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| import numpy as np | |
| import cv2 | |
| def overlay_alpha_image_lazy(background_rgb, overlay_rgba, alpha): | |
| # cf https://en.wikipedia.org/wiki/Alpha_compositing#Alpha_blending | |
| # If the destination background is opaque, then | |
| # out_rgb = overlay_rgb * overlay_alpha + background_rgb * (1 - overlay_alpha) | |
| overlay_alpha = overlay_rgba[: , : , 3].astype(np.float) / 255. * alpha | |
| overlay_alpha_3 = np.dstack((overlay_alpha, overlay_alpha, overlay_alpha)) | |
| overlay_rgb = overlay_rgba[: , : , : 3].astype(np.float) | |
| background_rgb_f = background_rgb.astype(np.float) | |
| out_rgb = overlay_rgb * overlay_alpha_3 + background_rgb_f * (1. - overlay_alpha_3) | |
| out_rgb = out_rgb.astype(np.uint8) | |
| return out_rgb | |
| def overlay_alpha_image_precise(background_rgb, overlay_rgba, alpha, gamma_factor=2.2): | |
| """ | |
| cf minute physics brilliant clip "Computer color is broken" : https://www.youtube.com/watch?v=LKnqECcg6Gw | |
| the RGB values are gamma-corrected by the sensor (in order to keep accuracy for lower luminancy), | |
| we need to undo this before averaging. | |
| """ | |
| overlay_alpha = overlay_rgba[: , : , 3].astype(np.float) / 255. * alpha | |
| overlay_alpha_3 = np.dstack((overlay_alpha, overlay_alpha, overlay_alpha)) | |
| overlay_rgb_squared = np.float_power(overlay_rgba[: , : , : 3].astype(np.float), gamma_factor) | |
| background_rgb_squared = np.float_power( background_rgb.astype(np.float), gamma_factor) | |
| out_rgb_squared = overlay_rgb_squared * overlay_alpha_3 + background_rgb_squared * (1. - overlay_alpha_3) | |
| out_rgb = np.float_power(out_rgb_squared, 1. / gamma_factor) | |
| out_rgb = out_rgb.astype(np.uint8) | |
| return out_rgb | |
| def test_overlay(): | |
| img = np.zeros((100, 800, 3), np.uint8) | |
| img[ : , : , : ] = (0, 0, 255) | |
| overlay = np.zeros((100, 800, 4), np.uint8) | |
| def make_gradient(x0, color): | |
| x1 = x0 + 40 | |
| for x in range(x0, x1): | |
| for y in range(0, 100): | |
| k = (x - x0) / (x1 - x0) | |
| alpha = int(round(k * 255.)) | |
| color_grad = (color[0], color[1], color[2], alpha) | |
| overlay[y, x, :] = color_grad | |
| make_gradient(100, (255, 0, 0)) | |
| make_gradient(200, (0, 255, 0)) | |
| make_gradient(300, (255, 255, 0)) | |
| make_gradient(400, (0, 255, 255)) | |
| make_gradient(500, (250, 50, 200)) | |
| mix_precise = overlay_alpha_image_precise(img, overlay, alpha=1.) | |
| mix_lazy = overlay_alpha_image_lazy(img, overlay, alpha=1.) | |
| cv2.imshow("mix_precise", mix_precise) | |
| cv2.imshow("mix_lazy", mix_lazy) | |
| #cv2.imshow("img", img) | |
| #cv2.imshow("overlay", overlay) | |
| cv2.waitKey() | |
| test_overlay() |
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nope, did a full-float version and it still looks harsh. guess 40 steps is just not enough. dithering may do something, but meh that's too much work.
doing gamma in 0-255 space is also not a great idea, but
it works out approximately. at leastit always works in cases of a simple power, just not for the more interesting curves like sRGB.sqrt((255 * x1)^2 * (1-a) + (255 * x2)^2 * a) = 255 * sqrt(x1^2 * (1-a) + x2^2 * a). The same does not quite hold for 2.2 (wolfram alpha), though it remains very close.difference is very hard to tell:
new code: