / sizeof(hm_script_t));
-
-  // Put camera into streaming mode - this makes it so that the camera
-  // constantly captures images.  It is still OK to read and image since the
-  // camera uses a double-buffered input.  This means there is always one valid
-  // image to read while the other buffer fills.  Streaming mode allows the
-  // camera to perform auto exposure constantly.
-  hm01b0_set_mode(&s_HM01B0Cfg, HM01B0_REG_MODE_SELECT_STREAMING, 0);
-
-  return kTfLiteOk;
-}
-
-// Capture single frame.  Frame pointer passed in to reduce memory usage.  This
-// allows the input tensor to be used instead of requiring an extra copy.
-TfLiteStatus GetImage(tflite::ErrorReporter* error_reporter, int frame_width,
-                      int frame_height, int channels, uint8_t* frame) {
-  if (!g_is_camera_initialized) {
-    TfLiteStatus init_status = InitCamera(error_reporter);
-    if (init_status != kTfLiteOk) {
-      return init_status;
-    }
-    // Drop a few frames until auto exposure is calibrated.
-    for (int i = 0; i < kFramesToInitialize; ++i) {
-      hm01b0_blocking_read_oneframe_scaled(frame, frame_width, frame_height,
-                                           channels);
-    }
-    g_is_camera_initialized = true;
-  }
-
-  hm01b0_blocking_read_oneframe_scaled(frame, frame_width, frame_height,
-                                       channels);
-
-#ifdef DEMO_HM01B0_FRAMEBUFFER_DUMP_ENABLE
-  // Allow some time to see result of previous inference before dumping image.
-  am_util_delay_ms(2000);
-  hm01b0_framebuffer_dump(frame, frame_width * frame_height * channels);
-#endif
-
-  return kTfLiteOk;
-}

tensorflow/lite/micro/examples/person_detection_experimental/arc_emsdp/Makefile.inc

@@ -1,43 +0,0 @@
-ifeq ($(TARGET), arc_emsdp)
-
-# Patch of arc make project to adjust it specifically 
-# for experimental person detection example. In particular:
-# - Use Linker command file with better usage of fast memory
-# - Stripout TFLM reference code by default.
-# - Optional: replace mli switchers with specialized kernels 
-#   for smaller code size
-
-  person_detection_HDRS += \
-  person_detection_int8_patch.txt
-  
-  person_detection_TEST_HDRS += \
-  person_detection_int8_patch.txt
-  
-  ARC_MLI_BACKEND_PATH = /tensorflow/lite/micro/kernels/arc_mli
-  
-# Apply changes in generated project files. 
-# See related comment echoed (@echo <comment>) after each change 
-# to get understanding on it's purpose.
-%/person_detection_int8_patch.txt: %/emsdp.lcf %/Makefile %$(ARC_MLI_BACKEND_PATH)/conv.cc %$(ARC_MLI_BACKEND_PATH)/depthwise_conv.cc %$(ARC_MLI_BACKEND_PATH)/pooling.cc
-	@cp tensorflow/lite/micro/examples/person_detection_experimental/arc_emsdp/emsdp.lcf $< 
-	@echo emsdp.lcf: Replace with example specific memory map  > $@
-
-	@sed -E -i 's#MLI_ONLY *\?= *false#MLI_ONLY \?= true#' $(word 2, $^)
-	@echo Makefile: No Reference fallback for MLI supported functions >> $@
-
-ifneq ($(filter $(ALL_TAGS), reduce_codesize),)
-# In case 'reduce_codesize' tag is present, we replace common MLI functions with 
-# specializations appropriate for this particular graph. But such changes of code 
-# with high probability may not be acceptable for other graphs and will need 
-# to be adjusted by the user
-
-	@sed -E -i 's#mli_krn_conv2d_nhwc_sa8_sa8_sa32#mli_krn_conv2d_nhwc_sa8_sa8_sa32_k1x1_nopad#' $(word 3, $^)
-	@sed -E -i 's#mli_krn_depthwise_conv2d_hwcn_sa8_sa8_sa32#mli_krn_depthwise_conv2d_hwcn_sa8_sa8_sa32_k3x3_krnpad#' $(word 4, $^)
-	@sed -E -i 's#mli_krn_avepool_hwc_sa8#mli_krn_avepool_hwc_sa8_k3x3_nopad#' $(word 5, $^)
-	@sed -E -i 's#mli_krn_maxpool_hwc_sa8\(in_ptr, \&cfg, out_ptr\);#return kTfLiteError;#' $(word 5, $^)
-	@echo $(word 3, $^): Use specialization >> $@
-	@echo $(word 4, $^): Use specialization >> $@
-	@echo $(word 5, $^): Use specialization and remove max pooling >> $@
-endif
-
-endif

tensorflow/lite/micro/examples/person_detection_experimental/arduino/detection_responder.cc

@@ -1,56 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/micro/examples/person_detection_experimental/detection_responder.h"
-
-#include "Arduino.h"
-
-// Flash the blue LED after each inference
-void RespondToDetection(tflite::ErrorReporter* error_reporter,
-                        int8_t person_score, int8_t no_person_score) {
-  static bool is_initialized = false;
-  if (!is_initialized) {
-    // Pins for the built-in RGB LEDs on the Arduino Nano 33 BLE Sense
-    pinMode(LEDR, OUTPUT);
-    pinMode(LEDG, OUTPUT);
-    pinMode(LEDB, OUTPUT);
-    is_initialized = true;
-  }
-
-  // Note: The RGB LEDs on the Arduino Nano 33 BLE
-  // Sense are on when the pin is LOW, off when HIGH.
-
-  // Switch the person/not person LEDs off
-  digitalWrite(LEDG, HIGH);
-  digitalWrite(LEDR, HIGH);
-
-  // Flash the blue LED after every inference.
-  digitalWrite(LEDB, LOW);
-  delay(100);
-  digitalWrite(LEDB, HIGH);
-
-  // Switch on the green LED when a person is detected,
-  // the red when no person is detected
-  if (person_score > no_person_score) {
-    digitalWrite(LEDG, LOW);
-    digitalWrite(LEDR, HIGH);
-  } else {
-    digitalWrite(LEDG, HIGH);
-    digitalWrite(LEDR, LOW);
-  }
-
-  TF_LITE_REPORT_ERROR(error_reporter, "Person score: %d No person score: %d",
-                       person_score, no_person_score);
-}

tensorflow/lite/micro/examples/person_detection_experimental/arduino/image_provider.cc

@@ -1,266 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/micro/examples/person_detection_experimental/image_provider.h"
-
-/*
- * The sample requires the following third-party libraries to be installed and
- * configured:
- *
- * Arducam
- * -------
- * 1. Download https://github.com/ArduCAM/Arduino and copy its `ArduCAM`
- *    subdirectory into `Arduino/libraries`. Commit #e216049 has been tested
- *    with this code.
- * 2. Edit `Arduino/libraries/ArduCAM/memorysaver.h` and ensure that
- *    "#define OV2640_MINI_2MP_PLUS" is not commented out. Ensure all other
- *    defines in the same section are commented out.
- *
- * JPEGDecoder
- * -----------
- * 1. Install "JPEGDecoder" 1.8.0 from the Arduino library manager.
- * 2. Edit "Arduino/Libraries/JPEGDecoder/src/User_Config.h" and comment out
- *    "#define LOAD_SD_LIBRARY" and "#define LOAD_SDFAT_LIBRARY".
- */
-
-// Required by Arducam library
-#include <SPI.h>
-#include <Wire.h>
-#include <memorysaver.h>
-// Arducam library
-#include <ArduCAM.h>
-// JPEGDecoder library
-#include <JPEGDecoder.h>
-
-// Checks that the Arducam library has been correctly configured
-#if !(defined OV2640_MINI_2MP_PLUS)
-#error Please select the hardware platform and camera module in the Arduino/libraries/ArduCAM/memorysaver.h
-#endif
-
-// The size of our temporary buffer for holding
-// JPEG data received from the Arducam module
-#define MAX_JPEG_BYTES 4096
-// The pin connected to the Arducam Chip Select
-#define CS 7
-
-// Camera library instance
-ArduCAM myCAM(OV2640, CS);
-// Temporary buffer for holding JPEG data from camera
-uint8_t jpeg_buffer[MAX_JPEG_BYTES] = {0};
-// Length of the JPEG data currently in the buffer
-uint32_t jpeg_length = 0;
-
-// Get the camera module ready
-TfLiteStatus InitCamera(tflite::ErrorReporter* error_reporter) {
-  TF_LITE_REPORT_ERROR(error_reporter, "Attempting to start Arducam");
-  // Enable the Wire library
-  Wire.begin();
-  // Configure the CS pin
-  pinMode(CS, OUTPUT);
-  digitalWrite(CS, HIGH);
-  // initialize SPI
-  SPI.begin();
-  // Reset the CPLD
-  myCAM.write_reg(0x07, 0x80);
-  delay(100);
-  myCAM.write_reg(0x07, 0x00);
-  delay(100);
-  // Test whether we can communicate with Arducam via SPI
-  myCAM.write_reg(ARDUCHIP_TEST1, 0x55);
-  uint8_t test;
-  test = myCAM.read_reg(ARDUCHIP_TEST1);
-  if (test != 0x55) {
-    TF_LITE_REPORT_ERROR(error_reporter, "Can't communicate with Arducam");
-    delay(1000);
-    return kTfLiteError;
-  }
-  // Use JPEG capture mode, since it allows us to specify
-  // a resolution smaller than the full sensor frame
-  myCAM.set_format(JPEG);
-  myCAM.InitCAM();
-  // Specify the smallest possible resolution
-  myCAM.OV2640_set_JPEG_size(OV2640_160x120);
-  delay(100);
-  return kTfLiteOk;
-}
-
-// Begin the capture and wait for it to finish
-TfLiteStatus PerformCapture(tflite::ErrorReporter* error_reporter) {
-  TF_LITE_REPORT_ERROR(error_reporter, "Starting capture");
-  // Make sure the buffer is emptied before each capture
-  myCAM.flush_fifo();
-  myCAM.clear_fifo_flag();
-  // Start capture
-  myCAM.start_capture();
-  // Wait for indication that it is done
-  while (!myCAM.get_bit(ARDUCHIP_TRIG, CAP_DONE_MASK)) {
-  }
-  TF_LITE_REPORT_ERROR(error_reporter, "Image captured");
-  delay(50);
-  // Clear the capture done flag
-  myCAM.clear_fifo_flag();
-  return kTfLiteOk;
-}
-
-// Read data from the camera module into a local buffer
-TfLiteStatus ReadData(tflite::ErrorReporter* error_reporter) {
-  // This represents the total length of the JPEG data
-  jpeg_length = myCAM.read_fifo_length();
-  TF_LITE_REPORT_ERROR(error_reporter, "Reading %d bytes from Arducam",
-                       jpeg_length);
-  // Ensure there's not too much data for our buffer
-  if (jpeg_length > MAX_JPEG_BYTES) {
-    TF_LITE_REPORT_ERROR(error_reporter, "Too many bytes in FIFO buffer (%d)",
-                         MAX_JPEG_BYTES);
-    return kTfLiteError;
-  }
-  if (jpeg_length == 0) {
-    TF_LITE_REPORT_ERROR(error_reporter, "No data in Arducam FIFO buffer");
-    return kTfLiteError;
-  }
-  myCAM.CS_LOW();
-  myCAM.set_fifo_burst();
-  for (int index = 0; index < jpeg_length; index++) {
-    jpeg_buffer[index] = SPI.transfer(0x00);
-  }
-  delayMicroseconds(15);
-  TF_LITE_REPORT_ERROR(error_reporter, "Finished reading");
-  myCAM.CS_HIGH();
-  return kTfLiteOk;
-}
-
-// Decode the JPEG image, crop it, and convert it to greyscale
-TfLiteStatus DecodeAndProcessImage(tflite::ErrorReporter* error_reporter,
-                                   int image_width, int image_height,
-                                   int8_t* image_data) {
-  TF_LITE_REPORT_ERROR(error_reporter,
-                       "Decoding JPEG and converting to greyscale");
-  // Parse the JPEG headers. The image will be decoded as a sequence of Minimum
-  // Coded Units (MCUs), which are 16x8 blocks of pixels.
-  JpegDec.decodeArray(jpeg_buffer, jpeg_length);
-
-  // Crop the image by keeping a certain number of MCUs in each dimension
-  const int keep_x_mcus = image_width / JpegDec.MCUWidth;
-  const int keep_y_mcus = image_height / JpegDec.MCUHeight;
-
-  // Calculate how many MCUs we will throw away on the x axis
-  const int skip_x_mcus = JpegDec.MCUSPerRow - keep_x_mcus;
-  // Roughly center the crop by skipping half the throwaway MCUs at the
-  // beginning of each row
-  const int skip_start_x_mcus = skip_x_mcus / 2;
-  // Index where we will start throwing away MCUs after the data
-  const int skip_end_x_mcu_index = skip_start_x_mcus + keep_x_mcus;
-  // Same approach for the columns
-  const int skip_y_mcus = JpegDec.MCUSPerCol - keep_y_mcus;
-  const int skip_start_y_mcus = skip_y_mcus / 2;
-  const int skip_end_y_mcu_index = skip_start_y_mcus + keep_y_mcus;
-
-  // Pointer to the current pixel
-  uint16_t* pImg;
-  // Color of the current pixel
-  uint16_t color;
-
-  // Loop over the MCUs
-  while (JpegDec.read()) {
-    // Skip over the initial set of rows
-    if (JpegDec.MCUy < skip_start_y_mcus) {
-      continue;
-    }
-    // Skip if we're on a column that we don't want
-    if (JpegDec.MCUx < skip_start_x_mcus ||
-        JpegDec.MCUx >= skip_end_x_mcu_index) {
-      continue;
-    }
-    // Skip if we've got all the rows we want
-    if (JpegDec.MCUy >= skip_end_y_mcu_index) {
-      continue;
-    }
-    // Pointer to the current pixel
-    pImg = JpegDec.pImage;
-
-    // The x and y indexes of the current MCU, ignoring the MCUs we skip
-    int relative_mcu_x = JpegDec.MCUx - skip_start_x_mcus;
-    int relative_mcu_y = JpegDec.MCUy - skip_start_y_mcus;
-
-    // The coordinates of the top left of this MCU when applied to the output
-    // image
-    int x_origin = relative_mcu_x * JpegDec.MCUWidth;
-    int y_origin = relative_mcu_y * JpegDec.MCUHeight;
-
-    // Loop through the MCU's rows and columns
-    for (int mcu_row = 0; mcu_row < JpegDec.MCUHeight; mcu_row++) {
-      // The y coordinate of this pixel in the output index
-      int current_y = y_origin + mcu_row;
-      for (int mcu_col = 0; mcu_col < JpegDec.MCUWidth; mcu_col++) {
-        // Read the color of the pixel as 16-bit integer
-        color = *pImg++;
-        // Extract the color values (5 red bits, 6 green, 5 blue)
-        uint8_t r, g, b;
-        r = ((color & 0xF800) >> 11) * 8;
-        g = ((color & 0x07E0) >> 5) * 4;
-        b = ((color & 0x001F) >> 0) * 8;
-        // Convert to grayscale by calculating luminance
-        // See https://en.wikipedia.org/wiki/Grayscale for magic numbers
-        float gray_value = (0.2126 * r) + (0.7152 * g) + (0.0722 * b);
-
-        // Convert to signed 8-bit integer by subtracting 128.
-        gray_value -= 128;
-
-        // The x coordinate of this pixel in the output image
-        int current_x = x_origin + mcu_col;
-        // The index of this pixel in our flat output buffer
-        int index = (current_y * image_width) + current_x;
-        image_data[index] = static_cast<int8_t>(gray_value);
-      }
-    }
-  }
-  TF_LITE_REPORT_ERROR(error_reporter, "Image decoded and processed");
-  return kTfLiteOk;
-}
-
-// Get an image from the camera module
-TfLiteStatus GetImage(tflite::ErrorReporter* error_reporter, int image_width,
-                      int image_height, int channels, int8_t* image_data) {
-  static bool g_is_camera_initialized = false;
-  if (!g_is_camera_initialized) {
-    TfLiteStatus init_status = InitCamera(error_reporter);
-    if (init_status != kTfLiteOk) {
-      TF_LITE_REPORT_ERROR(error_reporter, "InitCamera failed");
-      return init_status;
-    }
-    g_is_camera_initialized = true;
-  }
-
-  TfLiteStatus capture_status = PerformCapture(error_reporter);
-  if (capture_status != kTfLiteOk) {
-    TF_LITE_REPORT_ERROR(error_reporter, "PerformCapture failed");
-    return capture_status;
-  }
-
-  TfLiteStatus read_data_status = ReadData(error_reporter);
-  if (read_data_status != kTfLiteOk) {
-    TF_LITE_REPORT_ERROR(error_reporter, "ReadData failed");
-    return read_data_status;
-  }
-
-  TfLiteStatus decode_status = DecodeAndProcessImage(
-      error_reporter, image_width, image_height, image_data);
-  if (decode_status != kTfLiteOk) {
-    TF_LITE_REPORT_ERROR(error_reporter, "DecodeAndProcessImage failed");
-    return decode_status;
-  }
-
-  return kTfLiteOk;
-}

tensorflow/lite/micro/examples/person_detection_experimental/arduino/main.cc

@@ -1,20 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/micro/examples/person_detection_experimental/main_functions.h"
-
-// Arduino automatically calls the setup() and loop() functions in a sketch, so
-// where other systems need their own main routine in this file, it can be left
-// empty.

tensorflow/lite/micro/examples/person_detection_experimental/detection_responder.cc

@@ -1,25 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/micro/examples/person_detection_experimental/detection_responder.h"
-
-// This dummy implementation writes person and no person scores to the error
-// console. Real applications will want to take some custom action instead, and
-// should implement their own versions of this function.
-void RespondToDetection(tflite::ErrorReporter* error_reporter,
-                        int8_t person_score, int8_t no_person_score) {
-  TF_LITE_REPORT_ERROR(error_reporter, "person score:%d no person score %d",
-                       person_score, no_person_score);
-}

tensorflow/lite/micro/examples/person_detection_experimental/detection_responder.h

