python帧间差分

帧间差分是一种用于运动目标检测的方法，它通过对相邻的两帧图像进行差分运算，并取差分运算的绝对值来构成移动物体。在Python中，可以使用OpenCV库来实现帧间差分算法。下面是一个示例代码，展示了如何使用帧间差分方法进行运动目标检测：

import cv2

class FrameDifference(object):
    def run(self, Videopath='./video.avi'):
        frames = self.video_to_image(Videopath)
        c_frames = self.absdiff_(frames)
        
    def video_to_image(self, Videopath):
        capture = cv2.VideoCapture(Videopath)
        framesNum = capture.get(cv2.CAP_PROP_FRAME_COUNT)  # 得到视频的帧数
        frames = []
        for i in range(int(framesNum) - 1):
            ret, frame = capture.read()
            frames.append(frame)
        return frames
    
    def absdiff_(self, frames):
        c_frames = []
        for i in range(len(frames) - 2):
            frame_front = frames[i]
            frame_later = frames[i+1]
            d_frame = cv2.absdiff(frame_front, frame_later)  # 帧间做差
            c_frames.append(d_frame)
            cv2.imshow('d_frame', d_frame)
            cv2.waitKey()
        return c_frames

你也可以根据需要修改代码中的参数和方法，来适应不同的场景和需求。请注意，这仅是一个简化的示例，实际应用中可能需要根据具体情况进行调整和优化。建议参考《python调用opencv库中的KCF等跟踪算法》这篇文章，以获取更详细的代码和说明。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
- *1* *2* *3* [（1）Python-OpenCV视频帧间差分、高斯混合建模、背景差分提取前景目标轮廓、KCF目标跟踪、Meanshift算法...](https://blog.csdn.net/yohnyang/article/details/124834383)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_1"}}] [.reference_item style="max-width: 100%"]
[ .reference_list ]