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/*
* Created by Ryan Jay 30.10.16
* Covered by the GPL. v3 (see included LICENSE)
*/
#include <caffe/caffe.hpp>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <algorithm>
#include <iosfwd>
#include <memory>
#include <string>
#include <sstream>
#include <utility>
#include <vector>
#include <stdlib.h>
#include <unistd.h>
#include <dirent.h>
#include <fstream>
#include <boost/thread.hpp>
#include "cut_movie.hpp"
#include "util.hpp"
using namespace caffe; // NOLINT(build/namespaces)
using namespace std;
using std::string;
int global_ffmpeg_done = -1;
class Classifier
{
public:
Classifier(const string& model_file,
const string& trained_file,
const string& mean_file,
const string& label_file);
ScoreList Classify(const vector<cv::Mat>& imgs);
std::vector<string> labels_;
private:
void SetMean(const string& mean_file);
std::vector<vector<float> > Predict(const vector<cv::Mat>& imgs);
void WrapInputLayer(std::vector<cv::Mat>* input_channels, int n);
void Preprocess(const cv::Mat& img,
std::vector<cv::Mat>* input_channels);
private:
boost::shared_ptr<Net<float> > net_;
cv::Size input_geometry_;
int num_channels_;
cv::Mat mean_;
};
Classifier::Classifier(const string& model_file,
const string& trained_file,
const string& mean_file,
const string& label_file)
{
#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(trained_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);
/* Load labels. */
std::ifstream labels(label_file.c_str());
CHECK(labels) << "Unable to open labels file " << label_file;
string line;
while (std::getline(labels, line))
labels_.push_back(string(line));
Blob<float>* output_layer = net_->output_blobs()[0];
CHECK_EQ(labels_.size(), output_layer->channels())
<< "Number of labels is different from the output layer dimension.";
}
//Utility Functions
int IndexOf(string label, vector<string> labels)
{
for( int k=0; k<labels.size(); k++)
if(label == labels[k])
return k;
cerr << "Label not found in list: " << label << endl;
exit(EXIT_FAILURE);
}
string FormatFileNumber(int file_no)
{
ostringstream out;
out << std::internal << std::setfill('0') << std::setw(5) << file_no;
return out.str();
}
inline bool FileExists(const std::string& name)
{
ifstream f(name.c_str());
return f.good();
}
int CountFiles(string directory)
{
DIR *dir;
struct dirent *ent;
int n=0;
if ((dir = opendir (directory.c_str())) != NULL)
{
n = 0;
while ((ent = readdir(dir)) != NULL) n++;
closedir(dir);
return n-2; //-2 for . and ..
}
else
{
cerr << "Could not open directory: " << directory << endl;
exit(EXIT_FAILURE);
}
}
void CreateScreenShots(string movie_file, string screenshot_directory)
{
//turn movie into 1 second screenshots
string mkdir_cmd = "mkdir -p " + screenshot_directory;
if(system(mkdir_cmd.c_str()))
{
cerr << "Command failed: " << mkdir_cmd << endl;
exit(EXIT_FAILURE);
}
string screenshot_cmd = "ffmpeg -loglevel 8 -i \"" + movie_file + "\" -vf fps=1 -q:v 1 " +
screenshot_directory + "img_\%05d.jpg";
if(system(screenshot_cmd.c_str()))
{
cerr << "Error getting screenshots from: " << movie_file << endl;
exit(EXIT_FAILURE);
}
global_ffmpeg_done = CountFiles(screenshot_directory);
}
void PrintUsage(char* prog_name)
{
cout << "Usage: " << prog_name << " [-t target|-x|-a] [-b batch_size] [-o output_dir] [options] movie_file" << endl;
cout << "-h\tPrint more help information about options" << endl;
}
void PrintHelp()
{
cout << endl;
cout << "Main Options" << endl;
cout << "-t\tComma separated list of the Targets to search for (default:blowjob_handjob)" << endl;
cout << "-x\tRemove all non-sexual scenes. Same as all targets except \'other\'. Ignores -t." << endl;
cout << "-a\tCreate a tag file with the cuts for all categories. Ignores -t and -x" << endl;
cout << "-b\tBatch size (default: 32) - decrease if you run out of memory" << endl;
