在caffe中添加样本扩增的功能

在caffe-cudnn 中添加样本扩增的功能

有个样本扩增的代码。可以run

考虑到我的caffe的版本太多了。所以把所有的功能都merge 到一起。

首先merge 的是样本扩增的功能。

因为只有在imagedata 层里面用到样本扩增。里面用到了data_transfer 这一层。

data_transfer 这一层有四个函数。

我们只需要重载：

template<typename Dtype>

void DataTransformer<Dtype>::Transform(const cv::Mat& img, Blob<Dtype>* transformed_blob)

改成下面的code。

template<typename Dtype>
void DataTransformer<Dtype>::Transform(const cv::Mat& img,Blob<Dtype>* transformed_blob) {cv::Mat cv_img;img.copyTo(cv_img);                                       const int crop_size = param_.crop_size();const bool display = param_.display();const bool contrast_adjustment = param_.contrast_adjustment();const bool smooth_filtering = param_.smooth_filtering();const bool jpeg_compression = param_.jpeg_compression();const int img_channels = cv_img.channels();const int img_height = cv_img.rows;const int img_width = cv_img.cols;// Check dimensions.const int channels = transformed_blob->channels();const int height = transformed_blob->height();const int width = transformed_blob->width();const int num = transformed_blob->num();CHECK_EQ(channels, img_channels);CHECK_LE(height, img_height);CHECK_LE(width, img_width);CHECK_GE(num, 1);CHECK(cv_img.depth() == CV_8U) << "Image data type must be unsigned byte";const Dtype scale = param_.scale();//const bool do_mirror = param_.mirror() && Rand(2);const bool has_mean_file = param_.has_mean_file();const bool has_mean_values = mean_values_.size() > 0;CHECK_GT(img_channels, 0);CHECK_GE(img_height, crop_size);CHECK_GE(img_width, crop_size);// param for rotationconst float rotation_angle_interval = param_.rotation_angle_interval();if (display && phase_ == TRAIN)cv::imshow("Source", cv_img);// Flipping and Reflection -----------------------------------------------------------------int flipping_mode = (Rand(4)) - 1; // -1, 0, 1, 2bool apply_flipping = (flipping_mode != 2);if (apply_flipping) {cv::flip(cv_img,cv_img,flipping_mode);if (display && phase_ == TRAIN)cv::imshow("Flipping and Reflection", cv_img);}// Smooth Filtering -------------------------------------------------------------int smooth_param1 = 3;int apply_smooth = Rand(2);if ( smooth_filtering && apply_smooth ) {int smooth_type = Rand(4); // see opencv_util.hppsmooth_param1 = 3 + 2*(Rand(1));switch(smooth_type){case 0://cv::Smooth(cv_img, cv_img, smooth_type, smooth_param1);cv::GaussianBlur(cv_img, cv_img, cv::Size(smooth_param1,smooth_param1),0);break;case 1:cv::blur(cv_img, cv_img, cv::Size(smooth_param1,smooth_param1));break;case 2:cv::medianBlur(cv_img, cv_img, smooth_param1);break;case 3:cv::boxFilter(cv_img, cv_img, -1, cv::Size(smooth_param1*2,smooth_param1*2));break;}if (display && phase_ == TRAIN)cv::imshow("Smooth Filtering", cv_img);}cv::RNG rng;// Contrast and Brightness Adjuestment ----------------------------------------float alpha = 1, beta = 0;int apply_contrast = Rand(2);if ( contrast_adjustment && apply_contrast ) {float min_alpha = 0.8, max_alpha = 1.2;alpha = rng.uniform(min_alpha, max_alpha);beta = (float)(Rand(6));// flip signif ( Rand(2) ) beta = - beta;cv_img.convertTo(cv_img, -1 , alpha, beta);if (display && phase_ == TRAIN)cv::imshow("Contrast Adjustment", cv_img);}LOG(INFO) << "JPEG Compression";// JPEG Compression -------------------------------------------------------------// DO NOT use the following code as there is some memory leak which I cann't figure outint QF = 100;int apply_JPEG = Rand(2);if ( jpeg_compression && apply_JPEG ) {// JPEG quality factorQF = 95 + 1 * (Rand(6));int cp[] = {1, QF};vector<int> compression_params(cp,cp + 2);vector<unsigned char> img_jpeg;//cv::imencode(".jpg", cv_img, img_jpeg);cv::imencode(".jpg", cv_img, img_jpeg, compression_params);cv::Mat temp = cv::imdecode(img_jpeg, 1);temp.copyTo(cv_img);if (display && phase_ == TRAIN)cv::imshow("JPEG Compression", cv_img);}LOG(INFO) << "crop";// Cropping -------------------------------------------------------------int h_off = 0;int w_off = 0;cv::Mat cv_cropped_img = cv_img;if (crop_size) {CHECK_EQ(crop_size, height);CHECK_EQ(crop_size, width);// We only do random crop when we do training.if (phase_ == TRAIN) {h_off = Rand(img_height - crop_size + 1);w_off = Rand(img_width - crop_size + 1);} else {h_off = (img_height - crop_size) / 2;w_off = (img_width - crop_size) / 2;}cv::Rect roi(w_off, h_off, crop_size, crop_size);cv_cropped_img = cv_img(roi);if (display && phase_ == TRAIN)cv::imshow("Cropping", cv_cropped_img);} else {CHECK_EQ(img_height, height);CHECK_EQ(img_width, width);}// Rotation -------------------------------------------------------------double rotation_degree;if ( rotation_angle_interval!=1 ) {cv::Mat dst;int interval = 360/rotation_angle_interval;int apply_rotation = Rand(interval);cv::Size dsize = cv::Size(cv_cropped_img.cols*1.5,cv_cropped_img.rows*1.5);cv::Mat resize_img = cv::Mat(dsize,CV_32S);cv::resize(cv_cropped_img, resize_img,dsize);cv::Point2f pt(resize_img.cols/2., resize_img.rows/2.);    rotation_degree = apply_rotation*rotation_angle_interval;cv::Mat r = getRotationMatrix2D(pt, rotation_degree, 1.0);warpAffine(resize_img, dst, r, cv::Size(resize_img.cols, resize_img.rows));cv::Rect myROI(resize_img.cols/6, resize_img.rows/6, cv_cropped_img.cols, cv_cropped_img.rows);cv::Mat crop_after_rotate = dst(myROI);if (display && phase_ == TRAIN)cv::imshow("Rotation", crop_after_rotate);crop_after_rotate.copyTo(cv_img);}if (display && phase_ == TRAIN)cv::imshow("Final", cv_img);//--------------------!! for debug only !!-------------------if (display && phase_ == TRAIN) {LOG(INFO) << "----------------------------------------";LOG(INFO) << "src width: " << width << ", src height: " << height;LOG(INFO) << "dest width: " << crop_size << ", dest height: " << crop_size;if (apply_flipping) {LOG(INFO) << "* parameter for flipping: ";LOG(INFO) << "  flipping_mode: " << flipping_mode;}if ( smooth_filtering && apply_smooth ) {LOG(INFO) << "* parameter for smooth filtering: ";//LOG(INFO) << "  smooth type: " << smooth_type << ", smooth param1: " << smooth_param1;}if ( contrast_adjustment && apply_contrast ) {LOG(INFO) << "* parameter for contrast adjustment: ";LOG(INFO) << "  alpha: " << alpha << ", beta: " << beta;}if ( jpeg_compression && apply_JPEG ) {LOG(INFO) << "* parameter for JPEG compression: ";LOG(INFO) << "  QF: " << QF;}LOG(INFO) << "* parameter for cropping: ";LOG(INFO) << "  w: " << w_off << ", h: " << h_off;LOG(INFO) << "  roi_width: " << crop_size << ", roi_height: " << crop_size;LOG(INFO) << "* parameter for rotation: ";LOG(INFO) << "  angle_interval: " << rotation_angle_interval;LOG(INFO) << "  angle: " << rotation_degree;cvWaitKey(10);}Dtype* mean = NULL;if (has_mean_file) {CHECK_EQ(img_channels, data_mean_.channels());CHECK_EQ(img_height, data_mean_.height());CHECK_EQ(img_width, data_mean_.width());mean = data_mean_.mutable_cpu_data();}if (has_mean_values) {CHECK(mean_values_.size() == 1 || mean_values_.size() == img_channels) <<"Specify either 1 mean_value or as many as channels: " << img_channels;if (img_channels > 1 && mean_values_.size() == 1) {// Replicate the mean_value for simplicityfor (int c = 1; c < img_channels; ++c) {mean_values_.push_back(mean_values_[0]);}}}Dtype* transformed_data = transformed_blob->mutable_cpu_data();int top_index;for (int h = 0; h < height; ++h) {const uchar* ptr = cv_img.ptr<uchar>(h); // here!!int img_index = 0;for (int w = 0; w < width; ++w) {for (int c = 0; c < img_channels; ++c) {//if (do_mirror) {//  top_index = (c * height + h) * width + (width - 1 - w);//} else {top_index = (c * height + h) * width + w;//}// int top_index = (c * height + h) * width + w;Dtype pixel = static_cast<Dtype>(ptr[img_index++]);if (has_mean_file) {int mean_index = (c * img_height + h) * img_width + w;transformed_data[top_index] =(pixel - mean[mean_index]) * scale;} else {if (has_mean_values) {transformed_data[top_index] =(pixel - mean_values_[c]) * scale;} else {transformed_data[top_index] = pixel * scale;}}}}}}

