initial project version!

track_.py

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+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
+"""
+Run YOLOv5 detection inference on images, videos, directories, globs, YouTube, webcam, streams, etc.
+
+Usage - sources:
+    $ python detect.py --weights yolov5s.pt --source 0                               # webcam
+                                                     img.jpg                         # image
+                                                     vid.mp4                         # video
+                                                     screen                          # screenshot
+                                                     path/                           # directory
+                                                     list.txt                        # list of images
+                                                     list.streams                    # list of streams
+                                                     'path/*.jpg'                    # glob
+                                                     'https://youtu.be/Zgi9g1ksQHc'  # YouTube
+                                                     'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream
+
+Usage - formats:
+    $ python detect.py --weights yolov5s.pt                 # PyTorch
+                                 yolov5s.torchscript        # TorchScript
+                                 yolov5s.onnx               # ONNX Runtime or OpenCV DNN with --dnn
+                                 yolov5s_openvino_model     # OpenVINO
+                                 yolov5s.engine             # TensorRT
+                                 yolov5s.mlmodel            # CoreML (macOS-only)
+                                 yolov5s_saved_model        # TensorFlow SavedModel
+                                 yolov5s.pb                 # TensorFlow GraphDef
+                                 yolov5s.tflite             # TensorFlow Lite
+                                 yolov5s_edgetpu.tflite     # TensorFlow Edge TPU
+                                 yolov5s_paddle_model       # PaddlePaddle
+"""
+
+import argparse
+import csv
+import os
+import platform
+import sys
+from pathlib import Path
+import glob
+import numpy as np
+import pickle
+import torch
+
+FILE = Path(__file__).resolve()
+ROOT = FILE.parents[0]  # YOLOv5 root directory
+if str(ROOT) not in sys.path:
+    sys.path.append(str(ROOT))  # add ROOT to PATH
+ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
+
+from models.common import DetectMultiBackend
+from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams
+from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2,
+                           increment_path, non_max_suppression, print_args, scale_boxes, strip_optimizer, xyxy2xywh)
+from utils.torch_utils import select_device, smart_inference_mode
+
+'''集成跟踪模块，输出跟踪结果文件 .npy'''
+# from ultralytics.engine.results import Boxes    # Results
+# from ultralytics.utils import IterableSimpleNamespace, yaml_load
+from tracking.utils.plotting import Annotator, colors
+from tracking.utils import Boxes, IterableSimpleNamespace, yaml_load, boxes_add_fid
+from tracking.trackers import BOTSORT, BYTETracker
+from tracking.utils.showtrack import drawtracks
+
+
+def init_trackers(tracker_yaml = None, bs=1):
+    """
+    Initialize trackers for object tracking during prediction.
+    """   
+    # 需要将配置文件中的cmc_method改为gmc_method
+    # tracker_yaml = r"D:\DeepLearning\ultralytics\ultralytics\tracker\cfg\botsort.yaml"   
+    
+    tracker_yaml = r"./tracking/trackers/cfg/botsort.yaml"
+    
+    TRACKER_MAP = {'bytetrack': BYTETracker, 'botsort': BOTSORT}
+    
+    cfg = IterableSimpleNamespace(**yaml_load(tracker_yaml))
+    trackers = []
+    for _ in range(bs):
+        tracker = TRACKER_MAP[cfg.tracker_type](args=cfg, frame_rate=30)
+        trackers.append(tracker)
+    
+    return trackers
+
+
+@smart_inference_mode()
+def run(
+        weights=ROOT / 'yolov5s.pt',  # model path or triton URL
+        source=ROOT / 'data/images',  # file/dir/URL/glob/screen/0(webcam)
+        data=ROOT / 'data/coco128.yaml',  # dataset.yaml path
+        imgsz=(640, 640),  # inference size (height, width)
+        conf_thres=0.25,  # confidence threshold
+        iou_thres=0.45,  # NMS IOU threshold
+        max_det=1000,  # maximum detections per image
+        device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
