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。文章来源：https://www.toymoban.com/news/detail-455250.html
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。文章来源地址https://www.toymoban.com/news/detail-455250.html
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