Pre Merge pull request !14 from Daniel/lxm
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Daniel
Gitee
commit
3fb771cab2
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@ -275,6 +275,9 @@ target_link_libraries(GimbalUdpDetectionInfoSender sv_world)
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add_executable(EvalFpsOnVideo samples/test/eval_fps_on_video.cpp)
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target_link_libraries(EvalFpsOnVideo sv_world)
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add_executable(EvalModelOnCocoVal samples/test/eval_mAP_on_coco_val/eval_mAP_on_coco_val.cpp)
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target_link_libraries(EvalModelOnCocoVal sv_world)
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include_directories(${CMAKE_CURRENT_SOURCE_DIR}/samples/calib)
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add_executable(CameraCalibrarion samples/calib/calibrate_camera_charuco.cpp)
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target_link_libraries(CameraCalibrarion ${OpenCV_LIBS})
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@ -0,0 +1,25 @@
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from pycocotools.coco import COCO
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from pycocotools.cocoeval import COCOeval
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import os
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if __name__ == '__main__':
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path = os.path.abspath(os.path.join(os.getcwd(),"../../.."))
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pred_json = 'pd_coco.json'
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anno_json = path + '/val2017/instances_val2017.json'
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# use COCO API to load forecast results and annotations
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cocoGt = COCO(anno_json)
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cocoDt = cocoGt.loadRes(pred_json)
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# create COCO eval object
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cocoEval = COCOeval(cocoGt, cocoDt,'bbox')
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# assessment
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cocoEval.evaluate()
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cocoEval.accumulate()
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cocoEval.summarize()
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# save results
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with open('coco_eval.txt', 'w') as f:
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f.write(str(cocoEval.stats))
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@ -0,0 +1,100 @@
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#include <iostream>
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#include <string>
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// 包含SpireCV SDK头文件
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#include <sv_world.h>
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using namespace std;
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using namespace cv;
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//extract name
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std::string GetImageFileName(const std::string& imagePath) {
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size_t lastSlash = imagePath.find_last_of("/\\");
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if (lastSlash == std::string::npos) {
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return imagePath;
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} else {
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std::string fileName = imagePath.substr(lastSlash + 1);
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size_t lastDot = fileName.find_last_of(".");
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if (lastDot != std::string::npos) {
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return fileName.substr(0, lastDot);
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}
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return fileName;
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}
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}
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int main(int argc, char *argv[])
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{
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// 实例化 通用目标 检测器类
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sv::CommonObjectDetector cod;
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// 手动导入相机参数，如果使用Amov的G1等吊舱或相机，则可以忽略该步骤，将自动下载相机参数文件
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cod.loadCameraParams(sv::get_home() + "/SpireCV/calib_webcam_640x480.yaml");
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//load data
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string val_path = sv::get_home() + "/SpireCV/val2017/val2017";
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vector<string> val_image;
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glob(val_path, val_image, false);
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if (val_image.size() == 0)
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{
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printf("val_image error!!!\n");
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exit(1);
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}
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//preds folder
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std::string folder = sv::get_home() + "/SpireCV/val2017/preds";
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int checkStatus = std::system(("if [ -d \"" + folder + "\" ]; then echo; fi").c_str());
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if(checkStatus == 0)
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{
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int removeStatus = std::system(("rm -rf \"" + folder + "\"").c_str());
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if(removeStatus != 0)
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{
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printf("remove older preds folder error!!!\n");
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exit(1);
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}
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}
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int status = std::system(("mkdir \""+folder+"\"").c_str());
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if(status != 0)
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{
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printf("create preds folder error!!!\n");
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exit(1);
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}
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for (int i = 0; i < val_image.size(); i++) {
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//create pred file
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std::string val_image_name = GetImageFileName(val_image[i]);
