def train_dlc(path):
os.chdir(path)
folders = [name for name in os.listdir(path) if os.path.isdir(name) and name[0].isdigit()]
num_folder = len(folders)
for i in range(len(folders)):
start = time()
if os.path.exists(r"%s\%s\labeled-data"%(path, folders[i])) and os.path.exists(r"%s\%s\config.yaml"%(path, folders[i])) \
and os.path.exists(r"%s\%s\dlc-models\iteration-0"%(path, folders[i])) == False and get_size(r"%s\%s\labeled-data"%(path, folders[i])) > 0:
path_config_file = r"%s\%s\config.yaml" %(path, folders[i])
deeplabcut.check_labels(path_config_file)
deeplabcut.create_training_dataset(path_config_file)
deeplabcut.train_network(path_config_file, shuffle = 1, autotune = True, displayiters = 5000, saveiters = 5000, maxiters = 200000)
deeplabcut.analyze_videos(path_config_file, r"%s\%s\videos"%(path, folders[i]), videotype = '.mp4', save_as_csv = True)
print("%s training has been done, %s left"%(folders[i], num_folder - i))
elif os.path.exists(r"%s\%s\dlc-models\iteration-0"%(path, folders[i])):
path_config_file = r"%s\%s\config.yaml" %(path, folders[i])
print("%s model has been trained, do you want to retrain it? y/n"%(folders[i]))
feedback = input()
if feedback == "y":
deeplabcut.train_network(path_config_file, shuffle = 1, autotune = True, displayiters = 5000, saveiters = 5000, maxiters = 200000)
# If model was previously trained, read config.yaml to retrieve
deeplabcut.analyze_videos(path_config_file, r"%s\%s\videos"%(path, folders[i]), videotype='.mp4', save_as_csv=True)
print("%s training has been done, %s left"%(folders[i], num_folder - i))
else:
print("labeled-data folder not found OR empty OR config not found")
print("Running time for %s is %s sec" %(folders[i], time() - start))
def train():
videofile_path = ['videos/ostrich.mp4'
] #Enter a folder OR a list of videos to analyze.
if not os.path.isfile(videofile_path[0]):
sys.exit("File {} not found!!".format(videofile_path[0]))
path_config_file = '/home/braincreator/Pose_estimation/DeepLabCut/examples/ostrich/Testing-Shan-2019-08-07/config.yaml'
print("\n" + "=" * 80 + "\n\t\t\t\tTRAINING\n" + "=" * 80)
print("\nCreating Training dataset from labeled frames...\n")
deeplabcut.create_training_dataset(path_config_file)
print("\nDataset created, Next stop Training!\n\n")
deeplabcut.train_network(path_config_file,
shuffle=1,
saveiters=2000,
displayiters=500,
gputouse=None,
maxiters=50000)
print("\nEvauating network:\n" + "=" * 50)
deeplabcut.evaluate_network(path_config_file, plotting=True)
print("\n" + "=" * 80 + "\n\t\t\t\tANALYZE\n" + "=" * 80)
deeplabcut.analyze_videos(path_config_file,
videofile_path,
gputouse=None,
videotype='.mp4')
print("\n" + "=" * 80 + "\n\t\t\t\tCreating video\n" + "=" * 80)
deeplabcut.create_labeled_video(path_config_file,
videofile_path,
outputframerate=5,
draw_skeleton=True)
def run_program(path_config_file):
# import vars and check path
if not os.path.exists(path_config_file):
raise ValueError('no config file at %s' % path_config_file)
# train the network
deeplabcut.create_training_dataset(path_config_file, windows2linux=True)
deeplabcut.train_network(path_config_file,
gputouse=1,
max_snapshots_to_keep=5)
def DLCTrain(config_path, displayiters, saveiters, maxiters):
"""
Utility to facilitate training a DeepLabCut model from the MesoNet GUI.
:param config_path: (required) The path to a DeepLabCut configuration file (.yaml).
:param displayiters: (required) The interval between which intervals should be shown.
:param saveiters: (required) The interval after which the model should be saved.
:param maxiters: (required) The number of iterations after which the model should finish training.
