blend-modes

import numpy as np from blend_modes import soft_light # Simulate loading images (typically done with Pillow or OpenCV) # Images must be float arrays (0.0-255.0) and have shape (H, W, 4) for RGBA. height, width = 100, 150 # Background image: a gradient bg_img = np.zeros((height, width, 4), dtype=float) bg_img[:, :, 0] = np.linspace(0, 255, width) # Red gradient bg_img[:, :, 1] = np.linspace(0, 255, height).reshape(-1, 1) # Green gradient bg_img[:, :, 2] = 100.0 # Blue channel constant bg_img[:, :, 3] = 255.0 # Full opacity # Foreground image: a circle fg_img = np.zeros((height, width, 4), dtype=float) y, x = np.ogrid[0:height, 0:width] center_y, center_x = height // 2, width // 2 radius = min(height, width) // 3 mask = (x - center_x)**2 + (y - center_y)**2 < radius**2 fg_img[mask, 0] = 255.0 # Red circle fg_img[mask, 3] = 150.0 # Partial opacity for foreground opacity = 0.7 # Opacity of the foreground layer (0.0 to 1.0) # Perform blending blended_img = soft_light(bg_img, fg_img, opacity) print("Blended image shape:", blended_img.shape) print("Blended image min value:", blended_img.min()) print("Blended image max value:", blended_img.max()) # In a real application, you would save or display blended_img, # e.g., using Pillow: Image.fromarray(blended_img.astype(np.uint8)).save('output.png')