Anton Sobinov
lab: Sliman Bensmaia
"""
# %% 0 Imports
import os
import ncams
BASE_DIR = os.path.join('C:\\', 'FLIR_cameras', 'PublicExample')
# %% 1 Load camera_config
# Works if calibration and pose estimation has been done before and saved
cdatetime = '2019.12.09_16.23.02'
camera_config_dir = os.path.join(BASE_DIR, 'camconf_' + cdatetime)
camera_config = ncams.yaml_to_config(
os.path.join(camera_config_dir, 'config.yaml'))
# %% 2 Run the multi-calibration on all cameras
calibration_config = ncams.multi_camera_calibration(camera_config,
inspect=True)
# export to disk
ncams.export_calibration(calibration_config)
# %% 3 Do the pose estimation
pose_estimation_config = ncams.camera_pose.one_shot_multi_PnP(
camera_config, calibration_config)
# Does it look okay?
ncams.camera_pose.plot_poses(pose_estimation_config)
def main():
cdatetime = '2019.12.19_10.38.38'
camera_config_dir = os.path.join(BASE_DIR, 'camconf_' + cdatetime)
camera_config = ncams.yaml_to_config(
os.path.join(camera_config_dir, 'config.yaml'))
calibration_config, pose_estimation_config = ncams.load_camera_config(
camera_config)
session_shortnames = (
'exp_session_2019.12.20_09.49.42_AS_CMG_1',
'exp_session_2019.12.20_09.56.37_AS_CMG_2',
'exp_session_2019.12.20_09.57.31_AS_CMG_3',
'exp_session_2019.12.20_09.58.36_AS_CMG_4',
'exp_session_2019.12.20_10.09.44_AS_CMG_5',
'exp_session_2019.12.20_10.16.13_AS_CMG_6',
'exp_session_2019.12.20_10.34.40_AS_CMG_7',
'exp_session_2019.12.20_10.39.45_AS_CMG_8',
'exp_session_2019.12.20_10.45.01_AS_CMG_9',
'exp_session_2019.12.20_10.51.06_AS_CMG_10',
'exp_session_2019.12.20_11.11.21_AS_CMG_11',
'exp_session_2019.12.20_11.17.24_AS_CMG_12',
'exp_session_2019.12.20_11.21.52_AS_CMG_13',
)
for session_shortname in session_shortnames:
print('Processing session {}'.format(session_shortname))
session_full_filename = os.path.join(BASE_DIR, session_shortname,
'session_config.yaml')
session_config = ncams.import_session_config(session_full_filename)
session_config['video_path'] = 'videos'
session_config['ud_video_path'] = 'undistorted_videos'
for p in (os.path.join(session_config['session_path'],
session_config['video_path']),
os.path.join(session_config['session_path'],
session_config['ud_video_path'])):
if not os.path.isdir(p):
print('Making dir {}'.format(p))
os.mkdir(p)
for serial in camera_config['serials']:
session_config['cam_dicts'][serial]['pic_dir'] = session_config[
'cam_dicts'][serial]['name']
session_config['cam_dicts'][serial]['video'] = os.path.join(
session_config['video_path'],
session_config['cam_dicts'][serial]['name'] + '.mp4')
session_config['cam_dicts'][serial]['ud_video'] = os.path.join(
session_config['ud_video_path'],
session_config['cam_dicts'][serial]['name'] + '.mp4')
for cam_dict in session_config['cam_dicts'].values():
image_list = ncams.utils.get_image_list(
sort=True,
path=os.path.join(session_config['session_path'],
cam_dict['pic_dir']))
print('\tMaking a video for camera {} from {} images.'.format(
cam_dict['name'], len(image_list)))
ncams.images_to_video(image_list,
cam_dict['video'],
fps=session_config['frame_rate'],
output_folder=session_config['session_path'])
for icam, serial in enumerate(camera_config['serials']):
cam_dict = session_config['cam_dicts'][serial]
ncams.undistort_video(os.path.join(session_config['session_path'],
cam_dict['video']),
calibration_config['dicts'][serial],
crop_and_resize=False,
output_filename=os.path.join(
session_config['session_path'],
cam_dict['ud_video']))
print('\tCamera {} video undistorted.'.format(cam_dict['name']))
ncams.export_session_config(session_config)
def main():
cdatetime = '2019.12.19_10.38.38'
camera_config_dir = os.path.join(BASE_DIR, 'camconf_' + cdatetime)
camera_config = ncams.yaml_to_config(
os.path.join(camera_config_dir, 'config.yaml'))
calibration_config, pose_estimation_config = ncams.load_camera_config(
camera_config)
# # Load a session config from a file
# session_full_filename = os.path.join(BASE_DIR, 'exp_session_2019.12.09_16.40.45_AS_CMG_2',
# 'session_config.yaml')
# session_config = ncams.import_session_config(session_full_filename)
# %% 2b Load an existing DLC project with the labeled frames
dlc_prj_name = '2019.12.20_8camsNoMarkers'
scorer = 'AS'
prj_date = '2019-12-23'
dlc_proj_name = '-'.join([dlc_prj_name, scorer, prj_date])
proj_path = os.path.join(BASE_DIR, dlc_proj_name)
config_path = os.path.join(proj_path, 'config.yaml')
print('Existing config_path: "{}"'.format(config_path))
labeled_csv_path = os.path.join(proj_path, 'labeled_videos')
if not os.path.isdir(labeled_csv_path):
os.mkdir(labeled_csv_path)