@@ -1,34 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-// Provides an interface to take an action based on the output from the person
-// detection model.
-
-#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_DETECTION_RESPONDER_H_
-#define TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_DETECTION_RESPONDER_H_
-
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/micro/micro_error_reporter.h"
-
-// Called every time the results of a person detection run are available. The
-// `person_score` has the numerical confidence that the captured image contains
-// a person, and `no_person_score` has the numerical confidence that the image
-// does not contain a person. Typically if person_score > no person score, the
-// image is considered to contain a person.  This threshold may be adjusted for
-// particular applications.
-void RespondToDetection(tflite::ErrorReporter* error_reporter,
-                        int8_t person_score, int8_t no_person_score);
-
-#endif  // TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_DETECTION_RESPONDER_H_

tensorflow/lite/micro/examples/person_detection_experimental/detection_responder_test.cc

@@ -1,32 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/micro/examples/person_detection_experimental/detection_responder.h"
-
-#include "tensorflow/lite/micro/testing/micro_test.h"
-
-TF_LITE_MICRO_TESTS_BEGIN
-
-TF_LITE_MICRO_TEST(TestCallability) {
-  tflite::MicroErrorReporter micro_error_reporter;
-
-  // This will have external side-effects (like printing to the debug console
-  // or lighting an LED) that are hard to observe, so the most we can do is
-  // make sure the call doesn't crash.
-  RespondToDetection(&micro_error_reporter, -100, 100);
-  RespondToDetection(&micro_error_reporter, 100, 50);
-}
-
-TF_LITE_MICRO_TESTS_END

tensorflow/lite/micro/examples/person_detection_experimental/himax_driver/HM01B0.c

@@ -1,719 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "HM01B0.h"
-
-#include "HM01B0_Walking1s_01.h"
-#include "am_bsp.h"
-#include "am_mcu_apollo.h"
-#include "am_util.h"
-#include "platform_Sparkfun_Edge.h"
-
-//#define ENABLE_ASYNC
-
-const am_hal_gpio_pincfg_t g_HM01B0_pin_vsync = {
-    .uFuncSel = 3,
-    .eGPOutcfg = AM_HAL_GPIO_PIN_OUTCFG_DISABLE,
-#ifdef ENABLE_ASYNC
-    .eIntDir = AM_HAL_GPIO_PIN_INTDIR_BOTH,
-#endif
-    .eGPInput = AM_HAL_GPIO_PIN_INPUT_ENABLE,
-    .eGPRdZero = AM_HAL_GPIO_PIN_RDZERO_READPIN};
-
-const am_hal_gpio_pincfg_t g_HM01B0_pin_int = {
-    .uFuncSel = 3,
-    .eGPOutcfg = AM_HAL_GPIO_PIN_OUTCFG_DISABLE,
-    .eIntDir = AM_HAL_GPIO_PIN_INTDIR_LO2HI,
-    .eGPInput = AM_HAL_GPIO_PIN_INPUT_ENABLE,
-    .eGPRdZero = AM_HAL_GPIO_PIN_RDZERO_READPIN};
-
-#ifdef ENABLE_ASYNC
-static bool s_bVsyncAsserted = false;
-
-//*****************************************************************************
-//
-// GPIO ISR
-//
-//*****************************************************************************
-static void hm01b0_gpio_isr(void) {
-  //
-  // Clear the GPIO Interrupt (write to clear).
-  //
-  am_hal_gpio_interrupt_clear(1 << HM01B0_PIN_VSYNC);
-
-  if (read_vsync()) {
-    s_bVsyncAsserted = true;
-  } else {
-    s_bVsyncAsserted = false;
-  }
-}
-#endif
-
-//*****************************************************************************
-//
-//! @brief Write HM01B0 registers
-//!
-//! @param psCfg                - Pointer to HM01B0 configuration structure.
-//! @param ui16Reg              - Register address.
-//! @param pui8Value            - Pointer to the data to be written.
-//! @param ui32NumBytes         - Length of the data in bytes to be written.
-//!
-//! This function writes value to HM01B0 registers.
-//!
-//! @return Error code.
-//
-//*****************************************************************************
-static uint32_t hm01b0_write_reg(hm01b0_cfg_t* psCfg, uint16_t ui16Reg,
-                                 uint8_t* pui8Value, uint32_t ui32NumBytes) {
-  am_hal_iom_transfer_t Transaction;
-
-  //
-  // Create the transaction.
-  //
-  Transaction.ui32InstrLen = sizeof(uint16_t);
-  Transaction.ui32Instr = (ui16Reg & 0x0000FFFF);
-  Transaction.eDirection = AM_HAL_IOM_TX;
-  Transaction.ui32NumBytes = ui32NumBytes;
-  Transaction.pui32TxBuffer = (uint32_t*)pui8Value;
-  Transaction.uPeerInfo.ui32I2CDevAddr = (uint32_t)psCfg->ui16SlvAddr;
-  Transaction.bContinue = false;
-  Transaction.ui8RepeatCount = 0;
-  Transaction.ui32PauseCondition = 0;
-  Transaction.ui32StatusSetClr = 0;
-
-  //
-  // Execute the transction over IOM.
-  //
-  if (am_hal_iom_blocking_transfer(psCfg->pIOMHandle, &Transaction)) {
-    return HM01B0_ERR_I2C;
-  }
-
-  return HM01B0_ERR_OK;
-}
-
-//*****************************************************************************
-//
-//! @brief Read HM01B0 registers
-//!
-//! @param psCfg                - Pointer to HM01B0 configuration structure.
-//! @param ui16Reg              - Register address.
-//! @param pui8Value            - Pointer to the buffer for read data to be put
-//! into.
-//! @param ui32NumBytes         - Length of the data to be read.
-//!
-//! This function reads value from HM01B0 registers.
-//!
-//! @return Error code.
-//
-//*****************************************************************************
-static uint32_t hm01b0_read_reg(hm01b0_cfg_t* psCfg, uint16_t ui16Reg,
-                                uint8_t* pui8Value, uint32_t ui32NumBytes) {
-  am_hal_iom_transfer_t Transaction;
-
-  //
-  // Create the transaction.
-  //
-  Transaction.ui32InstrLen = sizeof(uint16_t);
-  Transaction.ui32Instr = (ui16Reg & 0x0000FFFF);
-  Transaction.eDirection = AM_HAL_IOM_RX;
-  Transaction.ui32NumBytes = ui32NumBytes;
-  Transaction.pui32RxBuffer = (uint32_t*)pui8Value;
-  ;
-  Transaction.uPeerInfo.ui32I2CDevAddr = (uint32_t)psCfg->ui16SlvAddr;
-  Transaction.bContinue = false;
-  Transaction.ui8RepeatCount = 0;
-  Transaction.ui32PauseCondition = 0;
-  Transaction.ui32StatusSetClr = 0;
-
-  //
-  // Execute the transction over IOM.
-  //
-  if (am_hal_iom_blocking_transfer(psCfg->pIOMHandle, &Transaction)) {
-    return HM01B0_ERR_I2C;
-  }
-
-  return HM01B0_ERR_OK;
-}
-
-//*****************************************************************************
-//
-//! @brief Load HM01B0 a given script
-//!
-//! @param psCfg                - Pointer to HM01B0 configuration structure.
-//! @param psScrip              - Pointer to the script to be loaded.
-//! @param ui32ScriptCmdNum     - Number of entries in a given script.
-//!
-//! This function loads HM01B0 a given script.
-//!
-//! @return Error code.
-//
-//*****************************************************************************
-static uint32_t hm01b0_load_script(hm01b0_cfg_t* psCfg, hm_script_t* psScript,
-                                   uint32_t ui32ScriptCmdNum) {
-  uint32_t ui32Err = HM01B0_ERR_OK;
-  for (uint32_t idx = 0; idx < ui32ScriptCmdNum; idx++) {
-    ui32Err = hm01b0_write_reg(psCfg, (psScript + idx)->ui16Reg,
-                               &((psScript + idx)->ui8Val), sizeof(uint8_t));
-    if (ui32Err != HM01B0_ERR_OK) {
-      break;
-    }
-  }
-
-  return ui32Err;
-}
-
-//*****************************************************************************
-//
-//! @brief Power up HM01B0
-//!
-//! @param psCfg                - Pointer to HM01B0 configuration structure.
-//!
-//! This function powers up HM01B0.
-//!
-//! @return none.
-//
-//*****************************************************************************
-void hm01b0_power_up(hm01b0_cfg_t* psCfg) {
-  // place holder
-}
-
-//*****************************************************************************
-//
-//! @brief Power down HM01B0
-//!
-//! @param psCfg                - Pointer to HM01B0 configuration structure.
-//!
-//! This function powers up HM01B0.
-//!
-//! @return none.
-//
-//*****************************************************************************
-void hm01b0_power_down(hm01b0_cfg_t* psCfg) {
-  // place holder
-}
-
-//*****************************************************************************
-//
-//! @brief Enable MCLK
-//!
-//! @param psCfg                - Pointer to HM01B0 configuration structure.
-//!
-//! This function utilizes CTimer to generate MCLK for HM01B0.
-//!
-//! @return none.
-//
-//*****************************************************************************
-void hm01b0_mclk_enable(hm01b0_cfg_t* psCfg) {
-#define MCLK_UI64PATTERN 0x55555555
-#define MCLK_UI64PATTERNLEN 31
-
-  am_hal_clkgen_control(AM_HAL_CLKGEN_CONTROL_SYSCLK_MAX, 0);
-
-  //
-  // Set up timer.
-  //
-  am_hal_ctimer_clear(psCfg->ui32CTimerModule, psCfg->ui32CTimerSegment);
-
-  am_hal_ctimer_config_single(
-      psCfg->ui32CTimerModule, psCfg->ui32CTimerSegment,
-      (AM_HAL_CTIMER_FN_PTN_REPEAT | AM_HAL_CTIMER_HFRC_12MHZ));
-
-  //
-  // Set the pattern in the CMPR registers.
-  //
-  am_hal_ctimer_compare_set(psCfg->ui32CTimerModule, psCfg->ui32CTimerSegment,
-                            0, (uint32_t)(MCLK_UI64PATTERN & 0xFFFF));
-  am_hal_ctimer_compare_set(psCfg->ui32CTimerModule, psCfg->ui32CTimerSegment,
-                            1, (uint32_t)((MCLK_UI64PATTERN >> 16) & 0xFFFF));
-
-  //
-  // Set the timer trigger and pattern length.
-  //
-  am_hal_ctimer_config_trigger(
-      psCfg->ui32CTimerModule, psCfg->ui32CTimerSegment,
-      ((MCLK_UI64PATTERNLEN << CTIMER_AUX0_TMRA0LMT_Pos) |
-       (CTIMER_AUX0_TMRB0TRIG_DIS << CTIMER_AUX0_TMRA0TRIG_Pos)));
-
-  //
-  // Configure timer output pin.
-  //
-  am_hal_ctimer_output_config(psCfg->ui32CTimerModule, psCfg->ui32CTimerSegment,
-                              psCfg->ui32CTimerOutputPin,
-                              AM_HAL_CTIMER_OUTPUT_NORMAL,
-                              AM_HAL_GPIO_PIN_DRIVESTRENGTH_12MA);
-
-  //
-  // Start the timer.
-  //
-  am_hal_ctimer_start(psCfg->ui32CTimerModule, psCfg->ui32CTimerSegment);
-}
-
-//*****************************************************************************
-//
-//! @brief Disable MCLK
-//!
-//! @param psCfg                - Pointer to HM01B0 configuration structure.
-//!
-//! This function disable CTimer to stop MCLK for HM01B0.
-//!
-//! @return none.
-//
-//*****************************************************************************
-void hm01b0_mclk_disable(hm01b0_cfg_t* psCfg) {
-  //
-  // Stop the timer.
-  //
-  am_hal_ctimer_stop(psCfg->ui32CTimerModule, psCfg->ui32CTimerSegment);
-  am_hal_gpio_pinconfig(psCfg->ui32CTimerOutputPin, g_AM_HAL_GPIO_DISABLE);
-}
-
-//*****************************************************************************
-//
-//! @brief Initialize interfaces
-//!
-//! @param psCfg                - Pointer to HM01B0 configuration structure.
-//!
-//! This function initializes interfaces.
-//!
-//! @return Error code.
-//
-//*****************************************************************************
-uint32_t hm01b0_init_if(hm01b0_cfg_t* psCfg) {
-  void* pIOMHandle = NULL;
-
-  if (psCfg->ui32IOMModule > AM_REG_IOM_NUM_MODULES) {
-    return HM01B0_ERR_I2C;
-  }
-
-  //
-  // Enable fault detection.
-  //
-#if AM_APOLLO3_MCUCTRL
-  am_hal_mcuctrl_control(AM_HAL_MCUCTRL_CONTROL_FAULT_CAPTURE_ENABLE, 0);
-#else   // AM_APOLLO3_MCUCTRL
-  am_hal_mcuctrl_fault_capture_enable();
-#endif  // AM_APOLLO3_MCUCTRL
-
-  //
-  // Initialize the IOM instance.
-  // Enable power to the IOM instance.
-  // Configure the IOM for Serial operation during initialization.
-  // Enable the IOM.
-  //
-  if (am_hal_iom_initialize(psCfg->ui32IOMModule, &pIOMHandle) ||
-      am_hal_iom_power_ctrl(pIOMHandle, AM_HAL_SYSCTRL_WAKE, false) ||
-      am_hal_iom_configure(pIOMHandle, &(psCfg->sIOMCfg)) ||
-      am_hal_iom_enable(pIOMHandle)) {
-    return HM01B0_ERR_I2C;
-  } else {
-    //
-    // Configure the IOM pins.
-    //
-    am_bsp_iom_pins_enable(psCfg->ui32IOMModule, psCfg->eIOMMode);
-
-    psCfg->pIOMHandle = pIOMHandle;
-  }
-
-  // initialize pins for camera parallel interface.
-  am_hal_gpio_fastgpio_disable(psCfg->ui8PinD0);
-  am_hal_gpio_fastgpio_disable(psCfg->ui8PinD1);
-  am_hal_gpio_fastgpio_disable(psCfg->ui8PinD2);
-  am_hal_gpio_fastgpio_disable(psCfg->ui8PinD3);
-  am_hal_gpio_fastgpio_disable(psCfg->ui8PinD4);
-  am_hal_gpio_fastgpio_disable(psCfg->ui8PinD5);
-  am_hal_gpio_fastgpio_disable(psCfg->ui8PinD6);
-  am_hal_gpio_fastgpio_disable(psCfg->ui8PinD7);
-
-  am_hal_gpio_fastgpio_clr(psCfg->ui8PinD0);
-  am_hal_gpio_fastgpio_clr(psCfg->ui8PinD1);
-  am_hal_gpio_fastgpio_clr(psCfg->ui8PinD2);
-  am_hal_gpio_fastgpio_clr(psCfg->ui8PinD3);
-  am_hal_gpio_fastgpio_clr(psCfg->ui8PinD4);
-  am_hal_gpio_fastgpio_clr(psCfg->ui8PinD5);