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std::string filename = folder+"/"+ val_image_name + ".txt";
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std::ofstream file(filename);
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file.is_open();
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file<<std::fixed<<std::setprecision(6);
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// 实例化SpireCV的 单帧检测结果 接口类 TargetsInFrame
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sv::TargetsInFrame tgts(i);
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cv::Mat img = imread(val_image[i]);
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int rows = img.rows;
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int cols = img.cols;
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// 执行通用目标检测
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cod.detect(img, tgts);
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// 可视化检测结果，叠加到img上
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sv::drawTargetsInFrame(img, tgts);
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// reslusts
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for (int j = 0; j < tgts.targets.size(); j++)
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{
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sv::Box b;
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tgts.targets[j].getBox(b);
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file<<tgts.targets[j].category_id<<" "<<(float)(b.x1+b.x2)/(2*cols)<<" "<<(float)(b.y1+b.y2)/(2*rows)<<" "<<(float)(b.x2-b.x1)/cols<<" "<<(float)(b.y2-b.y1)/rows<<" "<<(float)tgts.targets[j].score<<"\n";
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}
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file.close();
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cv::imshow("image", img);
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cv::waitKey(500);
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img.release();
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cv::destroyAllWindows();
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}
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return 0;
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}
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@ -0,0 +1,62 @@
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import datetime
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import json
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import os
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import cv2
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# revert prediction results to coco_json
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path = os.path.abspath(os.path.join(os.getcwd(),"../../.."))
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# all files dir
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images_path = path + '/val2017/val2017'
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preds_path = path + '/val2017/preds'
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coco_json_save = 'pd_coco.json'
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# config coco_json
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coco_json = []
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# remap the id of the coco dataset
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id_map = {1: 0, 2: 1, 3: 2, 4: 3, 5: 4, 6: 5, 7: 6, 8: 7, 9: 8, 10: 9, 11: 10, 13: 11, 14: 12, 15: 13, 16: 14, 17: 15, 18: 16, 19: 17, 20: 18, 21: 19, 22: 20, 23: 21, 24: 22, 25: 23, 27: 24, 28: 25, 31: 26, 32: 27, 33: 28, 34: 29, 35: 30, 36: 31, 37: 32, 38: 33, 39: 34, 40: 35, 41: 36, 42: 37, 43: 38, 44: 39, 46: 40, 47: 41, 48: 42, 49: 43, 50: 44, 51: 45, 52: 46, 53: 47, 54: 48, 55: 49, 56: 50, 57: 51, 58: 52, 59: 53, 60: 54, 61: 55, 62: 56, 63: 57, 64: 58, 65: 59, 67: 60, 70: 61, 72: 62, 73: 63, 74: 64, 75: 65, 76: 66, 77: 67, 78: 68, 79: 69, 80: 70, 81: 71, 82: 72, 84: 73, 85: 74, 86: 75, 87: 76, 88: 77, 89: 78, 90: 79}
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reid_mp = {value: key for key, value in id_map.items()}
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# load images dir
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images = os.listdir(images_path)
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for image in images:
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print(image)
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# get image name
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image_name, image_suffix = os.path.splitext(image)
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# get image W and H
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image_path = images_path + '/' + image
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img = cv2.imread(image_path)
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height, width, _ = img.shape
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# read pred's txt
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pred_path = preds_path + '/' + image_name + '.txt'
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if not os.path.exists(pred_path):
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continue
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with open(pred_path, 'r') as f:
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preds = f.readlines()
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preds = [l.strip() for l in preds]
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for j, pred in enumerate(preds):
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pred = pred.split(' ')
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category_id = int(pred[0])
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x = float(pred[1]) * width
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y = float(pred[2]) * height
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w = float(pred[3]) * width
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h = float(pred[4]) * height
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xmin = x - w / 2
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ymin = y - h / 2
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xmax = x + w / 2
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ymax = y + h / 2
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coco_json.append({
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'image_id': int(image_name),
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'category_id': int(reid_mp[category_id]),
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'bbox': [xmin, ymin, w, h],
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'score': float(pred[5]),
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'area': w * h})
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# save json
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with open(os.path.join(coco_json_save), 'w') as f:
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json.dump(coco_json, f, indent=2)
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print(len(coco_json), 'Done!')
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