一个朋友想用python来读取视频帧，根据帧和帧之间相似度判断剪辑痕迹；但是最后发现并没什么用……

原理就是遍历地读取图像相邻帧，将图像相邻帧前处理后，缩小什么的，计算d_hash,然后计算其汉明距离，然后把汉明距离变成相似度，比较相似度，如果相似度低于阈值，就标记时间什么的

不过我一想，欸！好像可以用来解GIF隐写

我就把代码放这里了

1.处理视频版本的

import cv2
import numpy as np
from PIL import Image# Constants
VIDEO_FILE = "faker.mp4"
FRAME_PREFIX = "frame"
FAIL_THRESHOLD = 0.77def d_hash(image):"""Calculate the difference hash for the given image."""hash_bits = []for i in range(8):for j in range(8):hash_bits.append(1 if image[i, j] > image[i, j + 1] else 0)return hash_bitsdef hamming_distance(hash1, hash2):"""Calculate the Hamming distance between two hashes."""return sum(1 for x, y in zip(hash1, hash2) if x != y)def process_frame(frame):"""Process frame to convert it to a format suitable for hash computation."""return np.array(Image.fromarray(frame).resize((9, 8), Image.LANCZOS).convert('L'), 'f')def main():video = cv2.VideoCapture(VIDEO_FILE)edit_detected = Falsewhile True:# Read two consecutive frames from the videosuccess, frame0 = video.read()success1, frame1 = video.read()if not success or not success1:break# Process framesframe0_processed = process_frame(frame0)frame1_processed = process_frame(frame1)# Calculate hashes and distancehash0 = d_hash(frame0_processed)hash1 = d_hash(frame1_processed)distance = hamming_distance(hash0, hash1)similarity = 1.0 - distance / 64.0# Check similarity against the thresholdif similarity < FAIL_THRESHOLD:msec = video.get(cv2.CAP_PROP_POS_MSEC)minute, second = divmod(msec // 1000, 60)print(f"{int(minute)} minute {int(second)} second detected with similarity {similarity}")edit_detected = Trueif not edit_detected:print("No edit detected.")if __name__ == "__main__":main()

2.处理GIF版本的

处理GIF的时候如果遇到相似度过低的帧，就标记出来，而不是时间

from PIL import Image, ImageSequence
import numpy as np# Constants
GIF_FILE = "aaa.gif"
FRAME_PREFIX = "frame"
FAIL_THRESHOLD = 0.77def d_hash(image):"""Calculate the difference hash for the given image."""hash_bits = []for i in range(8):for j in range(8):hash_bits.append(1 if image[i, j] > image[i, j + 1] else 0)return hash_bitsdef hamming_distance(hash1, hash2):"""Calculate the Hamming distance between two hashes."""return sum(1 for x, y in zip(hash1, hash2) if x != y)def process_frame(frame):"""Process frame to convert it to a format suitable for hash computation."""return np.array(frame.resize((9, 8), Image.LANCZOS).convert('L'), 'f')def main():gif = Image.open(GIF_FILE)frames = [frame.copy() for frame in ImageSequence.Iterator(gif)]edit_detected = Falseprevious_hash = Nonefor index, frame in enumerate(frames):# Process frameframe_processed = process_frame(frame)# Calculate hashcurrent_hash = d_hash(frame_processed)if previous_hash is not None:# Calculate distance and similaritydistance = hamming_distance(previous_hash, current_hash)similarity = 1.0 - distance / 64.0# Check similarity against the thresholdif similarity < FAIL_THRESHOLD:print(f"Frame {index} detected with similarity {similarity}")edit_detected = Trueprevious_hash = current_hashif not edit_detected:print("No edit detected.")if __name__ == "__main__":main()

FAIL_THRESHOLD那里可以调节灵敏度，越高越容易筛选帧对

不过话说回来，好像还是很鸡肋……算了……写都写了

兴许以后有用呢。