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chore: refactor and add different color distance methods selectable

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Hugh Bord
2025-08-27 12:31:47 +08:00
parent 06883d8a5f
commit 53d5a06455
2 changed files with 114 additions and 76 deletions
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README.md
+14 -12
View File
@@ -13,10 +13,11 @@ An example workflow is provided in `workflows/mircify_example_workflow.json`. At
## Features
- **Universal Image Support**: Works with any image dimensions
- **Color Transfer Integration**: Uses KMeans clustering with Manhattan distance for accurate color mapping
- **Color Transfer Integration**: Uses KMeans clustering with multiple distance methods for accurate color mapping
- **Block Processing**: Configurable block sizes (default 8x15 pixels)
- **Half Block Mode**: Option for 8x7.5 pixel blocks for finer resolution
- **Multiple Color Methods**: Choose between dominant, average, or median color extraction
- **Dominant Color Extraction**: Uses k-means clustering to find the most prominent color in each block
- **Multiple Distance Methods**: Choose between weighted manhattan, manhattan, euclidean, and perceptual weighted distance calculations
- **99-Color IRC Palette**: Maps colors to extended IRC color palette
- **16-Color Compatibility Mode**: Option to use traditional 16-color IRC palette
- **Dual Output**: Returns both processed image and mIRC-formatted text
@@ -52,13 +53,13 @@ Converts images into IRC art with both visual and text output.
- **block_height**: Height of each block in pixels (default: 15)
- **half_block_mode**: Enable half-height blocks (8x7.5 instead of 8x15)
- **use_16_colors**: Use only first 16 IRC colors for compatibility (default: False)
- **color_method**: Method for color selection (dominant/average/median)
- **distance_method**: Color distance calculation method (weighted_manhattan/manhattan/euclidean/perceptual_weighted)
**Outputs:**
- **processed_image**: Visual representation of the IRC art conversion
- **irc_text**: mIRC-formatted text with color codes ready for IRC clients
### IRC Text Saver
### IRC Text Saver (still not working too good with color matching)
**Category**: `text/output`
Saves IRC-formatted text to files for easy copying.
@@ -78,10 +79,11 @@ Saves IRC-formatted text to files for easy copying.
- Connect the `irc_text` output to "IRC Text Saver"
- Run the workflow
2. **Color Methods:**
- **dominant**: Uses k-means clustering to find the most prominent color in each block
- **average**: Calculates the mean RGB values for the block
- **median**: Uses the median RGB values for better noise resistance
2. **Distance Methods:**
- **weighted_manhattan**: Manhattan distance with perceptual weights (Green=1.0, Red=0.7, Blue=0.5) for better natural color matching
- **manhattan**: Standard Manhattan distance (L1 norm) - fastest calculation
- **euclidean**: Standard Euclidean distance (L2 norm) - geometric accuracy
- **perceptual_weighted**: Euclidean distance with CIE luminance weights for perceived brightness matching
3. **Block Modes:**
- **Full blocks (8x15)**: Standard IRC character block size
@@ -100,12 +102,12 @@ Saves IRC-formatted text to files for easy copying.
The node uses a two-stage approach:
1. **Color Transfer Phase**: Applies KMeans clustering with Manhattan distance to map the entire image to the 99-color IRC palette
2. **Block Processing Phase**: Divides the color-corrected image into blocks and generates the final IRC art output
1. **Color Transfer Phase**: Applies KMeans clustering with configurable distance methods to map the entire image to the 99-color IRC palette
2. **Block Processing Phase**: Divides the color-corrected image into blocks using dominant color extraction and generates the final IRC art output
## IRC Color Palette
The node uses an extended 99-color IRC palette built in.
The node uses an extended 99-color IRC palette built in. It also can support just 16 colors.
