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Image_Overlay: Add multi Base_Image Overlaying

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TSC
2023-06-15 20:58:06 -05:00
committed by GitHub
parent d67b1c246d
commit f1d2160d79
1 changed files with 53 additions and 34 deletions
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81
efficiency_nodes.py
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@@ -2451,62 +2451,81 @@ class TSC_ImageOverlay:
} }
RETURN_TYPES = ("IMAGE",) RETURN_TYPES = ("IMAGE",)
FUNCTION = "overlayimage" FUNCTION = "apply_overlay_image"
CATEGORY = "Efficiency Nodes/Image" CATEGORY = "Efficiency Nodes/Image"
def overlayimage(self, base_image, overlay_image, overlay_resize, resize_method, rescale_factor, width, height, x_offset, y_offset, rotation, opacity, optional_mask=None): def apply_overlay_image(self, base_image, overlay_image, overlay_resize, resize_method, rescale_factor,
result = self.apply_overlay(tensor2pil(base_image), overlay_image, overlay_resize, resize_method, rescale_factor, (int(width), int(height)), width, height, x_offset, y_offset, rotation, opacity, optional_mask):
(int(x_offset), int(y_offset)), int(rotation), opacity, optional_mask)
return (pil2tensor(result),)
def apply_overlay(self, base, overlay, size_option, resize_method, rescale_factor, size, location, rotation, opacity, mask): # Pack tuples and assign variables
size = width, height
location = x_offset, y_offset
mask = optional_mask
# Check for different sizing options # Check for different sizing options
if size_option != "None": if overlay_resize != "None":
#Extract overlay size and store in Tuple "overlay_size" (WxH) #Extract overlay_image size and store in Tuple "overlay_image_size" (WxH)
overlay_size = overlay.size() overlay_image_size = overlay_image.size()
overlay_size = (overlay_size[2], overlay_size[1]) overlay_image_size = (overlay_image_size[2], overlay_image_size[1])
if size_option == "Fit": if overlay_resize == "Fit":
overlay_size = (base.size[0],base.size[1]) overlay_image_size = (base_image.size[0],base_image.size[1])
elif size_option == "Resize by rescale_factor": elif overlay_resize == "Resize by rescale_factor":
overlay_size = tuple(int(dimension * rescale_factor) for dimension in overlay_size) overlay_image_size = tuple(int(dimension * rescale_factor) for dimension in overlay_image_size)
elif size_option == "Resize to width & heigth": elif overlay_resize == "Resize to width & heigth":
overlay_size = (size[0], size[1]) overlay_image_size = (size[0], size[1])
samples = overlay.movedim(-1, 1) samples = overlay_image.movedim(-1, 1)
overlay = comfy.utils.common_upscale(samples, overlay_size[0], overlay_size[1], resize_method, False) overlay_image = comfy.utils.common_upscale(samples, overlay_image_size[0], overlay_image_size[1], resize_method, False)
overlay = overlay.movedim(1, -1) overlay_image = overlay_image.movedim(1, -1)
overlay = tensor2pil(overlay) overlay_image = tensor2pil(overlay_image)
# Add Alpha channel to overlay # Add Alpha channel to overlay
overlay = overlay.convert('RGBA') overlay_image = overlay_image.convert('RGBA')
overlay.putalpha(Image.new("L", overlay.size, 255)) overlay_image.putalpha(Image.new("L", overlay_image.size, 255))
# If mask connected, check if the overlay image has an alpha channel # If mask connected, check if the overlay_image image has an alpha channel
if mask is not None: if mask is not None:
# Convert mask to pil and resize # Convert mask to pil and resize
mask = tensor2pil(mask) mask = tensor2pil(mask)
mask = mask.resize(overlay.size) mask = mask.resize(overlay_image.size)
# Apply mask as overlay's alpha # Apply mask as overlay's alpha
overlay.putalpha(ImageOps.invert(mask)) overlay_image.putalpha(ImageOps.invert(mask))
# Rotate the overlay image # Rotate the overlay image
overlay = overlay.rotate(rotation, expand=True) overlay_image = overlay_image.rotate(rotation, expand=True)
# Apply opacity on overlay image # Apply opacity on overlay image
r, g, b, a = overlay.split() r, g, b, a = overlay_image.split()
a = a.point(lambda x: max(0, int(x * (1 - opacity / 100)))) a = a.point(lambda x: max(0, int(x * (1 - opacity / 100))))
overlay.putalpha(a) overlay_image.putalpha(a)
# Split the base_image tensor along the first dimension to get a list of tensors
base_image_list = torch.unbind(base_image, dim=0)
# Convert each tensor to a PIL image, apply the overlay, and then convert it back to a tensor
processed_base_image_list = []
for tensor in base_image_list:
# Convert tensor to PIL Image
image = tensor2pil(tensor)
# Paste the overlay image onto the base image # Paste the overlay image onto the base image
if mask is None: if mask is None:
base.paste(overlay, location) image.paste(overlay_image, location)
else: else:
base.paste(overlay, location, overlay) image.paste(overlay_image, location, overlay_image)
# Convert PIL Image back to tensor
processed_tensor = pil2tensor(image)
# Append to list
processed_base_image_list.append(processed_tensor)
# Combine the processed images back into a single tensor
base_image = torch.stack([tensor.squeeze() for tensor in processed_base_image_list])
# Return the edited base image # Return the edited base image
return base return (base_image,)
######################################################################################################################## ########################################################################################################################
# Install simple_eval if missing from packages # Install simple_eval if missing from packages