4.9 KiB
4.9 KiB
Documentation
- Class name: AutoAdjust
- Category: 😺dzNodes/LayerColor
- Output node: False
- Repo Ref: https://github.com/chflame163/ComfyUI_LayerStyle
自动调整图片的亮度，对比度和白平衡。提供一些手动调整选项以弥补自动调整的不足。
Input types
Required
-
image
- 输入的图片。
- Comfy dtype: IMAGE
- Python dtype: torch.Tensor
-
strength
- 调整的强度。数值越大，与原图的差别越大。
- Comfy dtype: INT
- Python dtype: int
-
brightness
- 亮度手动调整。
- Comfy dtype: INT
- Python dtype: int
-
contrast
- 对比度手动调整。
- Comfy dtype: INT
- Python dtype: int
-
saturation
- 饱和度手动调整。
- Comfy dtype: INT
- Python dtype: int
-
red
- 红色通道手动调整。
- Comfy dtype: INT
- Python dtype: int
-
green
- 绿色通道手动调整。
- Comfy dtype: INT
- Python dtype: int
-
blue
- 蓝色通道手动调整。
- Comfy dtype: INT
- Python dtype: int
Output types
- image
- 输出的图片。
- Comfy dtype: IMAGE
- Python dtype: torch.Tensor
Usage tips
- Infra type: GPU
Source code
class AutoAdjust:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(self):
return {
"required": {
"image": ("IMAGE", ), #
"strength": ("INT", {"default": 100, "min": 0, "max": 100, "step": 1}),
"brightness": ("INT", {"default": 0, "min": -100, "max": 100, "step": 1}),
"contrast": ("INT", {"default": 0, "min": -100, "max": 100, "step": 1}),
"saturation": ("INT", {"default": 0, "min": -100, "max": 100, "step": 1}),
"red": ("INT", {"default": 0, "min": -100, "max": 100, "step": 1}),
"green": ("INT", {"default": 0, "min": -100, "max": 100, "step": 1}),
"blue": ("INT", {"default": 0, "min": -100, "max": 100, "step": 1}),
},
"optional": {
}
}
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("image",)
FUNCTION = 'auto_adjust'
CATEGORY = '😺dzNodes/LayerColor'
def auto_adjust(self, image, strength, brightness, contrast, saturation, red, green, blue):
if brightness < 0:
brightness_offset = brightness / 100 + 1
else:
brightness_offset = brightness / 50 + 1
if contrast < 0:
contrast_offset = contrast / 100 + 1
else:
contrast_offset = contrast / 50 + 1
if saturation < 0:
saturation_offset = saturation / 100 + 1
else:
saturation_offset = saturation / 50 + 1
red_gamma = self.balance_to_gamma(red)
green_gamma = self.balance_to_gamma(green)
blue_gamma = self.balance_to_gamma(blue)
l_images = []
l_masks = []
ret_images = []
for l in image:
l_images.append(torch.unsqueeze(l, 0))
m = tensor2pil(l)
if m.mode == 'RGBA':
l_masks.append(m.split()[-1])
else:
l_masks.append(Image.new('L', m.size, 'white'))
max_batch = max(len(l_images), len(l_masks))
for i in range(max_batch):
_image = l_images[i] if i < len(l_images) else l_images[-1]
_mask = l_masks[i] if i < len(l_masks) else l_masks[-1]
orig_image = tensor2pil(_image)
r, g, b, _ = image_channel_split(orig_image, mode = 'RGB')
r = normalize_gray(r)
g = normalize_gray(g)
b = normalize_gray(b)
if red:
r = gamma_trans(r, red_gamma).convert('L')
if green:
g = gamma_trans(g, green_gamma).convert('L')
if blue:
b = gamma_trans(b, blue_gamma).convert('L')
ret_image = image_channel_merge((r, g, b), 'RGB')
if brightness:
brightness_image = ImageEnhance.Brightness(ret_image)
ret_image = brightness_image.enhance(factor=brightness_offset)
if contrast:
contrast_image = ImageEnhance.Contrast(ret_image)
ret_image = contrast_image.enhance(factor=contrast_offset)
if saturation:
color_image = ImageEnhance.Color(ret_image)
ret_image = color_image.enhance(factor=saturation_offset)
ret_image = chop_image_v2(orig_image, ret_image, blend_mode="normal", opacity=strength)
if orig_image.mode == 'RGBA':
ret_image = RGB2RGBA(ret_image, orig_image.split()[-1])
ret_images.append(pil2tensor(ret_image))
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
return (torch.cat(ret_images, dim=0),)
def balance_to_gamma(self, balance:int) -> float:
return 0.00005 * balance * balance - 0.01 * balance + 1