def analyze_videos(_, *args, **kwargs):
"""Makes prediction.\n
CONFIG: Full path of the "config.yaml" file in the train directory of a project.\n
VIDEOS: Full path to video.\n
Example\n
----------
python3 dlc.py analyze_videos /home/project/reaching/config.yaml /home/project/reaching/newVideo/1.avi
"""
from deeplabcut.pose_estimation_tensorflow import predict_videos
predict_videos.analyze_videos(*args, **kwargs)
def triangulate(
config,
video_path,
videotype="avi",
filterpredictions=True,
filtertype="median",
gputouse=None,
destfolder=None,
save_as_csv=False,
):
"""
This function triangulates the detected DLC-keypoints from the two camera views
using the camera matrices (derived from calibration) to calculate 3D predictions.
Parameters
----------
config : string
Full path of the config.yaml file as a string.
video_path : string/list of list
Full path of the directory where videos are saved. If the user wants to analyze
only a pair of videos, the user needs to pass them as a list of list of videos,
i.e. [['video1-camera-1.avi','video1-camera-2.avi']]
videotype: string, optional
Checks for the extension of the video in case the input to the video is a directory.\n
Only videos with this extension are analyzed. The default is ``.avi``
filterpredictions: Bool, optional
Filter the predictions with filter specified by "filtertype". If specified it
should be either ``True`` or ``False``.
filtertype: string
Select which filter, 'arima' or 'median' filter (currently supported).
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
destfolder: string, optional
Specifies the destination folder for analysis data (default is the path of the video)
save_as_csv: bool, optional
Saves the predictions in a .csv file. The default is ``False``
Example
-------
Linux/MacOS
To analyze all the videos in the directory:
>>> deeplabcut.triangulate(config,'/data/project1/videos/')
To analyze only a few pairs of videos:
>>> deeplabcut.triangulate(config,[['/data/project1/videos/video1-camera-1.avi','/data/project1/videos/video1-camera-2.avi'],['/data/project1/videos/video2-camera-1.avi','/data/project1/videos/video2-camera-2.avi']])
Windows
To analyze all the videos in the directory:
>>> deeplabcut.triangulate(config,'C:\\yourusername\\rig-95\\Videos')
To analyze only a few pair of videos:
>>> deeplabcut.triangulate(config,[['C:\\yourusername\\rig-95\\Videos\\video1-camera-1.avi','C:\\yourusername\\rig-95\\Videos\\video1-camera-2.avi'],['C:\\yourusername\\rig-95\\Videos\\video2-camera-1.avi','C:\\yourusername\\rig-95\\Videos\\video2-camera-2.avi']])
"""
from deeplabcut.pose_estimation_tensorflow import predict_videos
from deeplabcut.post_processing import filtering
cfg_3d = auxiliaryfunctions.read_config(config)
cam_names = cfg_3d["camera_names"]
pcutoff = cfg_3d["pcutoff"]
scorer_3d = cfg_3d["scorername_3d"]
snapshots = {}
for cam in cam_names:
snapshots[cam] = cfg_3d[str("config_file_" + cam)]
# Check if the config file exists
if not os.path.exists(snapshots[cam]):
raise Exception(
str("It seems the file specified in the variable config_file_"
+ str(cam)) +
" does not exist. Please edit the config file with correct file path and retry."
)
# flag to check if the video_path variable is a string or a list of list
flag = False # assumes that video path is a list
if isinstance(video_path, str) == True:
flag = True
video_list = auxiliaryfunctions_3d.get_camerawise_videos(
video_path, cam_names, videotype=videotype)
else:
video_list = video_path
if video_list == []:
print("No videos found in the specified video path.", video_path)
print(
"Please make sure that the video names are specified with correct camera names as entered in the config file or"
)
print(
"perhaps the videotype is distinct from the videos in the path, I was looking for:",
videotype,
)
print("List of pairs:", video_list)
scorer_name = {}
run_triangulate = False
for i in range(len(video_list)):
dataname = []
for j in range(len(video_list[i])): # looping over cameras
if cam_names[j] in video_list[i][j]:
print("Analyzing video %s using %s" %
(video_list[i][j], str("config_file_" + cam_names[j])))
config_2d = snapshots[cam_names[j]]
cfg = auxiliaryfunctions.read_config(config_2d)
shuffle = cfg_3d[str("shuffle_" + cam_names[j])]
trainingsetindex = cfg_3d[str("trainingsetindex_" +
