Update Actions with Lychee and GitHub Token (#12592)
* Add Ultralytics Actions * Auto-format by Ultralytics actions * Update format.yml Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> --------- Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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@@ -46,23 +46,36 @@ from ultralytics.utils.plotting import Annotator, colors, save_one_box
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from models.common import DetectMultiBackend
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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, non_max_suppression, print_args, scale_boxes, scale_segments,
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strip_optimizer)
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from utils.general import (
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LOGGER,
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Profile,
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check_file,
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check_img_size,
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check_imshow,
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check_requirements,
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colorstr,
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cv2,
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increment_path,
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non_max_suppression,
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print_args,
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scale_boxes,
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scale_segments,
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strip_optimizer,
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)
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from utils.segment.general import masks2segments, process_mask, process_mask_native
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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-seg.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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weights=ROOT / "yolov5s-seg.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=(640, 640), # inference size (height, width)
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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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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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@@ -73,8 +86,8 @@ def run(
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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-seg', # save results to project/name
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name='exp', # save results to project/name
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project=ROOT / "runs/predict-seg", # 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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@@ -85,17 +98,17 @@ def run(
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retina_masks=False,
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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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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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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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(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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@@ -143,14 +156,14 @@ def run(
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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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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, 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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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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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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@@ -166,7 +179,8 @@ def run(
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if save_txt:
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segments = [
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scale_segments(im0.shape if retina_masks else im.shape[2:], x, im0.shape, normalize=True)
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for x in reversed(masks2segments(masks))]
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for x in reversed(masks2segments(masks))
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]
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# Print results
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for c in det[:, 5].unique():
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@@ -177,39 +191,42 @@ def run(
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annotator.masks(
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masks,
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colors=[colors(x, True) for x in det[:, 5]],
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im_gpu=torch.as_tensor(im0, dtype=torch.float16).to(device).permute(2, 0, 1).flip(0).contiguous() /
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255 if retina_masks else im[i])
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im_gpu=torch.as_tensor(im0, dtype=torch.float16).to(device).permute(2, 0, 1).flip(0).contiguous()
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/ 255
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if retina_masks
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else im[i],
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)
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# Write results
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for j, (*xyxy, conf, cls) in enumerate(reversed(det[:, :6])):
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if save_txt: # Write to file
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seg = segments[j].reshape(-1) # (n,2) to (n*2)
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line = (cls, *seg, conf) if save_conf else (cls, *seg) # label format
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with open(f'{txt_path}.txt', 'a') as f:
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f.write(('%g ' * len(line)).rstrip() % line + '\n')
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with open(f"{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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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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# annotator.draw.polygon(segments[j], outline=colors(c, True), width=3)
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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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save_one_box(xyxy, imc, file=save_dir / "crops" / names[c] / f"{p.stem}.jpg", BGR=True)
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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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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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if cv2.waitKey(1) == ord('q'): # 1 millisecond
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if cv2.waitKey(1) == ord("q"): # 1 millisecond
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exit()
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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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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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@@ -222,18 +239,18 @@ def run(
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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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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}{'' if len(det) else '(no detections), '}{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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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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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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@@ -241,34 +258,34 @@ def run(
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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-seg.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=[640], help='inference size h,w')
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parser.add_argument('--conf-thres', type=float, default=0.25, 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/predict-seg', 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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parser.add_argument('--vid-stride', type=int, default=1, help='video frame-rate stride')
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parser.add_argument('--retina-masks', action='store_true', help='whether to plot masks in native resolution')
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parser.add_argument("--weights", nargs="+", type=str, default=ROOT / "yolov5s-seg.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=[640], help="inference size h,w")
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parser.add_argument("--conf-thres", type=float, default=0.25, 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/predict-seg", 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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parser.add_argument("--vid-stride", type=int, default=1, help="video frame-rate stride")
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parser.add_argument("--retina-masks", action="store_true", help="whether to plot masks in native resolution")
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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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@@ -276,10 +293,10 @@ def parse_opt():
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def main(opt):
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check_requirements(ROOT / 'requirements.txt', exclude=('tensorboard', 'thop'))
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check_requirements(ROOT / "requirements.txt", exclude=("tensorboard", "thop"))
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run(**vars(opt))
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if __name__ == '__main__':
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if __name__ == "__main__":
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opt = parse_opt()
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main(opt)
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