LayerColor: Color of Shadow & Highlight¶
Documentation¶
- Class name:
LayerColor: Color of Shadow & Highlight - Category:
😺dzNodes/LayerColor - Output node:
False
This node is designed to adjust the color properties of shadows and highlights within an image. It allows for the fine-tuning of brightness, saturation, and hue for both shadows and highlights, enabling users to achieve a desired visual effect or correct color imbalances.
Input types¶
Required¶
image- The input image to be processed for shadow and highlight color correction.
- Comfy dtype:
IMAGE - Python dtype:
Image
shadow_brightness- Specifies the brightness level for shadows, affecting the overall darkness or lightness of shadow areas in the image.
- Comfy dtype:
FLOAT - Python dtype:
float
shadow_saturation- Determines the saturation level for shadows, influencing the intensity of colors in shadowed regions.
- Comfy dtype:
FLOAT - Python dtype:
float
shadow_hue- Adjusts the hue for shadows, allowing for the alteration of color tones in shadow areas.
- Comfy dtype:
INT - Python dtype:
int
shadow_level_offset- Offsets the level of shadows, modifying the threshold at which shadows are defined.
- Comfy dtype:
INT - Python dtype:
int
shadow_range- Defines the range of shadow levels, affecting the breadth of tones considered as shadows.
- Comfy dtype:
FLOAT - Python dtype:
float
highlight_brightness- Defines the brightness level for highlights, impacting the luminosity of highlighted areas in the image.
- Comfy dtype:
FLOAT - Python dtype:
float
highlight_saturation- Sets the saturation level for highlights, affecting the vibrancy of colors in highlighted regions.
- Comfy dtype:
FLOAT - Python dtype:
float
highlight_hue- Modifies the hue for highlights, enabling changes in color tones within highlighted areas.
- Comfy dtype:
INT - Python dtype:
int
highlight_level_offset- Offsets the level of highlights, modifying the threshold at which highlights are defined.
- Comfy dtype:
INT - Python dtype:
int
highlight_range- Defines the range of highlight levels, affecting the breadth of tones considered as highlights.
- Comfy dtype:
FLOAT - Python dtype:
float
Optional¶
mask- An optional mask to specify areas of the image to apply the shadow and highlight adjustments.
- Comfy dtype:
MASK - Python dtype:
Mask
Output types¶
image- Comfy dtype:
IMAGE - The output image after applying shadow and highlight color corrections.
- Python dtype:
Image
- Comfy dtype:
Usage tips¶
- Infra type:
CPU - Common nodes: unknown
Source code¶
class ColorCorrectShadowAndHighlight:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(self):
return {
"required": {
"image": ("IMAGE", ),
"shadow_brightness": ("FLOAT", {"default": 1, "min": 0.0, "max": 3, "step": 0.01}),
"shadow_saturation": ("FLOAT", {"default": 1, "min": 0.0, "max": 3, "step": 0.01}),
"shadow_hue": ("INT", {"default": 0, "min": -255, "max": 255, "step": 1}),
"shadow_level_offset": ("INT", {"default": 0, "min": -99, "max": 99, "step": 1}),
"shadow_range": ("FLOAT", {"default": 0.25, "min": 0.01, "max": 0.99, "step": 0.01}),
"highlight_brightness": ("FLOAT", {"default": 1, "min": 0.0, "max": 3, "step": 0.01}),
"highlight_saturation": ("FLOAT", {"default": 1, "min": 0.0, "max": 3, "step": 0.01}),
"highlight_hue": ("INT", {"default": 0, "min": -255, "max": 255, "step": 1}),
"highlight_level_offset": ("INT", {"default": 0, "min": -99, "max": 99, "step": 1}),
"highlight_range": ("FLOAT", {"default": 0.25, "min": 0.01, "max": 0.99, "step": 0.01}),
},
"optional": {