For more information about how IRC uses colors refer to these resources:
@@ -124,7 +126,7 @@ For more information about how IRC uses colors refer to these resources:
## Credits
- Color transfer logic adapted from [ComfyUI-Color_Transfer](https://github.com/45uee/ComfyUI-Color_Transfer) by 45uee
- Uses KMeans clustering and Manhattan distance for optimal color mapping
- Uses KMeans clustering with modified multiple distance methods for optimal color mapping
## License
src/nodes.py
+100 -64
View File
@@ -7,9 +7,28 @@ from datetime import datetime
class IRCArtConverter:
CATEGORY = "image/conversion"
# 99-color IRC palette (class constant)
# 99-color IRC palette (class constant) - matches Go IRCColorPalette exactly
IRC_COLORS = [
(255, 255, 255), (0, 0, 0), (0, 0, 127), (0, 147, 0), (255, 0, 0), (127, 0, 0), (156, 0, 156), (252, 127, 0), (255, 255, 0), (0, 252, 0), (0, 147, 147), (0, 255, 255), (0, 0, 252), (255, 0, 255), (127, 127, 127), (210, 210, 210), (71, 0, 0), (71, 33, 0), (71, 71, 0), (50, 71, 0), (0, 71, 0), (0, 71, 44), (0, 71, 71), (0, 39, 71), (0, 0, 71), (46, 0, 71), (71, 0, 71), (71, 0, 42), (116, 0, 0), (116, 58, 0), (116, 116, 0), (81, 116, 0), (0, 116, 0), (0, 116, 73), (0, 116, 116), (0, 64, 116), (0, 0, 116), (75, 0, 116), (116, 0, 116), (116, 0, 69), (181, 0, 0), (181, 99, 0), (181, 181, 0), (125, 181, 0), (0, 181, 0), (0, 181, 113), (0, 181, 181), (0, 99, 181), (0, 0, 181), (117, 0, 181), (181, 0, 181), (181, 0, 107), (255, 0, 0), (255, 140, 0), (255, 255, 0), (178, 255, 0), (0, 255, 0), (0, 255, 160), (0, 255, 255), (0, 140, 255), (0, 0, 255), (165, 0, 255), (255, 0, 255), (255, 0, 152), (255, 89, 89), (255, 180, 89), (255, 255, 113), (207, 255, 96), (111, 255, 111), (101, 255, 201), (109, 255, 255), (89, 180, 255), (89, 89, 255), (196, 89, 255), (255, 102, 255), (255, 89, 188), (255, 156, 156), (255, 211, 156), (255, 255, 156), (226, 255, 156), (156, 255, 156), (156, 255, 219), (156, 255, 255), (156, 211, 255), (156, 156, 255), (220, 156, 255), (255, 156, 255), (255, 148, 211), (0, 0, 0), (19, 19, 19), (40, 40, 40), (54, 54, 54), (77, 77, 77), (101, 101, 101), (129, 129, 129), (159, 159, 159), (188, 188, 188), (226, 226, 226), (255, 255, 255)
(255, 255, 255), (0, 0, 0), (0, 0, 127), (0, 147, 0), (255, 0, 0),
(127, 0, 0), (156, 0, 156), (252, 127, 0), (255, 255, 0), (0, 252, 0),
(0, 147, 147), (0, 255, 255), (0, 0, 252), (255, 0, 255), (127, 127, 127),
(210, 210, 210), (71, 0, 0), (71, 33, 0), (71, 71, 0), (50, 71, 0),
(0, 71, 0), (0, 71, 44), (0, 71, 71), (0, 39, 71), (0, 0, 71),
(46, 0, 71), (71, 0, 71), (71, 0, 42), (116, 0, 0), (116, 58, 0),
(116, 116, 0), (81, 116, 0), (0, 116, 0), (0, 116, 73), (0, 116, 116),
(0, 64, 116), (0, 0, 116), (75, 0, 116), (116, 0, 116), (116, 0, 69),
(181, 0, 0), (181, 99, 0), (181, 181, 0), (125, 181, 0), (0, 181, 0),
(0, 181, 113), (0, 181, 181), (0, 99, 181), (0, 0, 181), (117, 0, 181),
(181, 0, 181), (181, 0, 107), (255, 0, 0), (255, 140, 0), (255, 255, 0),
(178, 255, 0), (0, 255, 0), (0, 255, 160), (0, 255, 255), (0, 140, 255),
(0, 0, 255), (165, 0, 255), (255, 0, 255), (255, 0, 152), (255, 89, 89),
(255, 180, 89), (255, 255, 113), (207, 255, 96), (111, 255, 111), (101, 255, 201),
(109, 255, 255), (89, 180, 255), (89, 89, 255), (196, 89, 255), (255, 102, 255),
(255, 89, 188), (255, 156, 156), (255, 211, 156), (255, 255, 156), (226, 255, 156),
(156, 255, 156), (156, 255, 219), (156, 255, 255), (156, 211, 255), (156, 156, 255),
(220, 156, 255), (255, 156, 255), (255, 148, 211), (0, 0, 0), (19, 19, 19),
(40, 40, 40), (54, 54, 54), (77, 77, 77), (101, 101, 101), (129, 129, 129),
(159, 159, 159), (188, 188, 188), (226, 226, 226), (255, 255, 255)
]
@classmethod
@@ -21,7 +40,7 @@ class IRCArtConverter:
"block_height": ("INT", {"default": 15, "min": 1, "max": 32}),
"half_block_mode": ("BOOLEAN", {"default": False}),
"use_16_colors": ("BOOLEAN", {"default": False}),
"color_method": (["dominant", "average", "median"],)
"distance_method": (["weighted_manhattan", "manhattan", "euclidean", "perceptual_weighted"], {"default": "weighted_manhattan"}),
}
}
@@ -29,12 +48,38 @@ class IRCArtConverter:
RETURN_NAMES = ("processed_image", "irc_text")
FUNCTION = "convert_to_irc_art"
def manhattan_distance(self, detected_color, target_colors):
"""Manhattan distance function for color matching."""
