[人工智能]yolov5 python API（供其他程序调用）

你的yolov5🚀是否只局限于detect.py？如果其他程序要调用yolov5，就需要制作一个detect.py的python API。python无处不对象，制作detect API实际上就是制作detect类。

前言

yolov5源码版本：截止2022.2.3
链接：https://github.com/ultralytics/yolov5

作为一个“CV”主义者，在此之前在各平台都没有找到合适的API代码。其中有一篇不错的文章https://www.pythonheidong.com/blog/article/851830/44a42d351037d307d02d/
可惜代码版本过于“久远”，部分函数已经不适用了。本文以一种简单粗暴的方式制作与detect.py功能一样的API，即使源码更新，按照我的方法也能快速制作一个API供其他程序调用。

一、总体思路

其他程序调用yolo，实际上就是把图像传给detect.py。为了最大化实现detect.py的所有功能，最直接的方式是摄像头或者视频流把帧图像存储在‘date/images’目录中，然后把帧图像从‘runs/detect/exp’中读取出来。这种方法增加了处理时间，不过实测存储和读取图像这部分的延迟很低，即便是在树莓派上。

二、制作detect类

在detect.py中添加以下代码

class DetectAPI:
    def __init__(self, weights='weights/yolov5s.pt', data='data/coco128.yaml', imgsz=None, conf_thres=0.25,
                 iou_thres=0.45, max_det=1000, device='0', view_img=False, save_txt=False,
                 save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False,
                 visualize=False, update=False, project='runs/detect', name='myexp', exist_ok=False, line_thickness=3,
                 hide_labels=False, hide_conf=False, half=False, dnn=False):

        if imgsz is None:
            self.imgsz = [640, 640]
        self.weights = weights
        self.data = data
        self.source = 'data/myimages'
        self.imgsz = [640, 640]
        self.conf_thres = conf_thres
        self.iou_thres = iou_thres
        self.max_det = max_det
        self.device = device
        self.view_img = view_img
        self.save_txt = save_txt
        self.save_conf = save_conf
        self.save_crop = save_crop
        self.nosave = nosave
        self.classes = classes
        self.agnostic_nms = agnostic_nms
        self.augment = augment
        self.visualize = visualize
        self.update = update
        self.project = project
        self.name = name
        self.exist_ok = exist_ok
        self.line_thickness = line_thickness
        self.hide_labels = hide_labels
        self.hide_conf = hide_conf
        self.half = half
        self.dnn = dnn

    def run(self):
        source = str(self.source)
        save_img = not self.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

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

        # Load model
        device = select_device(self.device)
        model = DetectMultiBackend(self.weights, device=device, dnn=self.dnn, data=self.data)
        stride, names, pt, jit, onnx, engine = model.stride, model.names, model.pt, model.jit, model.onnx, model.engine
        imgsz = check_img_size(self.imgsz, s=stride)  # check image size

        # Half
        self.half &= (pt or jit or onnx or engine) and device.type != 'cpu'  # FP16 supported on limited backends with CUDA
        if pt or jit:
            model.model.half() if self.half else model.model.float()

        # 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)
            bs = len(dataset)  # batch_size
        else:
            dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt)
            bs = 1  # batch_size
        vid_path, vid_writer = [None] * bs, [None] * bs

        # Run inference
        model.warmup(imgsz=(1, 3, *imgsz), half=self.half)  # 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 self.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 self.visualize else False
            pred = model(im, augment=self.augment, visualize=visualize)
            t3 = time_sync()
            dt[1] += t3 - t2

            # NMS
            pred = non_max_suppression(pred, self.conf_thres, self.iou_thres, self.classes, self.agnostic_nms,
                                       max_det=self.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 self.save_crop else im0  # for save_crop
                annotator = Annotator(im0, line_width=self.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

                    mylabel = []
                    # Write results
                    for *xyxy, conf, cls in reversed(det):
                        if self.save_txt:  # Write to file
                            xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
                            line = (cls, *xywh, conf) if self.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 self.save_crop or self.view_img:  # Add bbox to image
                            c = int(cls)  # integer class
                            label = None if self.hide_labels else (names[c] if self.hide_conf else f'{names[c]} {conf:.2f}')
                            mylabel.append(str(label))
                            annotator.box_label(xyxy, label, color=colors(c, True))
                            if self.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 self.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[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 += '.mp4'
                            vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
                        vid_writer[i].write(im0)

        # Print results
        t = tuple(x / 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 self.save_txt or save_img:
            s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if self.save_txt \
                else ''
            LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
        if self.update:
            strip_optimizer(self.weights)  # update model (to fix SourceChangeWarning)

        return mylabel
        

代码与run函数基本相同。run函数的思路是加载模型和图片，进行模型预测和推理。类中run函数修改了images目录，可自行修改。函数会返回识别到的物体标签以及对应的置信度，可用于其他处理。

二、调用detect类

下面给出使用这个API的一个例程，需要将yolov5源码文件夹放到程序目录中。

import cv2
import yolov5-master.detect
import os

video_capture = cv2.VideoCapture(0)
detect_api = yolov5-master.detect.DetectAPI(exist_ok=True)

while True:
	k = cv2.waitKey(1)
    ret, frame = video_capture.read()
    
    path = '你的目录/yolov5-master/data/myimages'
    cv2.imwrite(os.path.join(path, 'test.jpg'), frame)
    
    label = detect_api.run()
    print(str(label))
    
    image = cv2.imread('你的目录/yolov5-master/runs/detect/myexp/test.jpg', flags=1)
    cv2.imshow("video", image)

    if k == 27:  # 按下ESC退出窗口
        break

video_capture.release()

实例化对象中参数exist_ok=True的作用是生成的exp目录会自行覆盖，不会有后面的exp1、exp2、exp3等，方便用于实时处理。

结语

本文假设你已经可以成功跑detect.py的基础上再去制作API接口。在使用IP摄像头或者视频流时，修改实例化中的参数即可。