Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

encapsulate GenerateColormap in Visualize #218

Merged
merged 2 commits into from
Jul 13, 2020
Merged

encapsulate GenerateColormap in Visualize #218

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
62daf2f
encapsulate GenerateColormap in Visualize
joey12300 Jul 13, 2020
398b7e4
remove time record
joey12300 Jul 13, 2020
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
26 changes: 0 additions & 26 deletions deploy/cpp/demo/classifier.cpp
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -62,8 +62,6 @@ int main(int argc, char** argv) {
FLAGS_use_ir_optim);

// 进行预测
double total_running_time_s = 0.0;
double total_imread_time_s = 0.0;
int imgs = 1;
if (FLAGS_image_list != "") {
std::ifstream inf(FLAGS_image_list);
Expand All @@ -79,7 +77,6 @@ int main(int argc, char** argv) {
}
imgs = image_paths.size();
for (int i = 0; i < image_paths.size(); i += FLAGS_batch_size) {
auto start = system_clock::now();
// 读图像
int im_vec_size =
std::min(static_cast<int>(image_paths.size()), i + FLAGS_batch_size);
Expand All @@ -91,19 +88,7 @@ int main(int argc, char** argv) {
for (int j = i; j < im_vec_size; ++j) {
im_vec[j - i] = std::move(cv::imread(image_paths[j], 1));
}
auto imread_end = system_clock::now();
model.predict(im_vec, &results, thread_num);

auto imread_duration = duration_cast<microseconds>(imread_end - start);
total_imread_time_s += static_cast<double>(imread_duration.count()) *
microseconds::period::num /
microseconds::period::den;

auto end = system_clock::now();
auto duration = duration_cast<microseconds>(end - start);
total_running_time_s += static_cast<double>(duration.count()) *
microseconds::period::num /
microseconds::period::den;
for (int j = i; j < im_vec_size; ++j) {
std::cout << "Path:" << image_paths[j]
<< ", predict label: " << results[j - i].category
Expand All @@ -112,23 +97,12 @@ int main(int argc, char** argv) {
}
}
} else {
auto start = system_clock::now();
PaddleX::ClsResult result;
cv::Mat im = cv::imread(FLAGS_image, 1);
model.predict(im, &result);
auto end = system_clock::now();
auto duration = duration_cast<microseconds>(end - start);
total_running_time_s += static_cast<double>(duration.count()) *
microseconds::period::num /
microseconds::period::den;
std::cout << "Predict label: " << result.category
<< ", label_id:" << result.category_id
<< ", score: " << result.score << std::endl;
}
std::cout << "Total running time: " << total_running_time_s
<< " s, average running time: " << total_running_time_s / imgs
<< " s/img, total read img time: " << total_imread_time_s
<< " s, average read time: " << total_imread_time_s / imgs
<< " s/img, batch_size = " << FLAGS_batch_size << std::endl;
return 0;
}
31 changes: 2 additions & 29 deletions deploy/cpp/demo/detector.cpp
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -65,11 +65,7 @@ int main(int argc, char** argv) {
FLAGS_gpu_id,
FLAGS_key,
FLAGS_use_ir_optim);

