alphard/Bjornulf_custom_nodes
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new node : cut image from mask

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justumen
2024-08-24 15:00:56 +02:00
parent 4f4f6be7ff
commit aedb53c326
6 changed files with 77 additions and 15 deletions
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30
combine_background_overlay.py
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@@ -8,7 +8,8 @@ class CombineBackgroundOverlay:
return {
"required": {
"background": ("IMAGE",),
"overlay_alpha": ("IMAGE",),
"overlay": ("IMAGE",),
"mask": ("MASK",),
"horizontal_position": ("FLOAT", {"default": 50, "min": -50, "max": 150, "step": 0.1}),
"vertical_position": ("FLOAT", {"default": 50, "min": -50, "max": 150, "step": 0.1}),
},
@@ -18,7 +19,7 @@ class CombineBackgroundOverlay:
FUNCTION = "combine_background_overlay"
CATEGORY = "Bjornulf"
def combine_background_overlay(self, background, overlay_alpha, horizontal_position, vertical_position):
def combine_background_overlay(self, background, overlay, mask, horizontal_position, vertical_position):
# Convert background from torch tensor to numpy array
bg = background[0].numpy()
bg = (bg * 255).astype(np.uint8)
@@ -26,17 +27,23 @@ class CombineBackgroundOverlay:
results = []
for overlay in overlay_alpha:
for ov, m in zip(overlay, mask):
# Convert overlay from torch tensor to numpy array
ov = overlay.numpy()
ov = ov.numpy()
ov = (ov * 255).astype(np.uint8)
# Convert mask from torch tensor to numpy array
m = m.numpy()
m = (m * 255).astype(np.uint8)
# Create PIL Image for overlay
if ov.shape[2] == 4:
ov_img = Image.fromarray(ov, 'RGBA')
else:
ov_img = Image.fromarray(ov, 'RGB')
ov_img = ov_img.convert('RGBA')
ov_img = Image.fromarray(ov, 'RGB')
# Create alpha channel from mask
alpha = Image.fromarray(m, 'L')
# Combine RGB overlay with alpha mask
ov_img.putalpha(alpha)
# Calculate horizontal position
x = int((horizontal_position / 100) * (bg_img.width - ov_img.width))
@@ -56,9 +63,8 @@ class CombineBackgroundOverlay:
# Convert back to numpy array and then to torch tensor
result_np = np.array(result)
# If the result is RGBA, convert to RGB
if result_np.shape[2] == 4:
result_np = result_np[:,:,:3]
# Convert RGBA to RGB
result_np = result_np[:,:,:3]
result_tensor = torch.from_numpy(result_np).float() / 255.0