annotatorplugin_caffe_ssd/detector.cpp at master · annotatorproject/annotatorplugin_caffe_ssd · GitHub

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// source:

// https://github.com/weiliu89/caffe/blob/ssd/examples/ssd/ssd_detect.cpp

#include "detector.h"

#include <opencv2/imgproc/imgproc.hpp>

using namespace caffe;

Detector::Detector(const string& model_file, const string& weights_file,

const string& mean_file, const string& mean_value) {

#ifdef CPU_ONLY

Caffe::set_mode(Caffe::CPU);

#else

Caffe::set_mode(Caffe::GPU);

#endif

/* Load the network. */

net_.reset(new Net<float>(model_file, TEST));

net_->CopyTrainedLayersFrom(weights_file);

CHECK_EQ(net_->num_inputs(), 1) << "Network should have exactly one input.";

CHECK_EQ(net_->num_outputs(), 1) << "Network should have exactly one output.";

Blob<float>* input_layer = net_->input_blobs()[0];

num_channels_ = input_layer->channels();

CHECK(num_channels_ == 3 || num_channels_ == 1)

<< "Input layer should have 1 or 3 channels.";

input_geometry_ = cv::Size(input_layer->width(), input_layer->height());

/* Load the binaryproto mean file. */

SetMean(mean_file, mean_value);

}

std::vector<vector<float> > Detector::Detect(const cv::Mat& img) {

Blob<float>* input_layer = net_->input_blobs()[0];

input_layer->Reshape(1, num_channels_, input_geometry_.height,

input_geometry_.width);

/* Forward dimension change to all layers. */

net_->Reshape();

std::vector<cv::Mat> input_channels;

WrapInputLayer(&input_channels);

Preprocess(img, &input_channels);

net_->Forward();

/* Copy the output layer to a std::vector */

Blob<float>* result_blob = net_->output_blobs()[0];

const float* result = result_blob->cpu_data();

const int num_det = result_blob->height();

vector<vector<float> > detections;

for (int k = 0; k < num_det; ++k) {

if (result[0] == -1) {

// Skip invalid detection.

result += 7;

continue;

}

vector<float> detection(result, result + 7);

detections.push_back(detection);

result += 7;

}

return detections;

}

/* Load the mean file in binaryproto format. */

void Detector::SetMean(const string& mean_file, const string& mean_value) {

cv::Scalar channel_mean;

if (!mean_file.empty()) {

CHECK(mean_value.empty())

<< "Cannot specify mean_file and mean_value at the same time";

BlobProto blob_proto;

ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto);

/* Convert from BlobProto to Blob<float> */

Blob<float> mean_blob;

mean_blob.FromProto(blob_proto);

CHECK_EQ(mean_blob.channels(), num_channels_)

<< "Number of channels of mean file doesn't match input layer.";

/* The format of the mean file is planar 32-bit float BGR or grayscale. */

std::vector<cv::Mat> channels;

float* data = mean_blob.mutable_cpu_data();

for (int i = 0; i < num_channels_; ++i) {

/* Extract an individual channel. */

cv::Mat channel(mean_blob.height(), mean_blob.width(), CV_32FC1, data);

channels.push_back(channel);

data += mean_blob.height() * mean_blob.width();

}

/* Merge the separate channels into a single image. */

cv::Mat mean;

cv::merge(channels, mean);

/* Compute the global mean pixel value and create a mean image

* filled with this value. */

channel_mean = cv::mean(mean);

mean_ = cv::Mat(input_geometry_, mean.type(), channel_mean);

}

if (!mean_value.empty()) {

CHECK(mean_file.empty())

<< "Cannot specify mean_file and mean_value at the same time";

stringstream ss(mean_value);

vector<float> values;

string item;

while (getline(ss, item, ',')) {

float value = std::atof(item.c_str());

values.push_back(value);

}

CHECK(values.size() == 1 || values.size() == num_channels_)

<< "Specify either 1 mean_value or as many as channels: "

<< num_channels_;

std::vector<cv::Mat> channels;

for (int i = 0; i < num_channels_; ++i) {

/* Extract an individual channel. */

cv::Mat channel(input_geometry_.height, input_geometry_.width, CV_32FC1,

cv::Scalar(values[i]));

channels.push_back(channel);

}

cv::merge(channels, mean_);

}

/* Wrap the input layer of the network in separate cv::Mat objects

* (one per channel). This way we save one memcpy operation and we

* don't need to rely on cudaMemcpy2D. The last preprocessing

* operation will write the separate channels directly to the input

* layer. */

void Detector::WrapInputLayer(std::vector<cv::Mat>* input_channels) {

Blob<float>* input_layer = net_->input_blobs()[0];

int width = input_layer->width();

int height = input_layer->height();

float* input_data = input_layer->mutable_cpu_data();

for (int i = 0; i < input_layer->channels(); ++i) {

cv::Mat channel(height, width, CV_32FC1, input_data);

input_channels->push_back(channel);

input_data += width * height;

}

void Detector::Preprocess(const cv::Mat& img,

std::vector<cv::Mat>* input_channels) {

/* Convert the input image to the input image format of the network. */

cv::Mat sample;

if (img.channels() == 3 && num_channels_ == 1)

cv::cvtColor(img, sample, cv::COLOR_BGR2GRAY);

else if (img.channels() == 4 && num_channels_ == 1)

cv::cvtColor(img, sample, cv::COLOR_BGRA2GRAY);

else if (img.channels() == 4 && num_channels_ == 3)

cv::cvtColor(img, sample, cv::COLOR_BGRA2BGR);

else if (img.channels() == 1 && num_channels_ == 3)

cv::cvtColor(img, sample, cv::COLOR_GRAY2BGR);

else

sample = img;

cv::Mat sample_resized;

if (sample.size() != input_geometry_)

cv::resize(sample, sample_resized, input_geometry_);

else

sample_resized = sample;

cv::Mat sample_float;

if (num_channels_ == 3)

sample_resized.convertTo(sample_float, CV_32FC3);

else

sample_resized.convertTo(sample_float, CV_32FC1);

cv::Mat sample_normalized;

cv::subtract(sample_float, mean_, sample_normalized);

/* This operation will write the separate BGR planes directly to the

* input layer of the network because it is wrapped by the cv::Mat

* objects in input_channels. */

cv::split(sample_normalized, *input_channels);

CHECK(reinterpret_cast<float*>(input_channels->at(0).data) ==

net_->input_blobs()[0]->cpu_data())

<< "Input channels are not wrapping the input layer of the network.";

}

annotatorplugin_caffe_ssd/detector.cpp at master · annotatorproject/annotatorplugin_caffe_ssd · GitHub

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