def start_videoanalyser():
print("Starting DeepLabCut")
model = load_model(
r"D:\DeepPoseKit-Data-master\datasets\fly\best_model_densenet.h5")
experiment_enabled = False
video_output = True
if experiment_enabled:
print("Initializing experiment")
experiment = ExampleExperiment()
experiment.start_experiment()
# some variables initialization
all_rows = []
index = 0
while video.isOpened():
ret, frame = video.read()
if ret:
scmap, locref, pose = get_pose(frame, config, sess, inputs,
outputs)
peaks = find_local_peaks_new(scmap, locref, ANIMALS_NUMBER, config)
skeletons = calculate_skeletons(peaks, ANIMALS_NUMBER)
if skeletons:
for skeleton in skeletons:
if experiment_enabled:
result, response = experiment.check_skeleton(
frame, skeleton)
plot_triggers_response(frame, response)
out_frame = plot_bodyparts(frame, skeletons)
else:
out_frame = frame
cv2.imshow('stream', out_frame)
if video_output:
video_file.write(out_frame)
if experiment_enabled:
all_rows.append(
create_row(index, skeletons, experiment_enabled,
experiment.get_trial()))
else:
all_rows.append(
create_row(index, skeletons, experiment_enabled, None))
index += 1
else:
break
if cv2.waitKey(1) & 0xFF == ord('q'):
break
if experiment_enabled:
experiment.stop_experiment()
if video_output:
print('Saving analyzed video for {}'.format(video_name))
video_file.release()
video.release()
create_dataframes(all_rows)
def get_analysed_frames(self) -> tuple:
"""
The main magic is happening here
Getting the data from DLC processes
Plotting the data
Checking data in experiments
Gathering data to a series
"""
if self._dlc_running:
analysed_frames = {}
analysis_time = None
if CROP:
frame_height = CROP_Y[1] - CROP_Y[0]
frame_width = CROP_X[1] - CROP_X[0]
else:
frame_width,frame_height = RESOLUTION
for camera in self._multiprocessing:
if self._multiprocessing[camera]['output'].full():
if self._start_time is None:
self._start_time = time.time() # getting the first frame here
# Getting the analysed data
analysed_index, peaks, analysis_time = self._multiprocessing[camera]['output'].get()
skeletons = calculate_skeletons(peaks, ANIMALS_NUMBER)
print('', end='\r', flush=True) # this is the line you should not remove
analysed_frame , depth_map, input_time = self.get_stored_frames(camera, analysed_index)
delay_time = time.time() - input_time
# Calculating FPS and plotting the data on frame
self.calculate_fps(analysis_time if analysis_time != 0 else 0.01)
frame_time = time.time() - self._start_time
analysed_image = plot_metadata_frame(
plot_bodyparts(analysed_frame, skeletons),
frame_width, frame_height, self._fps, frame_time)
# Experiments
if self._experiment.experiment_finished and self._experiment_running:
self._experiment_running = False
if self._experiment_running and not self._experiment.experiment_finished:
if ANIMALS_NUMBER > 1 and not FLATTEN_MA and not PASS_SEPARATE:
self._experiment.check_skeleton(analysed_image,skeletons)
else:
for skeleton in skeletons:
self._experiment.check_skeleton(analysed_image, skeleton)
# Gathering data as pd.Series for output
if self._data_output:
self.append_row(camera, analysed_index, skeletons,
self._experiment_running, self._experiment.get_trial(), self._start_time)
analysed_frames[camera] = analysed_image
return analysed_frames, analysis_time