cout << "-o\tOutput directory (default: same as input)" << endl;
cout << "-d\tTemporary Directory (default: /tmp)" << endl;
cout << endl;
cout << "Cutting Options" << endl;
cout << "-u\tMinimum cUt in seconds (default: 4)" << endl;
cout << "-g\tMax Gap (default: 2)- the largest section of non-target frames in a cut" << endl;
cout << "-s\tMinimum Score (default: 0.5) - minimum value considered a match [0-1]" << endl;
cout << "-v\tMinimum coVerage of target frames in a cut (default: 0.4) [0-1]" << endl;
cout << "-c\tDon't Concatenate. Output cut directory (default: off)" << endl;
cout << "-n\tDoN't ask to remove original movie file (default: off)" << endl;
cout << endl;
cout << "Model Options" << endl;
cout << "-m\tMean file .binaryproto" << endl;
cout << "-p\tDefinition of model .prototxt" << endl;
cout << "-w\tWeights for model .caffemodel" << endl;
cout << "-l\tLabel file" << endl;
}
vector<string> Split(const string &s, char delim)
{
stringstream ss(s);
string item;
vector<string> tokens;
while (getline(ss, item, delim))
{
tokens.push_back(item);
}
return tokens;
}
vector<string> allExceptOther(vector<string> labels)
{
vector<string> output;
for(int i=0; i<labels.size(); i++)
{
if(labels[i] != "other")
output.push_back(labels[i]);
}
return(output);
}
//Classifier Class Functions
/* Return the all predictions. */
ScoreList Classifier::Classify(const vector<cv::Mat>& imgs)
{
ScoreList outputs = Predict(imgs);
return outputs;
}
/* Load the mean file in binaryproto format. */
void Classifier::SetMean(const string& mean_file)
{
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. */
cv::Scalar channel_mean = cv::mean(mean);
mean_ = cv::Mat(input_geometry_, mean.type(), channel_mean);
}
std::vector<vector<float> > Classifier::Predict(const vector<cv::Mat>& imgs)
{
Blob<float>* input_layer = net_->input_blobs()[0];
input_layer->Reshape(imgs.size(), num_channels_,
input_geometry_.height, input_geometry_.width);
/* Forward dimension change to all layers. */
net_->Reshape();
for( int i=0; i < imgs.size(); ++i)
{
vector<cv::Mat> input_channels;
WrapInputLayer(&input_channels,i);
Preprocess(imgs[i], &input_channels);
}
net_->Forward();
vector<vector<float> > outputs;
Blob<float>* output_layer = net_->output_blobs()[0];
for( int i=0; i < output_layer->num(); ++i)
{
const float* begin = output_layer->cpu_data() + i * output_layer->channels();
const float* end = begin + output_layer->channels();
/* Copy the output layer to a std::vector */
outputs.push_back(vector<float>(begin, end));
}
return outputs;
}
/* 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 Classifier::WrapInputLayer(std::vector<cv::Mat>* input_channels, int n)
{
Blob<float>* input_layer = net_->input_blobs()[0];
int width = input_layer->width();
int height = input_layer->height();
int channels = input_layer->channels();
float* input_data = input_layer->mutable_cpu_data() + n * width * height * channels;
for (int i = 0; i < channels; ++i)
{
cv::Mat channel(height, width, CV_32FC1, input_data);
input_channels->push_back(channel);
input_data += width * height;
}
}
void Classifier::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);
}
int main(int argc, char** argv)
{
int batch_size = 32;
int MAX_IMG_IDX = 99999;
int report_interval = 100;
int sleep_time = 1;
int min_cut = 4;
int max_gap = 2;
double min_score = 0.5;
double min_coverage = 0.4;
vector<string> target_list;
target_list.push_back("blowjob_handjob"); //the default target
string movie_file;
string screenshot_directory = "/tmp/screenshots/";
string model_dir = "model/";
string model_weights = model_dir + "weights.caffemodel";
string model_def = model_dir + "deploy.prototxt";
string mean_file = model_dir + "mean.binaryproto";
string label_file = model_dir + "labels.txt";
string output_directory = "";
string temp_directory = "/tmp";
bool auto_tag = false;
bool do_concat = true;
bool remove_original = true;
