Merge pull request #832 from insight-platform/830-symmetric-padding-s…

…upport

Symmetric padding property support

samples/bypass_model/README.md

@@ -56,7 +56,7 @@ A result video can be viewed:
 - in a player using `rtsp://127.0.0.1:554/stream/video-with-preprocessed-frame
 
 The video consists of original video stream and the pre-processed video stream side by side from left to right. The white background highlights the original video frame because its size is different from the size of the pre-processed frame.
-The pre-processed frame has a black background at the bottom, which is the result of the `maintain_aspect_ratio` flag.
+The pre-processed frame has a black background to match the aspect ratio of the original frame, i.e. `maintain_aspect_ratio` flag is set to `true`. The frame is centered on the background, as the `symmetric_padding` flag is set to `true`.
 
 Example output frame:
 ![](assets/result.jpg)

samples/bypass_model/demo.yml

@@ -43,6 +43,7 @@ pipeline:
           shape: ${parameters.result_shape}
           # preprocessing settings
           maintain_aspect_ratio: true
+          symmetric_padding: true
 
         # configuration of model output
         output:

savant/base/model.py

@@ -119,7 +119,10 @@ class ModelInput:
     """Indicates whether the input preprocessing should maintain image aspect ratio.
     """
 
-    # TODO: Add `symmetric-padding` support.
+    symmetric_padding: bool = False
+    """Indicates whether the input preprocessing should symmetrically pad the image when it's scaled. 
+    By default the images are padded asymmetrically.
+    """
 
     # TODO: Enhance scaling options
     #  range: Tuple[] = (0, 255) or (0.0, 1.0)

savant/deepstream/nvinfer/file_config.py

@@ -171,6 +171,12 @@ class _FieldMap:
             lambda v: bool(int(v)),
             0,
         ),
+        _FieldMap(
+            'symmetric-padding',
+            'input.symmetric_padding',
+            lambda v: bool(int(v)),
+            0,
+        ),
         _FieldMap('net-scale-factor', 'input.scale_factor', float, 1.0),
         _FieldMap(
             'offsets',

savant/deepstream/nvinfer/model.py

@@ -205,9 +205,6 @@ class NvInferModel(Model):
     """Specifies the number of consecutive batches to be skipped for inference."""
 
     # TODO: Add support for Gst-nvinfer props
-    # symmetric-padding (with maintain-aspect-ratio) -
-    #     Indicates whether to pad image symmetrically while scaling input.
-    #     DeepStream pads the images asymmetrically by default.
     # network-input-order - Order of the network input layer
 
     workspace_size: int = 6144