-  am_hal_gpio_fastgpio_clr(psCfg->ui8PinD6);
-  am_hal_gpio_fastgpio_clr(psCfg->ui8PinD7);
-
-  am_hal_gpio_fast_pinconfig(
-      (uint64_t)0x1 << psCfg->ui8PinD0 | (uint64_t)0x1 << psCfg->ui8PinD1 |
-          (uint64_t)0x1 << psCfg->ui8PinD2 | (uint64_t)0x1 << psCfg->ui8PinD3 |
-          (uint64_t)0x1 << psCfg->ui8PinD4 | (uint64_t)0x1 << psCfg->ui8PinD5 |
-          (uint64_t)0x1 << psCfg->ui8PinD6 | (uint64_t)0x1 << psCfg->ui8PinD7,
-      g_AM_HAL_GPIO_INPUT, 0);
-
-  am_hal_gpio_pinconfig(psCfg->ui8PinVSYNC, g_HM01B0_pin_vsync);
-#ifdef ENABLE_ASYNC
-  psCfg->pfnGpioIsr = hm01b0_gpio_isr;
-  am_hal_gpio_interrupt_clear(AM_HAL_GPIO_BIT(psCfg->ui8PinVSYNC));
-  am_hal_gpio_interrupt_enable(AM_HAL_GPIO_BIT(psCfg->ui8PinVSYNC));
-  NVIC_EnableIRQ(GPIO_IRQn);
-#endif
-  am_hal_gpio_pinconfig(psCfg->ui8PinHSYNC, g_AM_HAL_GPIO_INPUT);
-  am_hal_gpio_pinconfig(psCfg->ui8PinPCLK, g_AM_HAL_GPIO_INPUT);
-
-  am_hal_gpio_pinconfig(psCfg->ui8PinTrig, g_AM_HAL_GPIO_OUTPUT);
-
-  am_hal_gpio_pinconfig(psCfg->ui8PinInt, g_AM_HAL_GPIO_DISABLE);
-  // am_hal_gpio_pinconfig(psCfg->ui8PinInt,     g_HM01B0_pin_int);
-  // am_hal_gpio_interrupt_clear(AM_HAL_GPIO_BIT(psCfg->ui8PinInt));
-  // am_hal_gpio_interrupt_enable(AM_HAL_GPIO_BIT(psCfg->ui8PinInt));
-  // NVIC_EnableIRQ(GPIO_IRQn);
-
-  return HM01B0_ERR_OK;
-}
-
-//*****************************************************************************
-//
-//! @brief Deinitialize interfaces
-//!
-//! @param psCfg                - Pointer to HM01B0 configuration structure.
-//!
-//! This function deinitializes interfaces.
-//!
-//! @return Error code.
-//
-//*****************************************************************************
-uint32_t hm01b0_deinit_if(hm01b0_cfg_t* psCfg) {
-  am_hal_iom_disable(psCfg->pIOMHandle);
-  am_hal_iom_uninitialize(psCfg->pIOMHandle);
-
-  am_hal_gpio_pinconfig(psCfg->ui8PinSCL, g_AM_HAL_GPIO_DISABLE);
-  am_hal_gpio_pinconfig(psCfg->ui8PinSDA, g_AM_HAL_GPIO_DISABLE);
-
-  // initialize pins for camera parallel interface.
-  am_hal_gpio_fastgpio_disable(psCfg->ui8PinD0);
-  am_hal_gpio_fastgpio_disable(psCfg->ui8PinD1);
-  am_hal_gpio_fastgpio_disable(psCfg->ui8PinD2);
-  am_hal_gpio_fastgpio_disable(psCfg->ui8PinD3);
-  am_hal_gpio_fastgpio_disable(psCfg->ui8PinD4);
-  am_hal_gpio_fastgpio_disable(psCfg->ui8PinD5);
-  am_hal_gpio_fastgpio_disable(psCfg->ui8PinD6);
-  am_hal_gpio_fastgpio_disable(psCfg->ui8PinD7);
-
-  am_hal_gpio_fastgpio_clr(psCfg->ui8PinD0);
-  am_hal_gpio_fastgpio_clr(psCfg->ui8PinD1);
-  am_hal_gpio_fastgpio_clr(psCfg->ui8PinD2);
-  am_hal_gpio_fastgpio_clr(psCfg->ui8PinD3);
-  am_hal_gpio_fastgpio_clr(psCfg->ui8PinD4);
-  am_hal_gpio_fastgpio_clr(psCfg->ui8PinD5);
-  am_hal_gpio_fastgpio_clr(psCfg->ui8PinD6);
-  am_hal_gpio_fastgpio_clr(psCfg->ui8PinD7);
-
-  am_hal_gpio_pinconfig(psCfg->ui8PinVSYNC, g_AM_HAL_GPIO_DISABLE);
-#ifdef ENABLE_ASYNC
-  NVIC_DisableIRQ(GPIO_IRQn);
-  am_hal_gpio_interrupt_disable(AM_HAL_GPIO_BIT(psCfg->ui8PinVSYNC));
-  am_hal_gpio_interrupt_clear(AM_HAL_GPIO_BIT(psCfg->ui8PinVSYNC));
-  psCfg->pfnGpioIsr = NULL;
-#endif
-  am_hal_gpio_pinconfig(psCfg->ui8PinHSYNC, g_AM_HAL_GPIO_DISABLE);
-  am_hal_gpio_pinconfig(psCfg->ui8PinPCLK, g_AM_HAL_GPIO_DISABLE);
-
-  am_hal_gpio_pinconfig(psCfg->ui8PinTrig, g_AM_HAL_GPIO_DISABLE);
-  am_hal_gpio_pinconfig(psCfg->ui8PinInt, g_AM_HAL_GPIO_DISABLE);
-
-  return HM01B0_ERR_OK;
-}
-
-//*****************************************************************************
-//
-//! @brief Get HM01B0 Model ID
-//!
-//! @param psCfg                - Pointer to HM01B0 configuration structure.
-//! @param pui16MID             - Pointer to buffer for the read back model ID.
-//!
-//! This function reads back HM01B0 model ID.
-//!
-//! @return Error code.
-//
-//*****************************************************************************
-uint32_t hm01b0_get_modelid(hm01b0_cfg_t* psCfg, uint16_t* pui16MID) {
-  uint8_t ui8Data[1];
-  uint32_t ui32Err;
-
-  *pui16MID = 0x0000;
-
-  ui32Err =
-      hm01b0_read_reg(psCfg, HM01B0_REG_MODEL_ID_H, ui8Data, sizeof(ui8Data));
-  if (ui32Err == HM01B0_ERR_OK) {
-    *pui16MID |= (ui8Data[0] << 8);
-  }
-
-  ui32Err =
-      hm01b0_read_reg(psCfg, HM01B0_REG_MODEL_ID_L, ui8Data, sizeof(ui8Data));
-  if (ui32Err == HM01B0_ERR_OK) {
-    *pui16MID |= ui8Data[0];
-  }
-
-  return ui32Err;
-}
-
-//*****************************************************************************
-//
-//! @brief Initialize HM01B0
-//!
-//! @param psCfg                - Pointer to HM01B0 configuration structure.
-//! @param psScript             - Pointer to HM01B0 initialization script.
-//! @param ui32ScriptCmdNum     - No. of commands in HM01B0 initialization
-//! script.
-//!
-//! This function initilizes HM01B0 with a given script.
-//!
-//! @return Error code.
-//
-//*****************************************************************************
-uint32_t hm01b0_init_system(hm01b0_cfg_t* psCfg, hm_script_t* psScript,
-                            uint32_t ui32ScriptCmdNum) {
-  return hm01b0_load_script(psCfg, psScript, ui32ScriptCmdNum);
-}
-
-//*****************************************************************************
-//
-//! @brief Set HM01B0 in the walking 1s test mode
-//!
-//! @param psCfg                - Pointer to HM01B0 configuration structure.
-//!
-//! This function sets HM01B0 in the walking 1s test mode.
-//!
-//! @return Error code.
-//
-//*****************************************************************************
-uint32_t hm01b0_test_walking1s(hm01b0_cfg_t* psCfg) {
-  uint32_t ui32ScriptCmdNum =
-      sizeof(sHM01b0TestModeScript_Walking1s) / sizeof(hm_script_t);
-  hm_script_t* psScript = (hm_script_t*)sHM01b0TestModeScript_Walking1s;
-
-  return hm01b0_load_script(psCfg, psScript, ui32ScriptCmdNum);
-}
-
-//*****************************************************************************
-//
-//! @brief Software reset HM01B0
-//!
-//! @param psCfg        - Pointer to HM01B0 configuration structure.
-//!
-//! This function resets HM01B0 by issuing a reset command.
-//!
-//! @return Error code.
-//
-//*****************************************************************************
-uint32_t hm01b0_reset_sw(hm01b0_cfg_t* psCfg) {
-  uint8_t ui8Data[1] = {0x00};
-  return hm01b0_write_reg(psCfg, HM01B0_REG_SW_RESET, ui8Data, sizeof(ui8Data));
-}
-
-//*****************************************************************************
-//
-//! @brief Get current HM01B0 operation mode.
-//!
-//! @param psCfg        - Pointer to HM01B0 configuration structure.
-//! @param pui8Mode     - Pointer to buffer
-//!                     - for the read back operation mode to be put into
-//!
-//! This function get HM01B0 operation mode.
-//!
-//! @return Error code.
-//
-//*****************************************************************************
-uint32_t hm01b0_get_mode(hm01b0_cfg_t* psCfg, uint8_t* pui8Mode) {
-  uint8_t ui8Data[1] = {0x01};
-  uint32_t ui32Err;
-
-  ui32Err =
-      hm01b0_read_reg(psCfg, HM01B0_REG_MODE_SELECT, ui8Data, sizeof(ui8Data));
-
-  *pui8Mode = ui8Data[0];
-
-  return ui32Err;
-}
-
-//*****************************************************************************
-//
-//! @brief Set HM01B0 operation mode.
-//!
-//! @param psCfg        - Pointer to HM01B0 configuration structure.
-//! @param ui8Mode      - Operation mode. One of:
-//!     HM01B0_REG_MODE_SELECT_STANDBY
-//!     HM01B0_REG_MODE_SELECT_STREAMING
-//!     HM01B0_REG_MODE_SELECT_STREAMING_NFRAMES
-//!     HM01B0_REG_MODE_SELECT_STREAMING_HW_TRIGGER
-//! @param ui8FrameCnt  - Frame count for
-//! HM01B0_REG_MODE_SELECT_STREAMING_NFRAMES.
-//!                     - Discarded if other modes.
-//!
-//! This function set HM01B0 operation mode.
-//!
-//! @return Error code.
-//
-//*****************************************************************************
-uint32_t hm01b0_set_mode(hm01b0_cfg_t* psCfg, uint8_t ui8Mode,
-                         uint8_t ui8FrameCnt) {
-  uint32_t ui32Err = HM01B0_ERR_OK;
-
-  if (ui8Mode == HM01B0_REG_MODE_SELECT_STREAMING_NFRAMES) {
-    ui32Err = hm01b0_write_reg(psCfg, HM01B0_REG_PMU_PROGRAMMABLE_FRAMECNT,
-                               &ui8FrameCnt, sizeof(ui8FrameCnt));
-  }
-
-  if (ui32Err == HM01B0_ERR_OK) {
-    ui32Err = hm01b0_write_reg(psCfg, HM01B0_REG_MODE_SELECT, &ui8Mode,
-                               sizeof(ui8Mode));
-  }
-
-  return ui32Err;
-}
-
-//*****************************************************************************
-//
-//! @brief Hardware trigger HM01B0 to stream.
-//!
-//! @param psCfg        - Pointer to HM01B0 configuration structure.
-//! @param bTrigger     - True to start streaming
-//!                     - False to stop streaming
-//!
-//! This function triggers HM01B0 to stream by toggling the TRIG pin.
-//!
-//! @return Error code.
-//
-//*****************************************************************************
-uint32_t hm01b0_hardware_trigger_streaming(hm01b0_cfg_t* psCfg, bool bTrigger) {
-  uint32_t ui32Err = HM01B0_ERR_OK;
-  uint8_t ui8Mode;
-
-  ui32Err = hm01b0_get_mode(psCfg, &ui8Mode);
-
-  if (ui32Err != HM01B0_ERR_OK) goto end;
-
-  if (ui8Mode != HM01B0_REG_MODE_SELECT_STREAMING_HW_TRIGGER) {
-    ui32Err = HM01B0_ERR_MODE;
-    goto end;
-  }
-
-  if (bTrigger) {
-    am_hal_gpio_output_set(psCfg->ui8PinTrig);
-  } else {
-    am_hal_gpio_output_clear(psCfg->ui8PinTrig);
-  }
-
-end:
-  return ui32Err;
-}
-
-//*****************************************************************************
-//
-//! @brief Set HM01B0 mirror mode.
-//!
-//! @param psCfg        - Pointer to HM01B0 configuration structure.
-//! @param bHmirror     - Horizontal mirror
-//! @param bVmirror     - Vertical mirror
-//!
-//! This function set HM01B0 mirror mode.
-//!
-//! @return Error code.
-//
-//*****************************************************************************
-uint32_t hm01b0_set_mirror(hm01b0_cfg_t* psCfg, bool bHmirror, bool bVmirror) {
-  uint8_t ui8Data = 0x00;
-  uint32_t ui32Err = HM01B0_ERR_OK;
-
-  if (bHmirror) {
-    ui8Data |= HM01B0_REG_IMAGE_ORIENTATION_HMIRROR;
-  }
-
-  if (bVmirror) {
-    ui8Data |= HM01B0_REG_IMAGE_ORIENTATION_VMIRROR;
-  }
-
-  ui32Err = hm01b0_write_reg(psCfg, HM01B0_REG_IMAGE_ORIENTATION, &ui8Data,
-                             sizeof(ui8Data));
-
-  if (ui32Err == HM01B0_ERR_OK) {
-    ui8Data = HM01B0_REG_GRP_PARAM_HOLD_HOLD;
-    ui32Err = hm01b0_write_reg(psCfg, HM01B0_REG_GRP_PARAM_HOLD, &ui8Data,
-                               sizeof(ui8Data));
-  }
-
-  return ui32Err;
-}
-
-//*****************************************************************************
-//
-//! @brief Read data of one frame from HM01B0.
-//!
-//! @param psCfg            - Pointer to HM01B0 configuration structure.
-//! @param pui8Buffer       - Pointer to the frame buffer.
-//! @param ui32BufferLen    - Framebuffer size.
-//!
-//! This function read data of one frame from HM01B0.
-//!
-//! @return Error code.
-//
-//*****************************************************************************
-uint32_t hm01b0_blocking_read_oneframe(hm01b0_cfg_t* psCfg, uint8_t* pui8Buffer,
-                                       uint32_t ui32BufferLen) {
-  uint32_t ui32Err = HM01B0_ERR_OK;
-  uint32_t ui32Idx = 0x00;
-
-  am_util_stdio_printf("[%s] +\n", __func__);
-#ifdef ENABLE_ASYNC
-  while (!s_bVsyncAsserted);
-
-  while (s_bVsyncAsserted) {
-    // we don't check HSYNC here on the basis of assuming HM01B0 in the gated
-    // PCLK mode which PCLK toggles only when HSYNC is asserted. And also to
-    // minimize the overhead of polling.
-
-    if (read_pclk()) {
-      *(pui8Buffer + ui32Idx++) = read_byte();
-
-      if (ui32Idx == ui32BufferLen) {
-        goto end;
-      }
-
-      while (read_pclk());
-    }
-  }
-#else
-  uint32_t ui32HsyncCnt = 0x00;
-
-  while ((ui32HsyncCnt < HM01B0_PIXEL_Y_NUM)) {
-    while (0x00 == read_hsync());
-
-    // read one row
-    while (read_hsync()) {
-      while (0x00 == read_pclk());
-
-      *(pui8Buffer + ui32Idx++) = read_byte();
-
-      if (ui32Idx == ui32BufferLen) {
-        goto end;
-      }
-
-      while (read_pclk());
-    }
-
-    ui32HsyncCnt++;
-  }
-#endif
-end:
-  am_util_stdio_printf("[%s] - Byte Counts %d\n", __func__, ui32Idx);
-  return ui32Err;
-}
-
-uint32_t hm01b0_single_frame_capture(hm01b0_cfg_t* psCfg) {
-  hm01b0_write_reg(psCfg, HM01B0_REG_PMU_PROGRAMMABLE_FRAMECNT, 0x01, 1);
-  hm01b0_write_reg(psCfg, HM01B0_REG_MODE_SELECT,
-                   HM01B0_REG_MODE_SELECT_STREAMING_NFRAMES, 1);
-  hm01b0_write_reg(psCfg, HM01B0_REG_GRP_PARAM_HOLD, 0x01, 1);
-}

