This is 66 images. Stabilization was done with a python script: import cv2 import numpy as np import matplotlib.pyplot as plt import pprint import math # /opt/homebrew/bin/ffmpeg -framerate 30 -pattern_type glob -i '*_EurchinsHg.jpg' -c:v libx264 -crf 20 EurchinsHg.mp4 # /opt/homebrew/bin/ffmpeg -i EurchinsHg.mp4 -vf "vidstabtransform=input=transforms.trf" -crf 20 EurchinsStabilized.mp4 def replace_color_with_image(background, overlay, target_color, tolerance, output_path): """ Replaces a specific color in an image with another image. Args: background_path (str): Path to the background image. overlay_path (str): Path to the overlay image (can be the same as background if no overlay needed). target_color (tuple): RGB color to replace (e.g., (255, 0, 0) for red). tolerance (int): Tolerance range for color matching. replacement_image_path (str): Path to the replacement image. output_path (str): Path to save the output image. """ if background is None or overlay is None: raise FileNotFoundError("One or more images not found.") # Create a mask for the target color lower_bound = np.array([max(0, c - tolerance) for c in target_color], dtype="uint8") upper_bound = np.array([min(255, c + tolerance) for c in target_color], dtype="uint8") mask = cv2.inRange(overlay, lower_bound, upper_bound) kernel = np.ones((3, 3), np.uint8) dialated_mask = cv2.dilate(mask, kernel, iterations=1) mask_rgb = cv2.merge((dialated_mask,dialated_mask,dialated_mask)) mask_inv = cv2.bitwise_not(dialated_mask) mask_inv_rgb = cv2.merge((mask_inv,mask_inv,mask_inv)) masked_background = cv2.bitwise_and(mask_rgb, background) # pixels from background where target_color is in the overlay. masked_overlay = cv2.bitwise_and(mask_inv_rgb, overlay) # pixels from background where target_color is in the overlay. combined = cv2.add(masked_background, masked_overlay) cv2.imwrite(output_path, combined) f = open("transforms.trf", "w") f.write("VID.STAB 1") f.write("# accuracy = 15") f.write("# shakiness = 3") f.write("# stepsize = 4") f.write("# mincontrast = 0.200000") f.write("Frame 1 (List 0 []) ") img1 = cv2.imread('0012_EurchinsHg.jpg') mask = cv2.imread('mask.png', cv2.IMREAD_GRAYSCALE) currentMask = mask #sift sift = cv2.SIFT_create() keypoints_1, descriptors_1 = sift.detectAndCompute(img1,mask) for n in range(1,67): # Remember to +1 the end number. print("Frame " + str(n)) img2 = cv2.imread("{:04d}_EurchinsHg.jpg".format(n)) #img2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY) keypoints_2, descriptors_2 = sift.detectAndCompute(img2,currentMask) #feature matching bf = cv2.BFMatcher(cv2.NORM_L1, crossCheck=True) matches = bf.match(descriptors_1,descriptors_2) matches = sorted(matches, key = lambda x:x.distance) # Get key points points1 = np.float32([keypoints_1[m.queryIdx].pt for m in matches]).reshape(-1, 1, 2) points2 = np.float32([keypoints_2[m.trainIdx].pt for m in matches]).reshape(-1, 1, 2) # Affine matrix matrix, _ = cv2.findHomography(points2, points1, cv2.RANSAC, ransacReprojThreshold=5.0) # Align new_background_color = (0, 255, 0) # Green aligned_image = cv2.warpPerspective(img2, matrix, (img1.shape[1], img1.shape[0]), borderValue=new_background_color) replace_color_with_image(img1, aligned_image, new_background_color, 1, "aligned_{:04d}_EurchinsHg.jpg".format(n)) f.close() The actual stabilization is done with ffmpeg: /opt/homebrew/bin/ffmpeg -framerate 30 -pattern_type glob -i 'aligned_*_EurchinsHg.jpg' -c:v libx264 -crf 20 AlignedEurchinsHg.mp4