/**
* Get the prediction response for the given image.
* @param originImage image, which should be predicted
* @param resultLayer the name of the result layer
* @param dataLayer the name of the data layer
* @param predictions the predictions
*/
void CaffeClassifier::predict(std::vector<cv::Mat> originImages, std::vector<int> labels, string resultLayer,
string dataLayer, vector<short> & predictions) {
vector<Datum> vecDatum;
for (int i = 0; i < originImages.size(); i++) {
cv::Mat originImage = originImages[i];
// resize image
Mat image;
if (originImage.cols != imageSize.width || originImage.rows != imageSize.height) {
resize(originImage, image, imageSize);
} else
image = originImage;
// check channels
if (channels != image.channels()) {
cerr << "Error: the channel number of input image is invalid for CNN classifier!" << endl;
exit(1);
}
// mat to datum
Datum datum;
CVMatToDatum(image, &datum);
datum.set_label(labels[i]);
vecDatum.push_back(datum);
image.release();
}
// get the data layer
const caffe::shared_ptr<MemoryDataLayer<float>> memDataLayer = boost::static_pointer_cast<MemoryDataLayer<float>> (caffeNet->layer_by_name(dataLayer));
// push new image data
memDataLayer->AddDatumVector(vecDatum);
//memDataLayer->ExactNumBottomBlobs();
// do forward pass
vector<Blob<float>*> inputVec;
caffeNet->Forward(inputVec);
// get results
const caffe::shared_ptr<Blob<float> > featureBlob = caffeNet->blob_by_name(resultLayer);
int batchSize = featureBlob->num();
int dimFeatures = featureBlob->count() / batchSize;
// std::cout << "Batch size is " << batchSize << "/ dim features is " << dimFeatures << std::endl;
// get output from each channel
for (int n = 0; n < batchSize; ++n) {
float* fs = featureBlob->mutable_cpu_data() + featureBlob->offset(n);
if (sizeof(fs) > 0) {
vector<float> feature_vector(fs, fs + dimFeatures);
predictions.insert(predictions.end(), feature_vector.begin(), feature_vector.end());
}
}
// release data
// for (Datum d : vecDatum) {
// d.release_data();
// }
}
void CaffeClassifier::predictHeatMap(cv::Mat& inputImage, int label, string predictionLayer, string dataLayer, cv::Mat& heatMap) {
const int IMAGE_SIZE = 227;
const int BATCH_SIZE = 64;
heatMap = cv::Mat(IMAGE_SIZE, IMAGE_SIZE, CV_32FC1, Scalar(26.932154));
const int STEP_SIZE = 9;
const int START_OFFSET = STEP_SIZE / 2;
const int FILLER_SIZE = 50;
cv::Scalar mean = cv::mean(inputImage);
std::vector<Point> middlePoints;
for (int i = START_OFFSET; i < IMAGE_SIZE; i += STEP_SIZE) {
for (int j = START_OFFSET; j < IMAGE_SIZE; j += STEP_SIZE) {
middlePoints.push_back(Point(i, j));
}
}
for (int i = 0; i < middlePoints.size(); i += BATCH_SIZE) {
std::cout << (i * 100) / middlePoints.size() << "% " << std::flush;
vector<Datum> vecDatum;
for (int j = 0; j < BATCH_SIZE; ++j) {
// do not go over the last middle point
int index = min(static_cast<int>(middlePoints.size() - 1), i + j);
Point p = middlePoints[index];
cv::Mat image = inputImage.clone();
// cv::Rect rect(Point(max(0, p.x - FILLER_SIZE), max(0, p.y - FILLER_SIZE)), Point(min(IMAGE_SIZE - 1, p.x + FILLER_SIZE), min(IMAGE_SIZE - 1, p.y + FILLER_SIZE)));
// cv::Mat subMat = image(rect);
// cv::Scalar mean = cv::mean(subMat);
circle(image,
p,
FILLER_SIZE,
mean,
CV_FILLED);
// rectangle(image,
// Point(max(0, p.x - FILLER_SIZE), max(0, p.y - FILLER_SIZE)),
// Point(min(IMAGE_SIZE - 1, p.x + FILLER_SIZE), min(IMAGE_SIZE - 1, p.y + FILLER_SIZE)),
// Scalar(0, 0, 0),
// CV_FILLED);
std::ostringstream o;
o << "/home/knub/Repositories/video-classification/nets/activity_recognition/caffenet/";
o << index;
o << "_heat.png";
cv::imwrite(o.str(), image);
// check channels
if (channels != image.channels()) {
cerr << "Error: the channel number of input image is invalid for CNN classifier!" << endl;
exit(1);
}
// mat to datum
Datum datum;
CVMatToDatum(image, &datum);
vecDatum.push_back(datum);
image.release();
}
// get the data layer