tensorflow/lite/micro/examples/person_detection_experimental/himax_driver/HM01B0.h

@@ -1,402 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_HIMAX_DRIVER_HM01B0_H_
-#define TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_HIMAX_DRIVER_HM01B0_H_
-
-#ifdef __cplusplus
-extern "C" {
-#endif
-#include "am_bsp.h"         // NOLINT
-#include "am_mcu_apollo.h"  // NOLINT
-#include "am_util.h"        // NOLINT
-
-#define HM01B0_DRV_VERSION (0)
-#define HM01B0_DRV_SUBVERSION (3)
-
-#define HM01B0_DEFAULT_ADDRESS (0x24)
-
-#define HM01B0_PIXEL_X_NUM (324)
-#define HM01B0_PIXEL_Y_NUM (244)
-
-#define HM01B0_REG_MODEL_ID_H (0x0000)
-#define HM01B0_REG_MODEL_ID_L (0x0001)
-#define HM01B0_REG_SILICON_REV (0x0002)
-#define HM01B0_REG_FRAME_COUNT (0x0005)
-#define HM01B0_REG_PIXEL_ORDER (0x0006)
-
-#define HM01B0_REG_MODE_SELECT (0x0100)
-#define HM01B0_REG_IMAGE_ORIENTATION (0x0101)
-#define HM01B0_REG_SW_RESET (0x0103)
-#define HM01B0_REG_GRP_PARAM_HOLD (0x0104)
-
-#define HM01B0_REG_I2C_ID_SEL (0x3400)
-#define HM01B0_REG_I2C_ID_REG (0x3401)
-
-#define HM01B0_REG_PMU_PROGRAMMABLE_FRAMECNT (0x3020)
-
-// #define HM01B0_REG_MODE_SELECT (0x0100)
-#define HM01B0_REG_MODE_SELECT_STANDBY (0x00)
-#define HM01B0_REG_MODE_SELECT_STREAMING (0x01)
-#define HM01B0_REG_MODE_SELECT_STREAMING_NFRAMES (0x03)
-#define HM01B0_REG_MODE_SELECT_STREAMING_HW_TRIGGER (0x05)
-
-// #define HM01B0_REG_IMAGE_ORIENTATION                    (0x0101)
-#define HM01B0_REG_IMAGE_ORIENTATION_DEFAULT (0x00)
-#define HM01B0_REG_IMAGE_ORIENTATION_HMIRROR (0x01)
-#define HM01B0_REG_IMAGE_ORIENTATION_VMIRROR (0x02)
-#define HM01B0_REG_IMAGE_ORIENTATION_HVMIRROR \
-  (HM01B0_REG_IMAGE_ORIENTATION_HMIRROR | HM01B0_REG_IMAGE_ORIENTATION_HVMIRROR)
-
-// #define HM01B0_REG_GRP_PARAM_HOLD                       (0x0104)
-#define HM01B0_REG_GRP_PARAM_HOLD_CONSUME (0x00)
-#define HM01B0_REG_GRP_PARAM_HOLD_HOLD (0x01)
-
-// Helpers for reading raw values from the camera.
-#define read_vsync() \
-  (AM_REGVAL(AM_REGADDR(GPIO, RDA)) & (1 << HM01B0_PIN_VSYNC))
-#define read_hsync() \
-  (AM_REGVAL(AM_REGADDR(GPIO, RDA)) & (1 << HM01B0_PIN_HSYNC))
-#define read_pclk() (AM_REGVAL(AM_REGADDR(GPIO, RDA)) & (1 << HM01B0_PIN_PCLK))
-#define read_byte() (APBDMA->BBINPUT)
-
-enum {
-  HM01B0_ERR_OK = 0x00,
-  HM01B0_ERR_I2C,
-  HM01B0_ERR_MODE,
-};
-
-typedef struct {
-  uint16_t ui16Reg;
-  uint8_t ui8Val;
-} hm_script_t;
-
-typedef struct {
-  uint16_t ui16SlvAddr;
-  am_hal_iom_mode_e eIOMMode;
-  uint32_t ui32IOMModule;
-  am_hal_iom_config_t sIOMCfg;
-  void *pIOMHandle;
-
-  uint32_t ui32CTimerModule;
-  uint32_t ui32CTimerSegment;
-  uint32_t ui32CTimerOutputPin;
-
-  uint8_t ui8PinSCL;
-  uint8_t ui8PinSDA;
-  uint8_t ui8PinD0;
-  uint8_t ui8PinD1;
-  uint8_t ui8PinD2;
-  uint8_t ui8PinD3;
-  uint8_t ui8PinD4;
-  uint8_t ui8PinD5;
-  uint8_t ui8PinD6;
-  uint8_t ui8PinD7;
-  uint8_t ui8PinVSYNC;
-  uint8_t ui8PinHSYNC;
-  uint8_t ui8PinPCLK;
-
-  uint8_t ui8PinTrig;
-  uint8_t ui8PinInt;
-  void (*pfnGpioIsr)(void);
-} hm01b0_cfg_t;
-
-//*****************************************************************************
-//
-//! @brief Write HM01B0 registers
-//!
-//! @param psCfg                - Pointer to HM01B0 configuration structure.
-//! @param ui16Reg              - Register address.
-//! @param pui8Value            - Pointer to the data to be written.
-//! @param ui32NumBytes         - Length of the data in bytes to be written.
-//!
-//! This function writes value to HM01B0 registers.
-//!
-//! @return Error code.
-//
-//*****************************************************************************
-static uint32_t hm01b0_write_reg(hm01b0_cfg_t *psCfg, uint16_t ui16Reg,
-                                 uint8_t *pui8Value, uint32_t ui32NumBytes);
-
-//*****************************************************************************
-//
-//! @brief Read HM01B0 registers
-//!
-//! @param psCfg                - Pointer to HM01B0 configuration structure.
-//! @param ui16Reg              - Register address.
-//! @param pui8Value            - Pointer to the buffer for read data to be put
-//! into.
-//! @param ui32NumBytes         - Length of the data to be read.
-//!
-//! This function reads value from HM01B0 registers.
-//!
-//! @return Error code.
-//
-//*****************************************************************************
-static uint32_t hm01b0_read_reg(hm01b0_cfg_t *psCfg, uint16_t ui16Reg,
-                                uint8_t *pui8Value, uint32_t ui32NumBytes);
-
-//*****************************************************************************
-//
-//! @brief Load HM01B0 a given script
-//!
-//! @param psCfg                - Pointer to HM01B0 configuration structure.
-//! @param psScrip              - Pointer to the script to be loaded.
-//! @param ui32ScriptCmdNum     - Number of entries in a given script.
-//!
-//! This function loads HM01B0 a given script.
-//!
-//! @return Error code.
-//
-//*****************************************************************************
-static uint32_t hm01b0_load_script(hm01b0_cfg_t *psCfg, hm_script_t *psScript,
-                                   uint32_t ui32ScriptCmdNum);
-
-//*****************************************************************************
-//
-//! @brief Power up HM01B0
-//!
-//! @param psCfg                - Pointer to HM01B0 configuration structure.
-//!
-//! This function powers up HM01B0.
-//!
-//! @return none.
-//
-//*****************************************************************************
-void hm01b0_power_up(hm01b0_cfg_t *psCfg);
-
-//*****************************************************************************
-//
-//! @brief Power down HM01B0
-//!
-//! @param psCfg                - Pointer to HM01B0 configuration structure.
-//!
-//! This function powers up HM01B0.
-//!
-//! @return none.
-//
-//*****************************************************************************
-void hm01b0_power_down(hm01b0_cfg_t *psCfg);
-
-//*****************************************************************************
-//
-//! @brief Enable MCLK
-//!
-//! @param psCfg                - Pointer to HM01B0 configuration structure.
-//!
-//! This function utilizes CTimer to generate MCLK for HM01B0.
-//!
-//! @return none.
-//
-//*****************************************************************************
-void hm01b0_mclk_enable(hm01b0_cfg_t *psCfg);
-
-//*****************************************************************************
-//
-//! @brief Disable MCLK
-//!
-//! @param psCfg                - Pointer to HM01B0 configuration structure.
-//!
-//! This function disable CTimer to stop MCLK for HM01B0.
-//!
-//! @return none.
-//
-//*****************************************************************************
-void hm01b0_mclk_disable(hm01b0_cfg_t *psCfg);
-
-//*****************************************************************************
-//
-//! @brief Initialize interfaces
-//!
-//! @param psCfg                - Pointer to HM01B0 configuration structure.
-//!
-//! This function initializes interfaces.
-//!
-//! @return Error code.
-//
-//*****************************************************************************
-uint32_t hm01b0_init_if(hm01b0_cfg_t *psCfg);
-
-//*****************************************************************************
-//
-//! @brief Deinitialize interfaces
-//!
-//! @param psCfg                - Pointer to HM01B0 configuration structure.
-//!
-//! This function deinitializes interfaces.
-//!
-//! @return Error code.
-//
-//*****************************************************************************
-uint32_t hm01b0_deinit_if(hm01b0_cfg_t *psCfg);
-
-//*****************************************************************************
-//
-//! @brief Get HM01B0 Model ID
-//!
-//! @param psCfg                - Pointer to HM01B0 configuration structure.
-//! @param pui16MID             - Pointer to buffer for the read back model ID.
-//!
-//! This function reads back HM01B0 model ID.
-//!
-//! @return Error code.
-//
-//*****************************************************************************
-uint32_t hm01b0_get_modelid(hm01b0_cfg_t *psCfg, uint16_t *pui16MID);
-
-//*****************************************************************************
-//
-//! @brief Initialize HM01B0
-//!
-//! @param psCfg                - Pointer to HM01B0 configuration structure.
-//! @param psScript             - Pointer to HM01B0 initialization script.
-//! @param ui32ScriptCmdNum     - No. of commands in HM01B0 initialization
-//! script.
-//!
-//! This function initilizes HM01B0 with a given script.
-//!
-//! @return Error code.
-//
-//*****************************************************************************
-uint32_t hm01b0_init_system(hm01b0_cfg_t *psCfg, hm_script_t *psScript,
-                            uint32_t ui32ScriptCmdNum);
-
-//*****************************************************************************
-//
-//! @brief Set HM01B0 in the walking 1s test mode
-//!
-//! @param psCfg                - Pointer to HM01B0 configuration structure.
-//!
-//! This function sets HM01B0 in the walking 1s test mode.
-//!
-//! @return Error code.
-//
-//*****************************************************************************
-uint32_t hm01b0_test_walking1s(hm01b0_cfg_t *psCfg);
-
-//*****************************************************************************
-//
-//! @brief Software reset HM01B0
-//!
-//! @param psCfg        - Pointer to HM01B0 configuration structure.
-//!
-//! This function resets HM01B0 by issuing a reset command.
-//!
-//! @return Error code.
-//
-//*****************************************************************************
-uint32_t hm01b0_reset_sw(hm01b0_cfg_t *psCfg);
-
-//*****************************************************************************
-//
-//! @brief Get current HM01B0 operation mode.
-//!
-//! @param psCfg        - Pointer to HM01B0 configuration structure.
-//! @param pui8Mode     - Pointer to buffer
-//!                     - for the read back operation mode to be put into
-//!
-//! This function get HM01B0 operation mode.
-//!
-//! @return Error code.
-//
-//*****************************************************************************
-uint32_t hm01b0_get_mode(hm01b0_cfg_t *psCfg, uint8_t *pui8Mode);
-
-//*****************************************************************************
-//
-//! @brief Set HM01B0 operation mode.
-//!
-//! @param psCfg        - Pointer to HM01B0 configuration structure.
-//! @param ui8Mode      - Operation mode. One of:
-//!     HM01B0_REG_MODE_SELECT_STANDBY
-//!     HM01B0_REG_MODE_SELECT_STREAMING
-//!     HM01B0_REG_MODE_SELECT_STREAMING_NFRAMES
-//!     HM01B0_REG_MODE_SELECT_STREAMING_HW_TRIGGER
-//! @param framecnt     - Frame count for
-//! HM01B0_REG_MODE_SELECT_STREAMING_NFRAMES.
-//!                     - Discarded if other modes.
-//!
-//! This function set HM01B0 operation mode.
-//!
-//! @return Error code.
-//
-//*****************************************************************************
-uint32_t hm01b0_set_mode(hm01b0_cfg_t *psCfg, uint8_t ui8Mode,
-                         uint8_t framecnt);
-
-//*****************************************************************************
-//
-//! @brief Hardware trigger HM01B0 to stream.
-//!
-//! @param psCfg        - Pointer to HM01B0 configuration structure.
-//! @param bTrigger     - True to start streaming
-//!                     - False to stop streaming
-//!
-//! This function triggers HM01B0 to stream by toggling the TRIG pin.
-//!
-//! @return Error code.
-//
-//*****************************************************************************
-uint32_t hm01b0_hardware_trigger_streaming(hm01b0_cfg_t *psCfg, bool bTrigger);
-
-//*****************************************************************************
-//
-//! @brief Set HM01B0 mirror mode.
-//!
-//! @param psCfg        - Pointer to HM01B0 configuration structure.
-//! @param bHmirror     - Horizontal mirror
-//! @param bVmirror     - Vertical mirror
-//!
-//! This function set HM01B0 mirror mode.
-//!
-//! @return Error code.
-//
-//*****************************************************************************
-uint32_t hm01b0_set_mirror(hm01b0_cfg_t *psCfg, bool bHmirror, bool bVmirror);
-
-//*****************************************************************************
-//
-//! @brief Read data of one frame from HM01B0.
-//!
-//! @param psCfg            - Pointer to HM01B0 configuration structure.
-//! @param pui8Buffer       - Pointer to the frame buffer.
-//! @param ui32BufferLen    - Framebuffer size.
-//!
-//! This function read data of one frame from HM01B0.
-//!
-//! @return Error code.
-//
-//*****************************************************************************
-uint32_t hm01b0_blocking_read_oneframe(hm01b0_cfg_t *psCfg, uint8_t *pui8Buffer,
-                                       uint32_t ui32BufferLen);
-
-//*****************************************************************************
-//
-//! @brief Read data of one frame from HM01B0.
-//!
-//! @param psCfg            - Pointer to HM01B0 configuration structure.
-//!
-//! This function wakes up the camera and captures a single frame.
-//!
-//! @return Error code.
-//
-//*****************************************************************************
-uint32_t hm01b0_single_frame_capture(hm01b0_cfg_t *psCfg);
-
-#ifdef __cplusplus
-}
-#endif
-
-#endif  // TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_HIMAX_DRIVER_HM01B0_H_

tensorflow/lite/micro/examples/person_detection_experimental/himax_driver/HM01B0_RAW8_QVGA_8bits_lsb_5fps.h

@@ -1,510 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_HIMAX_DRIVER_HM01B0_RAW8_QVGA_8BITS_LSB_5FPS_H_
-#define TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_HIMAX_DRIVER_HM01B0_RAW8_QVGA_8BITS_LSB_5FPS_H_
-
-#include "HM01B0.h"
-
-const hm_script_t sHM01B0InitScript[] = {
-    // ;*************************************************************************
-    // ; Sensor: HM01B0
-    // ; I2C ID: 24
-    // ; Resolution: 324x244
-    // ; Lens:
-    // ; Flicker:
-    // ; Frequency:
-    // ; Description: AE control enable
-    // ; 8-bit mode, LSB first
-    // ;
-    // ;
-    // ; Note:
-    // ;
-    // ; $Revision: 1338 $
-    // ; $Date:: 2017-04-11 15:43:45 +0800#$
-    // ;*************************************************************************
-    //
-    // // ---------------------------------------------------
-    // // HUB system initial
-    // // ---------------------------------------------------
-    // W 20 8A04 01 2 1
-    // W 20 8A00 22 2 1
-    // W 20 8A01 00 2 1
-    // W 20 8A02 01 2 1
-    // W 20 0035 93 2 1 ; [3]&[1] hub616 20bits in, [5:4]=1 mclk=48/2=24mhz
-    // W 20 0036 00 2 1
-    // W 20 0011 09 2 1
-    // W 20 0012 B6 2 1
-    // W 20 0014 08 2 1
-    // W 20 0015 98 2 1
-    // ;W 20 0130 16 2 1 ; 3m soc, signal buffer control
-    // ;W 20 0100 44 2 1 ; [6] hub616 20bits in
-    // W 20 0100 04 2 1 ; [6] hub616 20bits in
-    // W 20 0121 01 2 1 ; [0] Q1 Intf enable, [1]:4bit mode, [2] msb first, [3]
-    // serial mode
-    // W 20 0150 00 2 1 ;
-    // W 20 0150 04 2 1 ;
-    //
-    //
-    // //---------------------------------------------------
-    // // Initial
-    // //---------------------------------------------------
-    // W 24 0103 00 2 1 ; software reset-> was 0x22
-    {
-        0x0103,
-        0x00,
-    },
-    // W 24 0100 00 2 1; power up
-    {
-        0x0100,
-        0x00,
-    },
-    //
-    //
-    //
-    // //---------------------------------------------------
-    // // Analog
-    // //---------------------------------------------------
-    // L HM01B0_analog_setting.txt
-    {
-        0x1003,
-        0x08,
-    },
-    {
-        0x1007,
-        0x08,
-    },
-    {
-        0x3044,
-        0x0A,
-    },
-    {
-        0x3045,
-        0x00,
-    },
-    {
-        0x3047,
-        0x0A,
-    },
-    {
-        0x3050,
-        0xC0,
-    },
-    {
-        0x3051,
-        0x42,
-    },
-    {
-        0x3052,
-        0x50,
-    },
-    {
-        0x3053,
-        0x00,
-    },
-    {
-        0x3054,
-        0x03,
-    },
-    {
-        0x3055,
-        0xF7,
-    },
-    {
-        0x3056,
-        0xF8,
-    },
-    {
-        0x3057,
-        0x29,
-    },
-    {
-        0x3058,
-        0x1F,
-    },
-    {
-        0x3059,
-        0x1E,
-    },
-    {
-        0x3064,
-        0x00,
-    },
-    {
-        0x3065,
-        0x04,
-    },
-    //
-    //
-    // //---------------------------------------------------
-    // // Digital function
-    // //---------------------------------------------------
-    //
-    // // BLC
-    // W 24 1000 43 2 1 ; BLC_on, IIR
-    {
-        0x1000,
-        0x43,
-    },
-    // W 24 1001 40 2 1 ; [6] : BLC dithering en
-    {
-        0x1001,
-        0x40,
-    },
-    // W 24 1002 32 2 1 ; // blc_darkpixel_thd
-    {
-        0x1002,
-        0x32,
-    },
-    //
-    // // Dgain
-    // W 24 0350 7F 2 1 ; Dgain Control
-    {
-        0x0350,
-        0x7F,
-    },
-    //
-    // // BLI
-    // W 24 1006 01 2 1 ; [0] : bli enable
-    {
-        0x1006,
-        0x01,
-    },
-    //
-    // // DPC
-    // W 24 1008 00 2 1 ; [2:0] : DPC option 0: DPC off 1 : mono 3 : bayer1 5 :
-    // bayer2
-    {
-        0x1008,
-        0x00,
-    },
-    // W 24 1009 A0 2 1 ; cluster hot pixel th
-    {
-        0x1009,
-        0xA0,
-    },
-    // W 24 100A 60 2 1 ; cluster cold pixel th
-    {
-        0x100A,
-        0x60,
-    },
-    // W 24 100B 90 2 1 ; single hot pixel th
-    {
-        0x100B,
-        0x90,
-    },
-    // W 24 100C 40 2 1 ; single cold pixel th
-    {
-        0x100C,
-        0x40,
-    },
-    // //
-    // advance VSYNC by 1 row
-    {
-        0x3022,
-        0x01,
-    },
-    // W 24 1012 00 2 1 ; Sync. enable VSYNC shift
-    {
-        0x1012,
-        0x01,
-    },
-
-    //
-    // // ROI Statistic
-    // W 24 2000 07 2 1 ; [0] : AE stat en [1] : MD LROI stat en [2] : MD GROI
-    // stat en [3] : RGB stat ratio en [4] : IIR selection (1 -> 16, 0 -> 8)
-    {
-        0x2000,
-        0x07,
-    },
-    // W 24 2003 00 2 1 ; MD GROI 0 y start HB
-    {
-        0x2003,
-        0x00,
-    },
-    // W 24 2004 1C 2 1 ; MD GROI 0 y start LB
-    {
-        0x2004,
-        0x1C,
-    },
-    // W 24 2007 00 2 1 ; MD GROI 1 y start HB
-    {
-        0x2007,
-        0x00,
-    },
-    // W 24 2008 58 2 1 ; MD GROI 1 y start LB
-    {
-        0x2008,
-        0x58,
-    },
-    // W 24 200B 00 2 1 ; MD GROI 2 y start HB
-    {
-        0x200B,
-        0x00,
-    },
-    // W 24 200C 7A 2 1 ; MD GROI 2 y start LB
-    {
-        0x200C,
-        0x7A,
-    },
-    // W 24 200F 00 2 1 ; MD GROI 3 y start HB
-    {
-        0x200F,
-        0x00,
-    },
-    // W 24 2010 B8 2 1 ; MD GROI 3 y start LB
-    {
-        0x2010,
-        0xB8,
-    },
-    //
-    // W 24 2013 00 2 1 ; MD LRIO y start HB
-    {
-        0x2013,
-        0x00,
-    },
-    // W 24 2014 58 2 1 ; MD LROI y start LB
-    {
-        0x2014,
-        0x58,
-    },
-    // W 24 2017 00 2 1 ; MD LROI y end HB
-    {
-        0x2017,
-        0x00,
-    },
-    // W 24 2018 9B 2 1 ; MD LROI y end LB
-    {
-        0x2018,
-        0x9B,
-    },
-    //
-    // // AE
-    // W 24 2100 01 2 1 ; [0]: AE control enable
-    {
-        0x2100,
-        0x01,
-    },
-    // W 24 2101 07 2 1 ; AE target mean
-    {
-        0x2101,
-        0x5F,
-    },
-    // W 24 2102 0A 2 1 ; AE min mean
-    {
-        0x2102,
-        0x0A,
-    },
-    // W 24 2104 03 2 1 ; AE Threshold
-    {
-        0x2103,
-        0x03,
-    },
-    // W 24 2104 05 2 1 ; AE Threshold
-    {
-        0x2104,
-        0x05,
-    },
-    // W 24 2105 01 2 1 ; max INTG Hb
-    {
-        0x2105,
-        0x02,
-    },
-    // W 24 2106 54 2 1 ; max INTG Lb
-    {
-        0x2106,
-        0x14,
-    },
-    // W 24 2108 02 2 1 ; max AGain in full
-    {
-        0x2107,
-        0x02,
-    },
-    // W 24 2108 03 2 1 ; max AGain in full
-    {
-        0x2108,
-        0x03,
-    },
-    // W 24 2109 04 2 1 ; max AGain in bin2
-    {
-        0x2109,
-        0x03,
-    },
-    // W 24 210A 00 2 1 ; min AGAIN
-    {
-        0x210A,
-        0x00,
-    },
-    // W 24 210B C0 2 1 ; max DGain
-    {
-        0x210B,
-        0x80,
-    },
-    // W 24 210C 40 2 1 ; min DGain
-    {
-        0x210C,
-        0x40,
-    },
-    // W 24 210D 20 2 1 ; damping factor
-    {
-        0x210D,
-        0x20,
-    },
-    // W 24 210E 03 2 1 ; FS ctrl
-    {
-        0x210E,
-        0x03,
-    },
-    // W 24 210F 00 2 1 ; FS 60Hz Hb
-    {
-        0x210F,
-        0x00,
-    },
-    // W 24 2110 85 2 1 ; FS 60Hz Lb
-    {
-        0x2110,
-        0x85,
-    },
-    // W 24 2111 00 2 1 ; Fs 50Hz Hb
-    {
-        0x2111,
-        0x00,
-    },
-    // W 24 2112 A0 2 1 ; FS 50Hz Lb
-    {
-        0x2112,
-        0xA0,
-    },
-
-    //
-    //
-    // // MD
-    // W 24 2150 03 2 1 ; [0] : MD LROI en [1] : MD GROI en
-    {
-        0x2150,
-        0x03,
-    },
-    //
-    //
-    // //---------------------------------------------------
-    // // frame rate : 5 FPS
-    // //---------------------------------------------------
-    // W 24 0340 0C 2 1 ; smia frame length Hb
-    {
-        0x0340,
-        0x0C,
-    },
-    // W 24 0341 7A 2 1 ; smia frame length Lb 3192
-    {
-        0x0341,
-        0x7A,
-    },
-    //
-    // W 24 0342 01 2 1 ; smia line length Hb
-    {
-        0x0342,
-        0x01,
-    },
-    // W 24 0343 77 2 1 ; smia line length Lb 375
-    {
-        0x0343,
-        0x77,
-    },
-    //
-    // //---------------------------------------------------
-    // // Resolution : QVGA 324x244
-    // //---------------------------------------------------
-    // W 24 3010 01 2 1 ; [0] : window mode 0 : full frame 324x324 1 : QVGA
-    {
-        0x3010,
-        0x01,
-    },
-    //
-    //
-    // W 24 0383 01 2 1 ;
-    {
-        0x0383,
-        0x01,
-    },
-    // W 24 0387 01 2 1 ;
-    {
-        0x0387,
-        0x01,
-    },
-    // W 24 0390 00 2 1 ;
-    {
-        0x0390,
-        0x00,
-    },
-    //
-    // //---------------------------------------------------
-    // // bit width Selection
-    // //---------------------------------------------------
-    // W 24 3011 70 2 1 ; [0] : 6 bit mode enable
-    {
-        0x3011,
-        0x70,
-    },
-    //
-    //
-    // W 24 3059 02 2 1 ; [7]: Self OSC En, [6]: 4bit mode, [5]: serial mode,
-    // [4:0]: keep value as 0x02
-    {
-        0x3059,
-        0x02,
-    },
-    // W 24 3060 01 2 1 ; [5]: gated_clock, [4]: msb first,
-    {
-        0x3060,
-        0x20,
-    },
-    // ; [3:2]: vt_reg_div -> div by 4/8/1/2
-    // ; [1;0]: vt_sys_div -> div by 8/4/2/1
-    //
-    //
-    {
-        0x0101,
-        0x01,
-    },
-    // //---------------------------------------------------
-    // // CMU update
-    // //---------------------------------------------------
-    //
-    // W 24 0104 01 2 1 ; was 0100
-    {
-        0x0104,
-        0x01,
-    },
-    //
-    //
-    //
-    // //---------------------------------------------------
-    // // Turn on rolling shutter
-    // //---------------------------------------------------
-    // W 24 0100 01 2 1 ; was 0005 ; mode_select 00 : standby - wait fir I2C SW
-    // trigger 01 : streaming 03 : output "N" frame, then enter standby 04 :
-    // standby - wait for HW trigger (level), then continuous video out til HW
-    // TRIG goes off 06 : standby - wait for HW trigger (edge), then output "N"
-    // frames then enter standby
-    {
-        0x0100,
-        0x01,
-    },
-    //
-    // ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
-};
-
-#endif  // TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_HIMAX_DRIVER_HM01B0_RAW8_QVGA_8BITS_LSB_5FPS_H_

