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76 | # Automatically generating object masks with SAM
# https://github.com/facebookresearch/segment-anything/blob/main/notebooks/automatic_mask_generator_example.ipynb
import numpy as np
import torch
import matplotlib.pyplot as plt
import cv2
def show_anns(anns):
if len(anns)==0:
return
sorted_anns=sorted(anns, key=(lambda x: x['area']), reverse=True)
ax=plt.gca()
ax.set_autoscale_on(False)
img=np.ones((sorted_anns[0]['segmentation'].shape[0], sorted_anns[0]['segmentation'].shape[1], 4))
img[:, :, 3]=0
for ann in sorted_anns:
m=ann['segmentation']
color_mask=np.concatenate([np.random.random(3), [0.35]])
img[m]=color_mask
ax.imshow(img)
image=cv2.imread('D:/PyTest/kkk/dog.jpg')
image=cv2.cvtColor(image,cv2.COLOR_BGR2RGB)
# plt.figure(figsize=(20,20))
# plt.imshow(image)
# plt.axis('off')
# plt.show()
# plt.axis('off')
import sys
sys.path.append("..")
from segment_anything import sam_model_registry, SamAutomaticMaskGenerator, SamPredictor
sam_checkpoint='D:/PyTest/kkk/sam_vit_h_4b8939.pth'
model_type="vit_h"
# device = "cuda"
sam = sam_model_registry[model_type](checkpoint=sam_checkpoint)
# sam.to(device=device)
mask_generator = SamAutomaticMaskGenerator(sam)
masks=mask_generator.generate(image)
print(len(masks))
# print(masks[0].keys())
# plt.figure(figsize=(20, 20))
# plt.imshow(image)
# show_anns(masks)
# plt.axis('off')
# plt.show()
mask_generator_2 = SamAutomaticMaskGenerator(
model=sam,
points_per_side=32,
pred_iou_thresh=0.86,
stability_score_thresh=0.92,
crop_n_layers=1,
crop_n_points_downscale_factor=2,
min_mask_region_area=100,
)
mask2=mask_generator_2.generate(image)
print(len(mask2))
# plt.figure(figsize=(20, 20))
# plt.imshow(image)
# show_anns(mask2)
# plt.axis('off')
# plt.show()
|
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