def AnalyzeVideo(video,
DLCscorer,
DLCscorerlegacy,
trainFraction,
cfg,
dlc_cfg,
sess,
inputs,
outputs,
pdindex,
save_as_csv,
destfolder=None,
TFGPUinference=True,
dynamic=(False, .5, 10)):
''' Helper function for analyzing a video. '''
print("Starting to analyze % ", video)
vname = Path(video).stem
if destfolder is None:
destfolder = str(Path(video).parents[0])
notanalyzed, dataname, DLCscorer = auxiliaryfunctions.CheckifNotAnalyzed(
destfolder, vname, DLCscorer, DLCscorerlegacy)
if notanalyzed:
print("Loading ", video)
cap = cv2.VideoCapture(video)
if not cap.isOpened():
raise IOError(
'Video could not be opened. Please check that the the file integrity.'
)
fps = cap.get(
5
) #https://docs.opencv.org/2.4/modules/highgui/doc/reading_and_writing_images_and_video.html#videocapture-get
nframes = int(cap.get(7))
duration = nframes * 1. / fps
size = (int(cap.get(4)), int(cap.get(3)))
ny, nx = size
print("Duration of video [s]: ", round(duration, 2),
", recorded with ", round(fps, 2), "fps!")
print("Overall # of frames: ", nframes,
" found with (before cropping) frame dimensions: ", nx, ny)
dynamic_analysis_state, detectiontreshold, margin = dynamic
start = time.time()
print("Starting to extract posture")
if dynamic_analysis_state:
PredictedData, nframes = GetPoseDynamic(cfg, dlc_cfg, sess, inputs,
outputs, cap, nframes,
detectiontreshold, margin)
#GetPoseF_GTF(cfg,dlc_cfg, sess, inputs, outputs,cap,nframes,int(dlc_cfg["batch_size"]))
else:
if int(dlc_cfg["batch_size"]) > 1:
if TFGPUinference:
PredictedData, nframes = GetPoseF_GTF(
cfg, dlc_cfg, sess, inputs, outputs, cap, nframes,
int(dlc_cfg["batch_size"]))
else:
PredictedData, nframes = GetPoseF(
cfg, dlc_cfg, sess, inputs, outputs, cap, nframes,
int(dlc_cfg["batch_size"]))
else:
if TFGPUinference:
PredictedData, nframes = GetPoseS_GTF(
cfg, dlc_cfg, sess, inputs, outputs, cap, nframes)
else:
PredictedData, nframes = GetPoseS(cfg, dlc_cfg, sess,
inputs, outputs, cap,
nframes)
stop = time.time()
if cfg['cropping'] == True:
coords = [cfg['x1'], cfg['x2'], cfg['y1'], cfg['y2']]
else:
coords = [0, nx, 0, ny]
dictionary = {
"start": start,
"stop": stop,
"run_duration": stop - start,
"Scorer": DLCscorer,
"DLC-model-config file": dlc_cfg,
"fps": fps,
"batch_size": dlc_cfg["batch_size"],
"frame_dimensions": (ny, nx),
"nframes": nframes,
"iteration (active-learning)": cfg["iteration"],
"training set fraction": trainFraction,
"cropping": cfg['cropping'],
"cropping_parameters": coords
#"gpu_info": device_lib.list_local_devices()
}
metadata = {'data': dictionary}
print("Saving results in %s..." % (Path(video).parents[0]))
auxiliaryfunctions.SaveData(PredictedData[:nframes, :], metadata,
dataname, pdindex, range(nframes),
save_as_csv)
return DLCscorer
else:
return DLCscorer
def analyze_time_lapse_frames(config,
directory,
frametype='.png',
shuffle=1,
trainingsetindex=0,
gputouse=None,
save_as_csv=False,
rgb=True):
"""
Analyzed all images (of type = frametype) in a folder and stores the output in one file.
You can crop the frames (before analysis), by changing 'cropping'=True and setting 'x1','x2','y1','y2' in the config file.
Output: The labels are stored as MultiIndex Pandas Array, which contains the name of the network, body part name, (x, y) label position \n
in pixels, and the likelihood for each frame per body part. These arrays are stored in an efficient Hierarchical Data Format (HDF) \n
in the same directory, where the video is stored. However, if the flag save_as_csv is set to True, the data can also be exported in \n
comma-separated values format (.csv), which in turn can be imported in many programs, such as MATLAB, R, Prism, etc.