"""
deeplabcut.create_training_dataset(config_path)
deeplabcut.train_network(config_path,
displayiters=displayiters,
saveiters=saveiters,
maxiters=maxiters)
def train_network(self,event):
shuffle = self.shuffles.GetValue()
trainingsetindex = self.trainingindex.GetValue()
max_snapshots_to_keep = self.snapshots.GetValue()
displayiters = self.display_iters.GetValue()
saveiters = self.save_iters.GetValue()
maxiters = self.max_iters.GetValue()
deeplabcut.train_network(self.config,shuffle,
trainingsetindex,gputouse=None,
max_snapshots_to_keep=max_snapshots_to_keep,
autotune=None,
displayiters=displayiters,
saveiters=saveiters,
maxiters=maxiters)
def train_network(self, event):
if self.shuffles.Children:
shuffle = int(self.shuffles.Children[0].GetValue())
else:
shuffle = int(self.shuffles.GetValue())
if self.trainingindex.Children:
trainingsetindex = int(self.trainingindex.Children[0].GetValue())
else:
trainingsetindex = int(self.trainingindex.GetValue())
if self.snapshots.Children:
max_snapshots_to_keep = int(self.snapshots.Children[0].GetValue())
else:
max_snapshots_to_keep = int(self.snapshots.GetValue())
if self.display_iters.Children:
displayiters = int(self.display_iters.Children[0].GetValue())
else:
displayiters = int(self.display_iters.GetValue())
if self.save_iters.Children:
saveiters = int(self.save_iters.Children[0].GetValue())
else:
saveiters = int(self.save_iters.GetValue())
if self.max_iters.Children:
maxiters = int(self.max_iters.Children[0].GetValue())
else:
maxiters = int(self.max_iters.GetValue())
deeplabcut.train_network(
self.config,
shuffle,
trainingsetindex,
gputouse=None,
max_snapshots_to_keep=max_snapshots_to_keep,
autotune=None,
displayiters=displayiters,
saveiters=saveiters,
maxiters=maxiters,
)
projectpath=os.path.join(prefix,project)
config=os.path.join(projectpath,'config.yaml')
cfg=deeplabcut.auxiliaryfunctions.read_config(config)
previous_path=cfg['project_path']
cfg['project_path']=projectpath
deeplabcut.auxiliaryfunctions.write_config(config,cfg)
print("This is the name of the script: ", sys.argv[0])
print("Shuffle: ", shuffle)
print("config: ", config)
deeplabcut.create_training_dataset(config, Shuffles=[shuffle],windows2linux=True)
deeplabcut.train_network(config, shuffle=shuffle, max_snapshots_to_keep=5, maxiters=Maxiter)
print("Evaluating...")
deeplabcut.evaluate_network(config, Shuffles=[shuffle],plotting=True)
print("Analyzing videos..., switching to last snapshot...")
#cfg=deeplabcut.auxiliaryfunctions.read_config(config)
#cfg['snapshotindex']=-1
#deeplabcut.auxiliaryfunctions.write_config(config,cfg)
for vtype in ['.mp4','.m4v','.mpg']:
try:
deeplabcut.analyze_videos(config,[str(os.path.join(projectpath,'videos'))],shuffle=shuffle,videotype=vtype,save_as_csv=True)
except:
pass
print("DONE WITH ", project," resetting to original path")
)
path_config = '/home/weertman/Documents/Theresa_FHL/Armina_DeepLabCut/Arimina_model_1/'
if os.path.exists(path_config) != True:
os.mkdir(path_config)
path_config = deeplabcut.create_new_project(project,
experimenter,
videos,
copy_videos=True,
working_directory=path_config)
path_config = '/home/weertman/Documents/Theresa_FHL/Armina_DeepLabCut/Arimina_model_1/Armina_model_1-Theresa-2020-07-01/config.yaml'
'''
deeplabcut.extract_frames(path_config,
mode = 'automatic',
algo = 'uniform',
userfeedback = False,
crop = False)
'''
deeplabcut.label_frames(path_config)
deeplabcut.create_training_dataset(path_config)
deeplabcut.train_network(path_config, maxiters=200000, displayiters=10)
deeplabcut.evaluate_network(path_config)
deeplabcut.analyze_time_lapse_frames(
path_config,
'/home/weertman/Documents/Theresa_FHL/Armina_DeepLabCut/left_test_images/',
save_as_csv=True)
def write_config(configname,cfg):
with open(str(configname), 'w') as ymlfile:
yaml.dump(cfg, ymlfile,default_flow_style=False)