同时在caffe.proto 把transformer 层改成：
message TransformationParameter {
// For data pre-processing, we can do simple scaling and subtracting the
// data mean, if provided. Note that the mean subtraction is always carried
// out before scaling.
optional float scale = 1 [default = 1];
// Specify if we want to randomly mirror data.
optional bool mirror = 2 [default = false];
// Specify if we would like to randomly crop an image.
optional uint32 crop_size = 3 [default = 0];
// mean_file and mean_value cannot be specified at the same time
optional string mean_file = 4;
// if specified can be repeated once (would subtract it from all the channels)
// or can be repeated the same number of times as channels
// (would subtract them from the corresponding channel)
repeated float mean_value = 5;
// Force the decoded image to have 3 color channels.
optional bool force_color = 6 [default = false];
// Force the decoded image to have 1 color channels.
optional bool force_gray = 7 [default = false];
// change by ggj 20170331
optional bool self_preprocess = 15 [default = false];
// Specify the range of scaling factor for doing resizing

// 下面的部分是需要添加的部分
optional float min_scaling_factor = 8 [default = 0.75];
optional float max_scaling_factor = 9 [default = 1.50];
// Specify the angle interval for doing rotation
optional float rotation_angle_interval = 10 [default = 1];
optional bool contrast_adjustment = 11 [default = false];
optional bool smooth_filtering = 12 [default = false];
optional bool jpeg_compression = 13 [default = false];
optional bool display = 14 [default = false];
}

然后make clean && make all -j8 如果报错，就按照错误一步步看下去，看哪里出现问题，然后改掉

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