# Buggy code
def detect(save_img=False):
    source, weights, view_img, save_txt, imgsz = (
        opt.source,
        opt.weights,
        opt.view_img,
        opt.save_txt,
        opt.img_size,
    )
    webcam = (
        source.isnumeric()
        or source.endswith(".txt")
        or source.lower().startswith(("rtsp://", "rtmp://", "http://"))
    )

    # Directories
    save_dir = Path(
        increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)
    )  # increment run
    (save_dir / "labels" if save_txt else save_dir).mkdir(
        parents=True, exist_ok=True
    )  # make dir

    # Initialize
    set_logging()
    device = select_device(opt.device)
    half = device.type != "cpu"  # half precision only supported on CUDA

    # Load model
    model = attempt_load(weights, map_location=device)  # load FP32 model
    imgsz = check_img_size(imgsz, s=model.stride.max())  # check img_size
    if half:
        model.half()  # to FP16

    # Second-stage classifier
    classify = False
    if classify:
        modelc = load_classifier(name="resnet101", n=2)  # initialize
        modelc.load_state_dict(
            torch.load("weights/resnet101.pt", map_location=device)["model"]
        ).to(device).eval()

    # Set Dataloader
    vid_path, vid_writer = None, None
    if webcam:
        view_img = True
        cudnn.benchmark = True  # set True to speed up constant image size inference
        dataset = LoadStreams(source, img_size=imgsz)
    else:
        save_img = True
        dataset = LoadImages(source, img_size=imgsz)

    # Get names and colors
    names = model.module.names if hasattr(model, "module") else model.names
    colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]

    # Run inference
    t0 = time.time()
    img = torch.zeros((1, 3, imgsz, imgsz), device=device)  # init img
    _ = model(img.half() if half else img) if device.type != "cpu" else None  # run once
    for path, img, im0s, vid_cap in dataset:
        img = torch.from_numpy(img).to(device)
        img = img.half() if half else img.float()  # uint8 to fp16/32
        img /= 255.0  # 0 - 255 to 0.0 - 1.0
        if img.ndimension() == 3:
            img = img.unsqueeze(0)

        # Inference
        t1 = time_synchronized()
        pred = model(img, augment=opt.augment)[0]

        # Apply NMS
        pred = non_max_suppression(
            pred,
            opt.conf_thres,
            opt.iou_thres,
            classes=opt.classes,
            agnostic=opt.agnostic_nms,
        )
        t2 = time_synchronized()

        # Apply Classifier
        if classify:
            pred = apply_classifier(pred, modelc, img, im0s)

        # Process detections
        for i, det in enumerate(pred):  # detections per image
            if webcam:  # batch_size >= 1
                p, s, im0 = Path(path[i]), "%g: " % i, im0s[i].copy()
            else:
                p, s, im0 = Path(path), "", im0s

            save_path = str(save_dir / p.name)
            txt_path = str(save_dir / "labels" / p.stem) + (
                "_%g" % dataset.frame if dataset.mode == "video" else ""
            )
            s += "%gx%g " % img.shape[2:]  # print string
            gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
            if len(det):
                # Rescale boxes from img_size to im0 size
                det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()

                # Print results
                for c in det[:, -1].unique():
                    n = (det[:, -1] == c).sum()  # detections per class
                    s += "%g %ss, " % (n, names[int(c)])  # add to string

                # Write results
                for *xyxy, conf, cls in reversed(det):
                    if save_txt:  # Write to file
                        xywh = (
                            (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn)
                            .view(-1)
                            .tolist()
                        )  # normalized xywh
                        line = (
                            (cls, *xywh, conf) if opt.save_conf else (cls, *xywh)
                        )  # label format
                        with open(txt_path + ".txt", "a") as f:
                            f.write(("%g " * len(line)).rstrip() % line + "\n")

                    if save_img or view_img:  # Add bbox to image
                        label = "%s %.2f" % (names[int(cls)], conf)
                        plot_one_box(
                            xyxy,
                            im0,
                            label=label,
                            color=colors[int(cls)],
                            line_thickness=3,
                        )

            # Print time (inference + NMS)
            print("%sDone. (%.3fs)" % (s, t2 - t1))

            # Stream results
            if view_img:
                cv2.imshow(str(p), im0)
                if cv2.waitKey(1) == ord("q"):  # q to quit
                    raise StopIteration

            # Save results (image with detections)
            if save_img:
                if dataset.mode == "images":
                    cv2.imwrite(save_path, im0)
                else:
                    if vid_path != save_path:  # new video
                        vid_path = save_path
                        if isinstance(vid_writer, cv2.VideoWriter):
                            vid_writer.release()  # release previous video writer

                        fourcc = "mp4v"  # output video codec
                        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))
                        vid_writer = cv2.VideoWriter(
                            save_path, cv2.VideoWriter_fourcc(*fourcc), fps, (w, h)
                        )
                    vid_writer.write(im0)

    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 ""
        )
        print(f"Results saved to {save_dir}{s}")

    print("Done. (%.3fs)" % (time.time() - t0))


# Traceback (RuntimeError)
** On entry to DGEBAL parameter number  3 had an illegal value
** On entry to DGEHRD  parameter number  2 had an illegal value
** On entry to DORGHR DORGQR parameter number  2 had an illegal value
** On entry to DHSEQR parameter number  4 had an illegal value
Traceback (most recent call last):
File "D:\Softwares\Python\lib\site-packages\numpy\__init__.py", line 305, in <module>
_win_os_check()
File "D:\Softwares\Python\lib\site-packages\numpy\__init__.py", line 302, in _win_os_check
raise RuntimeError(msg.format(__file__)) from None
RuntimeError: The current Numpy installation ('D:\\Softwares\\Python\\lib\\site-packages\\numpy\\__init__.py') fails to pass a sanity check due to a bug in the windows runtime. See this issue for more information: https://tinyurl.com/y3dm3h86
Traceback (most recent call last):
File ".\detect.py", line 5, in <module>
import cv2
File "D:\Softwares\Python\lib\site-packages\cv2\__init__.py", line 5, in <module>
from .cv2 import *
ImportError: numpy.core.multiarray failed to import