Parameters
----------
config : string
Full path of the config.yaml file as a string.
directory: string
Full path to directory containing the frames that shall be analyzed
frametype: string, optional
Checks for the file extension of the frames. Only images with this extension are analyzed. The default is ``.png``
shuffle: int, optional
An integer specifying the shuffle index of the training dataset used for training the network. The default is 1.
trainingsetindex: int, optional
Integer specifying which TrainingsetFraction to use. By default the first (note that TrainingFraction is a list in config.yaml).
gputouse: int, optional. Natural number indicating the number of your GPU (see number in nvidia-smi). If you do not have a GPU put None.
See: https://nvidia.custhelp.com/app/answers/detail/a_id/3751/~/useful-nvidia-smi-queries
save_as_csv: bool, optional
Saves the predictions in a .csv file. The default is ``False``; if provided it must be either ``True`` or ``False``
rbg: bool, optional.
Whether to load image as rgb; Note e.g. some tiffs do not alow that option in imread, then just set this to false.
Examples
--------
If you want to analyze all frames in /analysis/project/timelapseexperiment1
>>> deeplabcutcore.analyze_videos('/analysis/project/reaching-task/config.yaml','/analysis/project/timelapseexperiment1')
--------
If you want to analyze all frames in /analysis/project/timelapseexperiment1
>>> deeplabcutcore.analyze_videos('/analysis/project/reaching-task/config.yaml','/analysis/project/timelapseexperiment1')
--------
Note: for test purposes one can extract all frames from a video with ffmeg, e.g. ffmpeg -i testvideo.avi thumb%04d.png
"""
if 'TF_CUDNN_USE_AUTOTUNE' in os.environ:
del os.environ[
'TF_CUDNN_USE_AUTOTUNE'] #was potentially set during training
if gputouse is not None: #gpu selection
os.environ['CUDA_VISIBLE_DEVICES'] = str(gputouse)
tf.compat.v1.reset_default_graph()
start_path = os.getcwd(
) #record cwd to return to this directory in the end
cfg = auxiliaryfunctions.read_config(config)
trainFraction = cfg['TrainingFraction'][trainingsetindex]
modelfolder = os.path.join(
cfg["project_path"],
str(auxiliaryfunctions.GetModelFolder(trainFraction, shuffle, cfg)))
path_test_config = Path(modelfolder) / 'test' / 'pose_cfg.yaml'
try:
dlc_cfg = load_config(str(path_test_config))
except FileNotFoundError:
raise FileNotFoundError(
"It seems the model for shuffle %s and trainFraction %s does not exist."
% (shuffle, trainFraction))
# Check which snapshots are available and sort them by # iterations
try:
Snapshots = np.array([
fn.split('.')[0]
for fn in os.listdir(os.path.join(modelfolder, 'train'))
if "index" in fn
])
except FileNotFoundError:
raise FileNotFoundError(
"Snapshots not found! It seems the dataset for shuffle %s has not been trained/does not exist.\n Please train it before using it to analyze videos.\n Use the function 'train_network' to train the network for shuffle %s."
% (shuffle, shuffle))
if cfg['snapshotindex'] == 'all':
print(
"Snapshotindex is set to 'all' in the config.yaml file. Running video analysis with all snapshots is very costly! Use the function 'evaluate_network' to choose the best the snapshot. For now, changing snapshot index to -1!"
