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September 6, 2019 16:12
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OpenCV Python script to put the Matrix falling letters behind Taylor Swift for trueheart78
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| #!/usr/bin/env python | |
| import os | |
| import sys | |
| import numpy as np | |
| import cv2 | |
| FRAME_DELAY = int(sys.argv[1]) if len(sys.argv) > 1 else 30 | |
| foreground = cv2.VideoCapture('glowy_eyes.gif') | |
| background = cv2.VideoCapture('matrix.gif') | |
| fg_bg = cv2.createBackgroundSubtractorKNN() | |
| def edge_detect(channel): | |
| sobel_x = cv2.Sobel(channel, cv2.CV_16S, 1, 0) | |
| sobel_y = cv2.Sobel(channel, cv2.CV_16S, 0, 1) | |
| sobel = np.hypot(sobel_x, sobel_y) | |
| sobel[sobel > 255] = 255; | |
| return sobel | |
| def find_significant_contours(frame, edge_image): | |
| image, contours, hierarchy = cv2.findContours(edge_image, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) | |
| # Find level 1 contours | |
| level_1 = [] | |
| for i, tupl in enumerate(hierarchy[0]): | |
| # Each array is in format (next, prev, first child, parent) | |
| # Filter ones without a parent | |
| if tupl[3] == -1: | |
| tupl = np.insert(tupl, 0, [i]) | |
| level_1.append(tupl) | |
| # From among them, find the contours with large surface area | |
| significant = [] | |
| too_small = edge_image.size * 0.025 | |
| for tupl in level_1: | |
| contour = contours[tupl[0]] | |
| area = cv2.contourArea(contour) | |
| if area > too_small: | |
| significant.append([contour, area]) | |
| # cv2.drawContours(frame, [contour], 0, (0, 255, 0), 1, cv2.LINE_AA, maxLevel=1) | |
| significant.sort(key=lambda x: x[1]) | |
| return [x[0] for x in significant] | |
| def process_frame(fg_frame, bg_frame): | |
| blurred = cv2.GaussianBlur(fg_frame, (11, 11), 0) | |
| x, y = fg_frame.shape[:2] | |
| # bg_frame = cv2.resize(bg_frame, (y, x)) | |
| bg_frame = bg_frame[0:x, 0:y] | |
| edge_image = np.max(np.array([ | |
| edge_detect(blurred[:, :, 0]), | |
| edge_detect(blurred[:, :, 1]), | |
| edge_detect(blurred[:, :, 2]) | |
| ]), axis=0) | |
| mean = 0.05 * np.mean(edge_image) | |
| # Zero any value less than mean to reduce noise | |
| edge_image[edge_image <= mean] = 0 | |
| edge_image_8u = np.asarray(edge_image, np.uint8) | |
| # Find contours | |
| significant = find_significant_contours(fg_frame, edge_image_8u) | |
| mask = edge_image.copy() | |
| mask[mask > 0] = 0 | |
| cv2.fillPoly(mask, significant, 255) | |
| # invert mask | |
| inv_mask = np.logical_not(mask) | |
| # remove the background | |
| fg_frame[inv_mask] = bg_frame[inv_mask] | |
| return fg_frame | |
| frames = [] | |
| with open('image_list.txt', 'w') as file_list: | |
| while(True): | |
| fg_has_frame, fg_frame = foreground.read() | |
| bg_has_frame, bg_frame = background.read() | |
| if not all([fg_has_frame, bg_has_frame]): | |
| break | |
| processed = process_frame(fg_frame, bg_frame) | |
| cv2.imshow('frame', processed) | |
| filename = 'frame_{}.png'.format(len(frames)) | |
| cv2.imwrite(filename, processed) | |
| file_list.write('{}\n'.format(filename)) | |
| frames.append(processed.copy()) | |
| k = cv2.waitKey(FRAME_DELAY) & 0xff | |
| if k == 27: | |
| break | |
| os.system('convert @image_list.txt glowy_output.gif') | |
| foreground.release() | |
| background.release() | |
| cv2.destroyAllWindows() |
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