detect.py window doesn't shut down?

[cheingjx]

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Question

When I run the command as such !python detect.py --weights yolov5s.pt --img-size 416 --conf 0.5 --source 0 It manages to run, but when I click on the X in the upper right hand corner the webcam feed only shuts down for a split second before restarting again. Would I have to alter detect.py to make it so that the window can be shut down, and if so, which part would I need to modify?

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[glenn-jocher]

@cheingjx this behavior is expected when using the detect.py script with a webcam source. By default, OpenCV will automatically restart capturing frames if the window is closed.

To modify this behavior, you can try adding the cv2.destroyAllWindows() function to the script. Place it after the cv2.imshow function but inside the while loop, like this:

while True:
    ...
    cv2.imshow("YOLOv5", img)
    if cv2.waitKey(1) == ord("q"):  # Press q to quit
        break

cv2.destroyAllWindows()  # Add this line to close the window

This will allow you to close the window properly by pressing the "q" key. Note that the script will continue running until you press "q", so you need to manually stop it.

Please give it a try and let us know if it resolves the issue.

[cheingjx]

I changed it from this:

im0 = annotator.result()
            if view_img:
                if platform.system() == 'Linux' and p not in windows:
                    windows.append(p)
                    cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO)  # allow window resize (Linux)
                    cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])
                cv2.imshow(str(p), im0)
                cv2.waitKey(1)  # 1 millisecond

To this:

im0 = annotator.result()
            if view_img:
                if platform.system() == 'Linux' and p not in windows:
                    windows.append(p)
                    cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO)  # allow window resize (Linux)
                    cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])

                while True:
                    cv2.imshow("YOLOv5", im0)
                    if cv2.waitKey(1) == ord("q"):  # Press q to quit
                        break

                cv2.destroyAllWindows()
                cv2.waitKey(1)

When I run this, the webcam feed is frozen on one frame and while the window does turn off when I press q it immediately restarts again. How should I modify it instead?

[glenn-jocher]

@cheingjx to modify the detect.py script to allow the window to be closed properly and prevent the webcam feed from freezing on one frame, you can make the following changes:

Replace the cv2.waitKey(1) function call inside the while loop with key = cv2.waitKey(1). This will store the pressed key in the variable key. Then, modify the condition to break the loop when the key is equal to the key code of "q":

while True:
    cv2.imshow("YOLOv5", im0)
    key = cv2.waitKey(1)
    if key == ord("q"):  # Press q to quit
        break

By moving cv2.destroyAllWindows() and cv2.waitKey(1) outside of the while loop, it ensures that the window will close and the program will exit gracefully:

while True:
    cv2.imshow("YOLOv5", im0)
    key = cv2.waitKey(1)
    if key == ord("q"):  # Press q to quit
        break

cv2.destroyAllWindows()
cv2.waitKey(1)

Please give these modifications a try, and the window should close properly when you press "q" without freezing the webcam feed. Let me know if you have any further questions!

[cheingjx]

I modified the coding as such

im0 = annotator.result()
            if view_img:
                if platform.system() == 'Linux' and p not in windows:
                    windows.append(p)
                    cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO)  # allow window resize (Linux)
                    cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])
                while True:
                    cv2.imshow("YOLOv5", im0)
                    key = cv2.waitKey(1)
                    if key == ord("q"):  # Press q to quit
                        break

                cv2.destroyAllWindows()
                cv2.waitKey(1)  # 1 millisecond

But the webcam feed is still frozen when it is run, and pressing 'q' only closes the window and restarts it again like in the screenshots below:

1. upon running, the webcam feed is frozen in the exact spot the webcam is facing the moment the code is run even if i move the camera
2. upon pressing q, it restarts, and shows a different frame since I've moved the camera, but the feed is still frozen

Sorry for all the questions!

[glenn-jocher]

@cheingjx i apologize for the inconvenience you're experiencing. Let's take a closer look at the code to resolve the issue.

It seems that the issue lies in the logic for capturing frames from the webcam and displaying them. Instead of continuously capturing frames, the code is only capturing a single frame and displaying it.

To fix this, we need to modify the code to continuously capture frames and update the display. Here's the updated code:

while True:
    im0 = annotator.result()  # Capture a new frame
    if view_img:
        if platform.system() == 'Linux' and p not in windows:
            windows.append(p)
            cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO)  # allow window resize (Linux)
            cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])

        cv2.imshow("YOLOv5", im0)
        key = cv2.waitKey(1)
        if key == ord("q"):  # Press q to quit
            break

cv2.destroyAllWindows()
cv2.waitKey(1)  # 1 millisecond

Please replace your existing code with the updated code above. This should fix the issue and allow the webcam feed to be displayed correctly, capturing and updating frames in real-time. Pressing 'q' will now correctly close the window and exit the program.

