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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
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"""
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Run inference on images, videos, directories, streams, etc.
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Usage:
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$ python path/to/detect.py --weights yolov5s.pt --source 0 # webcam
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img.jpg # image
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vid.mp4 # video
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path/ # directory
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path/*.jpg # glob
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'https://youtu.be/Zgi9g1ksQHc' # YouTube
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'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
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"""
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import argparse
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import os
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import sys
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from pathlib import Path
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import cv2
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import torch
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import torch.backends.cudnn as cudnn
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FILE = Path(__file__).resolve()
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ROOT = FILE.parents[0] # YOLOv5 root directory
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if str(ROOT) not in sys.path:
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sys.path.append(str(ROOT)) # add ROOT to PATH
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ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
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from models.common import DetectMultiBackend
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from utils.datasets import IMG_FORMATS, VID_FORMATS, LoadImages, LoadStreams
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from utils.general import (LOGGER, check_file, check_img_size, check_imshow, check_requirements, colorstr,
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increment_path, non_max_suppression, print_args, scale_coords, strip_optimizer, xyxy2xywh)
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from utils.plots import Annotator, colors, save_one_box
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from utils.torch_utils import select_device, time_sync
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@torch.no_grad()
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def run(weights=ROOT / 'yolov5s.pt', # model.pt path(s)
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source=ROOT / 'data/images', # file/dir/URL/glob, 0 for webcam
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imgsz=640, # inference size (pixels)
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conf_thres=0.25, # confidence threshold
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iou_thres=0.45, # NMS IOU threshold
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max_det=1000, # maximum detections per image
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device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu
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view_img=False, # show results
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save_txt=False, # save results to *.txt
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save_conf=False, # save confidences in --save-txt labels
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save_crop=False, # save cropped prediction boxes
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nosave=False, # do not save images/videos
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classes=None, # filter by class: --class 0, or --class 0 2 3
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agnostic_nms=False, # class-agnostic NMS
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augment=False, # augmented inference
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visualize=False, # visualize features
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update=False, # update all models
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project=ROOT / 'runs/detect', # save results to project/name
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name='exp', # save results to project/name
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exist_ok=False, # existing project/name ok, do not increment
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line_thickness=3, # bounding box thickness (pixels)
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hide_labels=False, # hide labels
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hide_conf=False, # hide confidences
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half=False, # use FP16 half-precision inference
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dnn=False, # use OpenCV DNN for ONNX inference
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):
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source = str(source)
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save_img = not nosave and not source.endswith('.txt') # save inference images
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is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
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is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))
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webcam = source.isnumeric() or source.endswith('.txt') or (is_url and not is_file)
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if is_url and is_file:
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source = check_file(source) # download
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# Directories
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save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run
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(save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
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# Load model
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device = select_device(device)
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model = DetectMultiBackend(weights, device=device, dnn=dnn)
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stride, names, pt, jit, onnx = model.stride, model.names, model.pt, model.jit, model.onnx
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imgsz = check_img_size(imgsz, s=stride) # check image size
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# Half
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half &= pt and device.type != 'cpu' # half precision only supported by PyTorch on CUDA
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if pt:
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model.model.half() if half else model.model.float()
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# Dataloader
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if webcam:
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view_img = check_imshow()
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cudnn.benchmark = True # set True to speed up constant image size inference
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dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt and not jit)
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bs = len(dataset) # batch_size
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else:
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dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt and not jit)
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bs = 1 # batch_size
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vid_path, vid_writer = [None] * bs, [None] * bs
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# Run inference
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if pt and device.type != 'cpu':
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model(torch.zeros(1, 3, *imgsz).to(device).type_as(next(model.model.parameters()))) # warmup
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dt, seen = [0.0, 0.0, 0.0], 0
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for path, im, im0s, vid_cap, s in dataset:
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t1 = time_sync()
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im = torch.from_numpy(im).to(device)
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im = im.half() if half else im.float() # uint8 to fp16/32
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im /= 255 # 0 - 255 to 0.0 - 1.0
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if len(im.shape) == 3:
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im = im[None] # expand for batch dim
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t2 = time_sync()
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dt[0] += t2 - t1
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# Inference
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visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
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pred = model(im, augment=augment, visualize=visualize)
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t3 = time_sync()
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dt[1] += t3 - t2
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# NMS
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pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
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dt[2] += time_sync() - t3
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# Second-stage classifier (optional)
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# pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)
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# Process predictions
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for i, det in enumerate(pred): # per image
