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mask_test.py
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mask_test.py
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import numpy as np
import torch
import matplotlib.pyplot as plt
from PIL import Image
from sam2.build_sam import build_sam2
from sam2.automatic_mask_generator import SAM2AutomaticMaskGenerator
# use bfloat16 for the entire notebook
import gc
torch.cuda.empty_cache()
gc.collect()
torch.autocast(device_type="cuda", dtype=torch.bfloat16).__enter__()
image_path = 'img0.jpg'
image = Image.open(image_path)
image = np.array(image.convert("RGB"))
if torch.cuda.get_device_properties(0).major >= 8:
# turn on tfloat32 for Ampere GPUs (https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices)
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
def show_anns(anns, borders=True):
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), [0.5]])
img[m] = color_mask
if borders:
import cv2
contours, _ = cv2.findContours(m.astype(np.uint8),cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
# Try to smooth contours
contours = [cv2.approxPolyDP(contour, epsilon=0.01, closed=True) for contour in contours]
cv2.drawContours(img, contours, -1, (0,0,1,0.4), thickness=1)
ax.imshow(img)
#sam2_checkpoint = "./checkpoints/sam2_hiera_tiny.pt"
#model_cfg = "sam2_hiera_t.yaml"
sam2_checkpoint = "./checkpoints/sam2_hiera_small.pt"
model_cfg = "sam2_hiera_s.yaml"
sam2 = build_sam2(model_cfg, sam2_checkpoint, device ='cuda', apply_postprocessing=False)
mask_generator = SAM2AutomaticMaskGenerator(sam2)
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()
torch.cuda.empty_cache()
gc.collect()
mask_generator_2 = SAM2AutomaticMaskGenerator(
model=sam2,
points_per_side=32,
points_per_batch=64,
pred_iou_thresh=0.7,
stability_score_thresh=0.92,
stability_score_offset=0.7,
crop_n_layers=1,
box_nms_thresh=0.7,
crop_n_points_downscale_factor=2,
min_mask_region_area=25.0,
use_m2m=True,
)
masks2 = mask_generator_2.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()