import numpy as np from PIL import Image import cv2 def hex_to_rgb(hex_color): hex_color = hex_color.lstrip('#') return tuple(int(hex_color[i:i+2], 16) for i in (0, 2, 4)) def rgb_to_bgr(rgb_color): return rgb_color[::-1] def create_mask(image_np, color_to_replace, tolerance=20): # Convert hex colors to RGB and then to BGR color_to_replace_rgb = hex_to_rgb(color_to_replace) # replacement_color_rgb = hex_to_rgb(replacement_color) color_to_replace_bgr = rgb_to_bgr(color_to_replace_rgb) # replacement_color_bgr = rgb_to_bgr(replacement_color_rgb) # Calculate the lower and upper bounds for color tolerance in BGR lower_bound = np.array([max(c - tolerance, 0) for c in color_to_replace_bgr]) upper_bound = np.array([min(c + tolerance, 255) for c in color_to_replace_bgr]) # Convert the image to BGR format for OpenCV processing image_bgr = cv2.cvtColor(image_np, cv2.COLOR_RGB2BGR) # Create a mask for pixels within the tolerance range using cv2.inRange mask = cv2.inRange(image_bgr, lower_bound, upper_bound) # Replace color in the masked area # image_bgr[mask > 0] = replacement_color_bgr # Convert back to RGB # result_image_np = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB) # Save the result result_image = Image.fromarray(mask) result_image.save('./result.jpg') return result_image