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更换文档检测模型

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liuyebo
2024-08-27 14:42:45 +08:00
parent aea6f19951
commit 1514e09c40
2072 changed files with 254336 additions and 4967 deletions
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paddle_detection/deploy/cpp/src/main_keypoint.cc Normal file
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// Copyright (c) 2021 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 <glog/logging.h>
#include <math.h>
#include <sys/stat.h>
#include <sys/types.h>
#include <algorithm>
#include <iostream>
#include <numeric>
#include <string>
#include <vector>
#ifdef _WIN32
#include <direct.h>
#include <io.h>
#elif LINUX
#include <stdarg.h>
#endif
#include <gflags/gflags.h>
#include "include/keypoint_detector.h"
#include "include/object_detector.h"
#include "include/preprocess_op.h"
DEFINE_string(model_dir, "", "Path of object detector inference model");
DEFINE_string(model_dir_keypoint,
"",
"Path of keypoint detector inference model");
DEFINE_string(image_file, "", "Path of input image");
DEFINE_string(image_dir,
"",
"Dir of input image, `image_file` has a higher priority.");
DEFINE_int32(batch_size, 1, "batch_size of object detector");
DEFINE_int32(batch_size_keypoint, 8, "batch_size of keypoint detector");
DEFINE_string(
video_file,
"",
"Path of input video, `video_file` or `camera_id` has a highest priority.");
DEFINE_int32(camera_id, -1, "Device id of camera to predict");
DEFINE_bool(
use_gpu,
false,
"Deprecated, please use `--device` to set the device you want to run.");
DEFINE_string(device,
"CPU",
"Choose the device you want to run, it can be: CPU/GPU/XPU, "
"default is CPU.");
DEFINE_double(threshold, 0.5, "Threshold of score.");
DEFINE_double(threshold_keypoint, 0.5, "Threshold of score.");
DEFINE_string(output_dir, "output", "Directory of output visualization files.");
DEFINE_string(run_mode,
"paddle",
"Mode of running(paddle/trt_fp32/trt_fp16/trt_int8)");
DEFINE_int32(gpu_id, 0, "Device id of GPU to execute");
DEFINE_bool(run_benchmark,
false,
"Whether to predict a image_file repeatedly for benchmark");
DEFINE_bool(use_mkldnn, false, "Whether use mkldnn with CPU");
DEFINE_int32(cpu_threads, 1, "Num of threads with CPU");
DEFINE_int32(trt_min_shape, 1, "Min shape of TRT DynamicShapeI");
DEFINE_int32(trt_max_shape, 1280, "Max shape of TRT DynamicShapeI");
DEFINE_int32(trt_opt_shape, 640, "Opt shape of TRT DynamicShapeI");
DEFINE_bool(trt_calib_mode,
false,
"If the model is produced by TRT offline quantitative calibration, "
"trt_calib_mode need to set True");
DEFINE_bool(use_dark, true, "Whether use dark decode in keypoint postprocess");
void PrintBenchmarkLog(std::vector<double> det_time, int img_num) {
LOG(INFO) << "----------------------- Config info -----------------------";
LOG(INFO) << "runtime_device: " << FLAGS_device;
LOG(INFO) << "ir_optim: "
<< "True";
LOG(INFO) << "enable_memory_optim: "
<< "True";
int has_trt = FLAGS_run_mode.find("trt");
if (has_trt >= 0) {
LOG(INFO) << "enable_tensorrt: "
<< "True";
std::string precision = FLAGS_run_mode.substr(4, 8);
LOG(INFO) << "precision: " << precision;
} else {
LOG(INFO) << "enable_tensorrt: "
<< "False";
LOG(INFO) << "precision: "
<< "fp32";
}
LOG(INFO) << "enable_mkldnn: " << (FLAGS_use_mkldnn ? "True" : "False");
LOG(INFO) << "cpu_math_library_num_threads: " << FLAGS_cpu_threads;
LOG(INFO) << "----------------------- Data info -----------------------";
