Python3.7+Opencv3.4.1编程从双目图像中恢复深度图像

环境：python3.7+opencv3.4.1
IDE：Win10+eclipse

一、示例一

参考教程链接1 | 我的图片下载地址

修改后的源码：

import cv2
import numpy as np
from matplotlib import pyplot as plt

img_L = cv2.imread('left.jpg', 0)
img_R = cv2.imread('right.jpg', 0)

stereo = cv2.StereoBM_create(
    numDisparities=16, blockSize=15)  # OpenCV 3.0的函数为StereoBM_create
disparity = stereo.compute(img_L, img_R)

plt.subplot(121), plt.imshow(img_L, 'gray'), plt.title(
    'img_left'), plt.xticks([]), plt.yticks([])
plt.subplot(122), plt.imshow(disparity, 'gray'), plt.title(
    'disparity'), plt.xticks([]), plt.yticks([])

plt.show()

运行效果：

遇见问题：
报错：AttributeError: module 'cv2.cv2' has no attribute 'createStereoBM'

解决方法：
第一步：
pip install opencv-python opencv-contrib-python
下载完成但安装不成功
解决方法：以管理员身份运行cmd安装：
pip install --user opencv-contrib-python

第二步：
把createStereoBM（opencv2.7版本）改为StereoBM_create（opencv3.4）

二、示例二

图片另一个示例原博链接

修改后的源码：

import numpy as np
import cv2
from matplotlib import pyplot as plt

IMAGE_WIDTH = 800
IMAGE_HEIGHT = 600

capL = cv2.VideoCapture(2)
capR = cv2.VideoCapture(0)

imgL = np.zeros((IMAGE_WIDTH, IMAGE_HEIGHT, 3), np.uint8)
imgR = np.zeros((IMAGE_WIDTH, IMAGE_HEIGHT, 3), np.uint8)

stereo = None

opencv_measure_version = int(cv2.__version__.split('.')[0])
windowSize = 5
minDisp = 10
numDisp = 250 - minDisp

# for OpenCV3
stereo = cv2.StereoSGBM_create(
    minDisparity=minDisp,
    numDisparities=numDisp,
    blockSize=16,
    P1=8 * 3 * windowSize**2,
    P2=32 * 3 * windowSize**2,
    disp12MaxDiff=1,
    uniquenessRatio=10,
    speckleWindowSize=100,
    speckleRange=32
)
capL.set(cv2.CAP_PROP_FRAME_WIDTH,  IMAGE_WIDTH)
capL.set(cv2.CAP_PROP_FRAME_HEIGHT, IMAGE_HEIGHT)
capR.set(cv2.CAP_PROP_FRAME_WIDTH,  IMAGE_WIDTH)
capR.set(cv2.CAP_PROP_FRAME_HEIGHT, IMAGE_HEIGHT)


imgL = cv2.imread('left.jpg')
imgR = cv2.imread('right.jpg')

# create gray images
imgGrayL = cv2.cvtColor(imgL, cv2.COLOR_BGR2GRAY)
imgGrayR = cv2.cvtColor(imgR, cv2.COLOR_BGR2GRAY)

# calculate histogram
imtGrayL = cv2.equalizeHist(imgGrayL)
imtGrayR = cv2.equalizeHist(imgGrayR)

# through gausiann filter
imgGrayL = cv2.GaussianBlur(imgGrayL, (5, 5), 0)
imgGrayR = cv2.GaussianBlur(imgGrayR, (5, 5), 0)

plt.subplot(131), plt.imshow(imgGrayL, 'gray'), plt.title(
    'image left'), plt.xticks([]), plt.yticks([])
plt.subplot(132), plt.imshow(imgGrayR, 'gray'), plt.title(
    'image right'), plt.xticks([]), plt.yticks([])

# calculate disparity
disparity = stereo.compute(imgGrayL, imgGrayR).astype(np.float32) / 16
disparity = (disparity - minDisp) / numDisp


plt.subplot(133), plt.imshow(disparity, 'gray'), plt.title(
    'disparity'), plt.xticks([]), plt.yticks([])
plt.show()

运行结果：