tensorflow/lite/micro/examples/person_detection_experimental/himax_driver/HM01B0_Walking1s_01.h

@@ -1,56 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_HIMAX_DRIVER_HM01B0_WALKING1S_01_H_
-#define TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_HIMAX_DRIVER_HM01B0_WALKING1S_01_H_
-
-#include "HM01B0.h"
-
-const hm_script_t sHM01b0TestModeScript_Walking1s[] = {
-    {
-        0x2100,
-        0x00,
-    },  // W 24 2100 00 2 1 ; AE
-    {
-        0x1000,
-        0x00,
-    },  // W 24 1000 00 2 1 ; BLC
-    {
-        0x1008,
-        0x00,
-    },  // W 24 1008 00 2 1 ; DPC
-    {
-        0x0205,
-        0x00,
-    },  // W 24 0205 00 2 1 ; AGain
-    {
-        0x020E,
-        0x01,
-    },  // W 24 020E 01 2 1 ; DGain
-    {
-        0x020F,
-        0x00,
-    },  // W 24 020F 00 2 1 ; DGain
-    {
-        0x0601,
-        0x11,
-    },  // W 24 0601 11 2 1 ; Test pattern
-    {
-        0x0104,
-        0x01,
-    },  // W 24 0104 01 2 1 ;
-};
-
-#endif  // TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_HIMAX_DRIVER_HM01B0_WALKING1S_01_H_

tensorflow/lite/micro/examples/person_detection_experimental/himax_driver/HM01B0_Walking1s_01.txt

@@ -1,8 +0,0 @@
-W 24 2100 00 2 1 ; AE
-W 24 1000 00 2 1 ; BLC
-W 24 1008 00 2 1 ; DPC
-W 24 0205 00 2 1 ; AGain
-W 24 020E 01 2 1 ; DGain
-W 24 020F 00 2 1 ; DGain
-W 24 0601 11 2 1 ; Test pattern
-W 24 0104 01 2 1 ;

tensorflow/lite/micro/examples/person_detection_experimental/himax_driver/HM01B0_debug.c

@@ -1,35 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "HM01B0_debug.h"
-#include "am_util.h" // NOLINT
-
-void hm01b0_framebuffer_dump(uint8_t* frame, uint32_t length) {
-  am_util_stdio_printf("+++ frame +++");
-
-  for (uint32_t i = 0; i < length; i++) {
-    if ((i & 0xF) == 0x00) {
-      am_util_stdio_printf("\n0x%08LX ", i);
-      // this delay is to let itm have time to flush out data.
-      am_util_delay_ms(1);
-    }
-
-    am_util_stdio_printf("%02X ", frame[i]);
-  }
-
-  am_util_stdio_printf("\n--- frame ---\n");
-  am_util_delay_ms(1);
-}
-

tensorflow/lite/micro/examples/person_detection_experimental/himax_driver/HM01B0_debug.h

@@ -1,49 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_HIMAX_DRIVER_HM01B0_DEBUG_H_
-#define TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_HIMAX_DRIVER_HM01B0_DEBUG_H_
-
-#ifdef __cplusplus
-extern "C" {
-#endif
-
-#include "HM01B0.h"
-
-//*****************************************************************************
-//
-//! @brief Read one frame of data from HM01B0 scaled to 96x96 RGB.
-//!
-//! @param buffer       - Pointer to the frame buffer.
-//! @param w            - Image width.
-//! @param h            - Image height.
-//! @param channels     - Number of channels per pixel.
-//!
-//! This function reads data of one frame from HM01B0. It trims the image to an
-//! even power of two multiple of the requested width and height.  It down
-//! samples the original image and duplicates the greyscale value for each color
-//! channel.
-//!
-//! @return Error code.
-//
-//*****************************************************************************
-
-void hm01b0_framebuffer_dump(uint8_t* frame, uint32_t len);
-
-#ifdef __cplusplus
-}
-#endif
-
-#endif  // TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_HIMAX_DRIVER_HM01B0_DEBUG_H_

tensorflow/lite/micro/examples/person_detection_experimental/himax_driver/HM01B0_optimized.c

@@ -1,87 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "HM01B0.h"
-#include "am_bsp.h" //NOLINT
-#include "am_mcu_apollo.h" //NOLINT
-#include "platform_Sparkfun_Edge.h"
-
-// Image is down-sampled by applying a stride of 2 pixels in both the x and y
-// directions.
-static const int kStrideShift = 1;
-
-//*****************************************************************************
-//
-//! @brief Read one frame of data from HM01B0 scaled to 96x96 RGB.
-//!
-//! @param buffer       - Pointer to the frame buffer.
-//! @param w            - Image width.
-//! @param h            - Image height.
-//! @param channels     - Number of channels per pixel.
-//!
-//! This function reads data of one frame from HM01B0. It trims the image to an
-//! even power of two mulitple of the requested width and height.  It down
-//! samples the original image and duplicates the greyscale value for each color
-//! channel.
-//!
-//! @return Error code.
-//
-//*****************************************************************************
-uint32_t hm01b0_blocking_read_oneframe_scaled(hm01b0_cfg_t* psCfg,
-                                              int8_t* buffer, int w, int h,
-                                              int channels) {
-  hm01b0_single_frame_capture(psCfg);
-
-  // Calculate the number of pixels to crop to get a centered image.
-  const int offset_x = (HM01B0_PIXEL_X_NUM - (w * (1 << kStrideShift))) / 2;
-  const int offset_y = (HM01B0_PIXEL_Y_NUM - (h * (1 << kStrideShift))) / 2;
-
-  uint32_t hsync_count = 0;
-
-  while ((hsync_count < HM01B0_PIXEL_Y_NUM)) {
-    // Wait for horizontal sync.
-    while (!read_hsync());
-
-    // Get resulting image position.  When hsync_count < offset_y, this will
-    // underflow resulting in an index out of bounds which we check later,
-    // avoiding an unnecessary conditional.
-    const uint32_t output_y = (hsync_count - offset_y) >> kStrideShift;
-    uint32_t rowidx = 0;
-
-    // Read one row. Hsync is held high for the duration of a row read.
-    while (read_hsync()) {
-      // Wait for pixel value to be ready.
-      while (!read_pclk());
-
-      // Read 8-bit value from camera.
-      const uint8_t value = read_byte();
-      const uint32_t output_x = (rowidx++ - offset_x) >> kStrideShift;
-      if (output_x < w && output_y < h) {
-        const int output_idx = (output_y * w + output_x) * channels;
-        for (int i=0; i<channels; i++) {
-          // See the top of main_functions.cc for an explanation of and
-          // rationale for our unsigned to signed input conversion.
-          buffer[output_idx + i] = value - 128;
-        }
-      }
-
-      // Wait for next pixel clock.
-      while (read_pclk());
-    }
-
-    hsync_count++;
-  }
-  return HM01B0_ERR_OK;
-}

tensorflow/lite/micro/examples/person_detection_experimental/himax_driver/HM01B0_optimized.h

@@ -1,50 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_HIMAX_DRIVER_HM01B0_OPTIMIZED_H_
-#define TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_HIMAX_DRIVER_HM01B0_OPTIMIZED_H_
-
-#ifdef __cplusplus
-extern "C" {
-#endif
-
-#include "HM01B0.h"
-
-//*****************************************************************************
-//
-//! @brief Read one frame of data from HM01B0 scaled to 96x96 RGB.
-//!
-//! @param buffer       - Pointer to the frame buffer.
-//! @param w            - Image width.
-//! @param h            - Image height.
-//! @param channels     - Number of channels per pixel.
-//!
-//! This function reads data of one frame from HM01B0. It trims the image to an
-//! even power of two multiple of the requested width and height.  It down
-//! samples the original image and duplicates the greyscale value for each color
-//! channel.
-//!
-//! @return Error code.
-//
-//*****************************************************************************
-uint32_t hm01b0_blocking_read_oneframe_scaled(hm01b0_cfg_t* psCfg,
-                                              int8_t* buffer, int w, int h,
-                                              int channels);
-
-#ifdef __cplusplus
-}
-#endif
-
-#endif  // TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_HIMAX_DRIVER_HM01B0_OPTIMIZED_H_

tensorflow/lite/micro/examples/person_detection_experimental/himax_driver/Makefile.inc

@@ -1,13 +0,0 @@
-ifeq ($(TARGET),$(filter $(TARGET),apollo3evb sparkfun_edge))
-  person_detection_SRCS += \
-  tensorflow/lite/micro/examples/person_detection_experimental/himax_driver/HM01B0.c \
-  tensorflow/lite/micro/examples/person_detection_experimental/himax_driver/HM01B0_debug.c \
-  tensorflow/lite/micro/examples/person_detection_experimental/himax_driver/HM01B0_optimized.c
-
-  person_detection_HDRS += \
-  tensorflow/lite/micro/examples/person_detection_experimental/himax_driver/HM01B0.h \
-  tensorflow/lite/micro/examples/person_detection_experimental/himax_driver/HM01B0_debug.h \
-  tensorflow/lite/micro/examples/person_detection_experimental/himax_driver/HM01B0_optimized.h \
-  tensorflow/lite/micro/examples/person_detection_experimental/himax_driver/HM01B0_RAW8_QVGA_8bits_lsb_5fps.h \
-  tensorflow/lite/micro/examples/person_detection_experimental/himax_driver/HM01B0_Walking1s_01.h
-endif

tensorflow/lite/micro/examples/person_detection_experimental/himax_driver/platform_Sparkfun_Edge.h

@@ -1,54 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_HIMAX_DRIVER_PLATFORM_SPARKFUN_EDGE_H_
-#define TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_HIMAX_DRIVER_PLATFORM_SPARKFUN_EDGE_H_
-
-#ifdef __cplusplus
-extern "C" {
-#endif
-
-#define HM01B0_PIN_D0 24
-#define HM01B0_PIN_D1 25
-#define HM01B0_PIN_D2 26
-#define HM01B0_PIN_D3 27
-#define HM01B0_PIN_D4 28
-#define HM01B0_PIN_D5 5
-#define HM01B0_PIN_D6 6
-#define HM01B0_PIN_D7 7
-#define HM01B0_PIN_VSYNC 15
-#define HM01B0_PIN_HSYNC 22
-#define HM01B0_PIN_PCLK 23
-#define HM01B0_PIN_TRIG 12
-#define HM01B0_PIN_INT 4
-#define HM01B0_PIN_SCL 8
-#define HM01B0_PIN_SDA 9
-#define HM01B0_PIN_DVDD_EN 10
-
-// Define AP3B's CTIMER and output pin for HM01B0 MCLK generation
-#define HM01B0_MCLK_GENERATOR_MOD 0
-#define HM01B0_MCLK_GENERATOR_SEG AM_HAL_CTIMER_TIMERB
-#define HM01B0_PIN_MCLK 13
-
-// Deifne I2C controller and SCL(pin8)/SDA(pin9) are configured automatically.
-#define HM01B0_IOM_MODE AM_HAL_IOM_I2C_MODE
-#define HM01B0_IOM_MODULE 1
-#define HM01B0_I2C_CLOCK_FREQ AM_HAL_IOM_100KHZ
-
-#ifdef __cplusplus
-}
-#endif
-
-#endif  // TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_HIMAX_DRIVER_PLATFORM_SPARKFUN_EDGE_H_

tensorflow/lite/micro/examples/person_detection_experimental/image_provider.cc

@@ -1,26 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/micro/examples/person_detection_experimental/image_provider.h"
-
-#include "tensorflow/lite/micro/examples/person_detection_experimental/model_settings.h"
-
-TfLiteStatus GetImage(tflite::ErrorReporter* error_reporter, int image_width,
-                      int image_height, int channels, int8_t* image_data) {
-  for (int i = 0; i < image_width * image_height * channels; ++i) {
-    image_data[i] = 0;
-  }
-  return kTfLiteOk;
-}

tensorflow/lite/micro/examples/person_detection_experimental/image_provider.h

@@ -1,39 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_IMAGE_PROVIDER_H_
-#define TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_IMAGE_PROVIDER_H_
-
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/micro/micro_error_reporter.h"
-
-// This is an abstraction around an image source like a camera, and is
-// expected to return 8-bit sample data.  The assumption is that this will be
-// called in a low duty-cycle fashion in a low-power application.  In these
-// cases, the imaging sensor need not be run in a streaming mode, but rather can
-// be idled in a relatively low-power mode between calls to GetImage().  The
-// assumption is that the overhead and time of bringing the low-power sensor out
-// of this standby mode is commensurate with the expected duty cycle of the
-// application.  The underlying sensor may actually be put into a streaming
-// configuration, but the image buffer provided to GetImage should not be
-// overwritten by the driver code until the next call to GetImage();
-//
-// The reference implementation can have no platform-specific dependencies, so
-// it just returns a static image. For real applications, you should
-// ensure there's a specialized implementation that accesses hardware APIs.
-TfLiteStatus GetImage(tflite::ErrorReporter* error_reporter, int image_width,
-                      int image_height, int channels, int8_t* image_data);
-
-#endif  // TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_IMAGE_PROVIDER_H_

tensorflow/lite/micro/examples/person_detection_experimental/image_provider_test.cc