LOG(INFO) << "batch_size: " << FLAGS_batch_size;
LOG(INFO) << "input_shape: "
<< "dynamic shape";
LOG(INFO) << "----------------------- Model info -----------------------";
FLAGS_model_dir.erase(FLAGS_model_dir.find_last_not_of(OS_PATH_SEP) + 1);
LOG(INFO) << "model_name: " << FLAGS_model_dir;
LOG(INFO) << "----------------------- Perf info ------------------------";
LOG(INFO) << "Total number of predicted data: " << img_num
<< " and total time spent(ms): "
<< std::accumulate(det_time.begin(), det_time.end(), 0.);
img_num = std::max(1, img_num);
LOG(INFO) << "preproce_time(ms): " << det_time[0] / img_num
<< ", inference_time(ms): " << det_time[1] / img_num
<< ", postprocess_time(ms): " << det_time[2] / img_num;
}
void PrintKptsBenchmarkLog(std::vector<double> det_time, int img_num) {
LOG(INFO) << "----------------------- Data info -----------------------";
LOG(INFO) << "batch_size_keypoint: " << FLAGS_batch_size_keypoint;
LOG(INFO) << "----------------------- Model info -----------------------";
FLAGS_model_dir_keypoint.erase(
FLAGS_model_dir_keypoint.find_last_not_of(OS_PATH_SEP) + 1);
LOG(INFO) << "keypoint_model_name: " << FLAGS_model_dir_keypoint;
LOG(INFO) << "----------------------- Perf info ------------------------";
LOG(INFO) << "Total number of predicted data: " << img_num
<< " and total time spent(ms): "
<< std::accumulate(det_time.begin(), det_time.end(), 0.);
img_num = std::max(1, img_num);
LOG(INFO) << "Average time cost per person:";
LOG(INFO) << "preproce_time(ms): " << det_time[0] / img_num
<< ", inference_time(ms): " << det_time[1] / img_num
<< ", postprocess_time(ms): " << det_time[2] / img_num;
}
static std::string DirName(const std::string& filepath) {
auto pos = filepath.rfind(OS_PATH_SEP);
if (pos == std::string::npos) {
return "";
}
return filepath.substr(0, pos);
}
static bool PathExists(const std::string& path) {
#ifdef _WIN32
struct _stat buffer;
return (_stat(path.c_str(), &buffer) == 0);
#else
struct stat buffer;
return (stat(path.c_str(), &buffer) == 0);
#endif // !_WIN32
}
static void MkDir(const std::string& path) {
if (PathExists(path)) return;
int ret = 0;
#ifdef _WIN32
ret = _mkdir(path.c_str());
#else
ret = mkdir(path.c_str(), 0755);
#endif // !_WIN32
if (ret != 0) {
std::string path_error(path);
path_error += " mkdir failed!";
throw std::runtime_error(path_error);
}
}
static void MkDirs(const std::string& path) {
if (path.empty()) return;
if (PathExists(path)) return;
MkDirs(DirName(path));
MkDir(path);
}
void PredictVideo(const std::string& video_path,
PaddleDetection::ObjectDetector* det,
PaddleDetection::KeyPointDetector* keypoint,
const std::string& output_dir = "output") {
// Open video
cv::VideoCapture capture;
std::string video_out_name = "output.mp4";
if (FLAGS_camera_id != -1) {
capture.open(FLAGS_camera_id);
} else {
capture.open(video_path.c_str());
video_out_name =
video_path.substr(video_path.find_last_of(OS_PATH_SEP) + 1);
}
if (!capture.isOpened()) {
printf("can not open video : %s\n", video_path.c_str());
return;
}
// Get Video info : resolution, fps, frame count
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));
int video_fps = static_cast<int>(capture.get(CV_CAP_PROP_FPS));
int video_frame_count =
static_cast<int>(capture.get(CV_CAP_PROP_FRAME_COUNT));
printf("fps: %d, frame_count: %d\n", video_fps, video_frame_count);
// Create VideoWriter for output
cv::VideoWriter video_out;
std::string video_out_path(output_dir);
