Merge branch 'main' of https://github.com/DLR-RM/BlenderProc into iss…

…_356_bop_update

.clabot

@@ -0,0 +1,5 @@
+{
+  "contributors": ["MartinSmeyer", "themasterlink", "cornerfarmer", "wboerdijk", "MarkusKnauer", "maximilianmuehlbauer", "wangg12", "alexander-soare", "heilaw", "wboerdijk"],
+  "message": "For contributing to BlenderProc you need to sign our Contributor License Agreement. As an individual please sign [CLA_individuals.pdf](https://github.com/DLR-RM/BlenderProc/blob/main/CLA_individuals.pdf), as a company please sign [CLA_entities.pdf](https://github.com/DLR-RM/BlenderProc/blob/main/CLA_entities.pdf) and send it to [email protected]",
+  "label": "cla-signed"
+}

.github/workflows/blenderprochelper.yml

@@ -0,0 +1,13 @@
+name: BlenderProcHelper
+
+on: [push, pull_request, workflow_dispatch]
+
+jobs:
+  Run-BlenderProcHelper:
+    runs-on: [self-hosted]
+    strategy:
+      max-parallel: 1
+    steps:
+    - uses: actions/checkout@v2
+    - name: Run BlenderProcHelper
+      run: python /home/$USER/workspace/BlenderProcHelper/github_action.py

README_BlenderProc4BOP.md

@@ -32,11 +32,11 @@ The cameras are positioned to cover the distribution of the ground-truth object
 
 ## Examples
 
-* [bop_challenge](datasets/examples/datasets/bop_challenge): Configuration files and information on how the official synthetic data for the BOP Challenge 2020 were created.
-* [bop_object_physics_positioning](datasets/examples/datasets/bop_object_physics_positioning): Drops BOP objects on a plane and randomizes materials.
-* [bop_object_on_surface_sampling](datasets/examples/datasets/bop_object_on_surface_sampling): Samples upright poses on a plane and randomizes materials.
-* [bop_scene_replication](datasets/examples/datasets/bop_scene_replication): Replicates test scenes (object poses, camera intrinsics and extrinsics) from the BOP datasets.
-* [bop_object_pose_sampling](datasets/examples/datasets/bop_object_pose_sampling): Loads BOP objects and samples the camera, light poses and object poses in a free space.
+* [bop_challenge](examples/datasets/bop_challenge): Configuration files and information on how the official synthetic data for the BOP Challenge 2020 were created.
+* [bop_object_physics_positioning](examples/datasets/bop_object_physics_positioning): Drops BOP objects on a plane and randomizes materials.
+* [bop_object_on_surface_sampling](examples/datasets/bop_object_on_surface_sampling): Samples upright poses on a plane and randomizes materials.
+* [bop_scene_replication](examples/datasets/bop_scene_replication): Replicates test scenes (object poses, camera intrinsics and extrinsics) from the BOP datasets.
+* [bop_object_pose_sampling](examples/datasets/bop_object_pose_sampling): Loads BOP objects and samples the camera, light poses and object poses in a free space.
 
 
 ## Results

blenderproc/python/camera/CameraValidation.py

@@ -150,7 +150,8 @@ def visible_objects(cam2world_matrix: Union[Matrix, np.ndarray], sqrt_number_of_
             # Send ray from the camera position through the current point on the plane
             _, _, _, _, hit_object, _ = bpy.context.scene.ray_cast(bpy.context.evaluated_depsgraph_get(), position, end - position)
             # Add hit object to set
-            visible_objects.add(MeshObject(hit_object))
+            if hit_object:
+                visible_objects.add(MeshObject(hit_object))
 
     return visible_objects
 

blenderproc/python/writer/CocoWriterUtility.py

@@ -21,7 +21,7 @@ def write_coco_annotations(output_dir: str, instance_segmaps: Optional[List[np.n
                            mask_encoding_format: str = "rle", supercategory: str = "coco_annotations",
                            append_to_existing_output: bool = True, segmap_output_key: str = "segmap",
                            segcolormap_output_key: str = "segcolormap", rgb_output_key: str = "colors",
-                           jpg_quality: int = 95, label_mapping: LabelIdMapping = None):
+                           jpg_quality: int = 95, label_mapping: LabelIdMapping = None, file_prefix: str = ""):
     """ Writes coco annotations in the following steps:
     1. Locate the seg images
     2. Locate the rgb maps
@@ -49,6 +49,7 @@ def write_coco_annotations(output_dir: str, instance_segmaps: Optional[List[np.n
     :param jpg_quality: The desired quality level of the jpg encoding
     :param label_mapping: The label mapping which should be used to label the categories based on their ids.
                           If None, is given then the `name` field in the csv files is used or - if not existing - the category id itself is used.
+    :param file_prefix: Optional prefix for image file names
     """
     if instance_segmaps is None:
         instance_segmaps = []
@@ -124,19 +125,19 @@ def write_coco_annotations(output_dir: str, instance_segmaps: Optional[List[np.n
             color_bgr[..., :3] = color_bgr[..., :3][..., ::-1]
 
             if color_file_format == 'PNG':
-                target_base_path = 'images/{:06d}.png'.format(frame + image_offset)
+                target_base_path = 'images/{}{:06d}.png'.format(file_prefix, frame + image_offset)
                 target_path = os.path.join(output_dir, target_base_path)
                 cv2.imwrite(target_path, color_bgr)
             elif color_file_format == 'JPEG':
-                target_base_path = 'images/{:06d}.jpg'.format(frame + image_offset)
+                target_base_path = 'images/{}{:06d}.jpg'.format(file_prefix, frame + image_offset)
                 target_path = os.path.join(output_dir, target_base_path)
                 cv2.imwrite(target_path, color_bgr, [int(cv2.IMWRITE_JPEG_QUALITY), jpg_quality])
             else:
                 raise Exception('Unknown color_file_format={}. Try "PNG" or "JPEG"'.format(color_file_format))
 
         else:
             source_path = rgb_output["path"] % frame
-            target_base_path = os.path.join('images', os.path.basename(rgb_output["path"] % (frame + image_offset)))
+            target_base_path = os.path.join('images', file_prefix + os.path.basename(rgb_output["path"] % (frame + image_offset)))
             target_path = os.path.join(output_dir, target_base_path)
             shutil.copyfile(source_path, target_path)