@@ -1,43 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/micro/examples/person_detection_experimental/image_provider.h"
-
-#include <limits>
-
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/micro/examples/person_detection_experimental/model_settings.h"
-#include "tensorflow/lite/micro/micro_error_reporter.h"
-#include "tensorflow/lite/micro/testing/micro_test.h"
-
-TF_LITE_MICRO_TESTS_BEGIN
-
-TF_LITE_MICRO_TEST(TestImageProvider) {
-  tflite::MicroErrorReporter micro_error_reporter;
-
-  int8_t image_data[kMaxImageSize];
-  TfLiteStatus get_status = GetImage(&micro_error_reporter, kNumCols, kNumRows,
-                                     kNumChannels, image_data);
-  TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, get_status);
-  TF_LITE_MICRO_EXPECT_NE(image_data, nullptr);
-
-  // Make sure we can read all of the returned memory locations.
-  uint32_t total = 0;
-  for (int i = 0; i < kMaxImageSize; ++i) {
-    total += image_data[i];
-  }
-}
-
-TF_LITE_MICRO_TESTS_END

tensorflow/lite/micro/examples/person_detection_experimental/main.cc

@@ -1,27 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/micro/examples/person_detection_experimental/main_functions.h"
-
-// This is the default main used on systems that have the standard C entry
-// point. Other devices (for example FreeRTOS or ESP32) that have different
-// requirements for entry code (like an app_main function) should specialize
-// this main.cc file in a target-specific subfolder.
-int main(int argc, char* argv[]) {
-  setup();
-  while (true) {
-    loop();
-  }
-}

tensorflow/lite/micro/examples/person_detection_experimental/main_functions.cc

@@ -1,117 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/micro/examples/person_detection_experimental/main_functions.h"
-
-#include "tensorflow/lite/micro/examples/person_detection_experimental/detection_responder.h"
-#include "tensorflow/lite/micro/examples/person_detection_experimental/image_provider.h"
-#include "tensorflow/lite/micro/examples/person_detection_experimental/model_settings.h"
-#include "tensorflow/lite/micro/examples/person_detection_experimental/person_detect_model_data.h"
-#include "tensorflow/lite/micro/micro_error_reporter.h"
-#include "tensorflow/lite/micro/micro_interpreter.h"
-#include "tensorflow/lite/micro/micro_mutable_op_resolver.h"
-#include "tensorflow/lite/schema/schema_generated.h"
-#include "tensorflow/lite/version.h"
-
-// Globals, used for compatibility with Arduino-style sketches.
-namespace {
-tflite::ErrorReporter* error_reporter = nullptr;
-const tflite::Model* model = nullptr;
-tflite::MicroInterpreter* interpreter = nullptr;
-TfLiteTensor* input = nullptr;
-
-// In order to use optimized tensorflow lite kernels, a signed int8_t quantized
-// model is preferred over the legacy unsigned model format. This means that
-// throughout this project, input images must be converted from unisgned to
-// signed format. The easiest and quickest way to convert from unsigned to
-// signed 8-bit integers is to subtract 128 from the unsigned value to get a
-// signed value.
-
-// An area of memory to use for input, output, and intermediate arrays.
-constexpr int kTensorArenaSize = 136 * 1024;
-static uint8_t tensor_arena[kTensorArenaSize];
-}  // namespace
-
-// The name of this function is important for Arduino compatibility.
-void setup() {
-  // Set up logging. Google style is to avoid globals or statics because of
-  // lifetime uncertainty, but since this has a trivial destructor it's okay.
-  // NOLINTNEXTLINE(runtime-global-variables)
-  static tflite::MicroErrorReporter micro_error_reporter;
-  error_reporter = &micro_error_reporter;
-
-  // Map the model into a usable data structure. This doesn't involve any
-  // copying or parsing, it's a very lightweight operation.
-  model = tflite::GetModel(g_person_detect_model_data);
-  if (model->version() != TFLITE_SCHEMA_VERSION) {
-    TF_LITE_REPORT_ERROR(error_reporter,
-                         "Model provided is schema version %d not equal "
-                         "to supported version %d.",
-                         model->version(), TFLITE_SCHEMA_VERSION);
-    return;
-  }
-
-  // Pull in only the operation implementations we need.
-  // This relies on a complete list of all the ops needed by this graph.
-  // An easier approach is to just use the AllOpsResolver, but this will
-  // incur some penalty in code space for op implementations that are not
-  // needed by this graph.
-  //
-  // tflite::AllOpsResolver resolver;
-  // NOLINTNEXTLINE(runtime-global-variables)
-  static tflite::MicroMutableOpResolver<5> micro_op_resolver;
-  micro_op_resolver.AddAveragePool2D();
-  micro_op_resolver.AddConv2D();
-  micro_op_resolver.AddDepthwiseConv2D();
-  micro_op_resolver.AddReshape();
-  micro_op_resolver.AddSoftmax();
-
-  // Build an interpreter to run the model with.
-  // NOLINTNEXTLINE(runtime-global-variables)
-  static tflite::MicroInterpreter static_interpreter(
-      model, micro_op_resolver, tensor_arena, kTensorArenaSize, error_reporter);
-  interpreter = &static_interpreter;
-
-  // Allocate memory from the tensor_arena for the model's tensors.
-  TfLiteStatus allocate_status = interpreter->AllocateTensors();
-  if (allocate_status != kTfLiteOk) {
-    TF_LITE_REPORT_ERROR(error_reporter, "AllocateTensors() failed");
-    return;
-  }
-
-  // Get information about the memory area to use for the model's input.
-  input = interpreter->input(0);
-}
-
-// The name of this function is important for Arduino compatibility.
-void loop() {
-  // Get image from provider.
-  if (kTfLiteOk != GetImage(error_reporter, kNumCols, kNumRows, kNumChannels,
-                            input->data.int8)) {
-    TF_LITE_REPORT_ERROR(error_reporter, "Image capture failed.");
-  }
-
-  // Run the model on this input and make sure it succeeds.
-  if (kTfLiteOk != interpreter->Invoke()) {
-    TF_LITE_REPORT_ERROR(error_reporter, "Invoke failed.");
-  }
-
-  TfLiteTensor* output = interpreter->output(0);
-
-  // Process the inference results.
-  int8_t person_score = output->data.uint8[kPersonIndex];
-  int8_t no_person_score = output->data.uint8[kNotAPersonIndex];
-  RespondToDetection(error_reporter, person_score, no_person_score);
-}

tensorflow/lite/micro/examples/person_detection_experimental/main_functions.h

@@ -1,28 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_MAIN_FUNCTIONS_H_
-#define TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_MAIN_FUNCTIONS_H_
-
-// Initializes all data needed for the example. The name is important, and needs
-// to be setup() for Arduino compatibility.
-void setup();
-
-// Runs one iteration of data gathering and inference. This should be called
-// repeatedly from the application code. The name needs to be loop() for Arduino
-// compatibility.
-void loop();
-
-#endif  // TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_MAIN_FUNCTIONS_H_

tensorflow/lite/micro/examples/person_detection_experimental/model_settings.cc

@@ -1,21 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/micro/examples/person_detection_experimental/model_settings.h"
-
-const char* kCategoryLabels[kCategoryCount] = {
-    "notperson",
-    "person",
-};

tensorflow/lite/micro/examples/person_detection_experimental/model_settings.h

@@ -1,35 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_MODEL_SETTINGS_H_
-#define TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_MODEL_SETTINGS_H_
-
-// Keeping these as constant expressions allow us to allocate fixed-sized arrays
-// on the stack for our working memory.
-
-// All of these values are derived from the values used during model training,
-// if you change your model you'll need to update these constants.
-constexpr int kNumCols = 96;
-constexpr int kNumRows = 96;
-constexpr int kNumChannels = 1;
-
-constexpr int kMaxImageSize = kNumCols * kNumRows * kNumChannels;
-
-constexpr int kCategoryCount = 2;
-constexpr int kPersonIndex = 1;
-constexpr int kNotAPersonIndex = 0;
-extern const char* kCategoryLabels[kCategoryCount];
-
-#endif  // TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_MODEL_SETTINGS_H_

tensorflow/lite/micro/examples/person_detection_experimental/no_person_image_data.h

@@ -1,30 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-// This data was created from a sample image from without a person in it.
-// Convert original image to simpler format:
-// convert -resize 96x96\! noperson.PNG noperson.bmp3
-// Skip the 54 byte bmp3 header and add the reset of the bytes to a C array:
-// xxd -s 54 -i /tmp/noperson.bmp3 > /tmp/noperson.cc
-
-#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_NO_PERSON_IMAGE_DATA_H_
-#define TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_NO_PERSON_IMAGE_DATA_H_
-
-#include <cstdint>
-
-extern const int g_no_person_data_size;
-extern const uint8_t g_no_person_data[];
-
-#endif  // TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_NO_PERSON_IMAGE_DATA_H_

tensorflow/lite/micro/examples/person_detection_experimental/person_detect_model_data.h

@@ -1,27 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-// This is a standard TensorFlow Lite model file that has been converted into a
-// C data array, so it can be easily compiled into a binary for devices that
-// don't have a file system. It was created using the command:
-// xxd -i person_detect.tflite > person_detect_model_data.cc
-
-#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_PERSON_DETECT_MODEL_DATA_H_
-#define TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_PERSON_DETECT_MODEL_DATA_H_
-
-extern const unsigned char g_person_detect_model_data[];
-extern const int g_person_detect_model_data_len;
-
-#endif  // TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_PERSON_DETECT_MODEL_DATA_H_

tensorflow/lite/micro/examples/person_detection_experimental/person_detection_test.cc

@@ -1,135 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/micro/examples/person_detection_experimental/model_settings.h"
-#include "tensorflow/lite/micro/examples/person_detection_experimental/no_person_image_data.h"
-#include "tensorflow/lite/micro/examples/person_detection_experimental/person_detect_model_data.h"
-#include "tensorflow/lite/micro/examples/person_detection_experimental/person_image_data.h"
-#include "tensorflow/lite/micro/micro_error_reporter.h"
-#include "tensorflow/lite/micro/micro_interpreter.h"
-#include "tensorflow/lite/micro/micro_mutable_op_resolver.h"
-#include "tensorflow/lite/micro/testing/micro_test.h"
-#include "tensorflow/lite/schema/schema_generated.h"
-#include "tensorflow/lite/version.h"
-
-// Create an area of memory to use for input, output, and intermediate arrays.
-constexpr int tensor_arena_size = 136 * 1024;
-uint8_t tensor_arena[tensor_arena_size];
-
-TF_LITE_MICRO_TESTS_BEGIN
-
-TF_LITE_MICRO_TEST(TestInvoke) {
-  // Set up logging.
-  tflite::MicroErrorReporter micro_error_reporter;
-
-  // Map the model into a usable data structure. This doesn't involve any
-  // copying or parsing, it's a very lightweight operation.
-  const tflite::Model* model = ::tflite::GetModel(g_person_detect_model_data);
-  if (model->version() != TFLITE_SCHEMA_VERSION) {
-    TF_LITE_REPORT_ERROR(&micro_error_reporter,
-                         "Model provided is schema version %d not equal "
-                         "to supported version %d.\n",
-                         model->version(), TFLITE_SCHEMA_VERSION);
-  }
-
-  // Pull in only the operation implementations we need.
-  // This relies on a complete list of all the ops needed by this graph.
-  // An easier approach is to just use the AllOpsResolver, but this will
-  // incur some penalty in code space for op implementations that are not
-  // needed by this graph.
-  tflite::MicroMutableOpResolver<5> micro_op_resolver;
-  micro_op_resolver.AddAveragePool2D();
-  micro_op_resolver.AddConv2D();
-  micro_op_resolver.AddDepthwiseConv2D();
-  micro_op_resolver.AddReshape();
-  micro_op_resolver.AddSoftmax();
-
-  // Build an interpreter to run the model with.
-  tflite::MicroInterpreter interpreter(model, micro_op_resolver, tensor_arena,
-                                       tensor_arena_size,
-                                       &micro_error_reporter);
-  interpreter.AllocateTensors();
-
-  // Get information about the memory area to use for the model's input.
-  TfLiteTensor* input = interpreter.input(0);
-
-  // Make sure the input has the properties we expect.
-  TF_LITE_MICRO_EXPECT_NE(nullptr, input);
-  TF_LITE_MICRO_EXPECT_EQ(4, input->dims->size);
-  TF_LITE_MICRO_EXPECT_EQ(1, input->dims->data[0]);
-  TF_LITE_MICRO_EXPECT_EQ(kNumRows, input->dims->data[1]);
-  TF_LITE_MICRO_EXPECT_EQ(kNumCols, input->dims->data[2]);
-  TF_LITE_MICRO_EXPECT_EQ(kNumChannels, input->dims->data[3]);
-  TF_LITE_MICRO_EXPECT_EQ(kTfLiteInt8, input->type);
-
-  // Copy an image with a person into the memory area used for the input.
-  TFLITE_DCHECK_EQ(input->bytes, static_cast<size_t>(g_person_data_size));
-  memcpy(input->data.int8, g_person_data, input->bytes);
-
-  // Run the model on this input and make sure it succeeds.
-  TfLiteStatus invoke_status = interpreter.Invoke();
-  if (invoke_status != kTfLiteOk) {
-    TF_LITE_REPORT_ERROR(&micro_error_reporter, "Invoke failed\n");
-  }
-  TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, invoke_status);
-
-  // Get the output from the model, and make sure it's the expected size and
-  // type.
-  TfLiteTensor* output = interpreter.output(0);
-  TF_LITE_MICRO_EXPECT_EQ(2, output->dims->size);
-  TF_LITE_MICRO_EXPECT_EQ(1, output->dims->data[0]);
-  TF_LITE_MICRO_EXPECT_EQ(kCategoryCount, output->dims->data[1]);
-  TF_LITE_MICRO_EXPECT_EQ(kTfLiteInt8, output->type);
-
-  // Make sure that the expected "Person" score is higher than the other class.
-  int8_t person_score = output->data.int8[kPersonIndex];
-  int8_t no_person_score = output->data.int8[kNotAPersonIndex];
-  TF_LITE_REPORT_ERROR(&micro_error_reporter,
-                       "person data.  person score: %d, no person score: %d\n",
-                       person_score, no_person_score);
-  TF_LITE_MICRO_EXPECT_GT(person_score, no_person_score);
-
-  // TODO(b/161461076): Update model to make this work on real negative inputs.
-  memset(input->data.int8, 0, input->bytes);
-
-  // Run the model on this "No Person" input.
-  invoke_status = interpreter.Invoke();
-  if (invoke_status != kTfLiteOk) {
-    TF_LITE_REPORT_ERROR(&micro_error_reporter, "Invoke failed\n");
-  }
-  TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, invoke_status);
-
-  // Get the output from the model, and make sure it's the expected size and
-  // type.
-  output = interpreter.output(0);
-  TF_LITE_MICRO_EXPECT_EQ(2, output->dims->size);
-  TF_LITE_MICRO_EXPECT_EQ(1, output->dims->data[0]);
-  TF_LITE_MICRO_EXPECT_EQ(kCategoryCount, output->dims->data[1]);
-  TF_LITE_MICRO_EXPECT_EQ(kTfLiteInt8, output->type);
-
-  // Make sure that the expected "No Person" score is higher.
-  person_score = output->data.int8[kPersonIndex];
-  no_person_score = output->data.int8[kNotAPersonIndex];
-  TF_LITE_REPORT_ERROR(
-      &micro_error_reporter,
-      "no person data.  person score: %d, no person score: %d\n", person_score,
-      no_person_score);
-  TF_LITE_MICRO_EXPECT_GT(no_person_score, person_score);
-
-  TF_LITE_REPORT_ERROR(&micro_error_reporter, "Ran successfully\n");
-}
-
-TF_LITE_MICRO_TESTS_END

tensorflow/lite/micro/examples/person_detection_experimental/person_image_data.h

@@ -1,30 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-// This data was created from a sample image from with a person in it.
-// Convert original image to simpler format:
-// convert -resize 96x96\! person.PNG person.bmp3
-// Skip the 54 byte bmp3 header and add the reset of the bytes to a C array:
-// xxd -s 54 -i /tmp/person.bmp3 > /tmp/person.cc
-
-#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_PERSON_IMAGE_DATA_H_
-#define TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_PERSON_IMAGE_DATA_H_
-
-#include <cstdint>
-
-extern const int g_person_data_size;
-extern const uint8_t g_person_data[];
-
-#endif  // TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_PERSON_IMAGE_DATA_H_

tensorflow/lite/micro/examples/person_detection_experimental/sparkfun_edge/detection_responder.cc