cam_names[j])]
trainFraction = cfg["TrainingFraction"][trainingsetindex]
if flag == True:
video = os.path.join(video_path, video_list[i][j])
else:
video_path = str(Path(video_list[i][j]).parents[0])
video = os.path.join(video_path, video_list[i][j])
if destfolder is None:
destfolder = str(Path(video).parents[0])
vname = Path(video).stem
prefix = str(vname).split(cam_names[j])[0]
suffix = str(vname).split(cam_names[j])[-1]
if prefix == "":
pass
elif prefix[-1] == "_" or prefix[-1] == "-":
prefix = prefix[:-1]
if suffix == "":
pass
elif suffix[0] == "_" or suffix[0] == "-":
suffix = suffix[1:]
if prefix == "":
output_file = os.path.join(destfolder, suffix)
else:
if suffix == "":
output_file = os.path.join(destfolder, prefix)
else:
output_file = os.path.join(destfolder,
prefix + "_" + suffix)
output_filename = os.path.join(
output_file + "_" + scorer_3d
) # Check if the videos are already analyzed for 3d
if os.path.isfile(output_filename + ".h5"):
if save_as_csv is True and not os.path.exists(
output_filename + ".csv"):
# In case user adds save_as_csv is True after triangulating
pd.read_hdf(output_filename + ".h5").to_csv(
str(output_filename + ".csv"))
print(
"Already analyzed...Checking the meta data for any change in the camera matrices and/or scorer names",
vname,
)
pickle_file = str(output_filename + "_meta.pickle")
metadata_ = auxiliaryfunctions_3d.LoadMetadata3d(
pickle_file)
(
img_path,
path_corners,
path_camera_matrix,
path_undistort,
) = auxiliaryfunctions_3d.Foldernames3Dproject(cfg_3d)
path_stereo_file = os.path.join(path_camera_matrix,
"stereo_params.pickle")
stereo_file = auxiliaryfunctions.read_pickle(
path_stereo_file)
cam_pair = str(cam_names[0] + "-" + cam_names[1])
if_video_analyzed = False # variable to keep track if the video was already analyzed
# Check for the camera matrix
for k in metadata_["stereo_matrix"].keys():
if np.all(metadata_["stereo_matrix"][k] ==
stereo_file[cam_pair][k]):
pass
else:
run_triangulate = True
# Check for scorer names in the pickle file of 3d output
DLCscorer, DLCscorerlegacy = auxiliaryfunctions.GetScorerName(
cfg,
shuffle,
trainFraction,
trainingsiterations="unknown")
if (metadata_["scorer_name"][cam_names[j]] == DLCscorer
): # TODO: CHECK FOR BOTH?
if_video_analyzed = True
elif metadata_["scorer_name"][
cam_names[j]] == DLCscorerlegacy:
if_video_analyzed = True
else:
if_video_analyzed = False
run_triangulate = True
if if_video_analyzed:
print("This file is already analyzed!")
dataname.append(
os.path.join(destfolder,
vname + DLCscorer + ".h5"))
scorer_name[cam_names[j]] = DLCscorer
else:
# Analyze video if score name is different
DLCscorer = predict_videos.analyze_videos(
config_2d,
[video],
videotype=videotype,
shuffle=shuffle,
trainingsetindex=trainingsetindex,
gputouse=gputouse,
destfolder=destfolder,
)
scorer_name[cam_names[j]] = DLCscorer
if_video_analyzed = False
run_triangulate = True
if filterpredictions:
filtering.filterpredictions(
config_2d,
[video],
videotype=videotype,
shuffle=shuffle,
trainingsetindex=trainingsetindex,
filtertype=filtertype,
destfolder=destfolder,
)
dataname.append(
os.path.join(destfolder,
vname + DLCscorer + ".h5"))
else: # need to do the whole jam.
DLCscorer = predict_videos.analyze_videos(
config_2d,
[video],
videotype=videotype,
shuffle=shuffle,
trainingsetindex=trainingsetindex,
gputouse=gputouse,
destfolder=destfolder,
)
scorer_name[cam_names[j]] = DLCscorer
run_triangulate = True
print(destfolder, vname, DLCscorer)
if filterpredictions:
filtering.filterpredictions(
config_2d,
[video],
videotype=videotype,
shuffle=shuffle,
trainingsetindex=trainingsetindex,
filtertype=filtertype,
destfolder=destfolder,
)
dataname.append(
os.path.join(destfolder,
vname + DLCscorer + ".h5"))
if run_triangulate:
# if len(dataname)>0:
# undistort points for this pair
print("Undistorting...")
(
dataFrame_camera1_undistort,
dataFrame_camera2_undistort,
stereomatrix,
path_stereo_file,
) = undistort_points(config, dataname,
str(cam_names[0] + "-" + cam_names[1]))
if len(dataFrame_camera1_undistort) != len(
dataFrame_camera2_undistort):
import warnings
warnings.warn(
"The number of frames do not match in the two videos. Please make sure that your videos have same number of frames and then retry! Excluding the extra frames from the longer video."