if (output_dir.rfind(OS_PATH_SEP) != output_dir.size() - 1) {
video_out_path += OS_PATH_SEP;
}
video_out_path += video_out_name;
video_out.open(video_out_path.c_str(),
0x00000021,
video_fps,
cv::Size(video_width, video_height),
true);
if (!video_out.isOpened()) {
printf("create video writer failed!\n");
return;
}
PaddleDetection::PoseSmooth smoother =
PaddleDetection::PoseSmooth(video_width, video_height);
std::vector<PaddleDetection::ObjectResult> result;
std::vector<int> bbox_num;
std::vector<double> det_times;
auto labels = det->GetLabelList();
auto colormap = PaddleDetection::GenerateColorMap(labels.size());
// Store keypoint results
std::vector<PaddleDetection::KeyPointResult> result_kpts;
std::vector<cv::Mat> imgs_kpts;
std::vector<std::vector<float>> center_bs;
std::vector<std::vector<float>> scale_bs;
std::vector<int> colormap_kpts = PaddleDetection::GenerateColorMap(20);
// Capture all frames and do inference
cv::Mat frame;
int frame_id = 1;
bool is_rbox = false;
while (capture.read(frame)) {
if (frame.empty()) {
break;
}
std::vector<cv::Mat> imgs;
imgs.push_back(frame);
printf("detect frame: %d\n", frame_id);
det->Predict(imgs, FLAGS_threshold, 0, 1, &result, &bbox_num, &det_times);
std::vector<PaddleDetection::ObjectResult> out_result;
for (const auto& item : result) {
if (item.confidence < FLAGS_threshold || item.class_id == -1) {
continue;
}
out_result.push_back(item);
if (item.rect.size() > 6) {
is_rbox = true;
printf("class=%d confidence=%.4f rect=[%d %d %d %d %d %d %d %d]\n",
item.class_id,
item.confidence,
item.rect[0],
item.rect[1],
item.rect[2],
item.rect[3],
item.rect[4],
item.rect[5],
item.rect[6],
item.rect[7]);
} else {
printf("class=%d confidence=%.4f rect=[%d %d %d %d]\n",
item.class_id,
item.confidence,
item.rect[0],
item.rect[1],
item.rect[2],
item.rect[3]);
}
}
if (keypoint) {
result_kpts.clear();
int imsize = out_result.size();
for (int i = 0; i < imsize; i++) {
auto item = out_result[i];
cv::Mat crop_img;
std::vector<double> keypoint_times;
std::vector<int> rect = {
item.rect[0], item.rect[1], item.rect[2], item.rect[3]};
std::vector<float> center;
std::vector<float> scale;
if (item.class_id == 0) {
PaddleDetection::CropImg(frame, crop_img, rect, center, scale);
center_bs.emplace_back(center);
scale_bs.emplace_back(scale);
imgs_kpts.emplace_back(crop_img);
}
if (imgs_kpts.size() == FLAGS_batch_size_keypoint ||
((i == imsize - 1) && !imgs_kpts.empty())) {
keypoint->Predict(imgs_kpts,
center_bs,
scale_bs,
FLAGS_threshold,
0,
1,
&result_kpts,
&keypoint_times);
imgs_kpts.clear();
center_bs.clear();
scale_bs.clear();
}
}
if (result_kpts.size() == 1) {
for (int i = 0; i < result_kpts.size(); i++) {
result_kpts[i] = smoother.smooth_process(&(result_kpts[i]));
}
}
cv::Mat out_im = VisualizeKptsResult(frame, result_kpts, colormap_kpts);
video_out.write(out_im);
} else {
// Visualization result
cv::Mat out_im = PaddleDetection::VisualizeResult(
frame, out_result, labels, colormap, is_rbox);
video_out.write(out_im);
}
frame_id += 1;
}
capture.release();
video_out.release();
}
void PredictImage(const std::vector<std::string> all_img_paths,
const int batch_size,
const double threshold,
const bool run_benchmark,
PaddleDetection::ObjectDetector* det,
PaddleDetection::KeyPointDetector* keypoint,
const std::string& output_dir = "output") {
std::vector<double> det_t = {0, 0, 0};
int steps = ceil(static_cast<float>(all_img_paths.size()) / batch_size);
int kpts_imgs = 0;
std::vector<double> keypoint_t = {0, 0, 0};
