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import torch
from kornia.morphology import dilation, closing
import requests
from transformers import SamModel, SamProcessor
print('Loading SAM...')
device = "cuda" if torch.cuda.is_available() else "cpu"
model = SamModel.from_pretrained("facebook/sam-vit-huge").to(device)
processor = SamProcessor.from_pretrained("facebook/sam-vit-huge")
print('DONE')
def build_mask(image, faces, hairs):
# 1. Segmentation
input_points = faces # 2D location of the face
with torch.no_grad():
inputs = processor(image, input_points=input_points, return_tensors="pt").to(device)
outputs = model(**inputs)
masks = processor.image_processor.post_process_masks(
outputs.pred_masks.cpu(), inputs["original_sizes"].cpu(), inputs["reshaped_input_sizes"].cpu()
)
scores = outputs.iou_scores
input_points = hairs # 2D location of the face
with torch.no_grad():
inputs = processor(image, input_points=input_points, return_tensors="pt").to(device)
outputs = model(**inputs)
h_masks = processor.image_processor.post_process_masks(
outputs.pred_masks.cpu(), inputs["original_sizes"].cpu(), inputs["reshaped_input_sizes"].cpu()
)
h_scores = outputs.iou_scores
# 2. Post-processing
mask=masks[0][0].all(0) | h_masks[0][0].all(0)
# dilation
tensor = mask[None,None,:,:]
kernel = torch.ones(3, 3)
mask = closing(tensor, kernel)[0,0].bool()
return mask
def build_mask_multi(image, faces, hairs):
all_masks = []
for face,hair in zip(faces,hairs):
# 1. Segmentation
input_points = [face] # 2D location of the face
with torch.no_grad():
inputs = processor(image, input_points=input_points, return_tensors="pt").to(device)
outputs = model(**inputs)
masks = processor.image_processor.post_process_masks(
outputs.pred_masks.cpu(), inputs["original_sizes"].cpu(), inputs["reshaped_input_sizes"].cpu()
)
scores = outputs.iou_scores
input_points = [hair] # 2D location of the face
with torch.no_grad():
inputs = processor(image, input_points=input_points, return_tensors="pt").to(device)
outputs = model(**inputs)
h_masks = processor.image_processor.post_process_masks(
outputs.pred_masks.cpu(), inputs["original_sizes"].cpu(), inputs["reshaped_input_sizes"].cpu()
)
h_scores = outputs.iou_scores
# 2. Post-processing
mask=masks[0][0].all(0) | h_masks[0][0].all(0)
# dilation
mask_T = mask[None,None,:,:]
kernel = torch.ones(3, 3)
mask = closing(mask_T, kernel)[0,0].bool()
all_masks.append(mask)
mask = all_masks[0]
for next_mask in all_masks[1:]:
mask = mask | next_mask
return mask |