# Loading example data set
path_config_file = os.path.join(os.getcwd(),'openfield-Pranav-2018-10-30/config.yaml')
deeplabcut.load_demo_data(path_config_file)
cfg=read_config(path_config_file)
posefile=os.path.join(cfg['project_path'],'dlc-models/iteration-'+str(cfg['iteration'])+'/'+ cfg['Task'] + cfg['date'] + '-trainset' + str(int(cfg['TrainingFraction'][0] * 100)) + 'shuffle' + str(1),'train/pose_cfg.yaml')
DLC_config=read_config(posefile)
DLC_config['save_iters']=10
DLC_config['display_iters']=2
DLC_config['multi_step']=[[0.005,15001]]
write_config(posefile,DLC_config)
print("TRAIN NETWORK")
deeplabcut.train_network(path_config_file, shuffle=1,saveiters=15000,displayiters=100)
print("EVALUATE")
deeplabcut.evaluate_network(path_config_file,plotting=False)
print("Analyze Video")
videofile_path = os.path.join(os.getcwd(),'openfield-Pranav-2018-10-30','videos','m3v1mp4.mp4')
deeplabcut.analyze_videos(path_config_file,[videofile_path])
print("Create Labeled Video")
deeplabcut.create_labeled_video(path_config_file,[videofile_path],save_frames=True)
base_dlc_folder = join(const.base_save_folder, const.rat_folder, const.date_folders[date],
'Analysis', 'Deeplabcut')
base_projects_folder = join(base_dlc_folder, 'projects')
cropped_video_filename = join(base_dlc_folder, 'BonsaiCroping', 'Croped_video.avi')
# ----------------------------------------------------------------------------------------------------------------------
config_path = deeplabcut.create_new_project(project='V1', experimenter='', videos=[cropped_video_filename],
working_directory=base_projects_folder, copy_videos=True)
# Use the line below to 'reload the existing project
config_path = join(base_projects_folder, 'V1--2019-06-30', 'config.yaml')
# Edit the config.yaml file
deeplabcut.extract_frames(config_path, 'manual')
deeplabcut.label_frames(config_path)
deeplabcut.check_labels(config_path)
deeplabcut.create_training_dataset(config_path)
deeplabcut.train_network(config_path, gputouse=1)
deeplabcut.evaluate_network(config_path, plotting=True)
deeplabcut.analyze_videos(config_path, [cropped_video_filename], gputouse=1,
shuffle=1, save_as_csv=False, videotype='avi')
deeplabcut.create_labeled_video(config_path, [cropped_video_filename])
keepdeconvweights=False
'''
print("CREATING TRAININGSET", net_type)
if 'resnet_50' == net_type: #this tests the default condition...
deeplabcut.create_training_dataset(path_config_file,
Shuffles=[shuffle],
augmenter_type=augmenter_type)
else:
deeplabcut.create_training_dataset(path_config_file,
Shuffles=[shuffle],
net_type=net_type,
augmenter_type=augmenter_type)
Cuttrainingschedule(path_config_file, shuffle, lastvalue=stoptrain)
print("TRAIN")
deeplabcut.train_network(path_config_file, shuffle=shuffle)
print("EVALUATE")
deeplabcut.evaluate_network(path_config_file,
Shuffles=[shuffle],
plotting=True)
print("CUT SHORT VIDEO AND ANALYZE")
if shuffle == 0:
# Make super short video (so the analysis is quick!)
newvideo = deeplabcut.ShortenVideo(video[0],
start='00:00:00',
stop='00:00:00.4',
outsuffix='short',
outpath=os.path.join(
cfg['project_path'], 'videos'))
"CollectedData_" + cfg['scorer'] + ".csv"))
dataFrame.to_hdf(os.path.join(cfg['project_path'], 'labeled-data', videoname,
"CollectedData_" + cfg['scorer'] + '.h5'),
'df_with_missing',
format='table',
mode='w')
deeplabcut.create_training_dataset(configfile, Shuffles=[1])
DLC_config = deeplabcut.auxiliaryfunctions.read_plainconfig(path_train_config)
DLC_config['save_iters'] = lastvalue
DLC_config['display_iters'] = 1
DLC_config['multi_step'] = [[0.001, lastvalue]]
DLC_config['init_weights'] = pretrainedDeeperCutweights.split('.index')[0]
deeplabcut.auxiliaryfunctions.write_plainconfig(path_train_config, DLC_config)
deeplabcut.train_network(configfile, shuffle=1)
print("Adding bodypart!")