+        view_img=False,  # show results
+        save_txt=False,  # save results to *.txt
+        save_csv=False,  # save results in CSV format
+        save_conf=False,  # save confidences in --save-txt labels
+        save_crop=False,  # save cropped prediction boxes
+        nosave=False,  # do not save images/videos
+        classes=None,  # filter by class: --class 0, or --class 0 2 3
+        agnostic_nms=False,  # class-agnostic NMS
+        augment=False,  # augmented inference
+        visualize=False,  # visualize features
+        update=False,  # update all models
+        project=ROOT / 'runs/detect',  # save results to project/name
+        name='exp',  # save results to project/name
+        exist_ok=False,  # existing project/name ok, do not increment
+        line_thickness=3,  # bounding box thickness (pixels)
+        hide_labels=False,  # hide labels
+        hide_conf=False,  # hide confidencesL
+        half=False,  # use FP16 half-precision inference
+        dnn=False,  # use OpenCV DNN for ONNX inference
+        vid_stride=1,  # video frame-rate stride
+):
+    source = str(source)
+    save_img = not nosave and not source.endswith('.txt')  # save inference images
+    is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
+    is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))
+    webcam = source.isnumeric() or source.endswith('.streams') or (is_url and not is_file)
+    screenshot = source.lower().startswith('screen')
+    if is_url and is_file:
+        source = check_file(source)  # download
+    
+    save_dir = Path(project) / Path(source).stem
+    if save_dir.exists():
+        print(Path(source).stem)
+        # return
+        
+        save_dir = increment_path(Path(project) / name, exist_ok=exist_ok)  # increment run
+        (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir
+    else:
+        save_dir.mkdir(parents=True, exist_ok=True) 
+        
+    # Load model
+    device = select_device(device)
+    model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
+    stride, names, pt = model.stride, model.names, model.pt
+    imgsz = check_img_size(imgsz, s=stride)  # check image size
+     
+    # Dataloader
+    bs = 1  # batch_size
+    
+    dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)    
+    vid_path, vid_writer = [None] * bs, [None] * bs
+   
+    
+    # Run inference
+    model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz))  # warmup
+    seen, windows, dt = 0, [], (Profile(), Profile(), Profile())
+
+    
+    ## ================================================================================== writed by WQG
+    tracker = init_trackers(bs)[0]
+    vid_path_track, vid_writer_track = [None] * bs, [None] * bs
+    tboxes = []
+    vboxes = []
+    
+    f_i = 1
+    for path, im, im0s, vid_cap, s in dataset:
+        if f_i == 1: 
+            f_i == 0
+            imgshow = im0s.copy()
+        
+        ## ============================= tracking 功能只处理视频，writed by WQG
+        if dataset.mode == 'image':
+            continue
+
+        with dt[0]:
+            im = torch.from_numpy(im).to(model.device)
+            im = im.half() if model.fp16 else im.float()  # uint8 to fp16/32
+            im /= 255  # 0 - 255 to 0.0 - 1.0
+            if len(im.shape) == 3:
+                im = im[None]  # expand for batch dim
+
+        # Inference
+        with dt[1]:
+            visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
+            pred = model(im, augment=augment, visualize=visualize)
+
+        # NMS
+        with dt[2]:
+            pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
+
+        # Second-stage classifier (optional)
+        # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)
+
+        # Define the path for the CSV file
+        csv_path = save_dir / 'predictions.csv'
+
+        # Create or append to the CSV file
+        def write_to_csv(image_name, prediction, confidence):
+            data = {'Image Name': image_name, 'Prediction': prediction, 'Confidence': confidence}
+            with open(csv_path, mode='a', newline='') as f:
+                writer = csv.DictWriter(f, fieldnames=data.keys())
+                if not csv_path.is_file():
+                    writer.writeheader()
+                writer.writerow(data)
+
+        # Process predictions
+        for i, det in enumerate(pred):  # per image
+            seen += 1
+            if webcam:  # batch_size >= 1