const caffe::shared_ptr<MemoryDataLayer<float>> memDataLayer = boost::static_pointer_cast<MemoryDataLayer<float>>(caffeNet->layer_by_name(dataLayer));
// push new image data
memDataLayer->AddDatumVector(vecDatum);
// do forward pass
vector<Blob<float>*> inputVec;
caffeNet->Forward(inputVec);
// get results
const caffe::shared_ptr<Blob<float> > featureBlob = caffeNet->blob_by_name(predictionLayer);
int dimFeatures = featureBlob->count() / BATCH_SIZE; // 101
assert(dimFeatures == 101);
// get output from each input image
for (int j = 0; j < BATCH_SIZE; ++j) {
int index = min(static_cast<int>(middlePoints.size() - 1), i + j);
Point p = middlePoints[index];
// std::cout << "Channels: " << featureBlob->channels() << ", Count: " << featureBlob->count() << ", Width: " << featureBlob->width() << ", Height: " << featureBlob->height() << std::endl;
// featureBlob = 64 x 101 matrix
float* fs = featureBlob->mutable_cpu_data() + featureBlob->offset(j);
vector<float> featureVector(fs, fs + dimFeatures);
// std::vector<float>::iterator result = std::max_element(featureVector.begin(), featureVector.end());
// int predicted = result - featureVector.begin();
// std::cout << "Predicted: " << predicted << ", Actual: " << label << std::endl;
// assert(predicted == label);
float confidence = featureVector[label];
rectangle(heatMap,
Point(p.x - START_OFFSET, p.y - START_OFFSET),
Point(p.x + START_OFFSET, p.y + START_OFFSET),
Scalar(confidence),
CV_FILLED);
}
}
}
float CaffeFeatExtractor<Dtype>::extractBatch_singleFeat_1D(vector<cv::Mat> &images, int new_batch_size, vector< vector<Dtype> > &features)
{
// Set the GPU/CPU mode for Caffe (here in order to be thread-safe)
if (gpu_mode)
{
Caffe::set_mode(Caffe::GPU);
Caffe::SetDevice(device_id);
}
else
{
Caffe::set_mode(Caffe::CPU);
}
cudaEvent_t start, stop;
if (timing)
{
cudaEventCreate(&start);
cudaEventCreate(&stop);
cudaEventRecord(start, NULL);
}
// Initialize labels to zero
vector<int> labels(images.size(), 0);
// Get pointer to data layer to set the input
caffe::shared_ptr<MemoryDataLayer<Dtype> > memory_data_layer = boost::dynamic_pointer_cast<caffe::MemoryDataLayer<Dtype> >(feature_extraction_net->layers()[0]);
// Set batch size
if (memory_data_layer->batch_size()!=new_batch_size)
{
if (images.size()%new_batch_size==0)
{
memory_data_layer->set_batch_size(new_batch_size);
cout << "BATCH SIZE = " << memory_data_layer->batch_size() << endl;
}
else
{
if (images.size()%memory_data_layer->batch_size()==0)
{
cout << "WARNING: image number is not multiple of requested batch size,leaving the old one." << endl;
cout << "BATCH SIZE = " << memory_data_layer->batch_size() << endl;
} else
{
cout << "WARNING: image number is not multiple of batch size, setting it to 1 (performance issue)." << endl;
memory_data_layer->set_batch_size(1);
cout << "BATCH SIZE = " << memory_data_layer->batch_size() << endl;
}
}
} else
{
if (images.size()%memory_data_layer->batch_size()!=0)
{
cout << "WARNING: image number is not multiple of batch size, setting it to 1 (performance issue)." << endl;
memory_data_layer->set_batch_size(1);
cout << "BATCH SIZE = " << memory_data_layer->batch_size() << endl;
}
}
int num_batches = images.size()/new_batch_size;
// Input preprocessing
// The image passed to AddMatVector must be same size as the mean image
// If not, it is resized:
// if it is downsampled, an anti-aliasing Gaussian Filter is applied
for (int i=0; i<images.size(); i++)
{
if (images[i].rows != mean_height || images[i].cols != mean_height)
{
if (images[i].rows > mean_height || images[i].cols > mean_height)
{
cv::resize(images[i], images[i], cv::Size(mean_height, mean_width), 0, 0, CV_INTER_LANCZOS4);
}
else
{
cv::resize(images[i], images[i], cv::Size(mean_height, mean_width), 0, 0, CV_INTER_LINEAR);
}
}
}
memory_data_layer->AddMatVector(images,labels);
size_t num_features = blob_names.size();
if (num_features!=1)
{
cout<< "Error! The list of features to be extracted has not size one!" << endl;
return -1;
}
// Run network and retrieve features!