printf("total images = %d, batch_size = %d, total steps = %d\n",
all_img_paths.size(),
batch_size,
steps);
for (int idx = 0; idx < steps; idx++) {
std::vector<cv::Mat> batch_imgs;
int left_image_cnt = all_img_paths.size() - idx * batch_size;
if (left_image_cnt > batch_size) {
left_image_cnt = batch_size;
}
for (int bs = 0; bs < left_image_cnt; bs++) {
std::string image_file_path = all_img_paths.at(idx * batch_size + bs);
cv::Mat im = cv::imread(image_file_path, 1);
batch_imgs.insert(batch_imgs.end(), im);
}
// Store all detected result
std::vector<PaddleDetection::ObjectResult> result;
std::vector<int> bbox_num;
std::vector<double> det_times;
// Store keypoint results
std::vector<PaddleDetection::KeyPointResult> result_kpts;
std::vector<cv::Mat> imgs_kpts;
std::vector<std::vector<float>> center_bs;
std::vector<std::vector<float>> scale_bs;
std::vector<int> colormap_kpts = PaddleDetection::GenerateColorMap(20);
bool is_rbox = false;
if (run_benchmark) {
det->Predict(
batch_imgs, threshold, 10, 10, &result, &bbox_num, &det_times);
} else {
det->Predict(batch_imgs, threshold, 0, 1, &result, &bbox_num, &det_times);
}
// get labels and colormap
auto labels = det->GetLabelList();
auto colormap = PaddleDetection::GenerateColorMap(labels.size());
int item_start_idx = 0;
for (int i = 0; i < left_image_cnt; i++) {
cv::Mat im = batch_imgs[i];
std::vector<PaddleDetection::ObjectResult> im_result;
int detect_num = 0;
for (int j = 0; j < bbox_num[i]; j++) {
PaddleDetection::ObjectResult item = result[item_start_idx + j];
if (item.confidence < threshold || item.class_id == -1) {
continue;
}
detect_num += 1;
im_result.push_back(item);
if (item.rect.size() > 6) {
is_rbox = true;
printf("class=%d confidence=%.4f rect=[%d %d %d %d %d %d %d %d]\n",
item.class_id,
item.confidence,
item.rect[0],
item.rect[1],
item.rect[2],
item.rect[3],
item.rect[4],
item.rect[5],
item.rect[6],
item.rect[7]);
} else {
printf("class=%d confidence=%.4f rect=[%d %d %d %d]\n",
item.class_id,
item.confidence,
item.rect[0],
item.rect[1],
item.rect[2],
item.rect[3]);
}
}
std::cout << all_img_paths.at(idx * batch_size + i)
<< " The number of detected box: " << detect_num << std::endl;
item_start_idx = item_start_idx + bbox_num[i];
std::vector<int> compression_params;
compression_params.push_back(CV_IMWRITE_JPEG_QUALITY);
compression_params.push_back(95);
std::string output_path(output_dir);
if (output_dir.rfind(OS_PATH_SEP) != output_dir.size() - 1) {
output_path += OS_PATH_SEP;
}
std::string image_file_path = all_img_paths.at(idx * batch_size + i);
if (keypoint) {
int imsize = im_result.size();
for (int i = 0; i < imsize; i++) {
auto item = im_result[i];
cv::Mat crop_img;
std::vector<double> keypoint_times;
std::vector<int> rect = {
item.rect[0], item.rect[1], item.rect[2], item.rect[3]};
std::vector<float> center;
std::vector<float> scale;
if (item.class_id == 0) {
PaddleDetection::CropImg(im, crop_img, rect, center, scale);
center_bs.emplace_back(center);
scale_bs.emplace_back(scale);
imgs_kpts.emplace_back(crop_img);
kpts_imgs += 1;
}
if (imgs_kpts.size() == FLAGS_batch_size_keypoint ||
((i == imsize - 1) && !imgs_kpts.empty())) {
if (run_benchmark) {
keypoint->Predict(imgs_kpts,
center_bs,
scale_bs,
0.5,
10,
10,
&result_kpts,
&keypoint_times);
} else {
keypoint->Predict(imgs_kpts,
center_bs,
scale_bs,
0.5,
0,
1,
&result_kpts,
&keypoint_times);
}
imgs_kpts.clear();
center_bs.clear();
scale_bs.clear();
keypoint_t[0] += keypoint_times[0];
keypoint_t[1] += keypoint_times[1];
