alphard/Bjornulf_custom_nodes
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v1.1.8

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justumen
2025-06-11 14:32:34 +02:00
parent 3480d8a4ce
commit f43bce3516
3 changed files with 88 additions and 19 deletions
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103
combine_background_overlay.py
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@@ -17,12 +17,14 @@ class CombineBackgroundOverlay:
},
}
RETURN_TYPES = ("IMAGE",)
RETURN_TYPES = ("IMAGE", "MASK")
RETURN_NAMES = ("image", "mask")
FUNCTION = "combine_background_overlay"
CATEGORY = "Bjornulf"
def combine_background_overlay(self, background, overlay, horizontal_position, vertical_position, mask=None):
results = []
output_masks = []
# Process the first background image
bg = background[0].cpu().numpy()
@@ -45,7 +47,7 @@ class CombineBackgroundOverlay:
else:
ov_img = Image.fromarray(ov, 'RGB')
# Apply mask if provided
# Apply mask if provided - INVERTED LOGIC: mask removes opacity
if mask is not None:
mask_idx = min(i, mask.shape[0] - 1)
m = mask[mask_idx].cpu().numpy()
@@ -56,15 +58,18 @@ class CombineBackgroundOverlay:
if mask_img.size != ov_img.size:
mask_img = mask_img.resize(ov_img.size, Image.LANCZOS)
# INVERT THE MASK - white areas in mask become transparent
inverted_mask = Image.eval(mask_img, lambda x: 255 - x)
if ov_img.mode == 'RGBA':
# Combine overlay’s alpha with mask
# Combine overlay's alpha with inverted mask
ov_alpha = np.array(ov_img.split()[3], dtype=np.float32) / 255.0
mask_alpha = np.array(mask_img, dtype=np.float32) / 255.0
effective_alpha = (ov_alpha * mask_alpha * 255).astype(np.uint8)
inverted_mask_alpha = np.array(inverted_mask, dtype=np.float32) / 255.0
effective_alpha = (ov_alpha * inverted_mask_alpha * 255).astype(np.uint8)
ov_img.putalpha(Image.fromarray(effective_alpha, 'L'))
else:
# Use mask as alpha for RGB overlay
ov_img.putalpha(mask_img)
# Use inverted mask as alpha for RGB overlay
ov_img.putalpha(inverted_mask)
else:
if ov_img.mode == 'RGB':
# Add fully opaque alpha for RGB overlay
@@ -82,26 +87,90 @@ class CombineBackgroundOverlay:
result = Image.new('RGBA', bg_img.size, (0, 0, 0, 0))
result.paste(bg_img, (0, 0))
# Paste overlay with alpha blending
# Create output mask - start with background alpha or white
if bg_has_alpha:
output_mask_img = bg_img.split()[3].copy()
else:
output_mask_img = Image.new('L', bg_img.size, 255)
# Paste overlay directly on top (no alpha blending)
if x + ov_img.width > 0 and y + ov_img.height > 0 and x < result.width and y < result.height:
temp = Image.new('RGBA', result.size, (0, 0, 0, 0))
temp.paste(ov_img, (x, y), ov_img)
result = Image.alpha_composite(result.convert('RGBA'), temp)
# Convert overlay to RGB if needed for direct paste
if ov_img.mode == 'RGBA':
ov_rgb = Image.new('RGB', ov_img.size, (255, 255, 255))
ov_rgb.paste(ov_img, mask=ov_img.split()[3])
ov_paste = ov_rgb
paste_mask = ov_img.split()[3]
else:
ov_paste = ov_img
paste_mask = None
# Apply input mask if provided - UPDATED LOGIC FOR INVERTED MASK
if mask is not None:
mask_idx = min(i, mask.shape[0] - 1)
m = mask[mask_idx].cpu().numpy()
m = np.clip(m * 255, 0, 255).astype(np.uint8)
input_mask = Image.fromarray(m, 'L')
if input_mask.size != ov_img.size:
input_mask = input_mask.resize(ov_img.size, Image.LANCZOS)
# INVERT THE INPUT MASK
inverted_input_mask = Image.eval(input_mask, lambda x: 255 - x)
if paste_mask is not None:
# Combine overlay alpha with inverted input mask
paste_mask_array = np.array(paste_mask, dtype=np.float32) / 255.0
inverted_input_mask_array = np.array(inverted_input_mask, dtype=np.float32) / 255.0
combined_mask_array = (paste_mask_array * inverted_input_mask_array * 255).astype(np.uint8)
paste_mask = Image.fromarray(combined_mask_array, 'L')
else:
# Use inverted input mask directly
paste_mask = inverted_input_mask
# Paste overlay directly onto result
result.paste(ov_paste, (x, y), paste_mask)
# Update output mask
if paste_mask is not None:
temp_mask = Image.new('L', result.size, 0)
temp_mask.paste(paste_mask, (x, y))
# Combine masks - overlay mask replaces background mask where it exists
output_mask_array = np.array(output_mask_img, dtype=np.float32)
temp_mask_array = np.array(temp_mask, dtype=np.float32)
combined_mask_array = np.maximum(output_mask_array, temp_mask_array).astype(np.uint8)
output_mask_img = Image.fromarray(combined_mask_array, 'L')
else:
# No mask - overlay covers background completely in paste area
temp_mask = Image.new('L', result.size, 0)
temp_mask.paste(Image.new('L', ov_paste.size, 255), (x, y))
output_mask_array = np.array(output_mask_img, dtype=np.float32)
temp_mask_array = np.array(temp_mask, dtype=np.float32)
combined_mask_array = np.maximum(output_mask_array, temp_mask_array).astype(np.uint8)
output_mask_img = Image.fromarray(combined_mask_array, 'L')
# Convert result back to tensor
result_np = np.array(result)
if bg_has_alpha:
result_tensor = torch.from_numpy(result_np).float() / 255.0
else:
# Convert RGBA to RGB, blending with white only where needed
if result_np.shape[2] == 4:
if result_np.shape[2] == 4:
# Convert RGBA back to RGB if background was RGB
if not bg_has_alpha:
alpha = result_np[:, :, 3:4] / 255.0
rgb = result_np[:, :, :3]
white_bg = np.ones_like(rgb) * 255
result_np = (rgb * alpha + white_bg * (1 - alpha)).astype(np.uint8)
result_tensor = torch.from_numpy(result_np).float() / 255.0
else:
result_tensor = torch.from_numpy(result_np).float() / 255.0
else:
result_tensor = torch.from_numpy(result_np).float() / 255.0
# Convert output mask to tensor
output_mask_tensor = torch.from_numpy(np.array(output_mask_img)).float() / 255.0
results.append(result_tensor)
output_masks.append(output_mask_tensor)
final_result = torch.stack(results)
return (final_result,)
final_masks = torch.stack(output_masks)
return (final_result, final_masks)