61c6c84964
This change moves //tensorflow/contrib/lite to //tensorflow/lite in preparation for TensorFlow 2.0's deprecation of contrib/. If you refer to TF Lite build targets or headers, you will need to update them manually. If you use TF Lite from the TensorFlow python package, "tf.contrib.lite" now points to "tf.lite". Please update your imports as soon as possible. For more details, see https://groups.google.com/a/tensorflow.org/forum/#!topic/tflite/iIIXOTOFvwQ @angersson and @aselle are conducting this migration. Please contact them if you have any further questions. PiperOrigin-RevId: 219536476
121 lines
3.8 KiB
C++
121 lines
3.8 KiB
C++
/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#include <cstdint>
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#include <cstdio>
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#include <cstdlib>
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#include <fstream>
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#include <iostream>
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#include <unistd.h> // NOLINT(build/include_order)
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#include "tensorflow/lite/examples/label_image/bitmap_helpers.h"
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#define LOG(x) std::cerr
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namespace tflite {
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namespace label_image {
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std::vector<uint8_t> decode_bmp(const uint8_t* input, int row_size, int width,
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int height, int channels, bool top_down) {
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std::vector<uint8_t> output(height * width * channels);
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for (int i = 0; i < height; i++) {
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int src_pos;
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int dst_pos;
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for (int j = 0; j < width; j++) {
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if (!top_down) {
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src_pos = ((height - 1 - i) * row_size) + j * channels;
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} else {
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src_pos = i * row_size + j * channels;
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}
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dst_pos = (i * width + j) * channels;
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switch (channels) {
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case 1:
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output[dst_pos] = input[src_pos];
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break;
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case 3:
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// BGR -> RGB
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output[dst_pos] = input[src_pos + 2];
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output[dst_pos + 1] = input[src_pos + 1];
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output[dst_pos + 2] = input[src_pos];
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break;
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case 4:
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// BGRA -> RGBA
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output[dst_pos] = input[src_pos + 2];
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output[dst_pos + 1] = input[src_pos + 1];
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output[dst_pos + 2] = input[src_pos];
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output[dst_pos + 3] = input[src_pos + 3];
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break;
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default:
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LOG(FATAL) << "Unexpected number of channels: " << channels;
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break;
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}
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}
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}
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return output;
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}
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std::vector<uint8_t> read_bmp(const std::string& input_bmp_name, int* width,
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int* height, int* channels, Settings* s) {
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int begin, end;
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std::ifstream file(input_bmp_name, std::ios::in | std::ios::binary);
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if (!file) {
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LOG(FATAL) << "input file " << input_bmp_name << " not found\n";
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exit(-1);
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}
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begin = file.tellg();
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file.seekg(0, std::ios::end);
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end = file.tellg();
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size_t len = end - begin;
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if (s->verbose) LOG(INFO) << "len: " << len << "\n";
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std::vector<uint8_t> img_bytes(len);
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file.seekg(0, std::ios::beg);
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file.read(reinterpret_cast<char*>(img_bytes.data()), len);
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const int32_t header_size =
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*(reinterpret_cast<const int32_t*>(img_bytes.data() + 10));
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*width = *(reinterpret_cast<const int32_t*>(img_bytes.data() + 18));
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*height = *(reinterpret_cast<const int32_t*>(img_bytes.data() + 22));
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const int32_t bpp =
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*(reinterpret_cast<const int32_t*>(img_bytes.data() + 28));
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*channels = bpp / 8;
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if (s->verbose)
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LOG(INFO) << "width, height, channels: " << *width << ", " << *height
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<< ", " << *channels << "\n";
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// there may be padding bytes when the width is not a multiple of 4 bytes
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// 8 * channels == bits per pixel
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const int row_size = (8 * *channels * *width + 31) / 32 * 4;
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// if height is negative, data layout is top down
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// otherwise, it's bottom up
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bool top_down = (*height < 0);
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// Decode image, allocating tensor once the image size is known
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const uint8_t* bmp_pixels = &img_bytes[header_size];
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return decode_bmp(bmp_pixels, row_size, *width, abs(*height), *channels,
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top_down);
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}
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} // namespace label_image
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} // namespace tflite
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