return np.sum(np.abs(detected_color - target_colors), axis=1)
def calculate_color_distance(self, detected_color, target_colors, method):
"""Calculate color distance using various methods."""
if method == "manhattan":
# Standard Manhattan distance (L1 norm)
return np.sum(np.abs(detected_color - target_colors), axis=1)
elif method == "weighted_manhattan":
# Weighted Manhattan distance for better perceptual matching
# Weights based on human visual sensitivity: [R, G, B]
weights = np.array([0.7, 1.0, 0.5])
diff = np.abs(detected_color - target_colors)
return np.sum(diff * weights, axis=1)
elif method == "euclidean":
# Standard Euclidean distance (L2 norm)
return np.sqrt(np.sum((detected_color - target_colors) ** 2, axis=1))
elif method == "perceptual_weighted":
# Perceptual weighted Euclidean distance
# Based on CIE recommendations for RGB weights
weights = np.array([0.299, 0.587, 0.114]) # Luminance weights
diff = (detected_color - target_colors) ** 2
return np.sqrt(np.sum(diff * weights, axis=1))
else:
# Fallback to weighted manhattan
weights = np.array([0.7, 1.0, 0.5])
diff = np.abs(detected_color - target_colors)
return np.sum(diff * weights, axis=1)
def apply_color_transfer(self, image, use_16_colors=False):
"""Apply color transfer using KMeans clustering and Manhattan distance matching."""
def apply_color_transfer(self, image, use_16_colors=False, distance_method="weighted_manhattan"):
"""Apply color transfer using KMeans clustering and configurable distance matching."""
# Convert image to numpy array
img_array = (image * 255.0).cpu().numpy().astype(np.uint8)
img_flat = img_array.reshape((-1, 3))
@@ -53,7 +98,7 @@ class IRCArtConverter:
closest_colors = []
for color in detected_colors:
distances = self.manhattan_distance(color, target_colors_array)
distances = self.calculate_color_distance(color, target_colors_array, distance_method)
closest_colors.append(target_colors_array[np.argmin(distances)])
closest_colors = np.array(closest_colors)
@@ -64,54 +109,45 @@ class IRCArtConverter:
# Convert back to torch tensor
return torch.from_numpy(processed_image.astype(np.float32) / 255.0)
def get_block_color(self, block, method):
"""Extract representative color from a block using specified method."""
if method == "average":
def get_block_color(self, block):
"""Extract dominant color from a block using k-means clustering."""
