import cv2
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from skimage.metrics import structural_similarity as ssim
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import numpy as np
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class VideoSSIM:
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def __init__(self, video_path):
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# 初始化视频路径和打开视频
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self.video_path = video_path
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self.cap = cv2.VideoCapture(video_path)
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if not self.cap.isOpened():
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raise ValueError(f"Error opening video file: {video_path}")
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# 获取视频帧率（fps）
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self.fps = self.cap.get(cv2.CAP_PROP_FPS)
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# 获取视频的尺寸
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self.frame_width = int(self.cap.get(cv2.CAP_PROP_FRAME_WIDTH))
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self.frame_height = int(self.cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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def calculate_ssim(self):
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# 读取视频的第一帧
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ret, prev_frame = self.cap.read()
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if not ret:
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raise ValueError("Error reading the first frame")
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prev_frame_gray = cv2.cvtColor(prev_frame, cv2.COLOR_BGR2GRAY) # 转换为灰度图
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# 存储相似性数值
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ssim_values = []
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# 从第二帧开始逐帧计算与前一帧的结构相似性
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frame_index = 2 # 从第二帧开始
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while True:
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ret, curr_frame = self.cap.read()
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if not ret:
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break
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# 转换当前帧为灰度图
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curr_frame_gray = cv2.cvtColor(curr_frame, cv2.COLOR_BGR2GRAY)
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# 计算SSIM
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ssim_value, _ = ssim(prev_frame_gray, curr_frame_gray, full=True)
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ssim_values.append((frame_index, ssim_value)) # 记录帧索引和SSIM值
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# 更新前一帧
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prev_frame_gray = curr_frame_gray
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frame_index += 1
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# 释放视频捕捉对象
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self.cap.release()
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return ssim_values
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def save_ssim_to_file(self, output_file="ssim_values.txt"):
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ssim_values = self.calculate_ssim()
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# 保存SSIM值到文件
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with open(output_file, "w") as f:
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for frame_idx, ssim_value in ssim_values:
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f.write(f"Frame {frame_idx}: SSIM = {ssim_value}\n")
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print(f"SSIM values saved to {output_file}")
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# 使用示例
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video_ssim = VideoSSIM("demo.mp4") # 替换成你的视频文件路径
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video_ssim.save_ssim_to_file("demo_ssim_output.txt")
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