gesture_classifier.load_state_dict(checkpoint) gesture_classifier.eval() # Concatenate feature extractor and met converter net = Pipe(feature_extractor, gesture_classifier) # Create inference engine, video streaming and display instances inference_engine = engine.InferenceEngine(net, use_gpu=use_gpu) video_source = camera.VideoSource(camera_id=camera_id, size=inference_engine.expected_frame_size, filename=path_in) video_stream = camera.VideoStream(video_source, inference_engine.fps) postprocessor = [ PostprocessClassificationOutput(INT2LAB, smoothing=4) ] display_ops = [ realtimenet.display.DisplayTopKClassificationOutputs(top_k=1, threshold=0.5), ] display_results = realtimenet.display.DisplayResults(title=title, display_ops=display_ops) engine.run_inference_engine(inference_engine, video_stream, postprocessor, display_results, path_out)
# Create inference engine, video streaming and display objects inference_engine = engine.InferenceEngine(net, use_gpu=use_gpu) video_source = camera.VideoSource(camera_id=camera_id, size=inference_engine.expected_frame_size, filename=path_in) framegrabber = camera.VideoStream(video_source, inference_engine.fps) post_processors = [ calorie_estimation.CalorieAccumulator(weight=weight, height=height, age=age, gender=gender, smoothing=12) ] display_ops = [ realtimenet.display.DisplayDetailedMETandCalories(), ] display_results = realtimenet.display.DisplayResults(title=title, display_ops=display_ops) # Run live inference engine.run_inference_engine(inference_engine, framegrabber, post_processors, display_results, path_out)