@@ -1,54 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/micro/examples/person_detection_experimental/detection_responder.h"
-
-#include "am_bsp.h"  // NOLINT
-
-// This implementation will light up LEDs on the board in response to the
-// inference results.
-void RespondToDetection(tflite::ErrorReporter* error_reporter,
-                        int8_t person_score, int8_t no_person_score) {
-  static bool is_initialized = false;
-  if (!is_initialized) {
-    // Setup LED's as outputs.  Leave red LED alone since that's an error
-    // indicator for sparkfun_edge in image_provider.
-    am_hal_gpio_pinconfig(AM_BSP_GPIO_LED_BLUE, g_AM_HAL_GPIO_OUTPUT_12);
-    am_hal_gpio_pinconfig(AM_BSP_GPIO_LED_GREEN, g_AM_HAL_GPIO_OUTPUT_12);
-    am_hal_gpio_pinconfig(AM_BSP_GPIO_LED_YELLOW, g_AM_HAL_GPIO_OUTPUT_12);
-    is_initialized = true;
-  }
-
-  // Toggle the blue LED every time an inference is performed.
-  static int count = 0;
-  if (++count & 1) {
-    am_hal_gpio_output_set(AM_BSP_GPIO_LED_BLUE);
-  } else {
-    am_hal_gpio_output_clear(AM_BSP_GPIO_LED_BLUE);
-  }
-
-  // Turn on the green LED if a person was detected.  Turn on the yellow LED
-  // otherwise.
-  am_hal_gpio_output_clear(AM_BSP_GPIO_LED_YELLOW);
-  am_hal_gpio_output_clear(AM_BSP_GPIO_LED_GREEN);
-  if (person_score > no_person_score) {
-    am_hal_gpio_output_set(AM_BSP_GPIO_LED_GREEN);
-  } else {
-    am_hal_gpio_output_set(AM_BSP_GPIO_LED_YELLOW);
-  }
-
-  TF_LITE_REPORT_ERROR(error_reporter, "Person score: %d No person score: %d",
-                       person_score, no_person_score);
-}

tensorflow/lite/micro/examples/person_detection_experimental/sparkfun_edge/image_provider.cc

@@ -1,203 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/micro/examples/person_detection_experimental/image_provider.h"
-
-#include "tensorflow/lite/micro/examples/person_detection_experimental/himax_driver/HM01B0.h"
-#include "tensorflow/lite/micro/examples/person_detection_experimental/himax_driver/HM01B0_RAW8_QVGA_8bits_lsb_5fps.h"
-#include "tensorflow/lite/micro/examples/person_detection_experimental/himax_driver/HM01B0_debug.h"
-#include "tensorflow/lite/micro/examples/person_detection_experimental/himax_driver/HM01B0_optimized.h"
-#include "tensorflow/lite/micro/examples/person_detection_experimental/himax_driver/platform_Sparkfun_Edge.h"
-
-// These are headers from Ambiq's Apollo3 SDK.
-#include "am_bsp.h"         // NOLINT
-#include "am_mcu_apollo.h"  // NOLINT
-#include "am_util.h"        // NOLINT
-
-// #define DEMO_HM01B0_FRAMEBUFFER_DUMP_ENABLE
-
-// Enabling logging increases power consumption by preventing low power mode
-// from being enabled.
-#define ENABLE_LOGGING
-
-namespace {
-
-//*****************************************************************************
-//
-// HM01B0 Configuration
-//
-//*****************************************************************************
-static hm01b0_cfg_t s_HM01B0Cfg = {
-  // i2c settings
-  ui16SlvAddr : HM01B0_DEFAULT_ADDRESS,
-  eIOMMode : HM01B0_IOM_MODE,
-  ui32IOMModule : HM01B0_IOM_MODULE,
-  sIOMCfg : {
-    eInterfaceMode : HM01B0_IOM_MODE,
-    ui32ClockFreq : HM01B0_I2C_CLOCK_FREQ,
-  },
-  pIOMHandle : NULL,
-
-  // MCLK settings
-  ui32CTimerModule : HM01B0_MCLK_GENERATOR_MOD,
-  ui32CTimerSegment : HM01B0_MCLK_GENERATOR_SEG,
-  ui32CTimerOutputPin : HM01B0_PIN_MCLK,
-
-  // data interface
-  ui8PinSCL : HM01B0_PIN_SCL,
-  ui8PinSDA : HM01B0_PIN_SDA,
-  ui8PinD0 : HM01B0_PIN_D0,
-  ui8PinD1 : HM01B0_PIN_D1,
-  ui8PinD2 : HM01B0_PIN_D2,
-  ui8PinD3 : HM01B0_PIN_D3,
-  ui8PinD4 : HM01B0_PIN_D4,
-  ui8PinD5 : HM01B0_PIN_D5,
-  ui8PinD6 : HM01B0_PIN_D6,
-  ui8PinD7 : HM01B0_PIN_D7,
-  ui8PinVSYNC : HM01B0_PIN_VSYNC,
-  ui8PinHSYNC : HM01B0_PIN_HSYNC,
-  ui8PinPCLK : HM01B0_PIN_PCLK,
-
-  ui8PinTrig : HM01B0_PIN_TRIG,
-  ui8PinInt : HM01B0_PIN_INT,
-  pfnGpioIsr : NULL,
-};
-
-static constexpr int kFramesToInitialize = 4;
-
-bool g_is_camera_initialized = false;
-
-void burst_mode_enable(tflite::ErrorReporter* error_reporter, bool bEnable) {
-  am_hal_burst_avail_e eBurstModeAvailable;
-  am_hal_burst_mode_e eBurstMode;
-
-  // Check that the Burst Feature is available.
-  if (AM_HAL_STATUS_SUCCESS ==
-      am_hal_burst_mode_initialize(&eBurstModeAvailable)) {
-    if (AM_HAL_BURST_AVAIL == eBurstModeAvailable) {
-      TF_LITE_REPORT_ERROR(error_reporter, "Apollo3 Burst Mode is Available\n");
-    } else {
-      TF_LITE_REPORT_ERROR(error_reporter,
-                           "Apollo3 Burst Mode is Not Available\n");
-      return;
-    }
-  } else {
-    TF_LITE_REPORT_ERROR(error_reporter,
-                         "Failed to Initialize for Burst Mode operation\n");
-  }
-
-  // Make sure we are in "Normal" mode.
-  if (AM_HAL_STATUS_SUCCESS == am_hal_burst_mode_disable(&eBurstMode)) {
-    if (AM_HAL_NORMAL_MODE == eBurstMode) {
-      TF_LITE_REPORT_ERROR(error_reporter,
-                           "Apollo3 operating in Normal Mode (48MHz)\n");
-    }
-  } else {
-    TF_LITE_REPORT_ERROR(error_reporter,
-                         "Failed to Disable Burst Mode operation\n");
-  }
-
-  // Put the MCU into "Burst" mode.
-  if (bEnable) {
-    if (AM_HAL_STATUS_SUCCESS == am_hal_burst_mode_enable(&eBurstMode)) {
-      if (AM_HAL_BURST_MODE == eBurstMode) {
-        TF_LITE_REPORT_ERROR(error_reporter,
-                             "Apollo3 operating in Burst Mode (96MHz)\n");
-      }
-    } else {
-      TF_LITE_REPORT_ERROR(error_reporter,
-                           "Failed to Enable Burst Mode operation\n");
-    }
-  }
-}
-
-}  // namespace
-
-TfLiteStatus InitCamera(tflite::ErrorReporter* error_reporter) {
-  TF_LITE_REPORT_ERROR(error_reporter, "Initializing HM01B0...\n");
-
-  am_hal_clkgen_control(AM_HAL_CLKGEN_CONTROL_SYSCLK_MAX, 0);
-
-  // Set the default cache configuration
-  am_hal_cachectrl_config(&am_hal_cachectrl_defaults);
-  am_hal_cachectrl_enable();
-
-  // Configure the board for low power operation. This breaks logging by
-  // turning off the itm and uart interfaces.
-#ifndef ENABLE_LOGGING
-  am_bsp_low_power_init();
-#endif
-
-  // Enable interrupts so we can receive messages from the boot host.
-  am_hal_interrupt_master_enable();
-
-  burst_mode_enable(error_reporter, true);
-
-  // Turn on the 1.8V regulator for DVDD on the camera.
-  am_hal_gpio_pinconfig(HM01B0_PIN_DVDD_EN, g_AM_HAL_GPIO_OUTPUT_12);
-  am_hal_gpio_output_set(HM01B0_PIN_DVDD_EN);
-
-  // Configure Red LED for debugging.
-  am_hal_gpio_pinconfig(AM_BSP_GPIO_LED_RED, g_AM_HAL_GPIO_OUTPUT_12);
-  am_hal_gpio_output_clear(AM_BSP_GPIO_LED_RED);
-
-  hm01b0_power_up(&s_HM01B0Cfg);
-
-  am_util_delay_ms(1);
-
-  hm01b0_mclk_enable(&s_HM01B0Cfg);
-
-  am_util_delay_ms(1);
-
-  if (HM01B0_ERR_OK != hm01b0_init_if(&s_HM01B0Cfg)) {
-    return kTfLiteError;
-  }
-
-  if (HM01B0_ERR_OK !=
-      hm01b0_init_system(&s_HM01B0Cfg, (hm_script_t*)sHM01B0InitScript,
-                         sizeof(sHM01B0InitScript) / sizeof(hm_script_t))) {
-    return kTfLiteError;
-  }
-
-  return kTfLiteOk;
-}
-
-// Capture single frame.  Frame pointer passed in to reduce memory usage.  This
-// allows the input tensor to be used instead of requiring an extra copy.
-TfLiteStatus GetImage(tflite::ErrorReporter* error_reporter, int frame_width,
-                      int frame_height, int channels, int8_t* frame) {
-  if (!g_is_camera_initialized) {
-    TfLiteStatus init_status = InitCamera(error_reporter);
-    if (init_status != kTfLiteOk) {
-      am_hal_gpio_output_set(AM_BSP_GPIO_LED_RED);
-      return init_status;
-    }
-    // Drop a few frames until auto exposure is calibrated.
-    for (int i = 0; i < kFramesToInitialize; ++i) {
-      hm01b0_blocking_read_oneframe_scaled(&s_HM01B0Cfg, frame, frame_width,
-                                           frame_height, channels);
-    }
-    g_is_camera_initialized = true;
-  }
-
-  hm01b0_blocking_read_oneframe_scaled(&s_HM01B0Cfg, frame, frame_width,
-                                       frame_height, channels);
-
-#ifdef DEMO_HM01B0_FRAMEBUFFER_DUMP_ENABLE
-  hm01b0_framebuffer_dump(frame, frame_width * frame_height * channels);
-#endif
-
-  return kTfLiteOk;
-}

