Fix async demo in exportable code

CHANGELOG.md

@@ -15,11 +15,12 @@ All notable changes to this project will be documented in this file.
 ### Enhancements
 
 - Make semantic segmentation OpenVINO models compatible with ModelAPI (<https://github.com/openvinotoolkit/training_extensions/pull/2029>).
-- Support label hierarchy through LabelTree in LabelSchema for classification task (<https://github.com/openvinotoolkit/training_extensions/pull/2149>, <https://github.com/openvinotoolkit/training_extensions/pull/2152>)
+- Support label hierarchy through LabelTree in LabelSchema for classification task (<https://github.com/openvinotoolkit/training_extensions/pull/2149>, <https://github.com/openvinotoolkit/training_extensions/pull/2152>).
+- Enhance exportable code file structure, video inference and default value for demo (<https://github.com/openvinotoolkit/training_extensions/pull/2051>).
 
 ### Bug fixes
 
--
+- Fix async mode inference for demo in exportable code (<https://github.com/openvinotoolkit/training_extensions/pull/2154>)
 
 ### Known issues
 

otx/api/usecases/exportable_code/demo/demo_package/executors/asynchronous.py

@@ -17,6 +17,7 @@
 )
 from otx.api.usecases.exportable_code.streamer import get_streamer
 from otx.api.usecases.exportable_code.visualizers import Visualizer
+from otx.api.usecases.exportable_code.prediction_to_annotation_converter import DetectionToAnnotationConverter
 from otx.api.utils.vis_utils import dump_frames
 
 
@@ -45,13 +46,16 @@ def run(self, input_stream: Union[int, str], loop: bool = False) -> None:
         for frame in streamer:
             results = self.async_pipeline.get_result(next_frame_id_to_show)
             while results:
+                start_time = time.perf_counter()
                 output = self.render_result(results)
                 next_frame_id_to_show += 1
                 self.visualizer.show(output)
                 if self.visualizer.output:
                     saved_frames.append(output)
                 if self.visualizer.is_quit():
                     stop_visualization = True
+                # visualize video not faster than the original FPS
+                self.visualizer.video_delay(time.perf_counter() - start_time, streamer)
                 results = self.async_pipeline.get_result(next_frame_id_to_show)
             if stop_visualization:
                 break
@@ -63,13 +67,19 @@ def run(self, input_stream: Union[int, str], loop: bool = False) -> None:
             results = self.async_pipeline.get_result(next_frame_id_to_show)
             output = self.render_result(results)
             self.visualizer.show(output)
+            if self.visualizer.output:
+                saved_frames.append(output)
             # visualize video not faster than the original FPS
             self.visualizer.video_delay(time.perf_counter() - start_time, streamer)
         dump_frames(saved_frames, self.visualizer.output, input_stream, streamer)
 
     def render_result(self, results: Tuple[Any, dict]) -> np.ndarray:
         """Render for results of inference."""
         predictions, frame_meta = results
+        if isinstance(self.converter, DetectionToAnnotationConverter):
+            # Predictions for the detection task
+            predictions = np.array([[pred.id, pred.score, *pred.get_coords()] for pred in predictions])
+            predictions.shape = len(predictions), 6
         annotation_scene = self.converter.convert_to_annotation(predictions, frame_meta)
         current_frame = frame_meta["frame"]
         output = self.visualizer.draw(current_frame, annotation_scene, frame_meta)

otx/api/usecases/exportable_code/demo/requirements.txt

@@ -1,4 +1,4 @@
 openvino==2022.3.0
 openmodelzoo-modelapi==2022.3.0
-otx @ git+https://github.com/openvinotoolkit/training_extensions/@fb357c9956cc3865797bfb9a52179625454da275#egg=otx
+otx @ git+https://github.com/openvinotoolkit/training_extensions/@5519de55fc0e16cdfd018e2cfa681ad47e61ce52#egg=otx
 numpy>=1.21.0,<=1.23.5  # np.bool was removed in 1.24.0 which was used in openvino runtime

otx/api/utils/vis_utils.py

@@ -69,8 +69,8 @@ def dump_frames(saved_frames: list, output: str, input_path: Union[str, int], ca
         out.release()
         print(f"Video was saved to {video_path}")
     else:
-        if len(filenames) < len(saved_frames):
-            filenames = [f"output_{i}" for i, _ in enumerate(saved_frames)]
+        if len(filenames) != len(saved_frames):
+            filenames = [f"output_{i}.jpeg" for i, _ in enumerate(saved_frames)]
         for filename, frame in zip(filenames, saved_frames):
             image_path = str(output_path / filename)
             cv2.imwrite(image_path, frame)

tests/test_suite/run_test_command.py

@@ -408,6 +408,26 @@ def otx_deploy_openvino_testing(template, root, otx_dir, args):
             "../model",
             "-i",
             os.path.join(otx_dir, args["--input"]),
+            "--inference_type",
+            "sync",
+            "--no_show",
+            "--output",
+            os.path.join(deployment_dir, "output"),
+        ],
+        cwd=os.path.join(deployment_dir, "python"),
+    )
+    assert os.path.exists(os.path.join(deployment_dir, "output"))
+
+    check_run(
+        [
+            "python3",
+            "demo.py",
+            "-m",
+            "../model",
+            "-i",
+            os.path.join(otx_dir, args["--input"]),
+            "--inference_type",
+            "async",
             "--no_show",
             "--output",
             os.path.join(deployment_dir, "output"),