python opencv sift光流.zip

import cv2 import numpy as np def OpticalFlow(): # 第一步，打开usb摄像头，或者读取视频文件，一样 cap = cv2.VideoCapture("1.mp4") # 第二步：sift特征检测器 sift = cv2.SIFT_create(nfeatures=500, contrastThreshold=0.1, edgeThreshold=50, sigma=1.0) lk_params = dict(winSize=(15, 15), # lucas kanade参数：搜索窗口、金字塔层数 maxLevel=3) color = [0,255,0] # 第三步：拿到第一帧图像并灰度化作为前一帧图片 ret, old_frame = cap.read() old_gray = cv2.cvtColor(old_frame, cv2.COLOR_BGR2GRAY) # 第四步:使用SIFT查找关键点key points和描述符descriptors kp1 = sift.detect(old_gray, None) p0 = [] for kp in kp1: p0.append( [[kp.pt[0], kp.pt[1]]] ) p0 = np.array(p0).astype(np.float32) # 第五步:创建一个mask, 用于进行横线的绘制 mask = np.zeros_like(old_frame) # 保存视频 fourcc = cv2.VideoWriter_fourcc(*"mp4v") out = cv2.VideoWriter('output.mp4',fourcc,20.0,(old_frame.shape[1] ,old_frame.shape[0]),True) # 黑白为false，彩色为True while True: # 第六步：读取图片灰度化作为后一张图片的输入 ret, frame = cap.read() if ret == False: break frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # 第七步：进行金字塔LK光流检测需要输入前一帧和当前图像及前一帧检测到的角点 pl, st, err = cv2.calcOpticalFlowPyrLK(old_gray, frame_gray, p0, None, **lk_params) if len(pl[st == 1]) <= 1: kp1 = sift.detect(frame_gray, None) p0 = [] for kp in kp1: p0.append( [[kp.pt[0], kp.pt[1]]] ) p0 = np.array(p0).astype(np.float32) old_gray = frame_gray.copy() mask = np.zeros_like(old_frame) continue # 第八步：读取运动了的角点st == 1表示检测到的运动物体，即v和u表示为0 good_new = pl[st == 1] good_old = p0[st == 1] # 第九步：绘制轨迹 for i, (new, old) in enumerate(zip(good_new, good_old)): a, b = new.ravel().astype(int) c, d = old.ravel().astype(int) mask = cv2.line(mask, (a, b), (c, d), color, 2) frame = cv2.circle(frame, (a, b), 5, color, -1) img = cv2.add(frame, mask) cv2.imshow('frame', img) out.write(img) k = cv2.waitKey(50) & 0xff if k == 27: break # 第十步：更新前一帧图片和角点的位置 old_gray = frame_gray.copy() p0 = good_new.reshape(-1, 1, 2) cv2.destroyAllWindows() cap.release() out.release() if __name__ == '__main__': OpticalFlow()