def generate_features_frames_old(frames, cube_depth=5, tile_size=10):
counter = 0
frame_sequence = []
original_frame_sequence = []
results = []
for frame in tqdm(frames):
original_frame_sequence.append(frame)
if counter % cube_depth == 0 and counter != 0:
# To grayscale
frame = frame_to_gray(frame)
# Divide pixels by 255
reduced_frame = reduce_frame(frame)
frame_sequence.append(reduced_frame)
features = generate_features(frame_sequence, cube_depth, tile_size)
result = features.reshape(1, features.shape[0] * features.shape[1])
results.append(result)
##Keep original frames
#jpeg encode
# jpeg_frames = np.array([cv2.imencode('.jpg', x)[1] for x in original_frame_sequence])
# origin_frames = frame_to_bytes_str(jpeg_frames)
# Reset frame sequences
original_frame_sequence = []
frame_sequence = []
counter += 1
else:
if counter == 0:
counter += 1
continue
# To grayscale
frame = frame_to_gray(frame)
# Divide pixels by 255
reduced_frame = reduce_frame(frame)
frame_sequence.append(reduced_frame)
counter += 1
return results
def generate_features_frames(frames,
cube_depth=5,
tile_size=10,
description=''):
results = []
sequences = list(divide_chunks(frames, cube_depth))
if len(
sequences[-1]
) != cube_depth: ## If last sequence does not have sufficient frames, leave it out
sequences = sequences[0:-1]
#for sequence in tqdm(sequences, desc=description): # Removing tqdm for now
for sequence in sequences:
sequence = [reduce_frame(frame_to_gray(frame)) for frame in sequence]
features = generate_features(sequence, cube_depth, tile_size)
result = features.reshape(1, features.shape[0] * features.shape[1])
results.append(result)
return results
def send_frames(self):
counter = 0
frame_sequence = []
original_frame_sequence = []
for msg in self.consumer.get_consumer():
frame = frame_from_bytes_str(msg.value['data'])
original_frame_sequence.append(frame)
if counter%self.cube_depth == 0 and counter!=0:
# To grayscale
frame = frame_to_gray(frame)
# Divide pixels by 255
reduced_frame = reduce_frame(frame)
frame_sequence.append(reduced_frame)
features = generate_features(frame_sequence, self.cube_depth, self.tile_size)
result = features.reshape(1, features.shape[0]*features.shape[1])
##Keep original frames
#jpeg encode
jpeg_frames = np.array([cv2.imencode('.jpg', x)[1] for x in original_frame_sequence])
origin_frames = frame_to_bytes_str(jpeg_frames)
end_frame_number = self.frame_counter
source_end_timestamp = msg.value['timestamp']
extra_fields = {'origin_frames':origin_frames,
'start_frame_number': start_frame_number,
'end_frame_number': end_frame_number,
'source_end_timestamp': source_end_timestamp,
'source_start_timestamp': source_start_timestamp}
self.producer.send_frame(result, extra_fields=extra_fields)
start_frame_number = end_frame_number
source_start_timestamp = source_end_timestamp
# Reset frame sequences
original_frame_sequence = []
frame_sequence = []
counter+=1
self.frame_counter+=1
else:
if counter == 0:
start_frame_number = self.frame_counter
source_start_timestamp = msg.value['timestamp']
counter+=1
self.frame_counter+=1
continue
# To grayscale
frame = frame_to_gray(frame)
# Divide pixels by 255
reduced_frame = reduce_frame(frame)
frame_sequence.append(reduced_frame)
counter+=1
self.frame_counter+=1
def gaussian_model_frame(self, frame):
frame = frame_to_gray(frame)
reduced_frame = reduce_frame(frame)
return reduced_frame
def send_frames(self):
counter = 0
frame_sequence = []
original_frame_sequence = []
frame_height, frame_width = self.output_frame_size
for msg in self.consumer.get_consumer():
frame = frame_from_bytes_str(msg.value['data'])
original_frame_sequence.append(frame)
if counter % self.sequence_size == 0 and counter != 0:
# Resize
frame = cv2.resize(frame, self.output_frame_size)
# To grayscale
if self.to_grayscale:
frame = frame_to_gray(frame)
# Divide pixels by 255
reduced_frame = reduce_frame(frame)
frame_sequence.append(reduced_frame)
#Todo: Think when image is RGB
#Todo: Maybe possible with resize, reshape?
clip = np.zeros(shape=(self.sequence_size, frame_height,
frame_width, 1))
clip[:, :, :, 0] = frame_sequence
#Keep original frames
#jpeg encode
jpeg_frames = np.array([
cv2.imencode('.jpg', x)[1] for x in original_frame_sequence
])
origin_frames = frame_to_bytes_str(jpeg_frames)
end_frame_number = self.frame_counter
source_end_timestamp = msg.value['timestamp']
extra_fields = {
'origin_frames': origin_frames,
'start_frame_number': start_frame_number,
'end_frame_number': end_frame_number,
'source_end_timestamp': source_end_timestamp,
'source_start_timestamp': source_start_timestamp
}
#send to producer
self.producer.send_frame(clip, extra_fields=extra_fields)
start_frame_number = end_frame_number
source_start_timestamp = source_end_timestamp
# Reset frame sequences
original_frame_sequence = []
frame_sequence = []
counter += 1
self.frame_counter += 1
else:
if counter == 0:
start_frame_number = self.frame_counter
source_start_timestamp = msg.value['timestamp']
counter += 1
self.frame_counter += 1
continue
# Resize
frame = cv2.resize(frame, self.output_frame_size)
# To grayscale
if self.to_grayscale:
frame = frame_to_gray(frame)
# Divide pixels by 255
reduced_frame = reduce_frame(frame)
frame_sequence.append(reduced_frame)
counter += 1
self.frame_counter += 1
def cnn_autoencoder_frame(self, frame, input_size):
frame = cv2.resize(frame, input_size)
frame = frame_to_gray(frame)
reduced_frame = reduce_frame(frame)
return reduced_frame