)
if len(dataFrame_camera1_undistort) > len(
dataFrame_camera2_undistort):
dataFrame_camera1_undistort = dataFrame_camera1_undistort[:len(
dataFrame_camera2_undistort)]
if len(dataFrame_camera2_undistort) > len(
dataFrame_camera1_undistort):
dataFrame_camera2_undistort = dataFrame_camera2_undistort[:len(
dataFrame_camera1_undistort)]
# raise Exception("The number of frames do not match in the two videos. Please make sure that your videos have same number of frames and then retry!")
X_final = []
triangulate = []
scorer_cam1 = dataFrame_camera1_undistort.columns.get_level_values(
0)[0]
scorer_cam2 = dataFrame_camera2_undistort.columns.get_level_values(
0)[0]
df_3d, scorer_3d, bodyparts = auxiliaryfunctions_3d.create_empty_df(
dataFrame_camera1_undistort, scorer_3d, flag="3d")
P1 = stereomatrix["P1"]
P2 = stereomatrix["P2"]
print("Computing the triangulation...")
for bpindex, bp in enumerate(bodyparts):
# Extract the indices of frames where the likelihood of a bodypart for both cameras are less than pvalue
likelihoods = np.array([
dataFrame_camera1_undistort[scorer_cam1][bp]
["likelihood"].values[:],
dataFrame_camera2_undistort[scorer_cam2][bp]
["likelihood"].values[:],
])
likelihoods = likelihoods.T
# Extract frames where likelihood for both the views is less than the pcutoff
low_likelihood_frames = np.any(likelihoods < pcutoff, axis=1)
# low_likelihood_frames = np.all(likelihoods < pcutoff, axis=1)
low_likelihood_frames = np.where(
low_likelihood_frames == True)[0]
points_cam1_undistort = np.array([
dataFrame_camera1_undistort[scorer_cam1][bp]
["x"].values[:],
dataFrame_camera1_undistort[scorer_cam1][bp]
["y"].values[:],
])
points_cam1_undistort = points_cam1_undistort.T
# For cam1 camera: Assign nans to x and y values of a bodypart where the likelihood for is less than pvalue
points_cam1_undistort[low_likelihood_frames] = np.nan, np.nan
points_cam1_undistort = np.expand_dims(points_cam1_undistort,
axis=1)
points_cam2_undistort = np.array([
dataFrame_camera2_undistort[scorer_cam2][bp]
["x"].values[:],
dataFrame_camera2_undistort[scorer_cam2][bp]
["y"].values[:],
])
points_cam2_undistort = points_cam2_undistort.T
# For cam2 camera: Assign nans to x and y values of a bodypart where the likelihood is less than pvalue
points_cam2_undistort[low_likelihood_frames] = np.nan, np.nan
points_cam2_undistort = np.expand_dims(points_cam2_undistort,
axis=1)
X_l = auxiliaryfunctions_3d.triangulatePoints(
P1, P2, points_cam1_undistort, points_cam2_undistort)
# ToDo: speed up func. below by saving in numpy.array
X_final.append(X_l)
triangulate.append(X_final)
triangulate = np.asanyarray(triangulate)
metadata = {}
metadata["stereo_matrix"] = stereomatrix
metadata["stereo_matrix_file"] = path_stereo_file
metadata["scorer_name"] = {
cam_names[0]: scorer_name[cam_names[0]],
cam_names[1]: scorer_name[cam_names[1]],
}
# Create an empty dataframe to store x,y,z of 3d data
for bpindex, bp in enumerate(bodyparts):
df_3d.iloc[:][scorer_3d, bp, "x"] = triangulate[0, bpindex,
0, :]
df_3d.iloc[:][scorer_3d, bp, "y"] = triangulate[0, bpindex,
1, :]
df_3d.iloc[:][scorer_3d, bp, "z"] = triangulate[0, bpindex,
2, :]
df_3d.to_hdf(
str(output_filename + ".h5"),
"df_with_missing",
format="table",
mode="w",
)
auxiliaryfunctions_3d.SaveMetadata3d(
str(output_filename + "_meta.pickle"), metadata)
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 deeplabcut.create_labeled_video_3d"
)
def triangulate(
config,
video_path,
videotype="",
filterpredictions=True,
filtertype="median",
gputouse=None,
destfolder=None,
save_as_csv=False,
track_method="",
):
"""
This function triangulates the detected DLC-keypoints from the two camera views
using the camera matrices (derived from calibration) to calculate 3D predictions.
Parameters
----------
config : string
Full path of the config.yaml file as a string.
video_path : string/list of list
Full path of the directory where videos are saved. If the user wants to analyze
only a pair of videos, the user needs to pass them as a list of list of videos,
i.e. [['video1-camera-1.avi','video1-camera-2.avi']]
videotype: string, optional
Checks for the extension of the video in case the input to the video is a directory.\n Only videos with this extension are analyzed.