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seen += 1
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if webcam: # batch_size >= 1
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p, im0, frame = path[i], im0s[i].copy(), dataset.count
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s += f'{i}: '
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else:
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p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)
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p = Path(p) # to Path
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save_path = str(save_dir / p.name) # im.jpg
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txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # im.txt
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s += '%gx%g ' % im.shape[2:] # print string
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gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
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imc = im0.copy() if save_crop else im0 # for save_crop
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annotator = Annotator(im0, line_width=line_thickness, example=str(names))
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if len(det):
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# Rescale boxes from img_size to im0 size
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det[:, :4] = scale_coords(im.shape[2:], det[:, :4], im0.shape).round()
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# Print results
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for c in det[:, -1].unique():
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n = (det[:, -1] == c).sum() # detections per class
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s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string
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# Write results
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for *xyxy, conf, cls in reversed(det):
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if save_txt: # Write to file
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xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
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line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format
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with open(txt_path + '.txt', 'a') as f:
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f.write(('%g ' * len(line)).rstrip() % line + '\n')
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if save_img or save_crop or view_img: # Add bbox to image
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c = int(cls) # integer class
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label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
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annotator.box_label(xyxy, label, color=colors(c, True))
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if save_crop:
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save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)
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# Print time (inference-only)
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LOGGER.info(f'{s}Done. ({t3 - t2:.3f}s)')
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# Stream results
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im0 = annotator.result()
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if view_img:
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cv2.imshow(str(p), im0)
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cv2.waitKey(1) # 1 millisecond
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# Save results (image with detections)
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if save_img:
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if dataset.mode == 'image':
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cv2.imwrite(save_path, im0)
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else: # 'video' or 'stream'
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if vid_path[i] != save_path: # new video
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vid_path[i] = save_path
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if isinstance(vid_writer[i], cv2.VideoWriter):
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vid_writer[i].release() # release previous video writer
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if vid_cap: # video
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fps = vid_cap.get(cv2.CAP_PROP_FPS)
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w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
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h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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else: # stream
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fps, w, h = 30, im0.shape[1], im0.shape[0]
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save_path += '.mp4'
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vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
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vid_writer[i].write(im0)
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# Print results
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t = tuple(x / seen * 1E3 for x in dt) # speeds per image
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LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t)
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if save_txt or save_img:
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s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
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LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
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if update:
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strip_optimizer(weights) # update model (to fix SourceChangeWarning)
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def parse_opt():
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parser = argparse.ArgumentParser()
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parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s.pt', help='model path(s)')
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parser.add_argument('--source', type=str, default=ROOT / 'data/images', help='file/dir/URL/glob, 0 for webcam')
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parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w')
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parser.add_argument('--conf-thres', type=float, default=0.5, help='confidence threshold')
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parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold')
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parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image')
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parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
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parser.add_argument('--view-img', action='store_true', help='show results')
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parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
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parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
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parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes')
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parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
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parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 0 2 3')
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parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
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parser.add_argument('--augment', action='store_true', help='augmented inference')
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parser.add_argument('--visualize', action='store_true', help='visualize features')
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parser.add_argument('--update', action='store_true', help='update all models')
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parser.add_argument('--project', default=ROOT / 'runs/detect', help='save results to project/name')
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parser.add_argument('--name', default='exp', help='save results to project/name')
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parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
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parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)')
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parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels')
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parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences')
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parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
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parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')
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opt = parser.parse_args()
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opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand
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print_args(FILE.stem, opt)
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return opt
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def main(opt):
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check_requirements(exclude=('tensorboard', 'thop'))
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run(**vars(opt))
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# 命令使用
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# python detect.py --weights runs/train/exp_yolov5s/weights/best.pt --source data/images/fishman.jpg # webcam
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if __name__ == "__main__":
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opt = parse_opt()
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main(opt)
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