Merge pull request DLR-RM#479 from DLR-RM/iss_477_fix_stereo_matching

Iss 477 fix stereo matching

blenderproc/python/postprocessing/StereoGlobalMatching.py

@@ -44,7 +44,7 @@ def stereo_global_matching(color_images: List[np.ndarray], depth_max: Optional[f
 
     depth_frames = []
     disparity_frames = []
-    for frame, color_image in enumerate(color_images):
+    for color_image in color_images:
         depth, disparity = StereoGlobalMatching._sgm(color_image[0], color_image[1], baseline, depth_max, focal_length, window_size, num_disparities, min_disparity, disparity_filter, depth_completion)
 
         depth_frames.append(depth)
@@ -114,10 +114,7 @@ def _sgm(left_color_image: np.ndarray, right_color_image: np.ndarray, baseline:
             filteredImg = cv2.normalize(src=filteredImg, dst=filteredImg, beta=0, alpha=255, norm_type=cv2.NORM_MINMAX)
 
         disparity_to_be_written = filteredImg if disparity_filter else displ
-        disparity = np.float64(np.copy(disparity_to_be_written)) / 16.0
-
-        # Crop and resize, due to baseline, a part of the image on the left can't be matched with the one on the right
-        disparity = resize(disparity[:, num_disparities:], (left_color_image.shape[1], left_color_image.shape[0]))
+        disparity = np.float32(np.copy(disparity_to_be_written)) / 16.0
 
         # Triangulation
         depth = (1.0 / disparity) * baseline * focal_length

blenderproc/scripts/visHdf5Files.py

@@ -11,7 +11,7 @@
 default_flow_keys = ["forward_flow", "backward_flow"]
 default_segmap_keys = ["segmap", ".*_segmaps"]
 default_segcolormap_keys = ["segcolormap"]
-default_depth_keys = ["distance", "depth"]
+default_depth_keys = ["distance", "depth", "stereo-depth"]
 all_default_keys = default_rgb_keys + default_flow_keys + default_segmap_keys + default_segcolormap_keys + default_depth_keys
 default_depth_max = 20
 

examples/advanced/stereo_matching/README.md

@@ -1,11 +1,14 @@
 # Stereo Matching
-![](../../../images/stereo_matching_stereo_pair.jpg)
-![](../../../images/stereo_matching_stereo_depth.jpg)
+<p align="center">
+<img src="../../../images/stereo_matching_color_left.jpg" alt="Front readme image" width=400>
+<img src="../../../images/stereo_matching_color_right.jpg" alt="Front readme image" width=400>
+</p>
+<p align="center">
+<img src="../../../images/stereo_matching_stereo_depth.jpg" alt="Front readme image" width=400>
+</p>
 
 In the first row we can see the rendered stereo RGB images, left and right respectively, and beneath them we can view
-the computed depth image using stereo matching. Note that due to a high discrepancy between the TV and the rest
-of the rendered scene, the visualization is not descriptive enough. This discrepancy or high depth values at the TV
-is due to a lack of gradient or useful features for stereo matching in this area. However, the depth values in other
+the computed depth image using stereo matching. The high depth values from the uniform yellow area to the right are due to a lack of gradient or useful features for stereo matching in this area. However, the depth values in other
 areas are consistent and close to the rendered depth images.
 
 ## Usage

examples/advanced/stereo_matching/main.py

@@ -41,12 +41,15 @@
 bproc.renderer.enable_depth_output(activate_antialiasing=False)
 bproc.material.add_alpha_channel_to_textures(blurry_edges=True)
 bproc.renderer.toggle_stereo(True)
+bproc.renderer.set_max_amount_of_samples(50)
 
 # render the whole pipeline
 data = bproc.renderer.render()
 
 # Apply stereo matching to each pair of images
-data["stereo-depth"], data["disparity"] = bproc.postprocessing.stereo_global_matching(data["colors"], disparity_filter=False)
+data["stereo-depth"], data["disparity"] = bproc.postprocessing.stereo_global_matching(data["colors"], 
+                                                                                      disparity_filter=False, 
+                                                                                      depth_completion=False)
 
 # write the data to a .hdf5 container
 bproc.writer.write_hdf5(args.output_dir, data)