If left unspecified, videos with common extensions ('avi', 'mp4', 'mov', 'mpeg', 'mkv') are kept.
filterpredictions: Bool, optional
Filter the predictions with filter specified by "filtertype". If specified it
should be either ``True`` or ``False``.
filtertype: string
Select which filter, 'arima' or 'median' filter (currently supported).
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
destfolder: string, optional
Specifies the destination folder for analysis data (default is the path of the video)
save_as_csv: bool, optional
Saves the predictions in a .csv file. The default is ``False``
Example
-------
Linux/MacOS
To analyze all the videos in the directory:
>>> deeplabcut.triangulate(config,'/data/project1/videos/')
To analyze only a few pairs of videos:
>>> deeplabcut.triangulate(config,[['/data/project1/videos/video1-camera-1.avi','/data/project1/videos/video1-camera-2.avi'],['/data/project1/videos/video2-camera-1.avi','/data/project1/videos/video2-camera-2.avi']])
Windows
To analyze all the videos in the directory:
>>> deeplabcut.triangulate(config,'C:\\yourusername\\rig-95\\Videos')
To analyze only a few pair of videos:
>>> deeplabcut.triangulate(config,[['C:\\yourusername\\rig-95\\Videos\\video1-camera-1.avi','C:\\yourusername\\rig-95\\Videos\\video1-camera-2.avi'],['C:\\yourusername\\rig-95\\Videos\\video2-camera-1.avi','C:\\yourusername\\rig-95\\Videos\\video2-camera-2.avi']])
"""
from deeplabcut.pose_estimation_tensorflow import predict_videos
from deeplabcut.post_processing import filtering
cfg_3d = auxiliaryfunctions.read_config(config)
cam_names = cfg_3d["camera_names"]
pcutoff = cfg_3d["pcutoff"]
scorer_3d = cfg_3d["scorername_3d"]
snapshots = {}
for cam in cam_names:
snapshots[cam] = cfg_3d[str("config_file_" + cam)]
# Check if the config file exists
if not os.path.exists(snapshots[cam]):
raise Exception(
str(
"It seems the file specified in the variable config_file_"
+ str(cam)
)
+ " does not exist. Please edit the config file with correct file path and retry."
)
# flag to check if the video_path variable is a string or a list of list
flag = False # assumes that video path is a list
if isinstance(video_path, str) == True:
flag = True
video_list = auxiliaryfunctions_3d.get_camerawise_videos(
video_path, cam_names, videotype=videotype
)
else:
video_list = video_path
if video_list == []:
print("No videos found in the specified video path.", video_path)
print(
"Please make sure that the video names are specified with correct camera names as entered in the config file or"
)
print(
"perhaps the videotype is distinct from the videos in the path, I was looking for:",
videotype,
)
print("List of pairs:", video_list)
scorer_name = {}
run_triangulate = False
for i in range(len(video_list)):
dataname = []
for j in range(len(video_list[i])): # looping over cameras
if cam_names[j] in video_list[i][j]:
print(
"Analyzing video %s using %s"
% (video_list[i][j], str("config_file_" + cam_names[j]))
)
config_2d = snapshots[cam_names[j]]
cfg = auxiliaryfunctions.read_config(config_2d)
# Get track_method and do related checks
track_method = auxfun_multianimal.get_track_method(
cfg, track_method=track_method
)
if len(cfg.get("multianimalbodyparts", [])) == 1 and track_method != "box":
warnings.warn(
"Switching to `box` tracker for single point tracking..."