cfg = deeplabcut.auxiliaryfunctions.read_config(configfile)
cfg['bodyparts'] = [
'ankle1', 'knee1', 'hip1', 'hip2', 'knee2', 'ankle2', 'wrist1', 'elbow1',
'shoulder1', 'shoulder2', 'elbow2', 'wrist2', 'chin', 'forehead',
'plus1more'
]
deeplabcut.auxiliaryfunctions.write_config(configfile, cfg)
print("CREATING-SOME More LABELS FOR THE FRAMES (including the new bodypart!)")
cfg = deeplabcut.auxiliaryfunctions.read_config(configfile)
frames = [
f for f in os.listdir(
os.path.join(cfg['project_path'], 'labeled-data', videoname))
"""
# ------------------------------------------------------------------
# Stage VII: training the network
# Step 9
# ------------------------------------------------------------------
"""
CRITICAL It is recommended to train for thousands of iterations (typically >100,000) until the loss
plateaus. The variables display_iters and save_iters in the pose_cfg.yaml file allow the user to
alter how often the loss is displayed and how often the (intermediate) weights are stored.
"""
if "step_9" in stages_to_run or "training_network" in stages_to_run:
deeplabcut.train_network(config_path, shuffle=1, trainingsetindex=0,
max_snapshots_to_keep=None, displayiters=500, saveiters=5000, maxiters=None,
allow_growth=False, gputouse=0, autotune=False, keepdeconvweights=True) # gputouse=0
# saveiters is at 50000 by default
"""
During training, checkpoints are stored in the subdirectory ‘train’ under the respective iteration
directory at user-specified iterations (‘save_iters’). If you wish to restart the training from a specific
checkpoint, specify the full path of the checkpoint for the variable init_weights in the pose_cfg.
yaml file under the ‘train’ subdirectory before starting to train
"""
# ------------------------------------------------------------------
# Stage VIII: evaluation of the trained network
# Step 10
# ------------------------------------------------------------------
"""
def create_training_dataset_CLARA(config,
num_shuffles=1,
Shuffles=None,
windows2linux=False,
trainIndexes=None,
testIndexes=None):
"""
Creates a training dataset. Labels from all the extracted frames are merged into a single .h5 file.\n
Only the videos included in the config file are used to create this dataset.\n
[OPTIONAL] Use the function 'add_new_video' at any stage of the project to add more videos to the project.
Parameter
----------
config : string
Full path of the config.yaml file as a string.
num_shuffles : int, optional
Number of shuffles of training dataset to create, i.e. [1,2,3] for num_shuffles=3. Default is set to 1.
Shuffles: list of shuffles.
Alternatively the user can also give a list of shuffles (integers!).
windows2linux: bool.
The annotation files contain path formated according to your operating system. If you label on windows
but train & evaluate on a unix system (e.g. ubunt, colab, Mac) set this variable to True to convert the paths.
Example
--------
>>> deeplabcut.create_training_dataset('/analysis/project/reaching-task/config.yaml',num_shuffles=1)
Windows:
>>> deeplabcut.create_training_dataset('C:\\Users\\Ulf\\looming-task\\config.yaml',Shuffles=[3,17,5])
--------
"""
from skimage import io
import scipy.io as sio
from deeplabcut.utils import auxiliaryfunctions, auxfun_models
# Loading metadata from config file:
cfg = auxiliaryfunctions.read_config(config)
scorer = cfg['scorer']
project_path = cfg['project_path']
# Create path for training sets & store data there
trainingsetfolder = auxiliaryfunctions.GetTrainingSetFolder(
cfg) #Path concatenation OS platform independent
auxiliaryfunctions.attempttomakefolder(Path(
os.path.join(project_path, str(trainingsetfolder))),
recursive=True)
"""
Merges all the h5 files for all labeled-datasets (from individual videos).
"""
AnnotationData = None
data_path = Path(os.path.join(project_path, 'labeled-data'))
videos = cfg['video_sets'].keys()
video_names = [Path(i).stem for i in videos]
for i in video_names:
try:
data = pd.read_hdf((str(data_path / Path(i)) + '/CollectedData_' +
cfg['scorer'] + '.h5'), 'df_with_missing')
smlData = data.dropna(how='all')
smlKeys = list(smlData.index.values)
smlKeyLong = list()
for sk in smlKeys:
smlKeyLong.append('labeled-data/' + str(Path(i)) + '/' + sk)
smlData.index = smlKeyLong
data = smlData
if AnnotationData is None:
AnnotationData = data
else:
AnnotationData = pd.concat([AnnotationData, data])
except FileNotFoundError:
print((str(data_path / Path(i)) + '/CollectedData_' +
cfg['scorer'] + '.h5'),
" not found (perhaps not annotated)")
trainingsetfolder_full = Path(os.path.join(project_path,
trainingsetfolder))
filename = str(
str(trainingsetfolder_full) + '/' + '/CollectedData_' + cfg['scorer'])
AnnotationData.to_hdf(filename + '.h5', key='df_with_missing', mode='w')
AnnotationData.to_csv(filename + '.csv') #human readable.