std::vector<Blob<Dtype>*> results;
for (int b=0; b<num_batches; ++b)
{
results = feature_extraction_net->Forward();
const caffe::shared_ptr<Blob<Dtype> > feature_blob = feature_extraction_net->blob_by_name(blob_names[0]);
int batch_size = feature_blob->num();
int feat_dim = feature_blob->count() / batch_size; // should be equal to: channels*width*height
if (feat_dim!=feature_blob->channels())
{
cout<< "Attention! The feature is not 1D: unrolling according to Caffe's order (i.e. channel, width, height)" << endl;
}
for (int i=0; i<batch_size; ++i)
{
features.push_back(vector <Dtype>(feature_blob->mutable_cpu_data() + feature_blob->offset(i), feature_blob->mutable_cpu_data() + feature_blob->offset(i) + feat_dim));
}
}
if (timing)
{
// Record the stop event
cudaEventRecord(stop, NULL);
// Wait for the stop event to complete
cudaEventSynchronize(stop);
float msecTotal = 0.0f;
cudaEventElapsedTime(&msecTotal, start, stop);
float msecPerImage = msecTotal/(float)images.size();
return msecPerImage;
}
else
{
return 0;
}
}
float CaffeFeatExtractor<Dtype>::extract_multipleFeat_1D(cv::Mat &image, vector< vector<Dtype> > &features)
{
// Set the GPU/CPU mode for Caffe (here in order to be thread-safe)
if (gpu_mode)
{
Caffe::set_mode(Caffe::GPU);
Caffe::SetDevice(device_id);
}
else
{
Caffe::set_mode(Caffe::CPU);
}
cudaEvent_t start, stop;
if (timing)
{
cudaEventCreate(&start);
cudaEventCreate(&stop);
cudaEventRecord(start, NULL);
}
// Initialize labels to zero
int label = 0;
// Get pointer to data layer to set the input
caffe::shared_ptr<MemoryDataLayer<Dtype> > memory_data_layer = boost::dynamic_pointer_cast<caffe::MemoryDataLayer<Dtype> >(feature_extraction_net->layers()[0]);
// Set batch size to 1
if (memory_data_layer->batch_size()!=1)
{
memory_data_layer->set_batch_size(1);
cout << "BATCH SIZE = " << memory_data_layer->batch_size() << endl;
}
// Input preprocessing
// The image passed to AddMatVector must be same size as the mean image
// If not, it is resized:
// if it is downsampled, an anti-aliasing Gaussian Filter is applied
if (image.rows != mean_height || image.cols != mean_height)
{
if (image.rows > mean_height || image.cols > mean_height)
{
cv::resize(image, image, cv::Size(mean_height, mean_width), 0, 0, CV_INTER_LANCZOS4);
}
else
{
cv::resize(image, image, cv::Size(mean_height, mean_width), 0, 0, CV_INTER_LINEAR);
}
}
memory_data_layer->AddMatVector(vector<cv::Mat>(1, image),vector<int>(1,label));
size_t num_features = blob_names.size();
// Run network and retrieve features!