keypoint_t[2] += keypoint_times[2];
}
}
std::string kpts_savepath =
output_path + "keypoint_" +
image_file_path.substr(image_file_path.find_last_of(OS_PATH_SEP) + 1);
cv::Mat kpts_vis_img =
VisualizeKptsResult(im, result_kpts, colormap_kpts);
cv::imwrite(kpts_savepath, kpts_vis_img, compression_params);
printf("Visualized output saved as %s\n", kpts_savepath.c_str());
} else {
// Visualization result
cv::Mat vis_img = PaddleDetection::VisualizeResult(
im, im_result, labels, colormap, is_rbox);
std::string det_savepath =
output_path +
image_file_path.substr(image_file_path.find_last_of(OS_PATH_SEP) + 1);
cv::imwrite(det_savepath, vis_img, compression_params);
printf("Visualized output saved as %s\n", det_savepath.c_str());
}
}
det_t[0] += det_times[0];
det_t[1] += det_times[1];
det_t[2] += det_times[2];
}
PrintBenchmarkLog(det_t, all_img_paths.size());
if (keypoint) {
PrintKptsBenchmarkLog(keypoint_t, kpts_imgs);
}
}
int main(int argc, char** argv) {
// Parsing command-line
google::ParseCommandLineFlags(&argc, &argv, true);
if (FLAGS_model_dir.empty() ||
(FLAGS_image_file.empty() && FLAGS_image_dir.empty() &&
FLAGS_video_file.empty())) {
std::cout << "Usage: ./main --model_dir=/PATH/TO/INFERENCE_MODEL/ "
"(--model_dir_keypoint=/PATH/TO/INFERENCE_MODEL/)"
<< "--image_file=/PATH/TO/INPUT/IMAGE/" << std::endl;
return -1;
}
if (!(FLAGS_run_mode == "paddle" || FLAGS_run_mode == "trt_fp32" ||
FLAGS_run_mode == "trt_fp16" || FLAGS_run_mode == "trt_int8")) {
std::cout
<< "run_mode should be 'paddle', 'trt_fp32', 'trt_fp16' or 'trt_int8'.";
return -1;
}
transform(FLAGS_device.begin(),
FLAGS_device.end(),
FLAGS_device.begin(),
::toupper);
if (!(FLAGS_device == "CPU" || FLAGS_device == "GPU" ||
FLAGS_device == "XPU")) {
std::cout << "device should be 'CPU', 'GPU' or 'XPU'.";
return -1;
}
if (FLAGS_use_gpu) {
std::cout << "Deprecated, please use `--device` to set the device you want "
"to run.";
return -1;
}
// Load model and create a object detector
PaddleDetection::ObjectDetector det(FLAGS_model_dir,
FLAGS_device,
FLAGS_use_mkldnn,
FLAGS_cpu_threads,
FLAGS_run_mode,
FLAGS_batch_size,
FLAGS_gpu_id,
FLAGS_trt_min_shape,
FLAGS_trt_max_shape,
FLAGS_trt_opt_shape,
FLAGS_trt_calib_mode);
PaddleDetection::KeyPointDetector* keypoint = nullptr;
if (!FLAGS_model_dir_keypoint.empty()) {
keypoint = new PaddleDetection::KeyPointDetector(FLAGS_model_dir_keypoint,
FLAGS_device,
FLAGS_use_mkldnn,
FLAGS_cpu_threads,
FLAGS_run_mode,
FLAGS_batch_size_keypoint,
FLAGS_gpu_id,
FLAGS_trt_min_shape,
FLAGS_trt_max_shape,
FLAGS_trt_opt_shape,
FLAGS_trt_calib_mode,
FLAGS_use_dark);
}
// Do inference on input video or image
if (!PathExists(FLAGS_output_dir)) {
MkDirs(FLAGS_output_dir);
}
if (!FLAGS_video_file.empty() || FLAGS_camera_id != -1) {
PredictVideo(FLAGS_video_file, &det, keypoint, FLAGS_output_dir);
} else if (!FLAGS_image_file.empty() || !FLAGS_image_dir.empty()) {
std::vector<std::string> all_img_paths;
std::vector<cv::String> cv_all_img_paths;
if (!FLAGS_image_file.empty()) {
all_img_paths.push_back(FLAGS_image_file);
if (FLAGS_batch_size > 1) {
std::cout << "batch_size should be 1, when set `image_file`."
<< std::endl;
return -1;
}
} else {
cv::glob(FLAGS_image_dir, cv_all_img_paths);
for (const auto& img_path : cv_all_img_paths) {
all_img_paths.push_back(img_path);
}
}
PredictImage(all_img_paths,
FLAGS_batch_size,
FLAGS_threshold,
FLAGS_run_benchmark,
&det,
keypoint,
FLAGS_output_dir);
}
delete keypoint;
keypoint = nullptr;
return 0;
}