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Extract segmentation masks via SAM
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import logging | |
import numpy as np | |
import PIL.Image | |
from segment_anything import SamPredictor, sam_model_registry | |
import matplotlib.pyplot as plt | |
logger = logging.getLogger(__name__) | |
model_type = "default" | |
_SAM_CKPT = "sam_vit_h_4b8939.pth" | |
sam = sam_model_registry[model_type](checkpoint=_SAM_CKPT).to(device="cuda") | |
predictor = SamPredictor(sam) | |
def predict(img_pil: PIL.Image.Image,points=None, labels=None, boxes=None) -> np.ndarray: | |
if boxes is not None: | |
boxes = boxes[None, :] | |
predictor.set_image(np.asarray(img_pil)) | |
masks, scores, logits = predictor.predict( | |
point_coords=points, | |
point_labels=labels, | |
box=boxes, | |
multimask_output=True, | |
) | |
return masks, scores | |
def get_best_mask(img_pil: PIL.Image.Image) -> np.ndarray: | |
points, labels = get_points(img_pil) | |
bbox = get_boxes(img_pil) | |
masks, scores = predict(img_pil, points=points, labels=labels, boxes=bbox) | |
best_mask = masks[0] | |
best_score = scores[0] | |
for i in range(1, len(masks)): | |
if scores[i] > best_score: | |
best_mask = masks[i] | |
best_score = scores[i] | |
return best_mask | |
def get_points(img: PIL.Image.Image) -> (np.ndarray, np.ndarray): | |
# center = [img.size[0] // 2, img.size[1] // 2] | |
# up = [center[0] - 20, center[1]] | |
# down = [center[0] + 20, center[1]] | |
# return np.array([center, up, down]) | |
offset = 35 | |
top_left = [0 + offset, 0 + offset] | |
top_right = [img.size[0] - offset, 0 + offset] | |
bottom_left = [0 + offset, img.size[1] - offset] | |
bottom_right = [img.size[0] - offset, img.size[1] - offset] | |
points = np.array([top_left, top_right, bottom_left, bottom_right]) | |
labels = np.array([0, 0, 0, 0]) | |
return points, labels | |
def get_boxes(img_pil: PIL.Image.Image) -> np.ndarray: | |
return np.array([0 + 25, 0, img_pil.size[0] - 25, img_pil.size[1]]) | |
def show_mask(mask, ax, random_color=False): | |
if random_color: | |
color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0) | |
else: | |
color = np.array([30/255, 144/255, 1, 0.6]) | |
h, w = mask.shape[-2:] | |
mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1) | |
ax.imshow(mask_image) | |
def show_points(coords, ax, marker_size=375): | |
pos_points = coords | |
ax.scatter(pos_points[:, 0], pos_points[:, 1], color='green', marker='*', s=marker_size, edgecolor='white', linewidth=1.25) | |
def show_box(box, ax): | |
x0, y0 = box[0], box[1] | |
w, h = box[2] - box[0], box[3] - box[1] | |
ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor='green', facecolor=(0,0,0,0), lw=2)) | |
if __main__: | |
# TODO: load your HF dataset | |
# Test on one image | |
img = img_pil=dataset[0]["image"] | |
points, labels = get_points(img) | |
masks, scores = predict(img, points=points, labels=labels, boxes=get_boxes(img)) | |
for i, (mask, score) in enumerate(zip(masks, scores)): | |
plt.figure(figsize=(10,10)) | |
plt.imshow(img) | |
show_mask(mask, plt.gca()) | |
show_points(points, plt.gca()) | |
show_box(get_boxes(img), plt.gca()) | |
plt.title(f"Mask {i+1}, Score: {score:.3f}", fontsize=18) | |
plt.axis('off') | |
plt.show() |
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