// depending on your net's architecture, the blobs will hold accuracy and/or labels, etc
std::vector<Blob<Dtype>*> results = feature_extraction_net->Forward();
for (int f = 0; f < num_features; ++f) {
const caffe::shared_ptr<Blob<Dtype> > feature_blob = feature_extraction_net->blob_by_name(blob_names[f]);
int batch_size = feature_blob->num(); // should be 1
if (batch_size!=1)
{
cout << "Error! Retrieved more than one feature, exiting..." << endl;
return -1;
}
int feat_dim = feature_blob->count(); // should be equal to: count/batch_size=channels*width*height
if (feat_dim!=feature_blob->channels())
{
cout<< "Attention! The feature is not 1D: unrolling according to Caffe's order (i.e. channel, width, height)" << endl;
}
features.push_back(vector <Dtype>(feature_blob->mutable_cpu_data() + feature_blob->offset(0), feature_blob->mutable_cpu_data() + feature_blob->offset(0) + feat_dim));
}
if (timing)
{
// Record the stop event
cudaEventRecord(stop, NULL);
// Wait for the stop event to complete
cudaEventSynchronize(stop);
float msecTotal = 0.0f;
cudaEventElapsedTime(&msecTotal, start, stop);
return msecTotal;
}
else
{
return 0;
}
}
float CaffeFeatExtractor<Dtype>::extractBatch_multipleFeat(vector<cv::Mat> &images, int new_batch_size, vector< Blob<Dtype>* > &features) {
// Set the GPU/CPU mode for Caffe (here in order to be thread-safe)
if (gpu_mode)
{
Caffe::set_mode(Caffe::GPU);
Caffe::SetDevice(device_id);
}
else
{
Caffe::set_mode(Caffe::CPU);
}
cudaEvent_t start, stop;
if (timing)
{
cudaEventCreate(&start);
cudaEventCreate(&stop);
cudaEventRecord(start, NULL);
}
// Initialize labels to zero
vector<int> labels(images.size(), 0);
// Get pointer to data layer to set the input
caffe::shared_ptr<MemoryDataLayer<Dtype> > memory_data_layer = boost::dynamic_pointer_cast<caffe::MemoryDataLayer<Dtype> >(feature_extraction_net->layers()[0]);
// Set batch size
if (memory_data_layer->batch_size()!=new_batch_size)
{
if (images.size()%new_batch_size==0)
{
memory_data_layer->set_batch_size(new_batch_size);
cout << "BATCH SIZE = " << memory_data_layer->batch_size() << endl;
}
else
{
if (images.size()%memory_data_layer->batch_size()==0)
{
cout << "WARNING: image number is not multiple of requested batch size, leaving the old one..." << endl;
cout << "BATCH SIZE = " << memory_data_layer->batch_size() << endl;
} else
{
cout << "WARNING: image number is not multiple of batch size, setting it to 1 (performance issue)..." << endl;
memory_data_layer->set_batch_size(1);
cout << "BATCH SIZE = " << memory_data_layer->batch_size() << endl;
}
}
} else
{
if (images.size()%memory_data_layer->batch_size()!=0)
{
cout << "WARNING: image number is not multiple of batch size, setting it to 1 (performance issue)..." << endl;
memory_data_layer->set_batch_size(1);
cout << "BATCH SIZE = " << memory_data_layer->batch_size() << endl;
}
}
int num_batches = images.size()/new_batch_size;
// Input preprocessing
// The image passed to AddMatVector must be same size as the mean image
// If not, it is resized anisotropically (BILINEAR)
// if it is downsampled, LANCZOS4 is used for antialiasing
for (int i=0; i<images.size(); i++)
{
if (images[i].rows != mean_height || images[i].cols != mean_height)
{
if (images[i].rows > mean_height || images[i].cols > mean_height)
{
cv::resize(images[i], images[i], cv::Size(mean_height, mean_width), 0, 0, CV_INTER_LANCZOS4);
}
else
{
cv::resize(images[i], images[i], cv::Size(mean_height, mean_width), 0, 0, CV_INTER_LINEAR);
}
}
}
memory_data_layer->AddMatVector(images,labels);
size_t num_features = blob_names.size();
// Run network and retrieve features!
// depending on your net's architecture, the blobs will hold accuracy and/or labels, etc
std::vector<Blob<Dtype>*> results;
for (int b=0; b<num_batches; b++)
{
results = feature_extraction_net->Forward();
for (int i = 0; i < num_features; ++i) {
const caffe::shared_ptr<Blob<Dtype> > feature_blob = feature_extraction_net->blob_by_name(blob_names[i]);
int batch_size = feature_blob->num();
int channels = feature_blob->channels();
int width = feature_blob->width();
int height = feature_blob->height();
features.push_back(new Blob<Dtype>(batch_size, channels, height, width));
features.back()->CopyFrom(*feature_blob);
}
}
if (timing)
{
// Record the stop event
cudaEventRecord(stop, NULL);
// Wait for the stop event to complete
cudaEventSynchronize(stop);
float msecTotal = 0.0f;
cudaEventElapsedTime(&msecTotal, start, stop);
float msecPerImage = msecTotal/(float)images.size();
return msecPerImage;
}
else
{
return 0;
}
}