COCO Model mAP eVAL
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lxm
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@ -269,6 +269,9 @@ target_link_libraries(GimbalUdpDetectionInfoSender sv_world)
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add_executable(EvalFpsOnVideo samples/test/eval_fps_on_video.cpp)
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add_executable(EvalFpsOnVideo samples/test/eval_fps_on_video.cpp)
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target_link_libraries(EvalFpsOnVideo sv_world)
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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(${CMAKE_CURRENT_SOURCE_DIR}/samples/calib)
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include_directories(${CMAKE_CURRENT_SOURCE_DIR}/samples/calib)
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add_executable(CameraCalibrarion samples/calib/calibrate_camera_charuco.cpp)
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add_executable(CameraCalibrarion samples/calib/calibrate_camera_charuco.cpp)
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target_link_libraries(CameraCalibrarion ${OpenCV_LIBS})
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target_link_libraries(CameraCalibrarion ${OpenCV_LIBS})
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@ -0,0 +1,32 @@
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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/gt_coco.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 +1,124 @@
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# -*- coding: UTF-8 -*-
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"""
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@Author: lxm
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@version V1.0
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"""
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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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# 将yolo格式的数据集转换成coco格式的数据集
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# 读取文件夹下的所有文件
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path = os.path.abspath(os.path.join(os.getcwd(),"../../.."))
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images_path = path+'/val2017/val2017'
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labels_path = path+'/val2017/labels'
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coco_json_save = path + '/val2017/gt_coco.json'
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# 创建coco格式的json文件
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coco_json = {
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'info': {
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"description": "COCOVal Dataset",
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"url": "www.amov.com",
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"version": "1.0",
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"year": 2023,
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"contributor": "lxm",
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"date_created": datetime.datetime.utcnow().isoformat(' ')
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},
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"licenses": [
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{
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"url": "http://creativecommons.org/licenses/by-nc-sa/2.0/",
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"id": 1,
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"name": "Attribution-NonCommercial-ShareAlike License"
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}
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],
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'images': [],
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'annotations': [],
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'categories': []
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}
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# COCO classes
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classes = ["person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck",
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"boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench",
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"bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra",
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"giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee",
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"skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove",
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"skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork",
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"knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli",
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"carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant",
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"bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard",
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"cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book",
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"clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"]
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# 创建coco格式的json文件
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for i, c in enumerate(classes):
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coco_json['categories'].append({'id': i + 1, 'name': c, 'supercategory': c})
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# 读取images文件夹下的所有文件
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images = os.listdir(images_path)
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for image in images:
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# 获取图片名和后缀
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image_name, image_suffix = os.path.splitext(image)
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# 获取图片的宽和高
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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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# 添加图片信息
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coco_json['images'].append({
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'id': int(image_name),
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'file_name': image,
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'width': width,
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'height': height,
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'date_captured': datetime.datetime.utcnow().isoformat(' '),
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'license': 1
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})
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# 读取图片对应的标签文件
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label_path = labels_path + '/' + image_name + '.txt'
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if not os.path.exists(label_path):
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continue
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with open(label_path, 'r') as f:
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labels = f.readlines()
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labels = [l.strip() for l in labels]
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for j, label in enumerate(labels):
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label = label.split(' ')
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# 获取类别id
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category_id = int(label[0])
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# 将yolo格式的数据转换成coco格式的数据
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x = float(label[1]) * width
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y = float(label[2]) * height
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w = float(label[3]) * width
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h = float(label[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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# 添加bbox信息
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# 添加bbox信息
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coco_json['annotations'].append({
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'image_id': int(image_name),
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'category_id': category_id + 1,
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'bbox': [xmin, ymin, w, h],
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'id': len(coco_json['annotations']),
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'area': w * h,
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'iscrowd': 0,
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'segmentation': [],
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'attributes': ""
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})
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# 保存json文件
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with open(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['images']), len(coco_json['annotations']), len(coco_json['categories']), 'Done!')
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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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# 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': category_id + 1,
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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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