blenderproc.python.postprocessing.StereoGlobalMatching module

class blenderproc.python.postprocessing.StereoGlobalMatching.StereoGlobalMatching

Bases: object

static _sgm(left_color_image, right_color_image, baseline, depth_max, focal_length, window_size=7, num_disparities=32, min_disparity=0, disparity_filter=True, depth_completion=True)

Semi global matching funciton, for more details on what this function does check the original paper https://elib.dlr.de/73119/1/180Hirschmueller.pdf

Parameters:

• left_color_image (ndarray) – The left color image.
• right_color_image (ndarray) – The right color image.
• baseline (float) – The baseline that was used for rendering the two images.
• depth_max (float) – The maximum depth value for clipping the resulting depth values.
• focal_length (float) – The focal length that was used for rendering the two images.
• window_size (int) – Semi-global matching kernel size. Should be an odd number.
• num_disparities (int) – Semi-global matching number of disparities. Should be > 0 and divisible by 16.
• min_disparity (int) – Semi-global matching minimum disparity.
• disparity_filter (bool) – Applies post-processing of the generated disparity map using WLS filter.
• depth_completion (bool) – Applies basic depth completion using image processing techniques.

Return type:

Tuple[ndarray, ndarray]

Returns:

depth, disparity

blenderproc.python.postprocessing.StereoGlobalMatching.stereo_global_matching(color_images, depth_max=None, window_size=7, num_disparities=32, min_disparity=0, disparity_filter=True, depth_completion=True)

Does the stereo global matching in the following steps: 1. Collect camera object and its state, 2. For each frame, load left and right images and call the sgm() methode. 3. Write the results to a numpy file.

Parameters:

• color_images (List[ndarray]) – A list of stereo images, where each entry has the shape [2, height, width, 3].
• depth_max (Optional[float]) – The maximum depth value for clipping the resulting depth values. If None, distance_start + distance_range that were configured for distance rendering are used.
• window_size (int) – Semi-global matching kernel size. Should be an odd number.
• num_disparities (int) – Semi-global matching number of disparities. Should be > 0 and divisible by 16.
• min_disparity (int) – Semi-global matching minimum disparity.
• disparity_filter (bool) – Applies post-processing of the generated disparity map using WLS filter.
• depth_completion (bool) – Applies basic depth completion using image processing techniques.

Return type:

Tuple[List[ndarray], List[ndarray]]

Returns:

Returns the computed depth and disparity images for all given frames.