如何使用带有自定义视频输入的 OpenCV 视频捕获？

Question

我正在尝试使用 this tutorial 中的 LK 光流来对使用 ROS+Gazebo 制作的机器人模拟进行一些运动估计。

我可以根据下面的代码通过 cv_bridge 设法在 ROS 和 OpenCV 之间建立适当的桥梁，并且可以实现一些示例功能，这些功能可以“逐帧”工作而不会出现重大问题。

然而，光流教程参考似乎只接受视频输入，例如网络摄像头 and/or 保存的视频文件，这就是我卡住的地方。

如何在“逐帧”方法中应用 LK 光流，或者如何配置我的 cv_bridge 以充当“自定义”相机设备？

到目前为止，这是我的cv_brige：

#!/usr/bin/env python

import roslib
import rospy
import sys
import cv2
import numpy as np

from sensor_msgs.msg import Image, CameraInfo
from cv_bridge import CvBridge, CvBridgeError


class OpticalFlow(object):



    def __init__(self):

        #Setting up the bridge and the subscriber
        self.bridge = CvBridge()
        self.image_sub = rospy.Subscriber("/realsense/color/image_raw", Image,self.image_callback)
        
        # Parameters for Good Features Detection
        self.feature_params = dict( maxCorners = 100,
                                qualityLevel = 0.3,
                                minDistance = 7,
                                blockSize = 7 )

        # Parameters for Lucas-Kanade optical flow
        self.lk_params = dict( winSize  = (150,150),
                                maxLevel = 2,
                                criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))

        # Create some random colors for the tracking points
        self.color = np.random.randint(0,255,(100,3))



    def image_callback(self,ros_image):

        # Using cv_bridge() to convert the ROS image to OpenCV format
        try:
            cv_image = self.bridge.imgmsg_to_cv2(ros_image, "bgr8")#bgr8
            hsv_image = cv2.cvtColor(cv_image, cv2.COLOR_BGR2HSV)
            gray = cv2.cvtColor(cv_image, cv2.COLOR_BGR2GRAY)
            
        except CvBridgeError as e:
            print(e)
    

        cv2.imshow("Robot Image", cv_image) # The actual (non-processed image from the simulation/robot)
        #cv2.imshow('Image HSV', hsv_image)
        #cv2.imshow('Image Gray', gray)
        frame = np.array(cv_image, dtype=np.uint8)


        # Calling the Optical Flow Function
        display = self.optical_flow(frame,self.feature_params,self.lk_params,self.color)


    
    def optical_flow(self,frame,feature_params,lk_params,color):
        
        
        old_frame = frame
        old_gray = cv2.cvtColor(old_frame, cv2.COLOR_BGR2GRAY)
        p0 = cv2.goodFeaturesToTrack(old_gray, mask = None, **feature_params)
        mask = np.zeros_like(old_frame)
        while(1):
            
            newframe = frame
            frame_gray = cv2.cvtColor(newframe, cv2.COLOR_BGR2GRAY)

            # Calculating the optical flow
            p1, st, err = cv2.calcOpticalFlowPyrLK(old_gray, frame_gray, p0, None, **lk_params)
            
            # Select good points
            good_new = p1[st==1]
            good_old = p0[st==1]

            # Draw the tracks
            for i,(new,old) in enumerate(zip(good_new, good_old)):
                a,b = new.ravel()
                c,d = old.ravel()
                mask = cv2.line(mask, (a,b),(c,d), color[i].tolist(), 2)
                frame = cv2.circle(frame,(a,b),5,color[i].tolist(),-1)
            img = cv2.add(frame,mask)
            
            cv2.imshow('LK_Flow',img)                    
            k = cv2.waitKey(30) & 0xff
            if k == 27:
                break

            # Now update the previous frame and previous points
            old_gray = frame_gray.copy()
            p0 = good_new.reshape(-1,1,2)
            


def main():

    optical_flow_object = OpticalFlow()
    rospy.init_node('KLT_Node', anonymous=True)
    rospy.loginfo("\nWaiting for image topics...\n...")
    try:
        rospy.spin()
    except KeyboardInterrupt:
        rospy.loginfo("\nShutting Down...\n...")
    cv2.destroyAllWindows()

if __name__ == '__main__':
    main()

如果可能的话，我还想知道在哪里可以找到有关如何操作这些参数的更多信息（来自教程）：

# params for ShiTomasi corner detection
feature_params = dict( maxCorners = 100,
                       qualityLevel = 0.3,
                       minDistance = 7,
                       blockSize = 7 )
# Parameters for lucas kanade optical flow
lk_params = dict( winSize  = (15,15),
                  maxLevel = 2,
                  criteria = (cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 10, 0.03))

Answer 1

我建议使用 cv2.calcOpticalFlowPyrLK(old_frame, cur_frame, ...) 或 cv2.calcOpticalFlowFarneback(old_frame, cur_frame, ...)（密集光流）。 the cv2 website. 上有很多关于这些方法的信息，根据个人经验，这些方法非常有效！

如果您有任何疑问或问题，请告诉我！

如何使用带有自定义视频输入的 OpenCV 视频捕获？

How to use OpenCV video capture with custom video input?

python

opencv

computer-vision

python-3.x

ros