met_value_converter = calorie_estimation.METValueMLPConverter()
checkpoint = engine.load_weights('resources/calorie_estimation/mobilenet_features_met_converter.ckpt')
met_value_converter.load_state_dict(checkpoint)
met_value_converter.eval()
# Concatenate feature extractor and met converter
net = Pipe(feature_extractor, met_value_converter)
# 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
num_out=30)
checkpoint = engine.load_weights('resources/gesture_detection/efficientnet_logistic_regression.ckpt')
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)
def _setup_inference_engine(self):
self.inference_engine = engine.InferenceEngine(self.net, use_gpu=True)
video_source = camera.VideoSource(
camera_id=0, size=self.inference_engine.expected_frame_size)
self.frame_grabber = camera.VideoStream(video_source,
self.inference_engine.fps)