Data = AnnotationData
Data = Data[scorer] #extract labeled data
#loading & linking pretrained models
net_type = 'resnet_' + str(cfg['resnet'])
import deeplabcut
parent_path = Path(os.path.dirname(deeplabcut.__file__))
defaultconfigfile = str(parent_path / 'pose_cfg.yaml')
model_path, num_shuffles = auxfun_models.Check4weights(
net_type, parent_path, num_shuffles)
if Shuffles == None:
Shuffles = range(1, num_shuffles + 1, 1)
else:
Shuffles = [i for i in Shuffles if isinstance(i, int)]
bodyparts = cfg['bodyparts']
if isinstance(bodyparts, list):
parts = bodyparts
else:
parts = list()
categories = list()
for cat in bodyparts.keys():
categories.append(cat)
for key in categories:
for ptname in bodyparts[key]:
parts.append(ptname)
bodyparts = parts
TrainingFraction = cfg['TrainingFraction']
for shuffle in Shuffles: # Creating shuffles starting from 1
for trainFraction in TrainingFraction:
#trainIndexes, testIndexes = SplitTrials(range(len(Data.index)), trainFraction)
if trainIndexes is None and testIndexes is None:
trainIndexes, testIndexes = SplitTrials_CLARA(
range(len(Data.index)), trainFraction)
else:
print(
"You passed a split with the following fraction:",
len(trainIndexes) * 1. /
(len(testIndexes) + len(trainIndexes)) * 100)
####################################################
# Generating data structure with labeled information & frame metadata (for deep cut)
####################################################
# Make training file!
data = []
for jj in trainIndexes:
H = {}
# load image to get dimensions:
filename = Data.index[jj]
im = io.imread(os.path.join(cfg['project_path'], filename))
H['image'] = filename
if np.ndim(im) == 3:
H['size'] = np.array(
[np.shape(im)[2],
np.shape(im)[0],
np.shape(im)[1]])
else:
# print "Grayscale!"
H['size'] = np.array([1, np.shape(im)[0], np.shape(im)[1]])
indexjoints = 0
joints = np.zeros((len(bodyparts), 3)) * np.nan
for bpindex, bodypart in enumerate(bodyparts):
if Data[bodypart]['x'][jj] < np.shape(
im)[1] and Data[bodypart]['y'][jj] < np.shape(
im)[0]: #are labels in image?
joints[indexjoints, 0] = int(bpindex)
joints[indexjoints, 1] = Data[bodypart]['x'][jj]
joints[indexjoints, 2] = Data[bodypart]['y'][jj]
indexjoints += 1
joints = joints[np.where(np.prod(
np.isfinite(joints),
1))[0], :] # drop NaN, i.e. lines for missing body parts
assert (np.prod(np.array(joints[:, 2]) < np.shape(im)[0])
) # y coordinate within image?
assert (np.prod(np.array(joints[:, 1]) < np.shape(im)[1])
) # x coordinate within image?
