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main.cc
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// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include <iostream>
#include <string>
#include "humanseg.h" // NOLINT
#include "humanseg_postprocess.h" // NOLINT
// Do predicting on a video file
int VideoPredict(const std::string& video_path, HumanSeg& seg)
{
cv::VideoCapture capture;
capture.open(video_path.c_str());
if (!capture.isOpened()) {
printf("can not open video : %s\n", video_path.c_str());
return -1;
}
int video_width = static_cast<int>(capture.get(CV_CAP_PROP_FRAME_WIDTH));
int video_height = static_cast<int>(capture.get(CV_CAP_PROP_FRAME_HEIGHT));
cv::VideoWriter video_out;
std::string video_out_path = "result.avi";
video_out.open(video_out_path.c_str(),
CV_FOURCC('M', 'J', 'P', 'G'),
30.0,
cv::Size(video_width, video_height),
true);
if (!video_out.isOpened()) {
printf("create video writer failed!\n");
return -1;
}
cv::Mat frame;
while (capture.read(frame)) {
if (frame.empty()) {
break;
}
cv::Mat out_im = seg.Predict(frame);
video_out.write(out_im);
}
capture.release();
video_out.release();
return 0;
}
// Do predicting on a image file
int ImagePredict(const std::string& image_path, HumanSeg& seg)
{
cv::Mat img = imread(image_path, cv::IMREAD_COLOR);
cv::Mat out_im = seg.Predict(img);
imwrite("result.jpeg", out_im);
return 0;
}
int main(int argc, char* argv[]) {
if (argc < 3 || argc > 4) {
std::cout << "Usage:"
<< "./humanseg ./models/ ./data/test.avi"
<< std::endl;
return -1;
}
bool use_gpu = (argc == 4 ? std::stoi(argv[3]) : false);
auto model_dir = std::string(argv[1]);
auto input_path = std::string(argv[2]);
// Init Model
std::vector<float> means = {104.008, 116.669, 122.675};
std::vector<float> scale = {1.000, 1.000, 1.000};
HumanSeg seg(model_dir, means, scale, use_gpu);
// Call ImagePredict while input_path is a image file path
// The output will be saved as result.jpeg
// ImagePredict(input_path, seg);
// Call VideoPredict while input_path is a video file path
// The output will be saved as result.avi
VideoPredict(input_path, seg);
return 0;
}