118 lines
4.3 KiB
Python
118 lines
4.3 KiB
Python
import numpy as np
|
|
import torch
|
|
|
|
|
|
class ImageAutotone:
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"image": ("IMAGE", ),
|
|
"shadows": ("STRING", {
|
|
"default": "0,0,0",
|
|
"tooltip": "The color to use for the shadows in the image. This should be a comma-separated RGB value (e.g., '0,0,0' for black) or HEX string (e.g. '#000000')."
|
|
}),
|
|
"highlights": ("STRING", {
|
|
"default": "255,255,255",
|
|
"tooltip": "The color to use for the highlights in the image. This should be a comma-separated RGB value (e.g., '255,255,255' for white) or HEX string (e.g. '#FFFFFF')."
|
|
}),
|
|
"shadow_clip": ("FLOAT", {
|
|
"default": 0.001,
|
|
"min": 0.0,
|
|
"max": 1.0,
|
|
"step": 0.001,
|
|
"tooltip": "The percentage of pixels to clip from the shadows. This is a value between 0 and 1."
|
|
}),
|
|
"highlight_clip": ("FLOAT", {
|
|
"default": 0.001,
|
|
"min": 0.0,
|
|
"max": 1.0,
|
|
"step": 0.001,
|
|
"tooltip": "The percentage of pixels to clip from the highlights. This is a value between 0 and 1."
|
|
}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("IMAGE", )
|
|
FUNCTION = "op"
|
|
CATEGORY = "image"
|
|
DESCRIPTION = """Clip color channels independently to increase contrast and alter color cast. This is a reinterpretation of Photoshop's "Auto Tone" algorithm."""
|
|
|
|
# Thank you to Gerald Bakker for the following writeup on the algorithm:
|
|
# https://geraldbakker.nl/psnumbers/auto-options.html
|
|
|
|
def op(self, image, highlights, shadows, shadow_clip, highlight_clip):
|
|
|
|
def str_to_rgb(color_string):
|
|
"""Converts a color string to a tuple of RGB values"""
|
|
if color_string[0].isdigit():
|
|
return tuple(map(int, color_string.split(",")))
|
|
elif color_string.startswith("#"):
|
|
return bytes.fromhex(color_string[1:])
|
|
|
|
def calculate_adjustment_values(hist, total_pixels, clip_percent):
|
|
clip_threshold = total_pixels * clip_percent
|
|
cumulative_hist = hist.cumsum()
|
|
|
|
# Find the first and last indices where the cumulative histogram exceeds the clip thresholds
|
|
lower_bound_idx = np.where(cumulative_hist > clip_threshold)[0][0]
|
|
upper_bound_idx = np.where(cumulative_hist < (total_pixels - clip_threshold))[0][-1]
|
|
|
|
return lower_bound_idx, upper_bound_idx
|
|
|
|
shadows = np.array(str_to_rgb(shadows))
|
|
# midtones are only used in other algorithms
|
|
# midtones = str_to_rgb(self.Unprompted.parse_arg("midtones", "128,128,128"))
|
|
highlights = np.array(str_to_rgb(highlights))
|
|
|
|
total_images = image.shape[0]
|
|
out_images = []
|
|
|
|
for i in range(total_images):
|
|
# image is a 4d tensor array in the format of [B, H, W, C]
|
|
img_array = 255. * image[i].cpu().numpy()
|
|
# img_array = np.clip(this_img, 0, 255).astype(np.uint8)
|
|
|
|
# Process each channel (R, G, B) separately
|
|
for channel in range(3):
|
|
# Calculate the histogram of the current channel
|
|
hist, _ = np.histogram(img_array[:, :, channel].flatten(), bins=256, range=[0, 255])
|
|
|
|
# Total number of pixels
|
|
total_pixels = img_array.shape[0] * img_array.shape[1]
|
|
|
|
# Calculate the adjustment values based on clipping percentages
|
|
dark_value, light_value = calculate_adjustment_values(hist, total_pixels, shadow_clip)
|
|
_, upper_light_value = calculate_adjustment_values(hist, total_pixels, highlight_clip)
|
|
|
|
# Adjust light_value using upper_light_value for highlights
|
|
light_value = max(light_value, upper_light_value)
|
|
|
|
# Avoid division by zero
|
|
if light_value == dark_value:
|
|
continue
|
|
|
|
# Scale and clip the channel values
|
|
img_array[:, :, channel] = (img_array[:, :, channel] - dark_value) * (highlights[channel] - shadows[channel]) / (light_value - dark_value) + shadows[channel]
|
|
img_array[:, :, channel] = np.clip(img_array[:, :, channel], 0, 255)
|
|
|
|
img_array = np.clip(img_array, 0, 255).astype(np.uint8)
|
|
|
|
out_images.append(img_array)
|
|
|
|
restored_img_np = np.array(out_images).astype(np.float32) / 255.0
|
|
restored_img_tensor = torch.from_numpy(restored_img_np)
|
|
|
|
return (restored_img_tensor, )
|
|
|
|
|
|
NODE_CLASS_MAPPINGS = {
|
|
"ImageAutotone": ImageAutotone,
|
|
}
|
|
|
|
# A dictionary that contains the friendly/humanly readable titles for the nodes
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
"ImageAutotone": "Image Autotone",
|
|
}
|