H['joints'] = np.array(joints, dtype=int)
if np.size(joints) > 0: #exclude images without labels
data.append(H)
if len(trainIndexes) > 0:
datafilename, metadatafilename = auxiliaryfunctions.GetDataandMetaDataFilenames(
trainingsetfolder, trainFraction, shuffle, cfg)
################################################################################
# Saving metadata (Pickle file)
################################################################################
auxiliaryfunctions.SaveMetadata(
os.path.join(project_path, metadatafilename), data,
trainIndexes, testIndexes, trainFraction)
################################################################################
# Saving data file (convert to training file for deeper cut (*.mat))
################################################################################
DTYPE = [('image', 'O'), ('size', 'O'), ('joints', 'O')]
MatlabData = np.array(
[(np.array([data[item]['image']],
dtype='U'), np.array([data[item]['size']]),
boxitintoacell_CLARA(data[item]['joints']))
for item in range(len(data))],
dtype=DTYPE)
sio.savemat(os.path.join(project_path, datafilename),
{'dataset': MatlabData})
################################################################################
# Creating file structure for training &
# Test files as well as pose_yaml files (containing training and testing information)
#################################################################################
modelfoldername = auxiliaryfunctions.GetModelFolder(
trainFraction, shuffle, cfg)
auxiliaryfunctions.attempttomakefolder(
Path(config).parents[0] / modelfoldername, recursive=True)
auxiliaryfunctions.attempttomakefolder(
str(Path(config).parents[0] / modelfoldername) + '/' +
'/train')
auxiliaryfunctions.attempttomakefolder(
str(Path(config).parents[0] / modelfoldername) + '/' +
'/test')
path_train_config = str(
os.path.join(cfg['project_path'], Path(modelfoldername),
'train', 'pose_cfg.yaml'))
path_test_config = str(
os.path.join(cfg['project_path'], Path(modelfoldername),
'test', 'pose_cfg.yaml'))
#str(cfg['proj_path']+'/'+Path(modelfoldername) / 'test' / 'pose_cfg.yaml')
items2change = {
"dataset": datafilename,
"metadataset": metadatafilename,
"num_joints": len(bodyparts),
"all_joints": [[i] for i in range(len(bodyparts))],
"all_joints_names": [str(bpt) for bpt in bodyparts],
"init_weights": model_path,
"project_path": str(cfg['project_path']),
"net_type": net_type,
"crop": 'False'
}
trainingdata = MakeTrain_pose_yaml_CLARA(
items2change, path_train_config, defaultconfigfile)
keys2save = [
"dataset", "num_joints", "all_joints", "all_joints_names",
"net_type", 'init_weights', 'global_scale',
'location_refinement', 'locref_stdev'
]
MakeTest_pose_yaml_CLARA(trainingdata, keys2save,
path_test_config)
deeplabcut.train_network(config)
config_path = 'path to yaml file'
# extract frame
dlc.extract_frames(config_path, 'automatic', 'kmeans')
# label the data
dlc.label_frames(config_path)
# check labels
#dlc.check_labels(config_path)
# creating training dataset
dlc.create_training_dataset(config_path, num_shuffles=1)
# start training
dlc.train_network(config_path)
# evaluate the trained network
#dlc.evaluate_network(config_path, plotting=True)
full_path_to_videos = []
root = 'add your path'
for path in videos:
full_path_to_videos.append(root + '/' + path)
# video analysis and plotting results
dlc.analyze_videos(config_path,
full_path_to_videos,
shuffle=1,
save_as_csv=False,
videotype='.mp4')
class tutorial():
def __init__(self, algo, crop, userfeedback, shuffle, saveiters, displayiters, angle, center, scale, filenames):
self.algo = "automatic"
self.crop = False
self.userfeedback = False
self.shuffle = 1
self.saveiters = 200
self.displayiters = 100
self.angle = -4.5
self.center = None
self.scale = 1.0
#Generate all rotating videos
self.filenames = glob.glob('*.mp4') #Return the file name with .mp4 extention
def rotate(self, image, angle, center=None, scale=1):
#scale = 1: original size
rows,cols,ch = image.shape
if center == None:
center = (cols / 2, rows / 2)
M = cv2.getRotationMatrix2D(center, angle, scale)
#Matrix: Rotate with center by angles
dst = cv2.warpAffine(image,M,(cols,rows))
#After rotation
return dst
def videorotate(self, filenames, output_name, display_video = False):
# capture video
cap = cv2.VideoCapture(filename)
#read video frame by frame
#extract original video frame features
sz = (int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)),
int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)))
fourcc = int(cap.get(cv2.CAP_PROP_FOURCC))
fps = int(cap.get(cv2.CAP_PROP_FPS))
#Make a directory to store the rotated videos
path = "./rotated"
try:
os.mkdir(path)
except OSError:
pass
else:
print ("Successfully created the directory %s " % path)
#Automatically name the rotated videos
file = "./rotated/" + output_name
out = cv2.VideoWriter(file, cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'), fps, sz)
#Integrate all frames to video
#Read videos and rotate by certain degrees
while(cap.isOpened()):
#flip for truning(fliping) frames of video
ret,img = cap.read()
try:
img2 = rotate(img, -4.5)
#Flipped Vertically
out.write(img2)
if display_video == True:
cv2.imshow('rotated video',img2)
k=cv2.waitKey(30) & 0xff
#once you inter Esc capturing will stop
if k==27:
break
except:
print (filename, 'successfully rotated!!!' )
break
cap.release()
out.release()
cv2.destroyAllWindows()
for i in filenames:
output_name = os.path.splitext(i)[0] + " rotated.mp4"
cwd = os.chdir("./rotated")
#we are using rotated videos
cwd = os.getcwd()
mp4files = [f for f in listdir(cwd) if isfile(join(cwd, f)) and os.path.splitext(f)[1] == ".mp4"]
#Get all mp4 files
task='Reaching' # Enter the name of your experiment Task
experimenter='Donghan' # Enter the name of the experimenter
video=mp4files # Enter the paths of your videos you want to grab frames from.