)
snapshotindex = -1
else:
snapshotindex = cfg['snapshotindex']
increasing_indices = np.argsort([int(m.split('-')[1]) for m in Snapshots])
Snapshots = Snapshots[increasing_indices]
print("Using %s" % Snapshots[snapshotindex], "for model", modelfolder)
##################################################
# Load and setup CNN part detector
##################################################
# Check if data already was generated:
dlc_cfg['init_weights'] = os.path.join(modelfolder, 'train',
Snapshots[snapshotindex])
trainingsiterations = (dlc_cfg['init_weights'].split(
os.sep)[-1]).split('-')[-1]
#update batchsize (based on parameters in config.yaml)
dlc_cfg['batch_size'] = cfg['batch_size']
# Name for scorer:
DLCscorer, DLCscorerlegacy = auxiliaryfunctions.GetScorerName(
cfg, shuffle, trainFraction, trainingsiterations=trainingsiterations)
sess, inputs, outputs = predict.setup_pose_prediction(dlc_cfg)
# update number of outputs and adjust pandas indices
dlc_cfg['num_outputs'] = cfg.get('num_outputs', 1)
xyz_labs_orig = ['x', 'y', 'likelihood']
suffix = [str(s + 1) for s in range(dlc_cfg['num_outputs'])]
suffix[0] = '' # first one has empty suffix for backwards compatibility
xyz_labs = [x + s for s in suffix for x in xyz_labs_orig]
pdindex = pd.MultiIndex.from_product(
[[DLCscorer], dlc_cfg['all_joints_names'], xyz_labs],
names=['scorer', 'bodyparts', 'coords'])
if gputouse is not None: #gpu selectinon
os.environ['CUDA_VISIBLE_DEVICES'] = str(gputouse)
##################################################
# Loading the images
##################################################
#checks if input is a directory
if os.path.isdir(directory) == True:
"""
Analyzes all the frames in the directory.
"""
print("Analyzing all frames in the directory: ", directory)
os.chdir(directory)
framelist = np.sort(
[fn for fn in os.listdir(os.curdir) if (frametype in fn)])
vname = Path(directory).stem
notanalyzed, dataname, DLCscorer = auxiliaryfunctions.CheckifNotAnalyzed(
directory, vname, DLCscorer, DLCscorerlegacy, flag='framestack')
if notanalyzed:
nframes = len(framelist)
if nframes > 0:
start = time.time()
PredictedData, nframes, nx, ny = GetPosesofFrames(
cfg, dlc_cfg, sess, inputs, outputs, directory, framelist,
nframes, dlc_cfg['batch_size'], rgb)
stop = time.time()
if cfg['cropping'] == True:
coords = [cfg['x1'], cfg['x2'], cfg['y1'], cfg['y2']]
else:
coords = [0, nx, 0, ny]
dictionary = {
"start": start,
"stop": stop,
"run_duration": stop - start,
"Scorer": DLCscorer,
"config file": dlc_cfg,
"batch_size": dlc_cfg["batch_size"],
"num_outputs": dlc_cfg["num_outputs"],
"frame_dimensions": (ny, nx),
"nframes": nframes,
"cropping": cfg['cropping'],
"cropping_parameters": coords
}
metadata = {'data': dictionary}
print("Saving results in %s..." % (directory))
auxiliaryfunctions.SaveData(PredictedData[:nframes, :],
metadata, dataname, pdindex,
framelist, save_as_csv)
print(
"The folder was analyzed. Now your research can truly start!"
)
print(
"If the tracking is not satisfactory for some frome, consider expanding the training set."
)
else:
print(
"No frames were found. Consider changing the path or the frametype."
)
os.chdir(str(start_path))
if save_as_csv:
df_3d.to_csv(str(output_filename + '.csv'))
print("Triangulated data for video", video_list[i])
print("Results are saved under: ", destfolder)
# have to make the dest folder none so that it can be updated for a new pair of videos
if destfolder == str(Path(video).parents[0]):
destfolder = None
if len(video_list) > 0:
print("All videos were analyzed...")
print(
"Now you can create 3D video(s) using deeplabcutcore.create_labeled_video_3d"
)
'''
ToDo: speed up func. below and check only for one cam individually
PredicteData = np.zeros((nframes, 3 * len(dlc_cfg['all_joints_names'])))
PredicteData[batch_num*batchsize:batch_num*batchsize+batch_ind, :] = pose[:batch_ind,:]
pdindex = pd.MultiIndex.from_product([[DLCscorer], dlc_cfg['all_joints_names'], ['x', 'y', 'likelihood']],names=['scorer', 'bodyparts', 'coords'])
auxiliaryfunctions.SaveData(PredicteData[:nframes,:], metadata, dataname, pdindex, framelist,save_as_csv)
'''
def undistort_points(config, dataframe, camera_pair, destfolder):
cfg_3d = auxiliaryfunctions.read_config(config)
img_path, path_corners, path_camera_matrix, path_undistort = auxiliaryfunctions_3d.Foldernames3Dproject(
cfg_3d)
'''
path_undistort = destfolder
filename_cam1 = Path(dataframe[0]).stem