import sys
import colorsys
from PIL import Image, ImageDraw
import webcolors
from colormath.color_objects import sRGBColor
from datetime import datetime
from tkinter import Tk, filedialog
from tqdm import tqdm
def calculate_color_difference(color1, color2):
"""
Calculates the perceptual difference between two color objects
using the Euclidean distance between their RGB value tuples.
This works by:
1. Extracting the RGB value tuples for each color object
2. Calculating the difference between the R, G, and B values
3. Squaring the differences
4. Summing the squared differences
5. Taking the square root of the sum
This gives a measure of the total geometric distance between the
two colors in RGB space, which corresponds to their visual difference.
Args:
color1: First color object
color2: Second color object
Returns:
Float value representing the perceptual distance between the colors
"""
r1, g1, b1 = color1.get_value_tuple()
r2, g2, b2 = color2.get_value_tuple()
r_diff = r1 - r2
g_diff = g1 - g2
b_diff = b1 - b2
return ((r_diff ** 2) + (g_diff ** 2) + (b_diff ** 2)) ** 0.5
def generate_color_palette(colors, palette_size):
# Generates a color palette image from a list of RGB color tuples.
#
# Args:
# colors: List of RGB color tuples.
# palette_size: Number of colors to include in the palette.
#
# Returns:
# Image object containing the generated color palette.
swatch_size = 100
palette_width = palette_size * swatch_size
palette_height = swatch_size
palette_image = Image.new("RGB", (palette_width, palette_height))
draw = ImageDraw.Draw(palette_image)
x = 0
for color in colors:
draw.rectangle([(x, 0), (x + swatch_size, palette_height)], fill=color, outline="black")
x += swatch_size
return palette_image
# Extracts detailed color information from the given image file
# and saves it to a text file and color palette image.
#
# Args:
# image_path: Path to image file to extract colors from.
#
# The function opens the image, loops through pixels to collect
# color data, calculates averages and distributions, finds closest
# CSS color names, generates a color palette image, and saves the
# output to a text file and image file.
def extract_color_information(image_path):
"""Extracts detailed color information from the given image file and saves it to a text file and color palette image.
Args:
image_path: Path to image file to extract colors from.
The function opens the image, loops through pixels to collect color data, calculates averages and distributions, finds closest CSS color names, generates a color palette image, and saves the output to a text file and image file.
"""
try:
# Open and convert the image
image = Image.open(image_path).convert("RGB")
width, height = image.size
# Extract color information
colors, color_counts = set(), {}
for x in tqdm(range(width), desc="Extracting colors", unit="column"):
for y in range(height):
r, g, b = image.getpixel((x, y))
color = (r, g, b)
colors.add(color)
color_counts[color] = color_counts.get(color, 0) + 1
# Calculate average color and convert to HSV
average_color = image.resize((1, 1)).getpixel((0, 0))
average_color_hsv = colorsys.rgb_to_hsv(*average_color)
# Extract dominant color
dominant_color = max(color_counts, key=color_counts.get)
# Calculate color distribution
total_pixels = width * height
color_distribution = {color: count / total_pixels * 100 for color, count in color_counts.items()}
# Find color names and HEX codes
# Loops through each extracted color from the image
# Uses webcolors module to find the closest CSS color name and HEX code
# Appends the name and code to separate lists
# Catches any errors and skips invalid colors
color_names, color_hex_codes = [], []
for color in tqdm(colors, desc="Finding color names", unit="color"):
try:
name = webcolors.rgb_to_name(color)
hex_code = webcolors.rgb_to_hex(color)
color_names.append(name)
color_hex_codes.append(hex_code)
except ValueError:
pass
# Find similar colors
# Looks up the closest predefined CSS color for each extracted color
# and stores the result in a dictionary mapping colors to their
# closest predefined CSS color name
predefined_colors = webcolors.CSS3_NAMES_TO_HEX.values()
similar_colors = {min(predefined_colors, key=lambda x: calculate_color_difference(
sRGBColor(color[0] / 255, color[1] / 255, color[2] / 255),
sRGBColor.new_from_rgb_hex(x)
)) for color in tqdm(colors, desc="Finding similar colors", unit="color")}
# Save color information to a text file
output_file = f"{image_path.rsplit('.', 1)[0]}.txt"
"""Writes extracted color information to output text file.
The output file path is generated from the input image path.
The file contains the following information:
- Image path
- Image dimensions
- Number of unique colors
- Average color in RGB and HSV
- Dominant color
- Color distribution percentages
- Color names and hex codes
- Similar colors mapped to closest CSS color names
"""
with open(output_file, "w") as f:
f.write(f"Image: {image_path}\n")
f.write(f"Size: {width}x{height}\n")
f.write(f"Number of unique colors: {len(colors)}\n")
f.write(f"Average color (RGB): {average_color}\n")
f.write(f"Average color (HSV): {average_color_hsv}\n")
f.write(f"Dominant color: {dominant_color}\n")
f.write("Color Distribution:\n")
for color, percentage in color_distribution.items():
f.write(f"{color}: {percentage:.2f}%\n")
f.write("Color Names and HEX Codes:\n")
for name, hex_code in zip(color_names, color_hex_codes):
f.write(f"{name} (HEX: {hex_code})\n")
f.write("Similar Colors:\n")
for color in similar_colors:
f.write(color + "\n")
# Generate color palette image
# Saves the generated color palette image to disk and prints completion messages.
# The color palette image path is generated from the input image path with a _palette suffix.
# Completion messages indicate where the color information text file and palette image were saved.
palette_image = generate_color_palette(list(colors), palette_size=10)
palette_image_path = f"{image_path.rsplit('.', 1)[0]}_palette.png"
palette_image.save(palette_image_path)
# Print completion message
print(f"Color information saved to {output_file}")
print(f"Color palette image saved to {palette_image_path}")
except Exception as e:
# Logs error details to a log file when an exception occurs.
#
# The current timestamp, error message, and stack trace are written
# to the error.log file. This allows errors to be tracked over time
# for debugging.
timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
error_message = str(e)
with open("error.log", "a") as error_file:
error_file.write(f"[{timestamp}] An error occurred: {error_message}\n")
def prompt_for_image():
# Prompts the user to select an image file using a file dialog,
# then extracts color information from the selected image.
#
# Opens a file dialog for the user to select an image file. The
# allowed file types are PNG, JPG, and JPEG images.
#
# If an image is selected, calls extract_color_information() on
# the path to extract colors.
#
# If no image is selected, prints a message indicating no image
# was chosen.
#
# No return value. Prompts the user and processes the selected
# image file if one is chosen.
Tk().withdraw()
image_path = filedialog.askopenfilename(title="Select an image file",
filetypes=(("Image files", "*.png *.jpg *.jpeg"), ("All files", "*.*")))
if image_path:
extract_color_information(image_path)
else:
print("No image file selected.")
if __name__ == "__main__":
# If run as a script, check command line arguments for an image path.
# If provided, extract colors from that image.
# Otherwise, prompt the user to select an image file with a dialog.
if len(sys.argv) > 1:
image_path = sys.argv[1]
extract_color_information(image_path)
else:
prompt_for_image()