now = datetime.datetime.now()
try:
path_config_file=deeplabcut.create_new_project(task,experimenter,video, working_directory='/home/donghan/DeepLabCut/data/rotated',copy_videos=True)
#change the working directory to where you want the folders created.
except:
overwrite = input("Do you want to overwrite the folder since it already exists? y/n:")
if overwrite == 'y':
os.rmdir(task + '-' + experimenter + '-' + str(now.year) + '-' + str(now.month) + '-' + str(now.day))
path_config_file=deeplabcut.create_new_project(task,experimenter,video, working_directory='/home/donghan/DeepLabCut/data/rotated',copy_videos=True)
else:
continue
# The function returns the path, where your project is.
# You could also enter this manually (e.g. if the project is already created and you want to pick up, where you stopped...)
#path_config_file = '/home/Mackenzie/Reaching/config.yaml' # Enter the path of the config file that was just created from the above step (check the folder)
%matplotlib inline
deeplabcut.extract_frames(path_config_file,'automatic',crop=False, userfeedback=False) #there are other ways to grab frames, such as by clustering 'kmeans'; please see the paper.
#You can change the cropping to false, then delete the checkcropping part!
#userfeedback: ask if users would like to continue or stop
%gui wx
deeplabcut.label_frames(path_config_file)
deeplabcut.check_labels(path_config_file) #this creates a subdirectory with the frames + your labels
deeplabcut.create_training_dataset(path_config_file)
deeplabcut.train_network(path_config_file, shuffle=1, saveiters=200, displayiters=10)
#Other parameters include trainingsetindex=0,gputouse=None,max_snapshots_to_keep=5,autotune=False,maxiters=None
#Detailed function explanation can be found here https://github.com/AlexEMG/DeepLabCut/blob/efa95129061b1ba1535f7361fe76e9267568a156/deeplabcut/pose_estimation_tensorflow/training.py
deeplabcut.evaluate_network(path_config_file)
videofile_path = ['1035 SI_A, Aug 15, 13 17 7 rotated.mp4'] #Enter the list of videos to analyze.
deeplabcut.analyze_videos(path_config_file,videofile_path)
deeplabcut.create_labeled_video(path_config_file,videofile_path)
# DLC Cropping
deeplabcut.extract_frames(config_path,
mode='automatic',
algo='uniform',
crop=False,
userfeedback=False)
deeplabcut.label_frames(config_path)
deeplabcut.check_labels(config_path)
deeplabcut.create_training_dataset(config_path)
# 0 is the GPU number, see in nvidia-smi
deeplabcut.train_network(config_path,
gputouse=0,
saveiters=25000,
maxiters=250000)
deeplabcut.evaluate_network(config_path, plotting=False)
# DLC estimation
deeplabcut.analyze_videos(config_path,
training_videos,
gputouse=0,
save_as_csv=True,
destfolder=labeled_csv_path)
deeplabcut.create_labeled_video(config_path,
training_videos,
destfolder=labeled_csv_path,
draw_skeleton=True)
import os
os.environ["DLClight"] = "True"
import sys
import deeplabcut as dlc
shuffleindex = int(sys.argv[1])
gputouse = int(sys.argv[2])
config_path = "/usr/users/onur.serce/dlc_real-alja_onur-2020-04-06/config.yaml"
print("'config_path' is:", config_path)
print("'dlc.__version__' is:'", dlc.__version__)
print("\n")
print("This is the name of the program:", sys.argv[0])
print("str(sys.argv):", str(sys.argv), "\n")
dlc.train_network(config_path,
shuffle=shuffleindex,
trainingsetindex=0,
max_snapshots_to_keep=None,
displayiters=250,
saveiters=None,
maxiters=None,
allow_growth=False,
gputouse=gputouse,
autotune=False,
keepdeconvweights=True)
print("dlc_start_training.py with the call", str(sys.argv), "is done!")