# Fixed code
def detect(save_img=False):
    source, weights, view_img, save_txt, imgsz = (
        opt.source,
        opt.weights,
        opt.view_img,
        opt.save_txt,
        opt.img_size,
    )
    webcam = (
        source.isnumeric()
        or source.endswith(".txt")
        or source.lower().startswith(("rtsp://", "rtmp://", "http://"))
    )

    # Directories
    save_dir = Path(
        increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)
    )  # increment run
    (save_dir / "labels" if save_txt else save_dir).mkdir(
        parents=True, exist_ok=True
    )  # make dir

    # Initialize
    set_logging()
    device = select_device(opt.device)
    half = device.type != "cpu"  # half precision only supported on CUDA

    # Load model
    model = attempt_load(weights, map_location=device)  # load FP32 model
    imgsz = check_img_size(imgsz, s=model.stride.max())  # check img_size
    if half:
        model.half()  # to FP16

    # Second-stage classifier
    classify = False
    if classify:
        modelc = load_classifier(name="resnet101", n=2)  # initialize
        modelc.load_state_dict(
            torch.load("weights/resnet101.pt", map_location=device)["model"]
        ).to(device).eval()

    # Set Dataloader
    vid_path, vid_writer = None, None
    if webcam:
        view_img = True
        cudnn.benchmark = True  # set True to speed up constant image size inference
        dataset = LoadStreams(source, img_size=imgsz)
    else:
        save_img = True
        dataset = LoadImages(source, img_size=imgsz)

    # Get names and colors
    names = model.module.names if hasattr(model, "module") else model.names
    colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]

    # Run inference
    t0 = time.time()
    img = torch.zeros((1, 3, imgsz, imgsz), device=device)  # init img
    _ = model(img.half() if half else img) if device.type != "cpu" else None  # run once
    for path, img, im0s, vid_cap in dataset:
        img = torch.from_numpy(img).to(device)
        img = img.half() if half else img.float()  # uint8 to fp16/32
        img /= 255.0  # 0 - 255 to 0.0 - 1.0
        if img.ndimension() == 3:
            img = img.unsqueeze(0)

        # Inference
        t1 = time_synchronized()
        pred = model(img, augment=opt.augment)[0]

        # Apply NMS
        pred = non_max_suppression(
            pred,
            opt.conf_thres,
            opt.iou_thres,
            classes=opt.classes,
            agnostic=opt.agnostic_nms,
        )
        t2 = time_synchronized()

        # Apply Classifier
        if classify:
            pred = apply_classifier(pred, modelc, img, im0s)

        # Process detections
        for i, det in enumerate(pred):  # detections per image
            if webcam:  # batch_size >= 1
                p, s, im0, frame = (
                    Path(path[i]),
                    "%g: " % i,
                    im0s[i].copy(),
                    dataset.count,
                )
            else:
                p, s, im0, frame = Path(path), "", im0s, getattr(dataset, "frame", 0)

            save_path = str(save_dir / p.name)
            txt_path = str(save_dir / "labels" / p.stem) + (
                "" if dataset.mode == "image" else f"_{frame}"
            )
            s += "%gx%g " % img.shape[2:]  # print string
            gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
            if len(det):
                # Rescale boxes from img_size to im0 size
                det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()

                # Print results
                for c in det[:, -1].unique():
                    n = (det[:, -1] == c).sum()  # detections per class
                    s += f"{n} {names[int(c)]}s, "  # add to string

                # Write results
                for *xyxy, conf, cls in reversed(det):
                    if save_txt:  # Write to file
                        xywh = (
                            (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn)
                            .view(-1)
                            .tolist()
                        )  # normalized xywh
                        line = (
                            (cls, *xywh, conf) if opt.save_conf else (cls, *xywh)
                        )  # label format
                        with open(txt_path + ".txt", "a") as f:
                            f.write(("%g " * len(line)).rstrip() % line + "\n")

                    if save_img or view_img:  # Add bbox to image
                        label = f"{names[int(cls)]} {conf:.2f}"
                        plot_one_box(
                            xyxy,
                            im0,
                            label=label,
                            color=colors[int(cls)],
                            line_thickness=3,
                        )

            # Print time (inference + NMS)
            print(f"{s}Done. ({t2 - t1:.3f}s)")

            # Stream results
            if view_img:
                cv2.imshow(str(p), im0)
                if cv2.waitKey(1) == ord("q"):  # q to quit
                    raise StopIteration

            # Save results (image with detections)
            if save_img:
                if dataset.mode == "image":
                    cv2.imwrite(save_path, im0)
                else:  # 'video'
                    if vid_path != save_path:  # new video
                        vid_path = save_path
                        if isinstance(vid_writer, cv2.VideoWriter):
                            vid_writer.release()  # release previous video writer

                        fourcc = "mp4v"  # output video codec
                        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))
                        vid_writer = cv2.VideoWriter(
                            save_path, cv2.VideoWriter_fourcc(*fourcc), fps, (w, h)
                        )
                    vid_writer.write(im0)

    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 ""
        )
        print(f"Results saved to {save_dir}{s}")

    print(f"Done. ({time.time() - t0:.3f}s)")