How to Create a Depth Map from Stereo Images in OpenCV Python?
Stereo images are two images captured from different perspectives. In computer vision, these images are used to create a 3D representation of a scene. A depth map is a 2D image that represents the distance from the camera to each pixel in the scene. In this article, we will explore how to create a depth map from stereo images using OpenCV in Python.
Stereo Vision
Stereo vision involves using two images to extract information about the depth of a scene. The images are taken from different positions and viewpoints and can be used to calculate the distance to objects in the scene. The process involves finding corresponding points in both images and using them to triangulate the position of each point in 3D space.
Building a Depth Map
Creating a depth map from stereo images involves finding the disparity between the two images. Disparity is the difference between the x-coordinate of the same feature point in the two images. We can use this information to calculate the depth of each point in the scene.
Here is a code snippet that shows how to compute the disparity map and output the depth of each pixel in the image:
import cv2
import numpy as np
# load left and right images
img_left = cv2.imread('left_image.jpg', 0)
img_right = cv2.imread('right_image.jpg', 0)
# initialize stereo block matching object
stereo = cv2.StereoBM_create(numDisparities=16, blockSize=15)
# compute disparity map
disparity = stereo.compute(img_left, img_right)
# convert disparity map to depth map
depth_map = np.zeros_like(disparity, dtype=np.float32)
fx = 718.856
baseline = 0.54 # meters
depth_map[disparity > 0] = fx * baseline / disparity[disparity > 0]
# display depth map
cv2.imshow('Depth Map', depth_map)
cv2.waitKey(0)
cv2.destroyAllWindows()
In the code above, we first load the left and right images. We then initialize a stereo block matching object using the cv2.StereoBM_create()
function. This function takes two arguments: numDisparities
and blockSize
. numDisparities
determines the maximum disparity search range, while blockSize
is the size of the window used for block matching.
Next, we compute the disparity map using the compute()
function of the stereo object. This function takes the left and right images as input and returns the disparity map.
To convert the disparity map to a depth map, we need to know the focal length fx
and baseline distance baseline
of the camera. We then use these values to calculate the depth of each pixel in meters using the equation depth = (fx * baseline) / disparity
.
Finally, we display the depth map using the cv2.imshow()
function and wait for the user to press a key before closing the window.
Conclusion
In this article, we have explored how to create a depth map from stereo images using OpenCV in Python. We have discussed the concept of stereo vision and how it can be used to calculate the depth of a scene. We have also provided a code snippet that shows how to compute the disparity map and convert it to a depth map. With this information, you can start building your own applications that use stereo vision to create 3D representations of the world around us.