+                p, im0, frame = path[i], im0s[i].copy(), dataset.count
+                s += f'{i}: '
+            else:
+                p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)
+
+            p = Path(p)  # to Path
+            save_path = str(save_dir / p.name)  # im.jpg
+
+            s += '%gx%g ' % im.shape[2:]  # print string
+            
+            annotator = Annotator(im0, line_width=line_thickness, example=str(names))
+            if len(det): 
+                # Rescale boxes from img_size to im0 size
+                det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round()
+                
+                vboxes.append((det.cpu().numpy(), im0, frame)) 
+                ## ================================================================ writed by WQG
+                det_tracking = Boxes(det, im0.shape).cpu().numpy()
+                tracks = tracker.update(det_tracking, im0)
+                
+                if len(tracks) > 0:
+                    det = torch.as_tensor(tracks[:, :-2])
+                    tboxes.append((det, frame))
+                else:
+                    idmark = -1 * np.ones([det.shape[0], 1])
+                    det = np.concatenate([det[:,:4], idmark, det[:, 4:]], axis=1)
+
+                for *xyxy, id, conf, cls in reversed(det):
+                    name = ('' if id==-1 else f'id:{int(id)} ') + names[int(cls)]                   
+                    label = None if hide_labels else (name if hide_conf else f'{name} {conf:.2f}')
+                    
+                    if id >=0 and cls==0:
+                        color = colors(int(cls), True)
+                    elif id >=0 and cls!=0:
+                        color = colors(int(id), True)
+                    else:
+                        color = colors(19, True)     # 19为调色板的最后一个元素
+           
+                    annotator.box_label(xyxy, label, color=color)
+
+            # Save results (image and video with tracking)
+            im0 = annotator.result()
+            if save_img:
+                save_path_img, ext = os.path.splitext(save_path)
+                imgpath = save_path_img + f"_{dataset.frame}.png"
+                cv2.imwrite(Path(imgpath), im0)
+                
+                if vid_path[i] != save_path:  # new video
+                    vid_path[i] = save_path
+                    if isinstance(vid_writer[i], cv2.VideoWriter):
+                        vid_writer[i].release()  # release previous video writer
+                    if vid_cap:  # video
+                        fps = vid_cap.get(cv2.CAP_PROP_FPS)
+                        w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+                        h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+                    else:  # stream
+                        fps, w, h = 30, im0.shape[1], im0.shape[0]
+                    save_path = str(Path(save_path).with_suffix('.mp4'))  # force *.mp4 suffix on results videos
+                    vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
+                vid_writer[i].write(im0)
+
+        # Print time (inference-only)
+        LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms")
+
+    
+    ## ======================================================================== written by WQG
+    ## tboxes: list, [(det, frame_index)]
+    ## bboxes: Array, [x, y, w, h, track_id, score, cls, frame_index]
+
+    filename = os.path.split(save_path_img)[-1]
+    file, ext = os.path.splitext(filename)
+
+    bboxes = boxes_add_fid(tboxes)  
+    
+    imgshow = drawtracks(bboxes, file=filename)    
+    
+    showpath_1 = save_path_img + "_show.png"
+    cv2.imwrite(Path(showpath_1), imgshow)
+
+    ##================================================== save .npy
+    with open(f'./tracking/vboxes/{file}.pkl', 'wb') as file:
+        pickle.dump(vboxes, file)
+    
+    
+    boxes_dir = Path('./runs/boxes/')
+    if not boxes_dir.exists():
+        boxes_dir.mkdir(parents=True, exist_ok=True)
+    bboxes_path = boxes_dir.joinpath(filename + ".npy") 
+    np.save(bboxes_path, bboxes)
+ 
+
+    # Print results
+    t = tuple(x.t / seen * 1E3 for x in dt)  # speeds per image
+    LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t)
+    if save_txt or save_img:
+        s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
+        LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
+    if update:
+        strip_optimizer(weights[0])  # update model (to fix SourceChangeWarning)
+
+
+def parse_opt():
+    modelpath = ROOT / 'ckpts/best_158734_cls11_noaug10.pt'       # 'ckpts/best_15000_0908.pt',  'ckpts/yolov5s.pt', 'ckpts/best_20000_cls30.pt'
+    
+    # datapath = r"D:/datasets/ym/videos/标记视频/"   # ROOT/'data/videos',  ROOT/'data/images'   images    
+    datapath = r"D:\datasets\ym\highvalue\videos"
+    
+    # datapath = r"D:/dcheng/videos/"
+    # modelpath = ROOT / 'ckpts/yolov5s.pt' 
+    
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--weights', nargs='+', type=str, default=modelpath, help='model path or triton URL')   # 'yolov5s.pt', best_15000_0908.pt
+    parser.add_argument('--source', type=str, default=datapath, help='file/dir/URL/glob/screen/0(webcam)')    # images, videos
+    parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path')
+    parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w')
+    parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold')
+    parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold')
+    parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image')
+    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
+    parser.add_argument('--view-img', action='store_true', help='show results')
+    parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
+    parser.add_argument('--save-csv', action='store_true', help='save results in CSV format')
+    parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
+    parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes')
+    parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
+    parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 0 2 3')
+    parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
+    parser.add_argument('--augment', action='store_true', help='augmented inference')
+    parser.add_argument('--visualize', action='store_true', help='visualize features')
+    parser.add_argument('--update', action='store_true', help='update all models')
+    parser.add_argument('--project', default=ROOT / 'runs/detect', help='save results to project/name')
+    parser.add_argument('--name', default='exp', help='save results to project/name')
+    parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
+    parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)')
+    parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels')
+    parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences')
+    parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
+    parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')
+    parser.add_argument('--vid-stride', type=int, default=1, help='video frame-rate stride')
+    opt = parser.parse_args()
+    opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1  # expand
+    print_args(vars(opt))
+    return opt
+
+def main_loop_folders(opt):
+    path1 = r"D:\datasets\ym\videos\标记视频" 
+    path2 = r"D:\datasets\ym\永辉双摄视频\退购_前摄\videos"
+    path3 = r"D:\datasets\ym\永辉双摄视频\退购_后摄\videos"
+    path4 = r"D:\datasets\ym\永辉双摄视频\加购_前摄\videos"
+    path5 = r"D:\datasets\ym\永辉双摄视频\加购_后摄\videos"
+    paths = [path1]   # [path1, path2, path3, path4, path5]
+    
+    check_requirements(ROOT / 'requirements.txt', exclude=('tensorboard', 'thop'))
+    optdict = vars(opt)
+    k = 0
+    for p in paths:
+        files = []
+        if os.path.isdir(p):
+            files.extend(sorted(glob.glob(os.path.join(p, '*.*'))))
+            for file in files:
+                # file = r"D:\datasets\ym\videos\标记视频\加购_100.mp4"
+                
+                optdict["source"] = file
+                run(**optdict)
+                
+                # k += 1
+                # if k == 100:
+                #     break
+        elif os.path.isfile(p):
+            run(**optdict)
+        
+
+
+
+def main(opt):
+    check_requirements(ROOT / 'requirements.txt', exclude=('tensorboard', 'thop'))
+    # files.extend(sorted(glob.glob(os.path.join(p, '*.*'))))
+
+    optdict = vars(opt)
+    p = optdict["source"]
+    
+    files = []
+    k = 0
+    if os.path.isdir(p):
+        files.extend(sorted(glob.glob(os.path.join(p, '*.*'))))
+        
+        for file in files:
+            optdict["source"] = file
+            run(**optdict)
+            
+            k += 1
+            if k == 100:
+                break
+            
+    elif os.path.isfile(p):
+        run(**vars(opt))
+
+
+if __name__ == '__main__':
+    opt = parse_opt()
+    main_loop_folders(opt)