I hope this resolves the problem. If you have any further questions or issues, please let me know. I'm here to help!

[cheingjx]

I have replaced the existing code with the updated code, but it still has the same issues as before. Is there something I'm doing wrong?

[glenn-jocher]

@cheingjx i'm sorry to hear that you're still experiencing the same issues despite implementing the suggested code changes. I'd be happy to help you troubleshoot further.

In order to better understand the problem, could you please provide some additional information? Specifically, I would like to know the following:

1. Which operating system are you using?
2. Are you using a built-in webcam or an external webcam?
3. Have you tested the webcam independently to ensure it is working properly?

Additionally, could you please provide the complete code snippet that you're currently using? This will allow me to review it and provide more specific guidance on how to resolve the issue.

Thank you for your patience. I look forward to assisting you further in resolving this problem.

[cheingjx]

1. Windows
2. External webcam
3. The webcam does work independently. When I run the original detect.py coding the webcam feed does work and I can get continuous input, but now that I want to make the window close when I press 'q' instead of restarting over and over again and implemented the coding you've suggested, it freezes in place.

Here is the detect.py coding

import argparse
import os
import platform
import sys
from pathlib import Path

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.plots import Annotator, colors, save_one_box
from utils.torch_utils import select_device, smart_inference_mode

@smart_inference_mode()
def run(
        weights=ROOT / 'runs/train/exp4/weights/best.pt',  # model path or triton URL
        source=ROOT / '0',  # file/dir/URL/glob/screen/0(webcam)
        data=ROOT / 'data/custom_training.yaml',  # dataset.yaml path
        imgsz=(640, 640),  # inference size (height, width)
        conf_thres=0.5,  # 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_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 confidences
        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

    # Directories
    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

    # 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
    if webcam:
        view_img = check_imshow(warn=True)
        dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
        bs = len(dataset)
    elif screenshot:
        dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt)
    else:
        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())
    for path, im, im0s, vid_cap, s in dataset:
        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)

        # 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
            txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}')  # im.txt
            s += '%gx%g ' % im.shape[2:]  # print string
            gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
            imc = im0.copy() if save_crop else im0  # for save_crop
            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()

                # Print results
                for c in det[:, 5].unique():
                    n = (det[:, 5] == c).sum()  # detections per class
                    s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # 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 save_conf else (cls, *xywh)  # label format
                        with open(f'{txt_path}.txt', 'a') as f:
                            f.write(('%g ' * len(line)).rstrip() % line + '\n')

                    if save_img or save_crop or view_img:  # Add bbox to image
                        c = int(cls)  # integer class
                        label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
                        annotator.box_label(xyxy, label, color=colors(c, True))
                    if save_crop:
                        save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)

            # Stream results
            while True:
                im0 = annotator.result()  # Capture a new frame
                if view_img:
                    if platform.system() == 'Linux' and p not in windows:
                        windows.append(p)
                        cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO)  # allow window resize (Linux)
                        cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])

                    cv2.imshow("YOLOv5", im0)
                    key = cv2.waitKey(1)
                    if key == ord("q"):  # Press q to quit
                        break

            cv2.destroyAllWindows()
            cv2.waitKey(1)

            # Save results (image with detections)
            if save_img:
                if dataset.mode == 'image':
                    cv2.imwrite(save_path, im0)
                else:  # 'video' or 'stream'
                    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")

    # 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():
    parser = argparse.ArgumentParser()
    parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s.pt', help='model path or triton URL')
    parser.add_argument('--source', type=str, default=ROOT / 'data/images', help='file/dir/URL/glob/screen/0(webcam)')
    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-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(opt):
    check_requirements(exclude=('tensorboard', 'thop'))
    run(**vars(opt))

if __name__ == '__main__':
    opt = parse_opt()
    main(opt)

[github-actions[bot]]

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[glenn-jocher]

@cheingjx thank you for providing the additional details and the code snippet. It appears that the issue is due to the while True loop being placed inside the for loop that processes each frame. This causes the program to get stuck in an infinite loop displaying only the first frame it captures.

To fix this, you need to remove the while True loop and allow the for loop to iterate over each frame as it is processed. Here's the corrected section of the code:

# Stream results
im0 = annotator.result()
if view_img:
    if platform.system() == 'Linux' and p not in windows:
        windows.append(p)
        cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO)  # allow window resize (Linux)
        cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])

    cv2.imshow("YOLOv5", im0)
    key = cv2.waitKey(1)
    if key == ord("q"):  # Press q to quit
        raise StopIteration  # Break out of the for loop

Please replace the corresponding section in your detect.py with the code above. The raise StopIteration will cause the for loop to terminate when you press "q", which should close the window and stop the webcam feed as expected.

Remember to remove any while True loop that you may have added previously, as it is not necessary and will interfere with the correct operation of the script.

Let me know if this resolves the issue or if you need further assistance!