185 lines
8.0 KiB
Python
185 lines
8.0 KiB
Python
import torch
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from PIL import Image, ImageChops
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from .imagefunc import log, tensor2pil, pil2tensor, image2mask
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from .imagefunc import get_gray_average, calculate_shadow_highlight_level, luminance_keyer
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def norm_value(value):
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if value < 0.01:
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value = 0.01
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if value > 0.99:
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value = 0.99
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return value
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class ShadowAndHighlightMask:
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def __init__(self):
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self.NODE_NAME = 'Shadow & Highlight Mask'
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@classmethod
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def INPUT_TYPES(self):
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return {
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"required": {
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"image": ("IMAGE", ),
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"shadow_level_offset": ("INT", {"default": 0, "min": -99, "max": 99, "step": 1}),
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"shadow_range": ("FLOAT", {"default": 0.25, "min": 0.01, "max": 0.99, "step": 0.01}),
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"highlight_level_offset": ("INT", {"default": 0, "min": -99, "max": 99, "step": 1}),
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"highlight_range": ("FLOAT", {"default": 0.25, "min": 0.01, "max": 0.99, "step": 0.01}),
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},
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"optional": {
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"mask": ("MASK",), #
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}
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}
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RETURN_TYPES = ("MASK", "MASK")
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RETURN_NAMES = ("shadow_mask", "highlight_mask")
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FUNCTION = 'shadow_and_highlight_mask'
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CATEGORY = '😺dzNodes/LayerMask'
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def shadow_and_highlight_mask(self, image,
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shadow_level_offset, shadow_range,
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highlight_level_offset, highlight_range,
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mask=None
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):
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ret_shadow_masks = []
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ret_highlight_masks = []
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input_images = []
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input_masks = []
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for i in image:
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input_images.append(torch.unsqueeze(i, 0))
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m = tensor2pil(i)
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if m.mode == 'RGBA':
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input_masks.append(m.split()[-1])
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else:
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input_masks.append(Image.new('L', size=m.size, color='white'))
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if mask is not None:
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if mask.dim() == 2:
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mask = torch.unsqueeze(mask, 0)
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input_masks = []
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for m in mask:
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input_masks.append(tensor2pil(torch.unsqueeze(m, 0)).convert('L'))
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max_batch = max(len(input_images), len(input_masks))
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for i in range(max_batch):
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_image = input_images[i] if i < len(input_images) else input_images[-1]
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_image = tensor2pil(_image).convert('RGB')
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_mask = input_masks[i] if i < len(input_masks) else input_masks[-1]
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avg_gray = get_gray_average(_image, _mask)
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shadow_level, highlight_level = calculate_shadow_highlight_level(avg_gray)
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shadow_low_threshold = (shadow_level + shadow_level_offset) / 100 + shadow_range / 2
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shadow_low_threshold = norm_value(shadow_low_threshold)
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shadow_high_threshold = (shadow_level + shadow_level_offset) / 100 - shadow_range / 2
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shadow_high_threshold = norm_value(shadow_high_threshold)
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_shadow_mask = luminance_keyer(_image, shadow_low_threshold, shadow_high_threshold)
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highlight_low_threshold = (highlight_level + highlight_level_offset) / 100 - highlight_range / 2
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highlight_low_threshold = norm_value(highlight_low_threshold)
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highlight_high_threshold = (highlight_level + highlight_level_offset) / 100 + highlight_range / 2
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highlight_high_threshold = norm_value(highlight_high_threshold)
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_highlight_mask = luminance_keyer(_image, highlight_low_threshold, highlight_high_threshold)
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black = Image.new('L', size=_image.size, color='black')
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_mask = ImageChops.invert(_mask)
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_shadow_mask.paste(black, mask=_mask)
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_highlight_mask.paste(black, mask=_mask)
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ret_shadow_masks.append(image2mask(_shadow_mask))
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ret_highlight_masks.append(image2mask(_highlight_mask))
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log(f"{self.NODE_NAME} Processed {len(ret_shadow_masks)} image(s).", message_type='finish')
