如何使用OpenCV视频捕获与自定义视频输入

Question

我试图使用本教程中的LK光流，在ROS+Gazebo制作的机器人模拟中获得一些运动估计。

根据下面的代码，我可以通过cv_bridge在ROS和OpenCV之间建立适当的桥梁，并可以实现一些工作的示例功能；"逐帧"；没有重大问题。

然而，光流教程参考似乎只接受视频输入，如网络摄像头和/或保存的视频文件，这就是我陷入困境的地方。

如何将LK光流应用于"；"逐帧"；方法，或将我的cv_bridge配置为充当"；自定义"；相机设备？

这是我迄今为止的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，我也想知道在哪里可以找到更多关于如何操作这些参数的信息(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(。在cv2网站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！

如果您有任何问题或问题，请告诉我！