double total_running_time_s = 0.0;
double total_imread_time_s = 0.0;
int imgs = 1;
auto colormap = PaddleX::GenerateColorMap(model.labels.size());
std::string save_dir = "output";
// 进行预测
if (FLAGS_image_list != "") {
Expand All @@ -85,7 +81,6 @@ int main(int argc, char** argv) {
}
imgs = image_paths.size();
for (int i = 0; i < image_paths.size(); i += FLAGS_batch_size) {
auto start = system_clock::now();
int im_vec_size =
std::min(static_cast<int>(image_paths.size()), i + FLAGS_batch_size);
std::vector<cv::Mat> im_vec(im_vec_size - i);
Expand All @@ -96,17 +91,7 @@ int main(int argc, char** argv) {
for (int j = i; j < im_vec_size; ++j) {
im_vec[j - i] = std::move(cv::imread(image_paths[j], 1));
}
auto imread_end = system_clock::now();
model.predict(im_vec, &results, thread_num);
auto imread_duration = duration_cast<microseconds>(imread_end - start);
total_imread_time_s += static_cast<double>(imread_duration.count()) *
microseconds::period::num /
microseconds::period::den;
auto end = system_clock::now();
auto duration = duration_cast<microseconds>(end - start);
total_running_time_s += static_cast<double>(duration.count()) *
microseconds::period::num /
microseconds::period::den;
// 输出结果目标框
for (int j = 0; j < im_vec_size - i; ++j) {
for (int k = 0; k < results[j].boxes.size(); ++k) {
Expand All @@ -124,23 +109,17 @@ int main(int argc, char** argv) {
// 可视化
for (int j = 0; j < im_vec_size - i; ++j) {
cv::Mat vis_img = PaddleX::Visualize(
im_vec[j], results[j], model.labels, colormap, FLAGS_threshold);
im_vec[j], results[j], model.labels, FLAGS_threshold);
std::string save_path =
PaddleX::generate_save_path(FLAGS_save_dir, image_paths[i + j]);
cv::imwrite(save_path, vis_img);
std::cout << "Visualized output saved as " << save_path << std::endl;
}
}
} else {
auto start = system_clock::now();
PaddleX::DetResult result;
cv::Mat im = cv::imread(FLAGS_image, 1);
model.predict(im, &result);
auto end = system_clock::now();
auto duration = duration_cast<microseconds>(end - start);
total_running_time_s += static_cast<double>(duration.count()) *
microseconds::period::num /
microseconds::period::den;
// 输出结果目标框
for (int i = 0; i < result.boxes.size(); ++i) {
std::cout << "image file: " << FLAGS_image << std::endl;
Expand All @@ -155,19 +134,13 @@ int main(int argc, char** argv) {

// 可视化
cv::Mat vis_img =
PaddleX::Visualize(im, result, model.labels, colormap, FLAGS_threshold);
PaddleX::Visualize(im, result, model.labels, FLAGS_threshold);
std::string save_path =
PaddleX::generate_save_path(FLAGS_save_dir, FLAGS_image);
cv::imwrite(save_path, vis_img);
result.clear();
std::cout << "Visualized output saved as " << save_path << std::endl;
}

std::cout << "Total running time: " << total_running_time_s
<< " s, average running time: " << total_running_time_s / imgs
<< " s/img, total read img time: " << total_imread_time_s
<< " s, average read img time: " << total_imread_time_s / imgs
<< " s, batch_size = " << FLAGS_batch_size << std::endl;

return 0;
}
31 changes: 2 additions & 29 deletions deploy/cpp/demo/segmenter.cpp
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -62,11 +62,7 @@ int main(int argc, char** argv) {
FLAGS_gpu_id,
FLAGS_key,
FLAGS_use_ir_optim);