//parse command line flags
int opt;
bool set_all_but_other = false;
while ((opt = getopt(argc, argv, "act:b:d:o:m:ng:s:hxp:w:u:l:v:")) != -1)
{
switch (opt) {
case 'a':
auto_tag = true;
break;
case 'c':
do_concat = false;
break;
case 't':
target_list = Split(optarg,',');
break;
case 'b':
batch_size = atoi(optarg);
break;
case 'd':
temp_directory = optarg;
break;
case 'o':
output_directory = optarg;
break;
case 'u':
min_cut = atoi(optarg);
break;
case 'g':
max_gap = atoi(optarg);
break;
case 's':
min_score = atof(optarg);
break;
case 'v':
min_coverage = atof(optarg);
break;
case 'm':
mean_file = optarg;
break;
case 'p':
model_def = optarg;
break;
case 'w':
model_weights = optarg;
break;
case 'l':
label_file = optarg;
break;
case 'n':
remove_original = false;
break;
case 'h':
PrintHelp();
exit(0);
case 'x':
set_all_but_other = true;
break;
default: /* '?' */
PrintUsage(argv[0]);
exit(EXIT_FAILURE);
}
}
if(optind >= argc)
{
cerr << "No input movie file." << endl;
PrintUsage(argv[0]);
exit(EXIT_FAILURE);
}
movie_file = argv[optind];
//keep Caffe quiet
FLAGS_minloglevel = 3;
::google::InitGoogleLogging(argv[0]);
//create the classifier
Classifier classifier(model_def, model_weights, mean_file, label_file);
if(set_all_but_other)
target_list = allExceptOther(classifier.labels_);
//print targets
if(auto_tag)
cout << "Auto-tag mode" << endl;
else
{
cout << "Targets: [";
for(int i=0; i<target_list.size(); i++)
{
cout << target_list[i];
if(i < target_list.size()-1)
cout << ", ";
}
cout << "]" << endl;
}
global_ffmpeg_done = MAX_IMG_IDX;
boost::thread first(CreateScreenShots, movie_file, screenshot_directory);
//first.join(); //uncomment to make predictions wait for screenshots
int epoch = 0;
bool no_more = false;
ScoreList score_list;
//loop till all screenshots have been
//extracted and classified
while(true)
{
vector<cv::Mat> imgs;
//fill a batch with screenshots to classify
for( int i=0; i < batch_size; i++ )
{
int idx = epoch * batch_size + i + 1;
//print some progress updates
if(idx % report_interval == 0)
{
if(global_ffmpeg_done < MAX_IMG_IDX)
cout << PrettyTime(idx) << "/" << PrettyTime(global_ffmpeg_done) << endl;
else
cout << PrettyTime(idx) << endl;
}
string the_image = "img_" + FormatFileNumber(idx) + ".jpg";
string the_image_path = screenshot_directory + the_image;
//wait for screenshots from ffmpeg thread
while( !FileExists( the_image_path ) )
{
//if ffmpeg is done getting screenshots quit waiting
if(idx >= global_ffmpeg_done)
{
no_more = true;
break;
}
cout << " Waiting for: " + the_image_path << endl;
sleep(sleep_time);
}
if(!no_more)
{
cv::Mat img = cv::imread(the_image_path,-1);
CHECK(!img.empty()) << "Unable to decode image " << the_image_path;
imgs.push_back(img);
}
else
break;
}
//don't try to classify an empty batch
if(imgs.size() == 0)
break;
//perform classification
ScoreList ordered_preds = classifier.Classify(imgs);
for( size_t i=0; i < ordered_preds.size(); ++i)
score_list.push_back(ordered_preds[i]);
if(no_more)
break;
epoch += 1;
}
//Either create a file out the cuts for all targets
//or make the cuts from the input list
if(auto_tag)
{
TagTargets( score_list, movie_file, output_directory, classifier.labels_,
classifier.labels_.size(), min_cut, max_gap, min_score ,min_coverage);
}
else
{
//make the cuts based on the predictions
vector<int> target_ints;
for(int i=0; i<target_list.size(); i++)
{
int target_idx = IndexOf(target_list[i],classifier.labels_);
target_ints.push_back(target_idx);
}
CutMovie( score_list, movie_file, target_ints, output_directory, temp_directory,
classifier.labels_.size(), min_cut, max_gap, min_score,
min_coverage, do_concat, remove_original );
}
//clean up screenshots
string clean_cmd = "rm -rf " + screenshot_directory;
if(system(clean_cmd.c_str()))
{
cerr << "Error cleaning up temporary files: " << clean_cmd << endl;
exit(EXIT_FAILURE);
}
}