def inference(rec_name):
text1("Now Analyzing...")
videofile_path=["/home/stada/tmp/{}".format(rec_name)]
path_config_file="/home/stada/mouse_tracking-stada-2020-12-23/config.yaml"
deeplabcut.analyze_videos(path_config_file, videofile_path, videotype='avi', save_as_csv=True)
text2("Analyze finish!", "Now labeling...")
deeplabcut.create_labeled_video(path_config_file, videofile_path, draw_skeleton=True)
newclip = VideoClip(make_frame, duration=1)
newclip.write_videofile(newvideo, fps=30)
dlc.analyze_videos(path_config_file, [newvideo],
save_as_csv=True,
destfolder=dfolder,
dynamic=(True, .1, 5))
print("analyze again...")
dlc.analyze_videos(path_config_file, [newvideo],
save_as_csv=True,
destfolder=dfolder)
print("CREATE VIDEO")
dlc.create_labeled_video(path_config_file, [newvideo],
destfolder=dfolder,
save_frames=True)
print("Making plots")
dlc.plot_trajectories(path_config_file, [newvideo], destfolder=dfolder)
print("EXTRACT OUTLIERS")
dlc.extract_outlier_frames(path_config_file, [newvideo],
outlieralgorithm='jump',
epsilon=0,
automatic=True,
destfolder=dfolder)
dlc.extract_outlier_frames(path_config_file, [newvideo],
outlieralgorithm='Fitting',
automatic=True,
def create_pretrained_project(
project,
experimenter,
videos,
model="full_human",
working_directory=None,
copy_videos=False,
videotype=None,
analyzevideo=True,
filtered=True,
createlabeledvideo=True,
trainFraction=None,
):
"""
Creates a new project directory, sub-directories and a basic configuration file.
Change its parameters to your projects need.
The project will also be initialized with a pre-trained model from the DeepLabCut model zoo!
http://www.mousemotorlab.org/dlc-modelzoo
Parameters
----------
project : string
String containing the name of the project.
experimenter : string
String containing the name of the experimenter.
model: string, options see http://www.mousemotorlab.org/dlc-modelzoo
Current option and default: 'full_human' Creates a demo human project and analyzes a video with ResNet 101 weights pretrained on MPII Human Pose. This is from the DeeperCut paper
by Insafutdinov et al. https://arxiv.org/abs/1605.03170 Please make sure to cite it too if you use this code!
videos : list
A list of string containing the full paths of the videos to include in the project.
working_directory : string, optional
The directory where the project will be created. The default is the ``current working directory``; if provided, it must be a string.
copy_videos : bool, optional ON WINDOWS: TRUE is often necessary!
If this is set to True, the videos are copied to the ``videos`` directory. If it is False,symlink of the videos are copied to the project/videos directory. The default is ``False``; if provided it must be either
``True`` or ``False``.
analyzevideo " bool, optional
If true, then the video is analzyed and a labeled video is created. If false, then only the project will be created and the weights downloaded. You can then access them
filtered: bool, default false
Boolean variable indicating if filtered pose data output should be plotted rather than frame-by-frame predictions.
Filtered version can be calculated with deeplabcutcore.filterpredictions
trainFraction: By default value from *new* projects. (0.95)
Fraction that will be used in dlc-model/trainingset folder name.
Example
--------
Linux/MacOs loading full_human model and analzying video /homosapiens1.avi
>>> deeplabcutcore.create_pretrained_project('humanstrokestudy','Linus',['/data/videos/homosapiens1.avi'], copy_videos=False)
Loading full_cat model and analzying video "felixfeliscatus3.avi"
>>> deeplabcutcore.create_pretrained_project('humanstrokestudy','Linus',['/data/videos/felixfeliscatus3.avi'], model='full_cat')
Windows:
>>> deeplabcutcore.create_pretrained_project('humanstrokestudy','Bill',[r'C:\yourusername\rig-95\Videos\reachingvideo1.avi'],r'C:\yourusername\analysis\project' copy_videos=True)
Users must format paths with either: r'C:\ OR 'C:\\ <- i.e. a double backslash \ \ )
"""
if model in globals()["Modeloptions"]:
cwd = os.getcwd()
cfg = deeplabcutcore.create_new_project(project, experimenter, videos,
working_directory, copy_videos,
videotype)
if trainFraction is not None:
auxiliaryfunctions.edit_config(
cfg, {"TrainingFraction": [trainFraction]})
config = auxiliaryfunctions.read_config(cfg)
if model == "full_human":
config["bodyparts"] = [
"ankle1",
"knee1",
"hip1",
"hip2",
"knee2",
"ankle2",
"wrist1",
"elbow1",
"shoulder1",
"shoulder2",
"elbow2",
"wrist2",
"chin",
"forehead",
]
config["skeleton"] = [
["ankle1", "knee1"],
["ankle2", "knee2"],
["knee1", "hip1"],
["knee2", "hip2"],
["hip1", "hip2"],
["shoulder1", "shoulder2"],
["shoulder1", "hip1"],
["shoulder2", "hip2"],
["shoulder1", "elbow1"],
["shoulder2", "elbow2"],
["chin", "forehead"],
["elbow1", "wrist1"],
["elbow2", "wrist2"],
]
config["default_net_type"] = "resnet_101"
else: # just make a case and put the stuff you want.