)
track_method = "box"
# Get track method suffix
tr_method_suffix = TRACK_METHODS.get(track_method, "")
shuffle = cfg_3d[str("shuffle_" + cam_names[j])]
trainingsetindex = cfg_3d[str("trainingsetindex_" + cam_names[j])]
trainFraction = cfg["TrainingFraction"][trainingsetindex]
if flag == True:
video = os.path.join(video_path, video_list[i][j])
else:
video_path = str(Path(video_list[i][j]).parents[0])
video = os.path.join(video_path, video_list[i][j])
if destfolder is None:
destfolder = str(Path(video).parents[0])
vname = Path(video).stem
prefix = str(vname).split(cam_names[j])[0]
suffix = str(vname).split(cam_names[j])[-1]
if prefix == "":
pass
elif prefix[-1] == "_" or prefix[-1] == "-":
prefix = prefix[:-1]
if suffix == "":
pass
elif suffix[0] == "_" or suffix[0] == "-":
suffix = suffix[1:]
if prefix == "":
output_file = os.path.join(destfolder, suffix)
else:
if suffix == "":
output_file = os.path.join(destfolder, prefix)
else:
output_file = os.path.join(destfolder, prefix + "_" + suffix)
output_filename = os.path.join(
output_file + "_" + scorer_3d
) # Check if the videos are already analyzed for 3d
if os.path.isfile(output_filename + ".h5"):
if save_as_csv is True and not os.path.exists(
output_filename + ".csv"
):
# In case user adds save_as_csv is True after triangulating
pd.read_hdf(output_filename + ".h5").to_csv(
str(output_filename + ".csv")
)
print(
"Already analyzed...Checking the meta data for any change in the camera matrices and/or scorer names",
vname,
)
pickle_file = str(output_filename + "_meta.pickle")
metadata_ = auxiliaryfunctions_3d.LoadMetadata3d(pickle_file)
(
img_path,
path_corners,
path_camera_matrix,
path_undistort,
_,
) = auxiliaryfunctions_3d.Foldernames3Dproject(cfg_3d)
path_stereo_file = os.path.join(
path_camera_matrix, "stereo_params.pickle"
)
stereo_file = auxiliaryfunctions.read_pickle(path_stereo_file)
cam_pair = str(cam_names[0] + "-" + cam_names[1])
is_video_analyzed = False # variable to keep track if the video was already analyzed
# Check for the camera matrix
for k in metadata_["stereo_matrix"].keys():
if np.all(
metadata_["stereo_matrix"][k] == stereo_file[cam_pair][k]
):
pass
else:
run_triangulate = True
# Check for scorer names in the pickle file of 3d output
DLCscorer, DLCscorerlegacy = auxiliaryfunctions.GetScorerName(
cfg, shuffle, trainFraction, trainingsiterations="unknown"
)
if (
metadata_["scorer_name"][cam_names[j]] == DLCscorer
): # TODO: CHECK FOR BOTH?
is_video_analyzed = True
elif metadata_["scorer_name"][cam_names[j]] == DLCscorerlegacy:
is_video_analyzed = True
else:
is_video_analyzed = False
run_triangulate = True
if is_video_analyzed:
print("This file is already analyzed!")
dataname.append(
os.path.join(
destfolder, vname + DLCscorer + tr_method_suffix + ".h5"
)
)
scorer_name[cam_names[j]] = DLCscorer
else:
# Analyze video if score name is different
DLCscorer = predict_videos.analyze_videos(
config_2d,
[video],
videotype=videotype,
shuffle=shuffle,
trainingsetindex=trainingsetindex,
gputouse=gputouse,
destfolder=destfolder,
)
scorer_name[cam_names[j]] = DLCscorer
is_video_analyzed = False
run_triangulate = True
suffix = tr_method_suffix
if filterpredictions:
filtering.filterpredictions(
config_2d,
[video],
videotype=videotype,
shuffle=shuffle,
trainingsetindex=trainingsetindex,
filtertype=filtertype,
destfolder=destfolder,
)
suffix += "_filtered"
dataname.append(
os.path.join(
destfolder, vname + DLCscorer + suffix + ".h5"
)
)
else: # need to do the whole jam.
DLCscorer = predict_videos.analyze_videos(
config_2d,
[video],
videotype=videotype,
shuffle=shuffle,
trainingsetindex=trainingsetindex,
gputouse=gputouse,
destfolder=destfolder,
)
scorer_name[cam_names[j]] = DLCscorer
run_triangulate = True
print(destfolder, vname, DLCscorer)
if filterpredictions:
filtering.filterpredictions(
config_2d,
[video],
videotype=videotype,
shuffle=shuffle,
trainingsetindex=trainingsetindex,
filtertype=filtertype,
destfolder=destfolder,
)
dataname.append(
os.path.join(
destfolder, vname + DLCscorer + tr_method_suffix + "_filtered.h5"
)
)
if run_triangulate:
# if len(dataname)>0:
# undistort points for this pair
print("Undistorting...")
(
dataFrame_camera1_undistort,
dataFrame_camera2_undistort,
stereomatrix,
path_stereo_file,
) = undistort_points(
config, dataname, str(cam_names[0] + "-" + cam_names[1])
)
if len(dataFrame_camera1_undistort) != len(dataFrame_camera2_undistort):
import warnings
warnings.warn(
"The number of frames do not match in the two videos. Please make sure that your videos have same number of frames and then retry! Excluding the extra frames from the longer video."
