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import numpy as np |
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import torch |
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import matplotlib.pyplot as plt |
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import cv2 |
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import sys |
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sys.path.append("..") |
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from segment_anything import sam_model_registry, SamPredictor |
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def show_mask(mask, ax, random_color=False): |
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if random_color: |
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color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0) |
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else: |
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color = np.array([30/255, 144/255, 255/255, 0.6]) |
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h, w = mask.shape[-2:] |
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mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1) |
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ax.imshow(mask_image) |
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def show_points(coords, labels, ax, marker_size=375): |
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pos_points = coords[labels==1] |
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neg_points = coords[labels==0] |
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ax.scatter(pos_points[:, 0], pos_points[:, 1], color='green', marker='*', s=marker_size, edgecolor='white', linewidth=1.25) |
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ax.scatter(neg_points[:, 0], neg_points[:, 1], color='red', marker='*', s=marker_size, edgecolor='white', linewidth=1.25) |
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def show_box(box, ax): |
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x0, y0 = box[0], box[1] |
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w, h = box[2] - box[0], box[3] - box[1] |
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ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor='green', facecolor=(0,0,0,0), lw=2)) |
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sam_checkpoint = "./script/sam_vit_h_4b8939.pth" |
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model_type = "vit_h" |
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device = "cuda" |
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sam = sam_model_registry[model_type](checkpoint=sam_checkpoint) |
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sam.to(device=device) |
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predictor = SamPredictor(sam) |
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save_path = "./validation_demo/Demo/fish/" |
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image = cv2.imread("./validation_demo/Demo/fish/demo.jpg") |
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image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) |
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predictor.set_image(image) |
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input_point = np.array([[714,250]]) |
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input_label = np.array([1]) |
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masks, scores, logits = predictor.predict( |
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point_coords=input_point, |
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point_labels=input_label, |
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multimask_output=True, |
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) |
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for i, (mask, score) in enumerate(zip(masks, scores)): |
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h, w = mask.shape[-2:] |
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mask = mask.reshape(h, w, 1) * 255 |
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cv2.imwrite(save_path+str(i)+"_fish2.jpg",mask) |
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print(masks.shape) |
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print(score) |
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