63 lines
1.9 KiB
Python
63 lines
1.9 KiB
Python
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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# image = cv2.resize(image,(512,256))
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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) !=10) * 255
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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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