double total_running_time_s = 0.0;
double total_imread_time_s = 0.0;
int imgs = 1;
auto colormap = PaddleX::GenerateColorMap(model.labels.size());
// 进行预测
if (FLAGS_image_list != "") {
std::ifstream inf(FLAGS_image_list);
Expand All @@ -81,7 +77,6 @@ int main(int argc, char** argv) {
}
imgs = image_paths.size();
for (int i = 0; i < image_paths.size(); i += FLAGS_batch_size) {
auto start = system_clock::now();
int im_vec_size =
std::min(static_cast<int>(image_paths.size()), i + FLAGS_batch_size);
std::vector<cv::Mat> im_vec(im_vec_size - i);
Expand All @@ -92,50 +87,28 @@ int main(int argc, char** argv) {
for (int j = i; j < im_vec_size; ++j) {
im_vec[j - i] = std::move(cv::imread(image_paths[j], 1));
}
auto imread_end = system_clock::now();
model.predict(im_vec, &results, thread_num);
auto imread_duration = duration_cast<microseconds>(imread_end - start);
total_imread_time_s += static_cast<double>(imread_duration.count()) *
microseconds::period::num /
microseconds::period::den;
auto end = system_clock::now();
auto duration = duration_cast<microseconds>(end - start);
total_running_time_s += static_cast<double>(duration.count()) *
microseconds::period::num /
microseconds::period::den;
// 可视化
for (int j = 0; j < im_vec_size - i; ++j) {
cv::Mat vis_img =
PaddleX::Visualize(im_vec[j], results[j], model.labels, colormap);
PaddleX::Visualize(im_vec[j], results[j], model.labels);
std::string save_path =
PaddleX::generate_save_path(FLAGS_save_dir, image_paths[i + j]);
cv::imwrite(save_path, vis_img);
std::cout << "Visualized output saved as " << save_path << std::endl;
}
}
} else {
auto start = system_clock::now();
PaddleX::SegResult result;
cv::Mat im = cv::imread(FLAGS_image, 1);
model.predict(im, &result);
auto end = system_clock::now();
auto duration = duration_cast<microseconds>(end - start);
total_running_time_s += static_cast<double>(duration.count()) *
microseconds::period::num /
microseconds::period::den;
// 可视化
cv::Mat vis_img = PaddleX::Visualize(im, result, model.labels, colormap);
cv::Mat vis_img = PaddleX::Visualize(im, result, model.labels);
std::string save_path =
PaddleX::generate_save_path(FLAGS_save_dir, FLAGS_image);
cv::imwrite(save_path, vis_img);
result.clear();
std::cout << "Visualized output saved as " << save_path << std::endl;
}
std::cout << "Total running time: " << total_running_time_s
<< " s, average running time: " << total_running_time_s / imgs
<< " s/img, total read img time: " << total_imread_time_s
<< " s, average read img time: " << total_imread_time_s / imgs
<< " s, batch_size = " << FLAGS_batch_size << std::endl;

return 0;
}
7 changes: 2 additions & 5 deletions deploy/cpp/include/paddlex/visualize.h
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -65,13 +65,12 @@ std::vector<int> GenerateColorMap(int num_class);
* @param img: initial image matrix
* @param results: the detection result
* @param labels: label map
* @param colormap: visualization color map
* @param threshold: minimum confidence to display
* @return visualized image matrix
* */
cv::Mat Visualize(const cv::Mat& img,
const DetResult& results,
const std::map<int, std::string>& labels,
const std::vector<int>& colormap,
float threshold = 0.5);

/*
Expand All @@ -81,13 +80,11 @@ cv::Mat Visualize(const cv::Mat& img,
* @param img: initial image matrix
* @param results: the detection result
* @param labels: label map
* @param colormap: visualization color map
* @return visualized image matrix
* */
cv::Mat Visualize(const cv::Mat& img,
const SegResult& result,
const std::map<int, std::string>& labels,
const std::vector<int>& colormap);
const std::map<int, std::string>& labels);

/*
* @brief
Expand Down
6 changes: 3 additions & 3 deletions deploy/cpp/src/visualize.cpp
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -34,8 +34,8 @@ std::vector<int> GenerateColorMap(int num_class) {
cv::Mat Visualize(const cv::Mat& img,
const DetResult& result,
const std::map<int, std::string>& labels,
const std::vector<int>& colormap,
float threshold) {
auto colormap = GenerateColorMap(labels.size());
cv::Mat vis_img = img.clone();
auto boxes = result.boxes;
for (int i = 0; i < boxes.size(); ++i) {
Expand Down Expand Up @@ -107,8 +107,8 @@ cv::Mat Visualize(const cv::Mat& img,

cv::Mat Visualize(const cv::Mat& img,
const SegResult& result,
const std::map<int, std::string>& labels,
const std::vector<int>& colormap) {
const std::map<int, std::string>& labels) {
auto colormap = GenerateColorMap(labels.size());
std::vector<uint8_t> label_map(result.label_map.data.begin(),
result.label_map.data.end());
cv::Mat mask(result.label_map.shape[0],
Expand Down