)
if len(dataFrame_camera1_undistort) > len(dataFrame_camera2_undistort):
dataFrame_camera1_undistort = dataFrame_camera1_undistort[
: len(dataFrame_camera2_undistort)
]
if len(dataFrame_camera2_undistort) > len(dataFrame_camera1_undistort):
dataFrame_camera2_undistort = dataFrame_camera2_undistort[
: len(dataFrame_camera1_undistort)
]
# raise Exception("The number of frames do not match in the two videos. Please make sure that your videos have same number of frames and then retry!")
scorer_cam1 = dataFrame_camera1_undistort.columns.get_level_values(0)[0]
scorer_cam2 = dataFrame_camera2_undistort.columns.get_level_values(0)[0]
scorer_3d = scorer_cam1
bodyparts = dataFrame_camera1_undistort.columns.get_level_values(
"bodyparts"
).unique()
P1 = stereomatrix["P1"]
P2 = stereomatrix["P2"]
F = stereomatrix["F"]
print("Computing the triangulation...")
num_frames = dataFrame_camera1_undistort.shape[0]
### Assign nan to [X,Y] of low likelihood predictions ###
# Convert the data to a np array to easily mask out the low likelihood predictions
data_cam1_tmp = dataFrame_camera1_undistort.to_numpy().reshape(
(num_frames, -1, 3)
)
data_cam2_tmp = dataFrame_camera2_undistort.to_numpy().reshape(
(num_frames, -1, 3)
)
# Assign [X,Y] = nan to low likelihood predictions
data_cam1_tmp[data_cam1_tmp[..., 2] < pcutoff, :2] = np.nan
data_cam2_tmp[data_cam2_tmp[..., 2] < pcutoff, :2] = np.nan
# Reshape data back to original shape
data_cam1_tmp = data_cam1_tmp.reshape(num_frames, -1)
data_cam2_tmp = data_cam2_tmp.reshape(num_frames, -1)
# put data back to the dataframes
dataFrame_camera1_undistort[:] = data_cam1_tmp
dataFrame_camera2_undistort[:] = data_cam2_tmp
if cfg.get("multianimalproject"):
# Check individuals are the same in both views
individuals_view1 = (
dataFrame_camera1_undistort.columns.get_level_values("individuals")
.unique()
.to_list()
)
individuals_view2 = (
dataFrame_camera2_undistort.columns.get_level_values("individuals")
.unique()
.to_list()
)
if individuals_view1 != individuals_view2:
raise ValueError(
"The individuals do not match between the two DataFrames"
)
# Cross-view match individuals
_, voting = auxiliaryfunctions_3d.cross_view_match_dataframes(
dataFrame_camera1_undistort, dataFrame_camera2_undistort, F
)
else:
# Create a dummy variables for single-animal
individuals_view1 = ["indie"]
voting = {0: 0}
# Cleaner variable (since inds view1 == inds view2)
individuals = individuals_view1
# Reshape: (num_framex, num_individuals, num_bodyparts , 2)
all_points_cam1 = dataFrame_camera1_undistort.to_numpy().reshape(
(num_frames, len(individuals), -1, 3)
)[..., :2]
all_points_cam2 = dataFrame_camera2_undistort.to_numpy().reshape(
(num_frames, len(individuals), -1, 3)
)[..., :2]
# Triangulate data
triangulate = []
for i, _ in enumerate(individuals):
# i is individual in view 1
# voting[i] is the matched individual in view 2
pts_indv_cam1 = all_points_cam1[:, i].reshape((-1, 2)).T
pts_indv_cam2 = all_points_cam2[:, voting[i]].reshape((-1, 2)).T
indv_points_3d = auxiliaryfunctions_3d.triangulatePoints(
P1, P2, pts_indv_cam1, pts_indv_cam2
)
indv_points_3d = indv_points_3d[:3].T.reshape((num_frames, -1, 3))
triangulate.append(indv_points_3d)
triangulate = np.asanyarray(triangulate)
metadata = {}
metadata["stereo_matrix"] = stereomatrix
metadata["stereo_matrix_file"] = path_stereo_file
metadata["scorer_name"] = {
cam_names[0]: scorer_name[cam_names[0]],
cam_names[1]: scorer_name[cam_names[1]],
}
# Create 3D DataFrame column and row indices
axis_labels = ("x", "y", "z")
if cfg.get("multianimalproject"):
columns = pd.MultiIndex.from_product(
[[scorer_3d], individuals, bodyparts, axis_labels],
names=["scorer", "individuals", "bodyparts", "coords"],
)
else:
columns = pd.MultiIndex.from_product(
[[scorer_3d], bodyparts, axis_labels],
names=["scorer", "bodyparts", "coords"],
)
inds = range(num_frames)
# Swap num_animals with num_frames axes to ensure well-behaving reshape
triangulate = triangulate.swapaxes(0, 1).reshape((num_frames, -1))
# Fill up 3D dataframe
df_3d = pd.DataFrame(triangulate, columns=columns, index=inds)
df_3d.to_hdf(
str(output_filename + ".h5"),
"df_with_missing",
format="table",
mode="w",
)
# Reorder 2D dataframe in view 2 to match order of view 1
if cfg.get("multianimalproject"):
df_2d_view2 = pd.read_hdf(dataname[1])
individuals_order = [individuals[i] for i in list(voting.values())]
df_2d_view2 = auxfun_multianimal.reorder_individuals_in_df(df_2d_view2, individuals_order)
df_2d_view2.to_hdf(dataname[1], "tracks", format="table", mode="w",)
auxiliaryfunctions_3d.SaveMetadata3d(
str(output_filename + "_meta.pickle"), metadata
)
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 deeplabcut.create_labeled_video_3d")
def triangulate(config,
video_path,
videotype='avi',
filterpredictions=True,
filtertype='median',
gputouse=None,
destfolder=None,
save_as_csv=False):
"""
This function triangulates the detected DLC-keypoints from the two camera views using the camera matrices (derived from calibration) to calculate 3D predictions.