# print('training network')
# deeplabcut.train_network(config_path, gputouse=0, saveiters=100, maxiters=250000,
# displayiters=10)
# print('Evaluating network...')
# deeplabcut.evaluate_network(config_path, plotting=False)
# which videos do you want to train on?
video_path = os.path.join(BASE_DIR, 'exp_session_2019.12.20_videos')
training_videos = [
os.path.join(video_path, fname)
for fname in ('4_cam19194005.mp4', '4_cam19194008.mp4',
'4_cam19194009.mp4', '4_cam19194013.mp4',
'4_cam19335177.mp4', '4_cam19340298.mp4',
'4_cam19340300.mp4', '4_cam19340396.mp4',
'5_cam19194005.mp4', '5_cam19194008.mp4',
'5_cam19194009.mp4', '5_cam19194013.mp4',
'5_cam19335177.mp4', '5_cam19340298.mp4',
'5_cam19340300.mp4', '5_cam19340396.mp4',
'6_cam19194005.mp4', '6_cam19194008.mp4',
'6_cam19194009.mp4', '6_cam19194013.mp4',
'6_cam19335177.mp4', '6_cam19340298.mp4',
'6_cam19340300.mp4', '6_cam19340396.mp4')
]
# print('analyzing videos')
# deeplabcut.analyze_videos(config_path, training_videos,
# gputouse=0, save_as_csv=True, destfolder=labeled_csv_path)
# print('making labeled videos')
# deeplabcut.create_labeled_video(config_path, training_videos, destfolder=labeled_csv_path,
# draw_skeleton=True)
# %% 3 Triangulation from multiple cameras
method = 'full_rank'
threshold = 0.9
triangulated_path = os.path.join(
proj_path, 'triangulated_{}_{}'.format(method, threshold))
if not os.path.exists(triangulated_path):
os.mkdir(triangulated_path)
# make all videos
# file_prefixes = ['4', '5', '6']
# for file_prefix in file_prefixes:
# print('Working on session {}'.format(file_prefix))
# triangulated_path2 = os.path.join(triangulated_path, 'session{}'.format(file_prefix))
# triangulated_csv = os.path.join(triangulated_path2, 'triangulated_points{}.csv'.format(
# '' if len(file_prefix) == 0 else '_'+file_prefix))
# triangulated_csv_p = os.path.join(
# triangulated_path2, 'triangulated_points{}_smoothed.csv'.format(
# '' if len(file_prefix) == 0 else '_'+file_prefix))
# # ncams.triangulate(
# # camera_config, triangulated_csv, calibration_config, pose_estimation_config, labeled_csv_path,
# # threshold=threshold, method=method, undistorted_data=True, file_prefix=file_prefix)
# # ncams.process_triangulated_data(triangulated_csv, output_csv=triangulated_csv_p)
# # videos = [i for i in training_videos if ntpath.basename(i)[0] == file_prefix]
# # # %% 4 Make markered videos
# # ncams.make_triangulation_videos(camera_config, camera_config['serials'], videos,
# # triangulated_csv_p, skeleton_config=config_path)
# # %% 5 Interactive demonstration with a slider
# # ncams.reconstruction.interactive_3d_plot(
# # camera_config['serials'][0], camera_config, session_config, triangulated_csv,
# # num_frames_limit=None)
# make 1 pretty one
file_prefix = '4'
print('Working on session {}'.format(file_prefix))
triangulated_path2 = os.path.join(triangulated_path,
'session{}'.format(file_prefix))
triangulated_csv_p = os.path.join(
triangulated_path2, 'triangulated_points{}_smoothed.csv'.format(
'' if len(file_prefix) == 0 else '_' + file_prefix))
serial = 19335177
frame_range = (260, 360)
video_path = [
i for i in training_videos
if ntpath.basename(i)[0] == file_prefix and str(serial) in i
][0]
output_path = os.path.join(triangulated_path2,
'pretty_{}_{}.mp4'.format(serial, file_prefix))
ik_video_path = os.path.join(proj_path, 'ik_videos', 'marshmallow.webm')
ncams.make_triangulation_video(
video_path,
triangulated_csv_p,
skeleton_config=config_path,
frame_range=frame_range,
output_path=output_path,
thrd_video_path=ik_video_path,
thrd_video_frame_offset=0,
third_video_crop_hw=[slice(50, -100),
slice(350, -700)],
figure_dpi=300,
ranges=((-0.33, 3), (-2, 2), (-1.33, 6.74)))