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return (torch.cat(ret_shadow_masks, dim=0),torch.cat(ret_highlight_masks, dim=0),)
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class LS_ShadowAndHighlightMaskV2:
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def __init__(self):
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self.NODE_NAME = 'Shadow Highlight Mask V2'
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@classmethod
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def INPUT_TYPES(self):
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return {
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"required": {
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"image": ("IMAGE", ),
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"shadow_level_offset": ("INT", {"default": 0, "min": -99, "max": 99, "step": 1}),
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"shadow_range": ("FLOAT", {"default": 0.25, "min": 0.01, "max": 0.99, "step": 0.01}),
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"highlight_level_offset": ("INT", {"default": 0, "min": -99, "max": 99, "step": 1}),
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"highlight_range": ("FLOAT", {"default": 0.25, "min": 0.01, "max": 0.99, "step": 0.01}),
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},
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"optional": {
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"mask": ("MASK",), #
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}
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}
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RETURN_TYPES = ("MASK", "MASK")
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RETURN_NAMES = ("shadow_mask", "highlight_mask")
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FUNCTION = 'shadow_and_highlight_mask_v2'
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CATEGORY = '😺dzNodes/LayerMask'
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def shadow_and_highlight_mask_v2(self, image,
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shadow_level_offset, shadow_range,
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highlight_level_offset, highlight_range,
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mask=None
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):
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ret_shadow_masks = []
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ret_highlight_masks = []
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input_images = []
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input_masks = []
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for i in image:
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input_images.append(torch.unsqueeze(i, 0))
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m = tensor2pil(i)
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if m.mode == 'RGBA':
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input_masks.append(m.split()[-1])
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else:
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input_masks.append(Image.new('L', size=m.size, color='white'))
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if mask is not None:
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if mask.dim() == 2:
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mask = torch.unsqueeze(mask, 0)
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input_masks = []
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for m in mask:
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input_masks.append(tensor2pil(torch.unsqueeze(m, 0)).convert('L'))
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max_batch = max(len(input_images), len(input_masks))
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for i in range(max_batch):
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_image = input_images[i] if i < len(input_images) else input_images[-1]
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_image = tensor2pil(_image).convert('RGB')
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_mask = input_masks[i] if i < len(input_masks) else input_masks[-1]
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avg_gray = get_gray_average(_image, _mask)
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shadow_level, highlight_level = calculate_shadow_highlight_level(avg_gray)
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shadow_low_threshold = (shadow_level + shadow_level_offset) / 100 + shadow_range / 2
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shadow_low_threshold = norm_value(shadow_low_threshold)
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shadow_high_threshold = (shadow_level + shadow_level_offset) / 100 - shadow_range / 2
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shadow_high_threshold = norm_value(shadow_high_threshold)
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_shadow_mask = luminance_keyer(_image, shadow_low_threshold, shadow_high_threshold)
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highlight_low_threshold = (highlight_level + highlight_level_offset) / 100 - highlight_range / 2
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highlight_low_threshold = norm_value(highlight_low_threshold)
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highlight_high_threshold = (highlight_level + highlight_level_offset) / 100 + highlight_range / 2
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highlight_high_threshold = norm_value(highlight_high_threshold)
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_highlight_mask = luminance_keyer(_image, highlight_low_threshold, highlight_high_threshold)
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black = Image.new('L', size=_image.size, color='black')
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_mask = ImageChops.invert(_mask)
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_shadow_mask.paste(black, mask=_mask)
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_highlight_mask.paste(black, mask=_mask)
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ret_shadow_masks.append(image2mask(_shadow_mask))
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ret_highlight_masks.append(image2mask(_highlight_mask))
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log(f"{self.NODE_NAME} Processed {len(ret_shadow_masks)} image(s).", message_type='finish')
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return (torch.cat(ret_shadow_masks, dim=0),torch.cat(ret_highlight_masks, dim=0),)
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NODE_CLASS_MAPPINGS = {
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"LayerMask: Shadow & Highlight Mask": ShadowAndHighlightMask,
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"LayerMask: ShadowHighlightMaskV2": LS_ShadowAndHighlightMaskV2
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"LayerMask: Shadow & Highlight Mask": "LayerMask: Shadow & Highlight Mask",
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"LayerMask: ShadowHighlightMaskV2": "LayerMask: Shadow Highlight Mask V2"
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} |