Parameters
----------
config : string
Full path of the config.yaml file as a string.
video_path : string
Full path of the directory where videos are saved.
videotype: string, optional
Checks for the extension of the video in case the input to the video is a directory.\n Only videos with this extension are analyzed. The default is ``.avi``
filterpredictions: Bool, optional
Filter the predictions with filter specified by "filtertype". If specified it should be either ``True`` or ``False``.
filtertype: string
Select which filter, 'arima' or 'median' filter.
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
destfolder: string, optional
Specifies the destination folder for analysis data (default is the path of the video)
save_as_csv: bool, optional
Saves the predictions in a .csv file. The default is ``False``
Example
-------
Linux/MacOs
>>> deeplabcut.triangulate(config,'/data/project1/videos/')
Windows
>>> deeplabcut.triangulate(config,'C:\\yourusername\\rig-95\\Videos')
"""
from deeplabcut.pose_estimation_tensorflow import predict_videos
from deeplabcut.post_processing import filtering
cfg_3d = auxiliaryfunctions.read_config(config)
cam_names = cfg_3d['camera_names']
pcutoff = cfg_3d['pcutoff']
scorer_3d = cfg_3d['scorername_3d']
snapshots = {}
for cam in cam_names:
snapshots[cam] = cfg_3d[str('config_file_' + cam)]
# Check if the config file exists
if not os.path.exists(snapshots[cam]):
raise Exception(
str("It seems the file specified in the variable config_file_"
+ str(cam)) +
" does not exist. Please edit the config file with correct file path and retry."
)
video_list = auxiliaryfunctions_3d.get_camerawise_videos(
video_path, cam_names, videotype=videotype)
if video_list == []:
print("No videos found in the specified video path.", video_path)
print(
"Please make sure that the video names are specified with correct camera names as entered in the config file or"
)
print(
"perhaps the videotype is distinct from the videos in the path, I was looking for:",
videotype)
print("List of pairs:", video_list)
file_name_3d_scorer = []
for i in range(len(video_list)):
dataname = []
for j in range(len(video_list[i])): #looping over cameras
if cam_names[j] in video_list[i][j]:
print("Analyzing video %s using %s" %
(video_list[i][j], str('config_file_' + cam_names[j])))
cfg = snapshots[cam_names[j]]
shuffle = cfg_3d[str('shuffle_' + cam_names[j])]
trainingsetindex = cfg_3d[str('trainingsetindex_' +
cam_names[j])]
video = os.path.join(video_path, video_list[i][j])
if destfolder is None:
destfolder = str(Path(video).parents[0])
vname = Path(video).stem
prefix = str(vname).split(cam_names[j])[0]
suffix = str(vname).split(cam_names[j])[-1]
if prefix == "":
pass
elif prefix[-1] == '_' or prefix[-1] == '-':
prefix = prefix[:-1]
if suffix == "":
pass
elif suffix[0] == '_' or suffix[0] == '-':
suffix = suffix[1:]
if prefix == '':
output_filename = os.path.join(destfolder, suffix)
else:
if suffix == '':
output_filename = os.path.join(destfolder, prefix)
else:
output_filename = os.path.join(destfolder,
prefix + '_' + suffix)
output_filename = os.path.join(output_filename + '_' +
scorer_3d)
if os.path.isfile(
output_filename + '.h5'
): #TODO: don't check twice and load the pickle file to check if the same snapshots + camera matrices were used.
print("Already analyzed...", vname)
else: # need to do the whole jam.