"mask": ("MASK",), #
}
}
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("image",)
FUNCTION = 'color_shadow_and_highlight'
CATEGORY = '😺dzNodes/LayerColor'
def color_shadow_and_highlight(self, image,
shadow_brightness, shadow_saturation,
shadow_level_offset, shadow_range, shadow_hue,
highlight_brightness, highlight_saturation, highlight_hue,
highlight_level_offset, highlight_range,
mask=None
):
ret_images = []
input_images = []
input_masks = []
for i in image:
input_images.append(torch.unsqueeze(i, 0))
m = tensor2pil(i)
if m.mode == 'RGBA':
input_masks.append(m.split()[-1])
else:
input_masks.append(Image.new('L', size=m.size, color='white'))
if mask is not None:
if mask.dim() == 2:
mask = torch.unsqueeze(mask, 0)
input_masks = []
for m in mask:
input_masks.append(tensor2pil(torch.unsqueeze(m, 0)).convert('L'))
max_batch = max(len(input_images), len(input_masks))
for i in range(max_batch):
_image = input_images[i] if i < len(input_images) else input_images[-1]
_image = tensor2pil(_image).convert('RGB')
_mask = input_masks[i] if i < len(input_masks) else input_masks[-1]
avg_gray = get_gray_average(_image, _mask)
shadow_level, highlight_level = calculate_shadow_highlight_level(avg_gray)
_canvas = _image.copy()
if shadow_saturation !=1 or shadow_brightness !=1 or shadow_hue:
shadow_low_threshold = (shadow_level + shadow_level_offset) / 100 + shadow_range / 2
shadow_low_threshold = norm_value(shadow_low_threshold)
shadow_high_threshold = (shadow_level + shadow_level_offset) / 100 - shadow_range / 2
shadow_high_threshold = norm_value(shadow_high_threshold)
_shadow_mask = luminance_keyer(_image, shadow_low_threshold, shadow_high_threshold)
_shadow = _image.copy()
if shadow_brightness != 1:
brightness_image = ImageEnhance.Brightness(_shadow)
_shadow = brightness_image.enhance(factor=shadow_brightness)
if shadow_saturation != 1:
color_image = ImageEnhance.Color(_shadow)
_shadow = color_image.enhance(factor=shadow_saturation)
if shadow_hue:
_h, _s, _v = _shadow.convert('HSV').split()
_h = image_hue_offset(_h, shadow_hue)
_shadow = image_channel_merge((_h, _s, _v), 'HSV')
_canvas.paste(_shadow, mask=gaussian_blur(_shadow_mask,(_shadow_mask.width + _shadow_mask.height)//800))
_canvas.paste(_image, mask=ImageChops.invert(_mask))
if highlight_saturation != 1 or highlight_brightness != 1 or highlight_hue:
highlight_low_threshold = (highlight_level + highlight_level_offset) / 100 - highlight_range / 2
highlight_low_threshold = norm_value(highlight_low_threshold)
highlight_high_threshold = (highlight_level + highlight_level_offset) / 100 + highlight_range / 2
highlight_high_threshold = norm_value(highlight_high_threshold)
_highlight_mask = luminance_keyer(_image, highlight_low_threshold, highlight_high_threshold)
_highlight = _image.copy()
if highlight_brightness != 1:
brightness_image = ImageEnhance.Brightness(_highlight)
_highlight = brightness_image.enhance(factor=highlight_brightness)
if highlight_saturation != 1:
color_image = ImageEnhance.Color(_highlight)
_highlight = color_image.enhance(factor=highlight_saturation)
if highlight_hue:
_h, _s, _v = _highlight.convert('HSV').split()
_h = image_hue_offset(_h, highlight_hue)
_highlight = image_channel_merge((_h, _s, _v), 'HSV')
_canvas.paste(_highlight, mask=gaussian_blur(_highlight_mask, (_highlight_mask.width + _highlight_mask.height)//800))
_canvas.paste(_image, mask=ImageChops.invert(_mask))
ret_images.append(pil2tensor(_canvas))
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
return (torch.cat(ret_images, dim=0),)