tensorflow/lite/micro/examples/person_detection_experimental/training_a_model.md

@@ -1,455 +0,0 @@
-## Training a model
-
-The following document will walk you through the process of training your own
-250 KB embedded vision model using scripts that are easy to run. You can use
-either the [Visual Wake Words dataset](https://arxiv.org/abs/1906.05721) for
-person detection, or choose one of the [80
-categories from the MSCOCO dataset](http://cocodataset.org/#explore).
-
-This model will take several days to train on a powerful machine with GPUs. We
-recommend using a [Google Cloud Deep
-Learning VM](https://cloud.google.com/deep-learning-vm/).
-
-### Training framework choice
-
-Keras is the recommended interface for building models in TensorFlow, but when
-the person detector model was being created it didn't yet support all the
-features we needed. For that reason, we'll be showing you how to train a model
-using tf.slim, an older interface. It is still widely used but deprecated, so
-future versions of TensorFlow may not support this approach. We hope to publish
-Keras instructions in the future.
-
-The model definitions for Slim are part of the
-[TensorFlow models repository](https://github.com/tensorflow/models), so to get
-started you'll need to download it from GitHub using a command like this:
-
-```
-! cd ~
-! git clone https://github.com/tensorflow/models.git
-```
-
-The following guide is going to assume that you've done this from your home
-directory, so the model repository code is at ~/models, and that all commands
-are run from the home directory too unless otherwise noted. You can place the
-repository somewhere else, but you'll need to update all references to it.
-
-To use Slim, you'll need to make sure its modules can be found by Python, and
-install one dependency. Here's how to do this in an iPython notebook:
-
-```
-! pip install contextlib2
-import os
-new_python_path = (os.environ.get("PYTHONPATH") or '') + ":models/research/slim"
-%env PYTHONPATH=$new_python_path
-```
-
-Updating `PYTHONPATH` through an `EXPORT` statement like this only works for the
-current Jupyter session, so if you're using bash directly, you should add it to
-a persistent startup script, running something like this:
-
-```
-echo 'export PYTHONPATH=$PYTHONPATH:models/research/slim' >> ~/.bashrc
-source ~/.bashrc
-```
-
-If you see import errors running the slim scripts, you should make sure the
-`PYTHONPATH` is set up correctly, and that contextlib2 has been installed. You
-can find more general information on tf.slim in the
-[repository's
-README](https://github.com/tensorflow/models/tree/master/research/slim).
-
-### Building the dataset
-
-In order to train a person detector model, we need a large collection of images
-that are labeled depending on whether or not they have people in them. The
-ImageNet one-thousand class data that's widely used for training image
-classifiers doesn't include labels for people, but luckily the
-[COCO dataset](http://cocodataset.org/#home) does. You can also download this
-data without manually registering too, and Slim provides a convenient script to
-grab it automatically:
-
-```
-! chmod +x models/research/slim/datasets/download_mscoco.sh
-! bash models/research/slim/datasets/download_mscoco.sh coco
-```
-
-This is a large download, about 40GB, so it will take a while and you'll need
-to make sure you have at least 100GB free on your drive to allow space for
-unpacking and further processing. The argument to the script is the path that
-the data will be downloaded to. If you change this, you'll also need to update
-the commands below that use it.
-
-The dataset is designed to be used for training models for localization, so the
-images aren't labeled with the "contains a person", "doesn't contain a person"
-categories that we want to train for. Instead each image comes with a list of
-bounding boxes for all of the objects it contains. "Person" is one of these
-object categories, so to get to the classification labels we want, we have to
-look for images with bounding boxes for people. To make sure that they aren't
-too tiny to be recognizable we also need to exclude very small bounding boxes.
-Slim contains a script to convert the bounding box into labels:
-
-```
-! python models/research/slim/datasets/build_visualwakewords_data.py
---logtostderr \
---train_image_dir=coco/raw-data/train2014 \
---val_image_dir=coco/raw-data/val2014 \
---train_annotations_file=coco/raw-data/annotations/instances_train2014.json \
---val_annotations_file=coco/raw-data/annotations/instances_val2014.json \
---output_dir=coco/processed \
---small_object_area_threshold=0.005 \
---foreground_class_of_interest='person'
-```
-
-Don't be surprised if this takes up to twenty minutes to complete. When it's
-done, you'll have a set of TFRecords in `coco/processed` holding the labeled
-image information. This data was created by Aakanksha Chowdhery and is known as
-the [Visual Wake Words dataset](https://arxiv.org/abs/1906.05721). It's designed
-to be useful for benchmarking and testing embedded computer vision, since it
-represents a very common task that we need to accomplish with tight resource
-constraints. We're hoping to see it drive even better models for this and
-similar tasks.
-
-### Training the model
-
-One of the nice things about using tf.slim to handle the training is that the
-parameters you commonly need to modify are available as command line arguments,
-so we can just call the standard `train_image_classifier.py` script to train
-our model. You can use this command to build the model we use in the example:
-
-```
-! python models/research/slim/train_image_classifier.py \
-    --train_dir=vww_96_grayscale \
-    --dataset_name=visualwakewords \
-    --dataset_split_name=train \
-    --dataset_dir=coco/processed \
-    --model_name=mobilenet_v1_025 \
-    --preprocessing_name=mobilenet_v1 \
-    --train_image_size=96 \
-    --input_grayscale=True \
-    --save_summaries_secs=300 \
-    --learning_rate=0.045 \
-    --label_smoothing=0.1 \
-    --learning_rate_decay_factor=0.98 \
-    --num_epochs_per_decay=2.5 \
-    --moving_average_decay=0.9999 \
-    --batch_size=96 \
-    --max_number_of_steps=1000000
-```
-
-This will take a couple of days on a single-GPU v100 instance to complete all
-one-million steps, but you should be able to get a fairly accurate model after
-a few hours if you want to experiment early.
-
--   The checkpoints and summaries will the saved in the folder given in the
-    `--train_dir` argument, so that's where you'll have to look for the results.
--   The `--dataset_dir` parameter should match the one where you saved the
-    TFRecords from the Visual Wake Words build script.
--   The architecture we'll be using is defined by the `--model_name` argument.
-    The 'mobilenet_v1' prefix tells the script to use the first version of
-    MobileNet. We did experiment with later versions, but these used more RAM
-    for their intermediate activation buffers, so for now we kept with the
-    original. The '025' is the depth multiplier to use, which mostly affects the
-    number of weight parameters, this low setting ensures the model fits within
-    250KB of Flash.
--   `--preprocessing_name` controls how input images are modified before they're
-    fed into the model. The 'mobilenet_v1' version shrinks the width and height
-    of the images to the size given in `--train_image_size` (in our case 96
-    pixels since we want to reduce the compute requirements). It also scales the
-    pixel values from 0 to 255 integers into -1.0 to +1.0 floating point numbers
-    (though we'll be quantizing those after training).
--   The
-    [HM01B0](https://himax.com.tw/products/cmos-image-sensor/image-sensors/hm01b0/)
-    camera we're using on the SparkFun Edge board is monochrome, so to get the
-    best results we have to train our model on black and white images too, so we
-    pass in the `--input_grayscale` flag to enable that preprocessing.
--   The `--learning_rate`, `--label_smoothing`, `--learning_rate_decay_factor`,
-    `--num_epochs_per_decay`, `--moving_average_decay` and `--batch_size` are
-    all parameters that control how weights are updated during the training
-    process. Training deep networks is still a bit of a dark art, so these exact
-    values we found through experimentation for this particular model. You can
-    try tweaking them to speed up training or gain a small boost in accuracy,
-    but we can't give much guidance for how to make those changes, and it's easy
-    to get combinations where the training accuracy never converges.
--   The `--max_number_of_steps` defines how long the training should continue.
-    There's no good way to figure out this threshold in advance, you have to
-    experiment to tell when the accuracy of the model is no longer improving to
-    tell when to cut it off. In our case we default to a million steps, since
-    with this particular model we know that's a good point to stop.
-
-Once you start the script, you should see output that looks something like this:
-
-```
-INFO:tensorflow:global step 4670: loss = 0.7112 (0.251 sec/step)
-I0928 00:16:21.774756 140518023943616 learning.py:507] global step 4670: loss =
-0.7112 (0.251 sec/step)
-INFO:tensorflow:global step 4680: loss = 0.6596 (0.227 sec/step)
-I0928 00:16:24.365901 140518023943616 learning.py:507] global step 4680: loss =
-0.6596 (0.227 sec/step)
-```
-
-Don't worry about the line duplication, this is just a side-effect of the way
-TensorFlow log printing interacts with Python. Each line has two key bits of
-information about the training process. The global step is a count of how far
-through the training we are. Since we've set the limit as a million steps, in
-this case we're nearly five percent complete. The steps per second estimate is
-also useful, since you can use it to estimate a rough duration for the whole
-training process. In this case, we're completing about four steps a second, so
-a million steps will take about 70 hours, or three days. The other crucial
-piece of information is the loss. This is a measure of how close the
-partially-trained model's predictions are to the correct values, and lower
-values are better. This will show a lot of variation but should on average
-decrease during training if the model is learning. Because it's so noisy, the
-amounts will bounce around a lot over short time periods, but if things are
-working well you should see a noticeable drop if you wait an hour or so and
-check back. This kind of variation is a lot easier to see in a graph, which is
-one of the main reasons to try TensorBoard.
-
-### TensorBoard
-
-TensorBoard is a web application that lets you view data visualizations from
-TensorFlow training sessions, and it's included by default in most cloud
-instances. If you're using Google Cloud's AI Platform, you can start up a new
-TensorBoard session by open the command palette from the left tabs on the
-notebook interface, and scrolling down to select "Create a new tensorboard".
-You'll be prompted for the location of the summary logs, enter the path you
-used for `--train_dir` in the training script, in our example
-'vww_96_grayscale'. One common error to watch out for is adding a slash to the
-end of the path, which will cause tensorboard to fail to find the directory. If
-you're starting tensorboard from the command line in a different environment
-you'll have to pass in this path as the `--logdir` argument to the tensorboard
-command line tool, and point your browser to http://localhost:6006 (or the
-address of the machine you're running it on).
-
-It may take a little while for the graphs to have anything useful in them, since
-the script only saves summaries every five minutes. The most important graph is
-called 'clone_loss', and this shows the progression of the same loss value
-that's displayed on the logging output. It fluctuates a lot, but the
-overall trend is downwards over time. If you don't see this sort of progression
-after a few hours of training, it's a good sign that your model isn't
-converging to a good solution, and you may need to debug what's going wrong
-either with your dataset or the training parameters.
-
-Tensorboard defaults to the 'Scalars' tab when it opens, but the other section
-that can be useful during training is 'Images'. This shows a
-random selection of the pictures the model is currently being trained on,
-including any distortions and other preprocessing. This information isn't as
-essential as the loss graphs, but it can be useful to ensure the dataset is what
-you expect, and it is interesting to see the examples updating as training
-progresses.
-
-### Evaluating the model
-
-The loss function correlates with how well your model is training, but it isn't
-a direct, understandable metric. What we really care about is how many people
-our model detects correctly, but to get calculate this we need to run a
-separate script. You don't need to wait until the model is fully trained, you
-can check the accuracy of any checkpoints in the `--train_dir` folder.
-
-```
-! python models/research/slim/eval_image_classifier.py \
-    --alsologtostderr \
-    --checkpoint_path=vww_96_grayscale/model.ckpt-698580 \
-    --dataset_dir=coco/processed/ \
-    --dataset_name=visualwakewords \
-    --dataset_split_name=val \
-    --model_name=mobilenet_v1_025 \
-    --preprocessing_name=mobilenet_v1 \
-    --input_grayscale=True \
-    --train_image_size=96
-```
-
-You'll need to make sure that `--checkpoint_path` is pointing to a valid set of
-checkpoint data. Checkpoints are stored in three separate files, so the value
-should be their common prefix. For example if you have a checkpoint file called
-'model.ckpt-5179.data-00000-of-00001', the prefix would be 'model.ckpt-5179'.
-The script should produce output that looks something like this:
-
-```
-INFO:tensorflow:Evaluation [406/406]
-I0929 22:52:59.936022 140225887045056 evaluation.py:167] Evaluation [406/406]
-eval/Accuracy[0.717438412]eval/Recall_5[1]
-```
-
-The important number here is the accuracy. It shows the proportion of the
-images that were classified correctly, which is 72% in this case, after
-converting to a percentage. If you follow the example script, you should expect
-a fully-trained model to achieve an accuracy of around 84% after one million
-steps, and show a loss of around 0.4.
-
-### Exporting the model to TensorFlow Lite
-
-When the model has trained to an accuracy you're happy with, you'll need to
-convert the results from the TensorFlow training environment into a form you
-can run on an embedded device. As we've seen in previous chapters, this can be
-a complex process, and tf.slim adds a few of its own wrinkles too.
-
-#### Exporting to a GraphDef protobuf file
-
-Slim generates the architecture from the model_name every time one of its
-scripts is run, so for a model to be used outside of Slim it needs to be saved
-in a common format. We're going to use the GraphDef protobuf serialization
-format, since that's understood by both Slim and the rest of TensorFlow.
-
-```
-! python models/research/slim/export_inference_graph.py \
-    --alsologtostderr \
-    --dataset_name=visualwakewords \
-    --model_name=mobilenet_v1_025 \
-    --image_size=96 \
-    --input_grayscale=True \
-    --output_file=vww_96_grayscale_graph.pb
-```
-
-If this succeeds, you should have a new 'vww_96_grayscale_graph.pb' file in
-your home folder. This contains the layout of the operations in the model, but
-doesn't yet have any of the weight data.
-
-#### Freezing the weights
-
-The process of storing the trained weights together with the operation graph is
-known as freezing. This converts all of the variables in the graph to
-constants, after loading their values from a checkpoint file. The command below
-uses a checkpoint from the millionth training step, but you can supply any
-valid checkpoint path. The graph freezing script is stored inside the main
-tensorflow repository, so we have to download this from GitHub before running
-this command.
-
-```
-! git clone https://github.com/tensorflow/tensorflow
-! python tensorflow/tensorflow/python/tools/freeze_graph.py \
---input_graph=vww_96_grayscale_graph.pb \
---input_checkpoint=vww_96_grayscale/model.ckpt-1000000 \
---input_binary=true --output_graph=vww_96_grayscale_frozen.pb \
---output_node_names=MobilenetV1/Predictions/Reshape_1
-```
-
-After this, you should see a file called 'vww_96_grayscale_frozen.pb'.
-
-#### Quantizing and converting to TensorFlow Lite
-
-Quantization is a tricky and involved process, and it's still very much an
-active area of research, so taking the float graph that we've trained so far
-and converting it down to eight bit takes quite a bit of code. You can find
-more of an explanation of what quantization is and how it works in the chapter
-on latency optimization, but here we'll show you how to use it with the model
-we've trained. The majority of the code is preparing example images to feed
-into the trained network, so that the ranges of the activation layers in
-typical use can be measured. We rely on the TFLiteConverter class to handle the
-quantization and conversion into the TensorFlow Lite flatbuffer file that we
-need for the inference engine.
-
-```
-import tensorflow as tf
-import io
-import PIL
-import numpy as np
-
-def representative_dataset_gen():
-
-  record_iterator =
-tf.python_io.tf_record_iterator(path='coco/processed/val.record-00000-of-00010')
-
-  count = 0
-  for string_record in record_iterator:
-    example = tf.train.Example()
-    example.ParseFromString(string_record)
-    image_stream =
-io.BytesIO(example.features.feature['image/encoded'].bytes_list.value[0])
-    image = PIL.Image.open(image_stream)
-    image = image.resize((96, 96))
-    image = image.convert('L')
-    array = np.array(image)
-    array = np.expand_dims(array, axis=2)
-    array = np.expand_dims(array, axis=0)
-    array = ((array / 127.5) - 1.0).astype(np.float32)
-    yield([array])
-    count += 1
-    if count > 300:
-        break
-
-converter =
-tf.lite.TFLiteConverter.from_frozen_graph('vww_96_grayscale_frozen.pb',
-['input'], ['MobilenetV1/Predictions/Reshape_1'])
-converter.optimizations = [tf.lite.Optimize.DEFAULT]
-converter.representative_dataset = representative_dataset_gen
-converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
-converter.inference_input_type = tf.int8
-converter.inference_output_type = tf.int8
-
-tflite_quant_model = converter.convert()
-open("vww_96_grayscale_quantized.tflite", "wb").write(tflite_quant_model)
-```
-
-#### Converting into a C source file
-
-The converter writes out a file, but most embedded devices don't have a file
-system. To access the serialized data from our program, we have to compile it
-into the executable and store it in Flash. The easiest way to do that is to
-convert the file into a C data array.
-
-```
-# Install xxd if it is not available
-!apt-get -qq install xxd
-# Save the file as a C source file
-!xxd -i vww_96_grayscale_quantized.tflite > person_detect_model_data.cc
-```
-
-You can now replace the existing person_detect_model_data.cc file with the
-version you've trained, and be able to run your own model on embedded devices.
-
-### Training for other categories
-
-There are over 60 different object types in the MS-COCO dataset, so an easy way
-to customize your model would be to choose one of those instead of 'person'
-when you build the training dataset. Here's an example that looks for cars:
-
-```
-! python models/research/slim/datasets/build_visualwakewords_data.py
---logtostderr \
---train_image_dir=coco/raw-data/train2014 \
---val_image_dir=coco/raw-data/val2014 \
---train_annotations_file=coco/raw-data/annotations/instances_train2014.json \
---val_annotations_file=coco/raw-data/annotations/instances_val2014.json \
---output_dir=coco/processed_cars \
---small_object_area_threshold=0.005 \
---foreground_class_of_interest='car'
-```
-
-You should be able to follow the same steps you did for the person detector,
-but substitute the new 'coco/processed_cars' path wherever 'coco/processed'
-used to be.
-
-If the kind of object you're interested in isn't present in MS-COCO, you may be
-able to use transfer learning to help you train on a custom dataset you've
-gathered, even if it's much smaller. We don't have an example of this
-yet, but we hope to share one soon.
-
-### Understanding the architecture
-
-[MobileNets](https://arxiv.org/abs/1704.04861) are a family of architectures
-designed to provide good accuracy for as few weight parameters and arithmetic
-operations as possible. There are now multiple versions, but in our case we're
-using the original v1 since it required the smallest amount of RAM at runtime.
-The core concept behind the architecture is depthwise separable convolution.
-This is a variant of classical two-dimensional convolutions that works in a
-much more efficient way, without sacrificing very much accuracy. Regular
-convolution calculates an output value based on applying a filter of a
-particular size across all channels of the input. This means the number of
-calculations involved in each output is width of the filter multiplied by
-height, multiplied by the number of input channels. Depthwise convolution
-breaks this large calculation into separate parts. First each input channel is
-filtered by one or more rectangular filters to produce intermediate values.
-These values are then combined using pointwise convolutions. This dramatically
-reduces the number of calculations needed, and in practice produces similar
-results to regular convolution.
-
-MobileNet v1 is a stack of 14 of these depthwise separable convolution layers
-with an average pool, then a fully-connected layer followed by a softmax at the
-end. We've specified a 'width multiplier' of 0.25, which has the effect of
-reducing the number of computations down to around 60 million per inference, by
-shrinking the number of channels in each activation layer by 75% compared to
-the standard model. In essence it's very similar to a normal convolutional
-neural network in operation, with each layer learning patterns in the input.
-Earlier layers act more like edge recognition filters, spotting low-level
-structure in the image, and later layers synthesize that information into more
-abstract patterns that help with the final object classification.

tensorflow/lite/micro/kernels/arc_mli/README.md

@@ -65,7 +65,7 @@ quantization only, `dilation_ratio==1` 3. Average Pooling 4. Max Pooling 5.
 Fully Connected
 
 Currently only
-[/tensorflow/lite/micro/examples/person_detection_experimental](/tensorflow/lite/micro/examples/person_detection_experimental)
+[/tensorflow/lite/micro/examples/person_detection](/tensorflow/lite/micro/examples/person_detection)
 is quantized using this specification. Other examples can be executed on
 ARC-based targets, but will only use reference kernels.
 

tensorflow/lite/micro/tools/make/fix_arduino_subfolders.py

@@ -42,7 +42,7 @@ def move_person_data(library_dir):
   """Moves the downloaded person model into the examples folder."""
   old_person_data_path = os.path.join(
       library_dir, 'src/tensorflow/lite/micro/tools/make/downloads/' +
-      'person_model_grayscale/person_detect_model_data.cpp')
+      'person_model_int8/person_detect_model_data.cpp')
   new_person_data_path = os.path.join(
       library_dir, 'examples/person_detection/person_detect_model_data.cpp')
   if os.path.exists(old_person_data_path):
@@ -58,28 +58,6 @@ def move_person_data(library_dir):
       source_file.write(file_contents)
 
 
-def move_person_data_experimental(library_dir):
-  """Moves the downloaded person model into the examples folder."""
-  old_person_data_path = os.path.join(
-      library_dir, 'src/tensorflow/lite/micro/tools/make/downloads/' +
-      'person_model_int8/person_detect_model_data.cpp')
-  new_person_data_path = os.path.join(
-      library_dir,
-      'examples/person_detection_experimental/person_detect_model_data.cpp')
-  if os.path.exists(old_person_data_path):
-    os.rename(old_person_data_path, new_person_data_path)
-    # Update include.
-    with open(new_person_data_path, 'r') as source_file:
-      file_contents = source_file.read()
-    file_contents = file_contents.replace(
-        six.ensure_str(
-            '#include "tensorflow/lite/micro/examples/' +
-            'person_detection_experimental/person_detect_model_data.h"'),
-        '#include "person_detect_model_data.h"')
-    with open(new_person_data_path, 'w') as source_file:
-      source_file.write(file_contents)
-
-
 def move_image_data_experimental(library_dir):
   """Moves the downloaded image detection model into the examples folder."""
   old_image_data_path = os.path.join(
@@ -117,7 +95,6 @@ def main(unparsed_args):
   rename_example_subfolder_files(library_dir)
   rename_example_main_inos(library_dir)
   move_person_data(library_dir)
-  move_person_data_experimental(library_dir)
   move_image_data_experimental(library_dir)
 
 

tensorflow/lite/micro/tools/make/fix_arduino_subfolders_test.sh

@@ -30,7 +30,7 @@ mkdir -p `dirname ${EXAMPLES_SUBDIR_HEADER}`
 touch ${EXAMPLES_SUBDIR_HEADER}
 
 TENSORFLOW_SRC_DIR=${LIBRARY_DIR}/src/
-PERSON_DATA_FILE=${TENSORFLOW_SRC_DIR}tensorflow/lite/micro/tools/make/downloads/person_model_grayscale/person_detect_model_data.cpp
+PERSON_DATA_FILE=${TENSORFLOW_SRC_DIR}tensorflow/lite/micro/tools/make/downloads/person_model_int8/person_detect_model_data.cpp
 mkdir -p `dirname ${PERSON_DATA_FILE}`
 echo '#include "tensorflow/lite/micro/examples/person_detection/person_detect_model_data.h"' > ${PERSON_DATA_FILE}
 mkdir -p ${LIBRARY_DIR}/examples/person_detection

tensorflow/lite/micro/tools/make/targets/stm32f4_makefile.inc

@@ -74,7 +74,7 @@ MICROLITE_TEST_SRCS := $(filter-out $(EXCLUDED_TESTS), $(MICROLITE_TEST_SRCS))
 EXCLUDED_EXAMPLE_TESTS := \
   tensorflow/lite/micro/examples/magic_wand/Makefile.inc \
   tensorflow/lite/micro/examples/micro_speech/Makefile.inc \
-  tensorflow/lite/micro/examples/person_detection_experimental/Makefile.inc \
+  tensorflow/lite/micro/examples/person_detection/Makefile.inc \
   tensorflow/lite/micro/examples/image_recognition_experimental/Makefile.inc
 MICRO_LITE_EXAMPLE_TESTS := $(filter-out $(EXCLUDED_EXAMPLE_TESTS), $(MICRO_LITE_EXAMPLE_TESTS))
 

tensorflow/lite/micro/tools/make/targets/xtensa_makefile.inc

@@ -56,7 +56,6 @@ EXCLUDED_EXAMPLE_TESTS := \
   tensorflow/lite/micro/examples/magic_wand/Makefile.inc \
   tensorflow/lite/micro/examples/micro_speech/Makefile.inc \
   tensorflow/lite/micro/examples/network_tester/Makefile.inc \
-  tensorflow/lite/micro/examples/person_detection/Makefile.inc \
+  tensorflow/lite/micro/examples/person_detection/Makefile.inc
-  tensorflow/lite/micro/examples/person_detection_experimental/Makefile.inc
 MICRO_LITE_EXAMPLE_TESTS := $(filter-out $(EXCLUDED_EXAMPLE_TESTS), $(MICRO_LITE_EXAMPLE_TESTS))
 

tensorflow/lite/micro/tools/make/third_party_downloads.inc

@@ -65,8 +65,8 @@ IMAGE_RECOGNITION_MODEL_MD5 := "1f4607b05ac45b8a6146fb883dbc2d7b"
 PERSON_MODEL_URL := "https://storage.googleapis.com/download.tensorflow.org/data/tf_lite_micro_person_data_grayscale_2020_05_27.zip"
 PERSON_MODEL_MD5 := "55b85f76e2995153e660391d4a209ef1"
 
-PERSON_MODEL_INT8_URL := "https://storage.googleapis.com/download.tensorflow.org/data/tf_lite_micro_person_data_int8_grayscale_2020_06_23.zip"
+PERSON_MODEL_INT8_URL := "https://storage.googleapis.com/download.tensorflow.org/data/tf_lite_micro_person_data_int8_grayscale_2020_12_1.zip"
-PERSON_MODEL_INT8_MD5 := "9b5b6d4677dd0a91b1bb992d1c4c0417"
+PERSON_MODEL_INT8_MD5 := "e765cc76889db8640cfe876a37e4ec00"
 
 EMBARC_MLI_URL := "https://github.com/foss-for-synopsys-dwc-arc-processors/embarc_mli/archive/ef7dd3c4e37d74a908f30713a7d0121387d3c678.zip"
 EMBARC_MLI_MD5 := "65c4ff3f4a2963e90fd014f97c69f451"