def extract_features(path_in, net, num_layers_finetune, use_gpu, minimum_frames=45):
# Create inference engine
inference_engine = engine.InferenceEngine(net, use_gpu=use_gpu)
# extract features
for dataset in ["train", "valid"]:
videos_dir = os.path.join(path_in, f"videos_{dataset}")
features_dir = os.path.join(path_in, f"features_{dataset}_num_layers_to_finetune={num_layers_finetune}")
video_files = glob.glob(os.path.join(videos_dir, "*", "*.mp4"))
print(f"\nFound {len(video_files)} videos to process in the {dataset}set")
for video_index, video_path in enumerate(video_files):
print(f"\rExtract features from video {video_index + 1} / {len(video_files)}",
end="")
path_out = video_path.replace(videos_dir, features_dir).replace(".mp4", ".npy")
if os.path.isfile(path_out):
print("\n\tSkipped - feature was already precomputed.")
else:
# Read all frames
video_source = camera.VideoSource(camera_id=None,
size=inference_engine.expected_frame_size,
filename=video_path)
video_fps = video_source.get_fps()
frames = []
while True:
images = video_source.get_image()
if images is None:
break
else:
image, image_rescaled = images
frames.append(image_rescaled)
frames = uniform_frame_sample(np.array(frames), inference_engine.fps / video_fps)
if frames.shape[0] < minimum_frames:
print(f"\nVideo too short: {video_path} - first frame will be duplicated")
num_missing_frames = minimum_frames - frames.shape[0]
frames = np.pad(frames, ((num_missing_frames, 0), (0, 0), (0, 0), (0, 0)),
mode='edge')
# Inference
clip = frames[None].astype(np.float32)
predictions = inference_engine.infer(clip)
features = np.array(predictions)
os.makedirs(os.path.dirname(path_out), exist_ok=True)
np.save(path_out, features)
print('\n')
checkpoint = engine.load_weights('resources/backbone/strided_inflated_efficientnet.ckpt')
feature_extractor.load_state_dict(checkpoint)
feature_extractor.eval()
# Load a logistic regression classifier
gesture_classifier = LogisticRegression(num_in=feature_extractor.feature_dim,
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),
]
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)
