106 lines
3.1 KiB
Python
106 lines
3.1 KiB
Python
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import cv2
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import torch
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import numpy as np
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import supervision as sv
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from torchvision.ops import box_convert
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from sam2.build_sam import build_sam2
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from sam2.sam2_image_predictor import SAM2ImagePredictor
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from grounding_dino.groundingdino.util.inference import load_model, load_image, predict
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# environment settings
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# use bfloat16
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# build SAM2 image predictor
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sam2_checkpoint = "./checkpoints/sam2_hiera_large.pt"
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model_cfg = "sam2_hiera_l.yaml"
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sam2_model = build_sam2(model_cfg, sam2_checkpoint, device="cuda")
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sam2_predictor = SAM2ImagePredictor(sam2_model)
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# build grounding dino model
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model_id = "IDEA-Research/grounding-dino-tiny"
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device = "cuda" if torch.cuda.is_available() else "cpu"
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grounding_model = load_model(
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model_config_path="grounding_dino/groundingdino/config/GroundingDINO_SwinT_OGC.py",
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model_checkpoint_path="gdino_checkpoints/groundingdino_swint_ogc.pth",
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device=device
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)
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# setup the input image and text prompt for SAM 2 and Grounding DINO
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# VERY important: text queries need to be lowercased + end with a dot
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text = "car. tire."
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img_path = 'notebooks/images/truck.jpg'
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image_source, image = load_image(img_path)
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sam2_predictor.set_image(image_source)
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boxes, confidences, labels = predict(
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model=grounding_model,
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image=image,
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caption=text,
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box_threshold=0.35,
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text_threshold=0.25
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)
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# process the box prompt for SAM 2
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h, w, _ = image_source.shape
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boxes = boxes * torch.Tensor([w, h, w, h])
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input_boxes = box_convert(boxes=boxes, in_fmt="cxcywh", out_fmt="xyxy").numpy()
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# FIXME: figure how does this influence the G-DINO model
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torch.autocast(device_type="cuda", dtype=torch.bfloat16).__enter__()
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if torch.cuda.get_device_properties(0).major >= 8:
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# turn on tfloat32 for Ampere GPUs (https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices)
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torch.backends.cuda.matmul.allow_tf32 = True
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torch.backends.cudnn.allow_tf32 = True
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masks, scores, logits = sam2_predictor.predict(
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point_coords=None,
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point_labels=None,
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box=input_boxes,
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multimask_output=False,
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)
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import pdb; pdb.set_trace()
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"""
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Post-process the output of the model to get the masks, scores, and logits for visualization
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"""
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# convert the shape to (n, H, W)
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if masks.ndim == 3:
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masks = masks[None]
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scores = scores[None]
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logits = logits[None]
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elif masks.ndim == 4:
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masks = masks.squeeze(1)
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confidences = confidences.numpy().tolist()
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class_names = labels
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labels = [
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f"{class_name} {confidence:.2f}"
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for class_name, confidence
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in zip(class_names, confidences)
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]
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"""
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Visualize image with supervision useful API
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"""
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img = cv2.imread(img_path)
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detections = sv.Detections(
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xyxy=input_boxes, # (n, 4)
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mask=masks, # (n, h, w)
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)
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box_annotator = sv.BoxAnnotator()
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annotated_frame = box_annotator.annotate(scene=img.copy(), detections=detections, labels=labels)
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cv2.imwrite("groundingdino_annotated_image.jpg", annotated_frame)
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mask_annotator = sv.MaskAnnotator()
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annotated_frame = mask_annotator.annotate(scene=annotated_frame, detections=detections)
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cv2.imwrite("grounded_sam2_annotated_image_with_mask.jpg", annotated_frame)
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