image.py

# pip install opencv-python numpy

import cv2
import numpy as np
import sys

def color_percentage(image_path, target_color):
    # Read the image
    image = cv2.imread(image_path, cv2.IMREAD_UNCHANGED)  # Use cv2.IMREAD_UNCHANGED to keep the alpha channel

    # Convert BGR to RGBA (if necessary)
    if image.shape[2] == 3:
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGBA)

    # Convert image to numpy array
    image_array = np.array(image)

    # Flatten the array
    flattened_array = image_array.reshape(-1, 4)

    # Count pixels matching the target color
    matching_pixels = (flattened_array[:, :3] == target_color).all(axis=1).sum()

    # Count transparent pixels
    transparent_pixels = (flattened_array[:, 3] == 0).sum()

    # Calculate total pixels excluding transparent ones
    total_pixels = flattened_array.shape[0] - transparent_pixels

    # Calculate percentage
    percentage = (matching_pixels / total_pixels) * 100 if total_pixels > 0 else 0

    return [percentage, matching_pixels, total_pixels, transparent_pixels]

def pixels_to_inches(area_pixels, ppi):
    # Convert area from square pixels to square inches
    area_inches = area_pixels / (ppi ** 2)
    return area_inches

def get_image_path():
    if len(sys.argv) > 1:
        return sys.argv[1]
    else:
        print("Please provide the image path as a command line argument.")
        sys.exit()

image_path = get_image_path()
target_color = [255, 255, 255]

image = cv2.imread(image_path)

# Get the height and width of the image
height, width, _ = image.shape

# Print the height and width
print(f"Image Size: Width: {width}, Height: {height}")

percentage, matching_pixels, total_pixels, transparent_pixels = color_percentage(image_path, target_color)

print(f"Matching pixels: {matching_pixels}, Total pixels: {total_pixels}, Transparent pixels: {transparent_pixels}")
print(f"Target color percentage (excl transparent px): {percentage}%")