# TBD: 'partaffinityfield_graph' >> use to set skeleton!
pass
auxiliaryfunctions.write_config(cfg, config)
config = auxiliaryfunctions.read_config(cfg)
train_dir = Path(
os.path.join(
config["project_path"],
str(
auxiliaryfunctions.GetModelFolder(
trainFraction=config["TrainingFraction"][0],
shuffle=1,
cfg=config,
)),
"train",
))
test_dir = Path(
os.path.join(
config["project_path"],
str(
auxiliaryfunctions.GetModelFolder(
trainFraction=config["TrainingFraction"][0],
shuffle=1,
cfg=config,
)),
"test",
))
# Create the model directory
train_dir.mkdir(parents=True, exist_ok=True)
test_dir.mkdir(parents=True, exist_ok=True)
modelfoldername = auxiliaryfunctions.GetModelFolder(
trainFraction=config["TrainingFraction"][0], shuffle=1, cfg=config)
path_train_config = str(
os.path.join(config["project_path"], Path(modelfoldername),
"train", "pose_cfg.yaml"))
path_test_config = str(
os.path.join(config["project_path"], Path(modelfoldername), "test",
"pose_cfg.yaml"))
# Download the weights and put then in appropriate directory
print("Dowloading weights...")
auxfun_models.DownloadModel(model, train_dir)
pose_cfg = deeplabcutcore.auxiliaryfunctions.read_plainconfig(
path_train_config)
print(path_train_config)
# Updating config file:
dict = {
"default_net_type": pose_cfg["net_type"],
"default_augmenter": pose_cfg["dataset_type"],
"bodyparts": pose_cfg["all_joints_names"],
"skeleton": [], # TODO: update with paf_graph
"dotsize": 6,
}
auxiliaryfunctions.edit_config(cfg, dict)
# Create the pose_config.yaml files
parent_path = Path(os.path.dirname(deeplabcutcore.__file__))
defaultconfigfile = str(parent_path / "pose_cfg.yaml")
trainingsetfolder = auxiliaryfunctions.GetTrainingSetFolder(config)
datafilename, metadatafilename = auxiliaryfunctions.GetDataandMetaDataFilenames(
trainingsetfolder,
trainFraction=config["TrainingFraction"][0],
shuffle=1,
cfg=config,
)
# downloading base encoder / not required unless on re-trains (but when a training set is created this happens anyway)
# model_path, num_shuffles=auxfun_models.Check4weights(pose_cfg['net_type'], parent_path, num_shuffles= 1)
# Updating training and test pose_cfg:
snapshotname = [fn for fn in os.listdir(train_dir)
if ".meta" in fn][0].split(".meta")[0]
dict2change = {
"init_weights": str(os.path.join(train_dir, snapshotname)),
"project_path": str(config["project_path"]),
}
UpdateTrain_pose_yaml(pose_cfg, dict2change, path_train_config)
keys2save = [
"dataset",
"dataset_type",
"num_joints",
"all_joints",
"all_joints_names",
"net_type",
"init_weights",
"global_scale",
"location_refinement",
"locref_stdev",
]
MakeTest_pose_yaml(pose_cfg, keys2save, path_test_config)
video_dir = os.path.join(config["project_path"], "videos")
if analyzevideo == True:
print("Analyzing video...")
deeplabcutcore.analyze_videos(cfg, [video_dir],
videotype,
save_as_csv=True)
if createlabeledvideo == True:
if filtered:
deeplabcutcore.filterpredictions(cfg, [video_dir], videotype)
print("Plotting results...")
deeplabcutcore.create_labeled_video(cfg, [video_dir],
videotype,
draw_skeleton=True,
filtered=filtered)
deeplabcutcore.plot_trajectories(cfg, [video_dir],
videotype,
filtered=filtered)
os.chdir(cwd)
return cfg, path_train_config
else:
return "N/A", "N/A"