# Use k-means to find dominant color
block_flat = block.reshape(-1, 3).cpu().numpy()
if len(block_flat) < 2:
return tuple(block_flat[0] * 255)
# Check for color variance
color_std = np.std(block_flat, axis=0)
if np.max(color_std) < 0.01: # Very uniform color
return tuple(np.mean(block_flat, axis=0) * 255)
# Find unique colors to determine appropriate cluster count
unique_colors = np.unique(block_flat, axis=0)
n_clusters = min(3, len(unique_colors))
if n_clusters == 1:
return tuple(unique_colors[0] * 255)
# Use k-means with appropriate cluster count
try:
import warnings
with warnings.catch_warnings():
warnings.simplefilter("ignore")
kmeans = KMeans(n_clusters=n_clusters, random_state=42, n_init=10)
kmeans.fit(block_flat)
# Find the cluster with most points
labels = kmeans.labels_
unique, counts = np.unique(labels, return_counts=True)
dominant_cluster = unique[np.argmax(counts)]
dominant_color = kmeans.cluster_centers_[dominant_cluster]
return tuple(dominant_color * 255)
except:
# Fallback to average if k-means fails
return tuple(block.mean(dim=(0, 1)).cpu().numpy() * 255)
elif method == "median":
block_flat = block.reshape(-1, 3)
median_vals = torch.median(block_flat, dim=0)[0]
return tuple(median_vals.cpu().numpy() * 255)
elif method == "dominant":
# Use k-means to find dominant color
block_flat = block.reshape(-1, 3).cpu().numpy()
if len(block_flat) < 2:
return tuple(block_flat[0] * 255)
# Check for color variance
color_std = np.std(block_flat, axis=0)
if np.max(color_std) < 0.01: # Very uniform color
return tuple(np.mean(block_flat, axis=0) * 255)
# Find unique colors to determine appropriate cluster count
unique_colors = np.unique(block_flat, axis=0)
n_clusters = min(3, len(unique_colors))
if n_clusters == 1:
return tuple(unique_colors[0] * 255)
# Use k-means with appropriate cluster count
try:
import warnings
with warnings.catch_warnings():
warnings.simplefilter("ignore")
kmeans = KMeans(n_clusters=n_clusters, random_state=42, n_init=10)
kmeans.fit(block_flat)
# Find the cluster with most points
labels = kmeans.labels_
unique, counts = np.unique(labels, return_counts=True)
dominant_cluster = unique[np.argmax(counts)]
dominant_color = kmeans.cluster_centers_[dominant_cluster]
return tuple(dominant_color * 255)
except:
# Fallback to average if k-means fails
return tuple(block.mean(dim=(0, 1)).cpu().numpy() * 255)
def find_irc_color_index(self, block_color, use_16_colors):
def find_irc_color_index(self, block_color, use_16_colors, distance_method="weighted_manhattan"):
"""Find the IRC color index for a given block color."""
# Get representative color for this block
block_color_rounded = tuple(np.round(block_color).astype(int))
@@ -131,7 +167,7 @@ class IRCArtConverter:
else:
target_colors_array = np.array(self.IRC_COLORS)
distances = self.manhattan_distance(block_color_array, target_colors_array)
distances = self.calculate_color_distance(block_color_array, target_colors_array, distance_method)
color_idx = np.argmin(distances)
return color_idx
@@ -163,7 +199,7 @@ class IRCArtConverter:
return line_text
def process_block(self, color_transferred_img, x, y, block_width, block_height,
actual_block_height, width, height, color_method, use_16_colors):
actual_block_height, width, height, use_16_colors, distance_method):
"""Process a single block and return its color index."""
# Calculate block boundaries
start_x = x * block_width
@@ -178,10 +214,10 @@ class IRCArtConverter:
if block.numel() == 0:
return 0, start_y, end_y
else:
block_color = self.get_block_color(block, color_method)
return self.find_irc_color_index(block_color, use_16_colors), start_y, end_y
block_color = self.get_block_color(block)
return self.find_irc_color_index(block_color, use_16_colors, distance_method), start_y, end_y
def convert_to_irc_art(self, image, block_width, block_height, half_block_mode, use_16_colors, color_method):
def convert_to_irc_art(self, image, block_width, block_height, half_block_mode, use_16_colors, distance_method):
# Input validation
batch_size, height, width, channels = image.shape
@@ -192,7 +228,7 @@ class IRCArtConverter:
img = image[0] # Shape: (height, width, 3)
# Apply color transfer to map image to IRC palette
color_transferred_img = self.apply_color_transfer(img, use_16_colors)
color_transferred_img = self.apply_color_transfer(img, use_16_colors, distance_method)
# Calculate number of blocks
blocks_x = width // block_width
@@ -218,7 +254,7 @@ class IRCArtConverter:
# Process first half-block
color_idx, start_y, end_y = self.process_block(
color_transferred_img, x, y, block_width, block_height,
actual_block_height, width, height, color_method, use_16_colors
actual_block_height, width, height, use_16_colors, distance_method
)
# Apply color to image and store for text
@@ -231,7 +267,7 @@ class IRCArtConverter:
if y + 1 < blocks_y:
color_idx2, start_y2, end_y2 = self.process_block(
color_transferred_img, x, y + 1, block_width, block_height,
actual_block_height, width, height, color_method, use_16_colors
actual_block_height, width, height, use_16_colors, distance_method
)
# Apply color to second block
@@ -248,7 +284,7 @@ class IRCArtConverter:
for x in range(blocks_x):
color_idx, start_y, end_y = self.process_block(
color_transferred_img, x, y, block_width, block_height,
actual_block_height, width, height, color_method, use_16_colors
actual_block_height, width, height, use_16_colors, distance_method
)
# Apply color to image and store for text