DLCscorer = predict_videos.analyze_videos(
cfg, [video],
videotype=videotype,
shuffle=shuffle,
trainingsetindex=trainingsetindex,
gputouse=gputouse,
destfolder=destfolder)
file_name_3d_scorer.append(DLCscorer)
if filterpredictions:
filtering.filterpredictions(
cfg, [video],
videotype=videotype,
shuffle=shuffle,
trainingsetindex=trainingsetindex,
filtertype=filtertype,
destfolder=destfolder)
dataname.append(
os.path.join(destfolder, vname + DLCscorer + '.h5'))
if len(dataname) > 0:
#undistort points for this pair
print("Undistorting...")
dataFrame_camera1_undistort, dataFrame_camera2_undistort, stereomatrix, path_stereo_file = undistort_points(
config, dataname, str(cam_names[0] + '-' + cam_names[1]),
destfolder)
if len(dataFrame_camera1_undistort) != len(
dataFrame_camera2_undistort):
raise Exception(
"The number of frames do not match in the two videos. Please make sure that your videos have same number of frames and then retry!"
)
print("Computing the triangulation...")
X_final = []
triangulate = []
scorer_cam1 = dataFrame_camera1_undistort.columns.get_level_values(
0)[0]
scorer_cam2 = dataFrame_camera2_undistort.columns.get_level_values(
0)[0]
df_3d, scorer_3d, bodyparts = auxiliaryfunctions_3d.create_empty_df(
dataFrame_camera1_undistort, scorer_3d, flag='3d')
P1 = stereomatrix['P1']
P2 = stereomatrix['P2']
for bpindex, bp in enumerate(bodyparts):
# Extract the indices of frames where the likelihood of a bodypart for both cameras are less than pvalue
likelihoods = np.array([
dataFrame_camera1_undistort[scorer_cam1][bp]
['likelihood'].values[:],
dataFrame_camera2_undistort[scorer_cam2][bp]
['likelihood'].values[:]
])
likelihoods = likelihoods.T
#Extract frames where likelihood for both the views is less than the pcutoff
low_likelihood_frames = np.any(likelihoods < pcutoff, axis=1)
#low_likelihood_frames = np.all(likelihoods < pcutoff, axis=1)
low_likelihood_frames = np.where(
low_likelihood_frames == True)[0]
points_cam1_undistort = np.array([
dataFrame_camera1_undistort[scorer_cam1][bp]
['x'].values[:],
dataFrame_camera1_undistort[scorer_cam2][bp]['y'].values[:]
])
points_cam1_undistort = points_cam1_undistort.T
# For cam1 camera: Assign nans to x and y values of a bodypart where the likelihood for is less than pvalue
points_cam1_undistort[low_likelihood_frames] = np.nan, np.nan
points_cam1_undistort = np.expand_dims(points_cam1_undistort,
axis=1)
points_cam2_undistort = np.array([
dataFrame_camera2_undistort[scorer_cam1][bp]
['x'].values[:],
dataFrame_camera2_undistort[scorer_cam2][bp]['y'].values[:]
])
points_cam2_undistort = points_cam2_undistort.T
# For cam2 camera: Assign nans to x and y values of a bodypart where the likelihood is less than pvalue
points_cam2_undistort[low_likelihood_frames] = np.nan, np.nan
points_cam2_undistort = np.expand_dims(points_cam2_undistort,
axis=1)
X_l = auxiliaryfunctions_3d.triangulatePoints(
P1, P2, points_cam1_undistort, points_cam2_undistort)
#ToDo: speed up func. below by saving in numpy.array
X_final.append(X_l)
triangulate.append(X_final)
triangulate = np.asanyarray(triangulate)
metadata = {}
metadata['stereo_matrix'] = stereomatrix
metadata['stereo_matrix_file'] = path_stereo_file
metadata['scorer_name'] = {
cam_names[0]: file_name_3d_scorer[0],
cam_names[1]: file_name_3d_scorer[1]
}
# Create an empty dataframe to store x,y,z of 3d data
for bpindex, bp in enumerate(bodyparts):
df_3d.iloc[:][scorer_3d, bp, 'x'] = triangulate[0, bpindex,
0, :]
df_3d.iloc[:][scorer_3d, bp, 'y'] = triangulate[0, bpindex,
1, :]
df_3d.iloc[:][scorer_3d, bp, 'z'] = triangulate[0, bpindex,
2, :]
df_3d.to_hdf(str(output_filename + '.h5'),
'df_with_missing',
format='table',
mode='w')
auxiliaryfunctions_3d.SaveMetadata3d(
str(output_filename + '_includingmetadata.pickle'), metadata)
if save_as_csv:
df_3d.to_csv(str(output_filename + '.csv'),
'df_with_missing',
format='table',
mode='w')
print("Triangulated data for video", vname)
print("Results are saved under: ", destfolder)
if len(video_list) > 0:
print("All videos were analyzed...")
print(
"Now you can create 3D video(s) using deeplabcut.create_labeled_video_3d"
)
