73 lines
3.0 KiB
Python
73 lines
3.0 KiB
Python
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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from pathlib import Path
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from ultralytics import SAM, YOLO
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def auto_annotate(
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data,
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det_model="yolo11x.pt",
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sam_model="sam_b.pt",
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device="",
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conf=0.25,
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iou=0.45,
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imgsz=640,
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max_det=300,
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classes=None,
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output_dir=None,
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):
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"""
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Automatically annotates images using a YOLO object detection model and a SAM segmentation model.
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This function processes images in a specified directory, detects objects using a YOLO model, and then generates
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segmentation masks using a SAM model. The resulting annotations are saved as text files.
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Args:
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data (str): Path to a folder containing images to be annotated.
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det_model (str): Path or name of the pre-trained YOLO detection model.
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sam_model (str): Path or name of the pre-trained SAM segmentation model.
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device (str): Device to run the models on (e.g., 'cpu', 'cuda', '0').
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conf (float): Confidence threshold for detection model; default is 0.25.
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iou (float): IoU threshold for filtering overlapping boxes in detection results; default is 0.45.
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imgsz (int): Input image resize dimension; default is 640.
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max_det (int): Limits detections per image to control outputs in dense scenes.
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classes (list): Filters predictions to specified class IDs, returning only relevant detections.
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output_dir (str | None): Directory to save the annotated results. If None, a default directory is created.
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Examples:
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>>> from ultralytics.data.annotator import auto_annotate
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>>> auto_annotate(data="ultralytics/assets", det_model="yolo11n.pt", sam_model="mobile_sam.pt")
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Notes:
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- The function creates a new directory for output if not specified.
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- Annotation results are saved as text files with the same names as the input images.
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- Each line in the output text file represents a detected object with its class ID and segmentation points.
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"""
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det_model = YOLO(det_model)
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sam_model = SAM(sam_model)
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data = Path(data)
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if not output_dir:
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output_dir = data.parent / f"{data.stem}_auto_annotate_labels"
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Path(output_dir).mkdir(exist_ok=True, parents=True)
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det_results = det_model(
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data, stream=True, device=device, conf=conf, iou=iou, imgsz=imgsz, max_det=max_det, classes=classes
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)
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for result in det_results:
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class_ids = result.boxes.cls.int().tolist() # noqa
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if len(class_ids):
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boxes = result.boxes.xyxy # Boxes object for bbox outputs
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sam_results = sam_model(result.orig_img, bboxes=boxes, verbose=False, save=False, device=device)
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segments = sam_results[0].masks.xyn # noqa
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with open(f"{Path(output_dir) / Path(result.path).stem}.txt", "w") as f:
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for i in range(len(segments)):
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s = segments[i]
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if len(s) == 0:
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continue
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segment = map(str, segments[i].reshape(-1).tolist())
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f.write(f"{class_ids[i]} " + " ".join(segment) + "\n")
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