yolov5 口罩检测flask部署

• 这个报错不知道怎么解决，网上查了解决方案加 import matplotlib 和 matplotlib.use(‘Agg’) 还是没用。不过只是关闭界面才出现的，应该不影响使用。

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)

使用手机摄像头通过 rtsp 推流

省了三百块钱去买摄像头做毕设

• 安卓机下载 ip 摄像头
• 设置-用户名全删掉-密码全删掉-无客户端关闭摄像头选项关闭-打开后启动rtsp 服务器开启-返回-打开摄像头服务器-勾选 rtsp-后面的地址即为所需

参考资料

阿里云Ubuntu 部署
github flask yolov3 部署
flask 在浏览器中播放rtsp实时流

只能调用电脑摄像头

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

'''
***模型初始化***
'''


@torch.no_grad()
def model_load(weights="/Users/sinkarsenic/Downloads/mask_detect/权重/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("模型加载完成!")
    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/权重/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)

使用 rtsp 推流

用的是伊拉克电视台的

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

'''
***模型初始化***
'''


@torch.no_grad()
def model_load(weights="/Users/sinkarsenic/Downloads/mask_detect/权重/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("模型加载完成!")
    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/权重/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)