Deployment of mask detection flash for yolov5

• I don't know how to solve this error report , I checked the solution online and added import matplotlib and matplotlib.use(‘Agg’) It's still useless . But it only appears when the interface is closed , It should not affect the use .

OSError: [Errno 41] Protocol wrong type for socket
Assertion failed: (NSViewIsCurrentlyBuildingLayerTreeForDisplay() != currentlyBuildingLayerTree), function NSViewSetCurrentlyBuildingLayerTreeForDisplay, file /System/Volumes/Data/SWE/macOS/BuildRoots/e90674e518/Library/Caches/com.apple.xbs/Sources/AppKit/AppKit-2022.50.114/AppKit.subproj/NSView.m, line 13412.
Process finished with exit code 132 (interrupted by signal 4: SIGILL)

Reference material

Alibaba cloud Ubuntu Deploy
github flask yolov3 Deploy
flask Play in browser rtsp Real time flow

Only computer cameras can be called

from flask import Flask, render_template, Response
import argparse
from pathlib import Path
import os
import cv2
import torch
import torch.backends.cudnn as cudnn
from numpy import random
import numpy as np
import sys
from models.common import DetectMultiBackend
from utils.datasets import IMG_FORMATS, VID_FORMATS, LoadImages, LoadStreams
from utils.general import (LOGGER, check_file, check_img_size, check_imshow, check_requirements, colorstr,
                           increment_path, non_max_suppression, print_args, scale_coords, strip_optimizer, xyxy2xywh)
from utils.plots import Annotator, colors, save_one_box
from utils.torch_utils import select_device, time_sync

''' *** Model initialization *** '''


@torch.no_grad()
def model_load(weights="/Users/sinkarsenic/Downloads/mask_detect/ The weight /best.pt",  # model.pt path(s)
               device='cpu',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
               half=False,  # use FP16 half-precision inference
               dnn=False,  # use OpenCV DNN for ONNX inference
               ):
    device = select_device(device)
    half &= device.type != 'cpu'  # half precision only supported on CUDA
    device = select_device(device)
    model = DetectMultiBackend(weights, device=device, dnn=dnn)
    stride, names, pt, jit, onnx = model.stride, model.names, model.pt, model.jit, model.onnx
    # Half
    half &= pt and device.type != 'cpu'  # half precision only supported by PyTorch on CUDA
    if pt:
        model.model.half() if half else model.model.float()
    print(" Model loading completed !")
    return model

app = Flask(__name__, static_url_path='', 
            static_folder='static',
            template_folder='templates')

@app.route('/')
def index():
    return render_template('index.html')



def gen():
    model = model_load()
    device = select_device('cpu')
    imgsz = [640, 640]  # inference size (pixels)
    conf_thres = 0.25  # confidence threshold
    iou_thres = 0.45  # NMS IOU threshold
    max_det = 1000  # maximum detections per image
    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  # ugmented inference
    visualize = False  # visualize features
    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
    source = str(0)
    webcam = True
    stride, names, pt, jit, onnx = model.stride, model.names, model.pt, model.jit, model.onnx
    imgsz = check_img_size(imgsz, s=stride)  # check image size
    save_img = not nosave and not source.endswith('.txt')  # save inference images

    # Dataloader
    if webcam:
        view_img = check_imshow()
        cudnn.benchmark = True  # set True to speed up constant image size inference
        dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt and not jit)
        bs = len(dataset)  # batch_size
    else:
        dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt and not jit)
        bs = 1  # batch_size
    vid_path, vid_writer = [None] * bs, [None] * bs
    # Run inference
    if pt and device.type != "cpu":
        model(torch.zeros(1, 3, *imgsz).to(device).type_as(next(model.model.parameters())))  # warmup
    dt, seen = [0.0, 0.0, 0.0], 0
    for path, im, im0s, vid_cap, s in dataset:
        t1 = time_sync()
        im = torch.from_numpy(im).to(device)
        im = im.half() if half 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
        t2 = time_sync()
        dt[0] += t2 - t1
        # Inference
        # visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
        pred = model(im, augment=augment, visualize=visualize)
        t3 = time_sync()
        dt[1] += t3 - t2
        # NMS
        pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
        dt[2] += time_sync() - t3
        # 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_coords(im.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' * (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(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)
            # Print time (inference-only)
            LOGGER.info(f'{
      s}Done. ({
      t3 - t2:.3f}s)')
            # Stream results
            im0 = annotator.result()
            if view_img:
                cv2.imshow(str(p), im0)
                cv2.waitKey(1)  # 1 millisecond

        cv2.imwrite('frame.jpg', im0)
        yield (b'--frame\r\n'
               b'Content-Type: image/jpeg\r\n\r\n' + open('frame.jpg', 'rb').read() + b'\r\n')
        # String results
        print(s)
        # wait key to break
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break


@app.route('/video_feed')
def video_feed():
    return Response(gen(),
                    mimetype='multipart/x-mixed-replace; boundary=frame')


