YOLO拉流推理，并将结果采用MQTT进行消息发送

import argparse
from http import client
import time
from pathlib import Path
import cv2
import torch
import torch.backends.cudnn as cudnn
from numpy import random
from models.experimental import attempt_load
from utils.datasets import LoadStreams, LoadImages
from utils.general import check_img_size, check_requirements, check_imshow, non_max_suppression, apply_classifier, scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path
from utils.plots import plot_one_box
from utils.torch_utils import select_device, load_classifier, time_synchronized
import base64, json
from flask import jsonify
import paho.mqtt.client as mqtt
import copy


# cv2转base64
def cv2_to_base64(img):
    img = cv2.imencode('.jpg', img)[1]
    image_code = str(base64.b64encode(img))[2:-1]
    return image_code

client = mqtt.Client()
client.connect("*.***", 1883, 60)
topic = "qly_drone/data"



def detect():
    source, weights, view_img, save_txt, imgsz = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size
    save_img = not opt.nosave and not source.endswith('.txt')  # save inference images
    save_img = False
    print("save_img:", save_img)
    print("save_txt:", save_txt)
    webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
        ('rtsp://', 'rtmp://', 'http://', 'https://'))
    # 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
    stride = int(model.stride.max())  # model stride
    imgsz = check_img_size(imgsz, s=stride)  # 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
    print("webcam:", webcam)
    if webcam:
        # view_img = check_imshow()
        view_img = False
        cudnn.benchmark = True  # set True to speed up constant image size inference
        dataset = LoadStreams(source, img_size=imgsz, stride=stride)
        print("Using video stream!!!")
    else:
        dataset = LoadImages(source, img_size=imgsz, stride=stride)

    # 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
    if device.type != 'cpu':
        model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters())))  # run once
    t0 = time.time()
    frame_count = 0
    last_s = None
    for path, img, im0s, vid_cap in dataset:
        # print("dataset's is --->", len(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
            send_ok = False
            frame_count += 1
            if frame_count % 10 == 0:
                continue
            if frame_count % 100000 == 0:
                frame_count = 0 
            # print("pred len ---->", len(pred))
            if webcam:  # batch_size >= 1
                p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count
            else:
                p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0)

            p = Path(p)  # to Path
            save_path = str(save_dir / p.name)  # img.jpg
            txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}')  # img.txt
            s += '%gx%g ' % img.shape[2:]  # print string
            gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
            json_res = {}
            json_res["serial_number"] = frame_count
            json_res["drone_id"] = "CAR001"
            json_res["timestamp"] = str(time.strftime('%Y-%m-%d %H:%M:%S', time.localtime()))
            json_res["data_type"] = "ai"
            # json_res['data']
            obj_data = {}
            obj_data["frame_id"] = frame_count
            obj_data["obj_number"] = len(det)
            obj_list = []
            # obj_data["obj_list"] = ["22rr"]
            if len(det):
                # Rescale boxes from img_size to im0 size
                det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
                s += f"{len(det)} objects detected, "
                # 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
                # print(s, "--", last_s)
                if last_s is None or last_s != s:
                    send_ok = True
                last_s = copy.deepcopy(s)
                # Write results
                obj_id = 0
                for *xyxy, conf, cls in reversed(det):
                    
                    xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4))).view(-1).tolist()  # normalized xywh
                    # print(xywh)
                    if int(xywh[2]) == 0 or int(xywh[3]) == 0:
                        continue
                    obj_info = {}
                    obj_info["class_id"] = int(cls)
                    obj_info["obj_id"] = obj_id
                    obj_info["obj_left"] = int(xywh[0])
                    obj_info["obj_top"] = int(xywh[1])
                    obj_info["obj_width"] = int(xywh[2])
                    obj_info["obj_height"] = int(xywh[3])
                    obj_info["obj_conf"] = float(conf)
                    obj_img = im0[int(xywh[1]):int(xywh[1] + xywh[3]), int(xywh[0]):int(xywh[0] + xywh[2])]
                    obj_img_base64 = cv2_to_base64(obj_img)
                    obj_info["image_data"] = obj_img_base64
                    obj_list.append(obj_info)
                    # print(obj_info)
                    
                    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}'
                        # if int(cls) == 3:  # 只标注3的类别
                        #     plot_one_box(xyxy, im0, label="no-mask", color=[0, 0, 255], line_thickness=3)
                        plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=2)
                    obj_id += 1
            obj_data["obj_list"] = obj_list
            json_res["data"] = obj_data

            json_dict = json.dumps(json_res, indent=2, sort_keys=True, ensure_ascii=False)  # 行缩进和键值排序
            # json_dict = jsonify(json_res)
            # json_dict = json.dumps(json_res)
            # print(json_dict)
            if send_ok:
                print("send ok")
                client.publish(topic, json_dict)

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

            # Stream results
            if view_img:
                cv2.imshow(str(p), im0)
                cv2.waitKey(1)  # 1 millisecond

            # Save results (image with detections)
            if save_img:
                if dataset.mode == 'image':
                    cv2.imwrite(save_path, im0)
                else:  # 'video' or 'stream'
                    if vid_path != save_path:  # new video
                        vid_path = save_path
                        if isinstance(vid_writer, cv2.VideoWriter):
                            vid_writer.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 += '.mp4'
                        vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), 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}")
    client.disconnect()
    print(f'Done. ({time.time() - t0:.3f}s)')


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--weights', nargs='+', type=str, default='./weights/ktxx5/weights/best.pt', help='model.pt path(s)')
    # parser.add_argument('--source', type=str, default='/home/ubuntu/codes/data/tower_test/nc.JPG', help='source')
    parser.add_argument('--source', type=str, default='rtmp://*.*.*.*:1935/live/CAR002', help='source')  # file/folder, 0 for webcam  ./data/poppy/421/images_split
    parser.add_argument('--save-det-img', type=bool, default=False, help='do not save images/videos(no detect label)')  # True表示只保存检测了目标的图像
    parser.add_argument('--name', default='uav_ai', help='save results to project/name')
    parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)') # 640
    parser.add_argument('--conf-thres', type=float, default=0.4, help='object confidence threshold')
    parser.add_argument('--iou-thres', type=float, default=0.5, help='IOU threshold for NMS')
    parser.add_argument('--device', default='0', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
    parser.add_argument('--view-img', action='store_true', help='display 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('--nosave', action='store_true', help='do not save images/videos')
    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('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
    opt = parser.parse_args()
    print(opt)
    # check_requirements(exclude=('pycocotools', 'thop'))

    with torch.no_grad():
        detect()