python - 要与StereoBM一起使用，是否必须校准摄像机？

Question

在我的情况下，无论参数调整如何，OpenCV StereoBM深度图返回的数据都没有意义。

我正在为一个涉及OpenCV的设计项目进行研究，并使用立体视觉生成深度图。我目前能够成功加载两个摄像头并使用StereoBM生成深度图。但是，如下面的屏幕快照所示，结果数据目前无用。因此，我创建了一个小型python应用程序，可帮助我调整StereoBM参数，但没有帮助。

我的问题是摄像头是否必须经过校准才能与StereoBM功能一起使用？

如果没有，有什么替代方法可以帮助我改善结果(即提高分辨率，使用StereoSBGM等)



代码

import cv2
import time
import numpy as np
from Tkinter import *

oldVal = 15
def oddVals(n):
        global oldVal
        n = int(n)
        if not n % 2:
                window_size.set(n+1 if n > oldVal else n-1)
                oldVal = window_size.get()

minDispValues = [16,32,48,64]
def minDispCallback(n):
        n = int(n)
        newvalue = min(minDispValues, key=lambda x:abs(x-float(n)))
        min_disp.set(newvalue)

# Display the sliders to control the stereo vision 
master = Tk()

master.title("StereoBM Settings");

min_disp = Scale(master, from_=16, to=64, command=minDispCallback, length=600, orient=HORIZONTAL, label="Minimum Disparities")
min_disp.pack()
min_disp.set(16)

window_size = Scale(master, from_=5, to=255, command=oddVals, length=600, orient=HORIZONTAL, label="Window Size")
window_size.pack()
window_size.set(15)

Disp12MaxDiff = Scale(master, from_=5, to=30, length=600, orient=HORIZONTAL, label="Max Difference")
Disp12MaxDiff.pack()
Disp12MaxDiff.set(0)

UniquenessRatio = Scale(master, from_=0, to=30, length=600, orient=HORIZONTAL, label="Uniqueness Ratio")
UniquenessRatio.pack()
UniquenessRatio.set(15)

SpeckleRange = Scale(master, from_=0, to=60, length=600, orient=HORIZONTAL, label="Speckle Range")
SpeckleRange.pack()
SpeckleRange.set(34)

SpeckleWindowSize = Scale(master, from_=60, to=150, length=600, orient=HORIZONTAL, label="Speckle Window Size")
SpeckleWindowSize.pack()
SpeckleWindowSize.set(100)

master.update()

vcLeft = cv2.VideoCapture(0) # Load video campture for the left camera
#vcLeft.set(cv2.cv.CV_CAP_PROP_FRAME_WIDTH,420);
#vcLeft.set(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT,340);
vcLeft.set(3,640) # Set camera width
vcLeft.set(4,480) # Set camera height

vcRight = cv2.VideoCapture(1) # Load video capture for the right camera
#vcRight.set(cv2.cv.CV_CAP_PROP_FRAME_WIDTH,420);
#vcRight.set(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT,340);

firstTime = time.time() # First time log

totalFramesPassed = 0 # Number of frames passed

if vcLeft.isOpened() and vcRight.isOpened():
        rvalLeft, frameLeft = vcLeft.read()
        rvalRight, frameRight = vcRight.read()

else:
        rvalLeft = False
        rvalRight = False

while rvalLeft and rvalRight: # If the cameras are opened

        rvalLeft, frameLeft = vcLeft.read()

        rvalRight, frameRight = vcRight.read()

        cv2.putText(frameLeft, "FPS : " + str(totalFramesPassed / (time.time() - firstTime)),(40, 40), cv2.FONT_HERSHEY_SIMPLEX, 0.8, 150, 2, 10)

        cv2.imshow("Left Camera", frameLeft)

        cv2.putText(frameRight, "FPS : " + str(totalFramesPassed / (time.time() - firstTime)),(40, 40), cv2.FONT_HERSHEY_SIMPLEX, 0.8, 150, 2, 10)

        cv2.imshow("Right Camera", frameRight)

        frameLeftNew = cv2.cvtColor(frameLeft, cv2.COLOR_BGR2GRAY)

        frameRightNew = cv2.cvtColor(frameRight, cv2.COLOR_BGR2GRAY)

        num_disp = 112 - min_disp.get()

        stereo = cv2.StereoBM_create(numDisparities = num_disp, blockSize = window_size.get())

        stereo.setMinDisparity(min_disp.get())

        stereo.setNumDisparities(num_disp)

        stereo.setBlockSize(window_size.get())

        stereo.setDisp12MaxDiff(Disp12MaxDiff.get())

        stereo.setUniquenessRatio(UniquenessRatio.get())

        stereo.setSpeckleRange(SpeckleRange.get())

        stereo.setSpeckleWindowSize(SpeckleWindowSize.get())

        disparity = stereo.compute(frameLeftNew, frameRightNew).astype(np.float32) / 16.0

        disp_map = (disparity - min_disp.get())/num_disp

        cv2.imshow("Disparity", disp_map)

        master.update() # Update the slider options

        key = cv2.waitKey(20)

        totalFramesPassed = totalFramesPassed + 1 # One frame passed, increment

        if key == 27:

                break


vcLeft.release()

vcRight.release()

Answer 1

如StereoBM opencv stereoBM doc的opencv文档中所述，两个图像必须是“整流立体声对”。

这意味着在计算视差之前，您将需要校正两个摄像机。

看一看stereo_match，您可以在计算视差之前看到如何校正两个摄像机。

当您使用stereoBM计算视差时，您正在查看两幅图像中平行极线的对应关系。
这意味着期望将图像对齐，以使两个图像中的相同行对应于空间中的相同线。整改过程将解决这一问题。

有关更多信息，请查看Rectification with opencv