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--weights', nargs='+', type=str, default='/Users/sinkarsenic/Downloads/mask_detect/ The weight /best.pt', help='model.pt path(s)')
    parser.add_argument('--source', type=str, default='0', help='source')  # file/folder, 0 for webcam
    parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
    parser.add_argument('--conf-thres', type=float, default=0.15, help='object confidence threshold')
    parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS')
    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
    parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 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('--update', action='store_true', help='update all models')
    parser.add_argument('--project', default='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('--frame-rate', default=0, type=int, help='sample rate')
    opt = parser.parse_args()
    app.run(debug=True)

Use rstp Push flow

It's from Iraqi television

from flask import Flask, render_template, Response
import argparse
from pathlib import Path
import os
import cv2
import torch
import torch.backends.cudnn as cudnn
from numpy import random
import numpy as np
import sys
from models.common import DetectMultiBackend
from utils.datasets import IMG_FORMATS, VID_FORMATS, LoadImages, LoadStreams
from utils.general import (LOGGER, check_file, check_img_size, check_imshow, check_requirements, colorstr,
                           increment_path, non_max_suppression, print_args, scale_coords, strip_optimizer, xyxy2xywh)
from utils.plots import Annotator, colors, save_one_box
from utils.torch_utils import select_device, time_sync

''' *** Model initialization *** '''


@torch.no_grad()
def model_load(weights="/Users/sinkarsenic/Downloads/mask_detect/ The weight /best.pt",  # model.pt path(s)
               device='cpu',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
               half=False,  # use FP16 half-precision inference
               dnn=False,  # use OpenCV DNN for ONNX inference
               ):
    device = select_device(device)
    half &= device.type != 'cpu'  # half precision only supported on CUDA
    device = select_device(device)
    model = DetectMultiBackend(weights, device=device, dnn=dnn)
    stride, names, pt, jit, onnx = model.stride, model.names, model.pt, model.jit, model.onnx
    # Half
    half &= pt and device.type != 'cpu'  # half precision only supported by PyTorch on CUDA
    if pt:
        model.model.half() if half else model.model.float()
    print(" Model loading completed !")
    return model

app = Flask(__name__, static_url_path='', 
            static_folder='static',
            template_folder='templates')

@app.route('/')
def index():
    return render_template('index.html')

def gen():
    model = model_load()
    sourse = "rtmp://ns8.indexforce.com/home/mystream"
    device = select_device('cpu')
    imgsz = [640, 640]  # inference size (pixels)
    conf_thres = 0.25  # confidence threshold
    iou_thres = 0.45  # NMS IOU threshold
    max_det = 1000  # maximum detections per image
    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 = True  # 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  # ugmented inference
    visualize = False  # visualize features
    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
    source = str(sourse)
    #webcam = False
    stride, names, pt, jit, onnx = model.stride, model.names, model.pt, model.jit, model.onnx
    imgsz = check_img_size(imgsz, s=stride)  # check image size
    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('.txt') or (is_url and not is_file)
    if is_url and is_file:
        source = check_file(source)  # download
    # Dataloader
    if webcam:
        view_img = check_imshow()
        cudnn.benchmark = True  # set True to speed up constant image size inference
        dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt and not jit)
        bs = len(dataset)  # batch_size
    else:
        dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt and not jit)
        bs = 1  # batch_size
    vid_path, vid_writer = [None] * bs, [None] * bs
    # Run inference
    if pt and device.type != "cpu":
        model(torch.zeros(1, 3, *imgsz).to(device).type_as(next(model.model.parameters())))  # warmup
    dt, seen = [0.0, 0.0, 0.0], 0
    for path, im, im0s, vid_cap, s in dataset:
        t1 = time_sync()
        im = torch.from_numpy(im).to(device)
        im = im.half() if half 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
        t2 = time_sync()
        dt[0] += t2 - t1
        # Inference
        # visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
        pred = model(im, augment=augment, visualize=visualize)
        t3 = time_sync()
        dt[1] += t3 - t2
        # NMS
        pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
        dt[2] += time_sync() - t3
        # 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_coords(im.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' * (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(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)
            # Print time (inference-only)
            LOGGER.info(f'{
      s}Done. ({
      t3 - t2:.3f}s)')
            # Stream results
            im0 = annotator.result()
            if view_img:
                cv2.imshow(str(p), im0)
                cv2.waitKey(1)  # 1 millisecond

        cv2.imwrite('frame.jpg', im0)
        yield (b'--frame\r\n'
               b'Content-Type: image/jpeg\r\n\r\n' + open('frame.jpg', 'rb').read() + b'\r\n')
        # String results
        print(s)
        # wait key to break
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

@app.route('/video_feed')
def video_feed():
    return Response(gen(),
                    mimetype='multipart/x-mixed-replace; boundary=frame')

if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--weights', nargs='+', type=str, default='/Users/sinkarsenic/Downloads/mask_detect/ The weight /best.pt', help='model.pt path(s)')
    parser.add_argument('--source', type=str, default='rtmp://ns8.indexforce.com/home/mystream', help='source')  # file/folder, 0 for webcam
    parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
    parser.add_argument('--conf-thres', type=float, default=0.15, help='object confidence threshold')
    parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS')
    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
    parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 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('--update', action='store_true', help='update all models')
    parser.add_argument('--project', default='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('--frame-rate', default=0, type=int, help='sample rate')
    opt = parser.parse_args()
    app.run(debug=False)