Python cv2.reprojectImageTo3D() Examples

The following are 3 code examples of cv2.reprojectImageTo3D(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module cv2 , or try the search function .
Example #1
Source File: StereoMatching.py    From ImageProcessingProjects with MIT License 7 votes vote down vote up 
def stereo_match(imgL, imgR):
    # disparity range is tuned for 'aloe' image pair
    window_size = 15
    min_disp = 16
    num_disp = 96 - min_disp
    stereo = cv2.StereoSGBM_create(minDisparity=min_disp,
                                   numDisparities=num_disp,
                                   blockSize=16,
                                   P1=8 * 3 * window_size ** 2,
                                   P2=32 * 3 * window_size ** 2,
                                   disp12MaxDiff=1,
                                   uniquenessRatio=10,
                                   speckleWindowSize=150,
                                   speckleRange=32
                                   )

    # print('computing disparity...')
    disp = stereo.compute(imgL, imgR).astype(np.float32) / 16.0

    # print('generating 3d point cloud...',)
    h, w = imgL.shape[:2]
    f = 0.8 * w  # guess for focal length
    Q = np.float32([[1, 0, 0, -0.5 * w],
                    [0, -1, 0, 0.5 * h],  # turn points 180 deg around x-axis,
                    [0, 0, 0, -f],  # so that y-axis looks up
                    [0, 0, 1, 0]])
    points = cv2.reprojectImageTo3D(disp, Q)
    colors = cv2.cvtColor(imgL, cv2.COLOR_BGR2RGB)
    mask = disp > disp.min()
    out_points = points[mask]
    out_colors = colors[mask]
    append_ply_array(out_points, out_colors)

    disparity_scaled = (disp - min_disp) / num_disp
    disparity_scaled += abs(np.amin(disparity_scaled))
    disparity_scaled /= np.amax(disparity_scaled)
    disparity_scaled[disparity_scaled < 0] = 0
    return np.array(255 * disparity_scaled, np.uint8)
Example #2
Source File: blockmatchers.py    From StereoVision with GNU General Public License v3.0 5 votes vote down vote up 
def get_3d(cls, disparity, disparity_to_depth_map):
        """Compute point cloud."""
        return cv2.reprojectImageTo3D(disparity, disparity_to_depth_map)
Example #3
Source File: Camera.py    From SimpleCV2 with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def get3DImageFromDisparity(self, disparity, Q):
        """
        **SUMMARY**

        This method returns the 3D depth image using reprojectImageTo3D method.

        **PARAMETERS**
        * *disparity* - Disparity Image
        * *Q* - reprojection Matrix (disparity to depth matrix)

        **RETURNS**

        SimpleCV.Image representing 3D depth Image
        also StereoCamera.Image3D gives OpenCV 3D Depth Image of CV_32F type.

        **EXAMPLE**

        >>> lImage = Image("l.jpg")
        >>> rImage = Image("r.jpg")
        >>> stereo = StereoCamera()
        >>> Q = cv.Load("Q.yml")
        >>> disp = stereo.findDisparityMap()
        >>> stereo.get3DImageFromDisparity(disp, Q)
        """
        cv2flag = True
        try:
            import cv2
        except ImportError:
            cv2flag = False
            import cv2.cv as cv

        if cv2flag:
            if not isinstance(Q, np.ndarray):
                Q = np.array(Q)
            disparity = disparity.getNumpyCv2()    
            Image3D = cv2.reprojectImageTo3D(disparity, Q, ddepth=cv2.cv.CV_32F)
            Image3D_normalize = cv2.normalize(Image3D, alpha=0, beta=255, norm_type=cv2.cv.CV_MINMAX, dtype=cv2.cv.CV_8UC3)
            retVal = Image(Image3D_normalize, cv2image=True)
        else:
            disparity = disparity.getMatrix()
            Image3D = cv.CreateMat(self.LeftImage.size()[1], self.LeftImage.size()[0], cv2.cv.CV_32FC3)
            Image3D_normalize = cv.CreateMat(self.LeftImage.size()[1], self.LeftImage.size()[0], cv2.cv.CV_8UC3)
            cv.ReprojectImageTo3D(disparity, Image3D, Q)
            cv.Normalize(Image3D, Image3D_normalize, 0, 255, cv.CV_MINMAX, CV_8UC3)
            retVal = Image(Image3D_normalize)
        self.Image3D = Image3D
        return retVal