227 lines
12 KiB
Python
227 lines
12 KiB
Python
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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
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"""
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Run YOLOv5 classification inference on images, videos, directories, globs, YouTube, webcam, streams, etc.
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Usage - sources:
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$ python classify/predict.py --weights yolov5s-cls.pt --source 0 # webcam
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img.jpg # image
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vid.mp4 # video
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screen # screenshot
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path/ # directory
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list.txt # list of images
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list.streams # list of streams
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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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Usage - formats:
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$ python classify/predict.py --weights yolov5s-cls.pt # PyTorch
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yolov5s-cls.torchscript # TorchScript
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yolov5s-cls.onnx # ONNX Runtime or OpenCV DNN with --dnn
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yolov5s-cls_openvino_model # OpenVINO
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yolov5s-cls.engine # TensorRT
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yolov5s-cls.mlmodel # CoreML (macOS-only)
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yolov5s-cls_saved_model # TensorFlow SavedModel
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yolov5s-cls.pb # TensorFlow GraphDef
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yolov5s-cls.tflite # TensorFlow Lite
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yolov5s-cls_edgetpu.tflite # TensorFlow Edge TPU
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yolov5s-cls_paddle_model # PaddlePaddle
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"""
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import argparse
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import os
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import platform
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import sys
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from pathlib import Path
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import torch
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import torch.nn.functional as F
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FILE = Path(__file__).resolve()
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ROOT = FILE.parents[1] # 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.augmentations import classify_transforms
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from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams
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from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2,
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increment_path, print_args, strip_optimizer)
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from utils.plots import Annotator
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from utils.torch_utils import select_device, smart_inference_mode
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@smart_inference_mode()
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def run(
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weights=ROOT / 'yolov5s-cls.pt', # model.pt path(s)
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source=ROOT / 'data/images', # file/dir/URL/glob/screen/0(webcam)
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data=ROOT / 'data/coco128.yaml', # dataset.yaml path
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imgsz=(224, 224), # inference size (height, width)
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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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nosave=False, # do not save images/videos
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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/predict-cls', # 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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half=False, # use FP16 half-precision inference
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dnn=False, # use OpenCV DNN for ONNX inference
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vid_stride=1, # video frame-rate stride
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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('.streams') or (is_url and not is_file)
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screenshot = source.lower().startswith('screen')
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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, data=data, fp16=half)
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stride, names, pt = model.stride, model.names, model.pt
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imgsz = check_img_size(imgsz, s=stride) # check image size
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# Dataloader
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bs = 1 # batch_size
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if webcam:
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view_img = check_imshow(warn=True)
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dataset = LoadStreams(source, img_size=imgsz, transforms=classify_transforms(imgsz[0]), vid_stride=vid_stride)
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bs = len(dataset)
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elif screenshot:
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dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt)
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else:
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dataset = LoadImages(source, img_size=imgsz, transforms=classify_transforms(imgsz[0]), vid_stride=vid_stride)
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vid_path, vid_writer = [None] * bs, [None] * bs
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# Run inference
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model.warmup(imgsz=(1 if pt else bs, 3, *imgsz)) # warmup
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seen, windows, dt = 0, [], (Profile(), Profile(), Profile())
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for path, im, im0s, vid_cap, s in dataset:
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with dt[0]:
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im = torch.Tensor(im).to(model.device)
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im = im.half() if model.fp16 else im.float() # uint8 to fp16/32
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if len(im.shape) == 3:
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im = im[None] # expand for batch dim
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# Inference
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with dt[1]:
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results = model(im)
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# Post-process
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with dt[2]:
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pred = F.softmax(results, dim=1) # probabilities
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# Process predictions
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for i, prob 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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annotator = Annotator(im0, example=str(names), pil=True)
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# Print results
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top5i = prob.argsort(0, descending=True)[:5].tolist() # top 5 indices
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s += f"{', '.join(f'{names[j]} {prob[j]:.2f}' for j in top5i)}, "
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# Write results
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text = '\n'.join(f'{prob[j]:.2f} {names[j]}' for j in top5i)
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if save_img or view_img: # Add bbox to image
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annotator.text((32, 32), text, txt_color=(255, 255, 255))
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if save_txt: # Write to file
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with open(f'{txt_path}.txt', 'a') as f:
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f.write(text + '\n')
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# Stream results
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im0 = annotator.result()
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if view_img:
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if platform.system() == 'Linux' and p not in windows:
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windows.append(p)
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cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux)
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cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])
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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 = str(Path(save_path).with_suffix('.mp4')) # force *.mp4 suffix on results videos
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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 time (inference-only)
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LOGGER.info(f"{s}{dt[1].dt * 1E3:.1f}ms")
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# Print results
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t = tuple(x.t / 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[0]) # 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-cls.pt', help='model path(s)')
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parser.add_argument('--source', type=str, default=ROOT / 'data/images', help='file/dir/URL/glob/screen/0(webcam)')
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parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path')
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parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[224], help='inference size h,w')
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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('--nosave', action='store_true', help='do not save images/videos')
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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/predict-cls', 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('--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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parser.add_argument('--vid-stride', type=int, default=1, help='video frame-rate stride')
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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(vars(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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if __name__ == "__main__":
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opt = parse_opt()
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main(opt)
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