TRAIN_SIZE, 1, cfg, cfg["project_path"]
)
pose_config_path = os.path.join(model_folder, "train", "pose_cfg.yaml")
edits = {
"global_scale": 0.5,
"batch_size": 1,
"save_iters": N_ITER,
"display_iters": N_ITER // 2,
"crop_size": [200, 200],
# "multi_step": [[0.001, N_ITER]],
}
deeplabcut.auxiliaryfunctions.edit_config(pose_config_path, edits)
print("Pose config edited.")
print("Training network...")
deeplabcut.train_network(config_path, maxiters=N_ITER)
print("Network trained.")
print("Evaluating network...")
deeplabcut.evaluate_network(config_path, plotting=True)
print("Network evaluated....")
print("Extracting maps...")
deeplabcut.extract_save_all_maps(config_path, Indices=[0, 1, 2])
new_video_path = deeplabcut.ShortenVideo(
video_path,
start="00:00:00",
stop="00:00:01",
outsuffix="short",
print("CREATING TRAININGSET")
deeplabcut.create_training_dataset(path_config_file)
posefile=os.path.join(cfg['project_path'],'dlc-models/iteration-'+str(cfg['iteration'])+'/'+ cfg['Task'] + cfg['date'] + '-trainset' + str(int(cfg['TrainingFraction'][0] * 100)) + 'shuffle' + str(1),'train/pose_cfg.yaml')
DLC_config=deeplabcut.auxiliaryfunctions.read_plainconfig(posefile)
DLC_config['save_iters']=10
DLC_config['display_iters']=2
DLC_config['multi_step']=[[0.001,10]]
print("CHANGING training parameters to end quickly!")
deeplabcut.auxiliaryfunctions.write_plainconfig(posefile,DLC_config)
print("TRAIN")
deeplabcut.train_network(path_config_file)
print("EVALUATE")
deeplabcut.evaluate_network(path_config_file,plotting=True)
print("CUT SHORT VIDEO AND ANALYZE")
# Make super short video (so the analysis is quick!)
vname='brief'
newvideo=os.path.join(cfg['project_path'],'videos',vname+'.mp4')
try: #you need ffmpeg command line interface
subprocess.call(['ffmpeg','-i',video[0],'-ss','00:00:00','-to','00:00:00.4','-c','copy',newvideo])
except:
#for windows:
from moviepy.editor import VideoFileClip,VideoClip
clip = VideoFileClip(video[0])
def train_network(self):
deeplabcut.train_network(self.full_config_path(),
shuffle=1,
gputouse=0)
posefile, _, _ = deeplabcut.return_train_network_path(path_config_file,
shuffle=shuffle)
if shuffle % 3 == 1: # imgaug
edits = {"rotation": 180, "motion_blur": True}
DLC_config = deeplabcut.auxiliaryfunctions.edit_config(posefile, edits)
elif shuffle % 3 == 0: # Tensorpack:
edits = {"rotation": 180, "noise_sigma": 0.01}
DLC_config = deeplabcut.auxiliaryfunctions.edit_config(posefile, edits)
print("TRAIN NETWORK", shuffle)
deeplabcut.train_network(
path_config_file,
shuffle=shuffle,
saveiters=10000,
displayiters=200,
maxiters=maxiters,
max_snapshots_to_keep=11,
)
print("EVALUATE")
deeplabcut.evaluate_network(path_config_file,
Shuffles=[shuffle],
plotting=True)
print("Analyze Video")
videofile_path = os.path.join(os.getcwd(), "openfield-Pranav-2018-10-30",
"videos", "m3v1mp4.mp4")
deeplabcut.analyze_videos(path_config_file, [videofile_path],
# Loading example data set
path_config_file = os.path.join(os.getcwd(), "openfield-Pranav-2018-10-30/config.yaml")
deeplabcut.load_demo_data(path_config_file)
shuffle = 13
deeplabcut.create_training_dataset(path_config_file, Shuffles=[shuffle])
cfg = deeplabcut.auxiliaryfunctions.read_config(path_config_file)
# example how to set pose config variables:
posefile, _, _ = deeplabcut.return_train_network_path(path_config_file, shuffle=shuffle)
edits = {"save_iters": 15000, "display_iters": 1000, "multi_step": [[0.005, 15001]]}
DLC_config = deeplabcut.auxiliaryfunctions.edit_config(posefile, edits)
print("TRAIN NETWORK")
deeplabcut.train_network(path_config_file, shuffle=shuffle, max_snapshots_to_keep=3)
print("EVALUATE")
deeplabcut.evaluate_network(path_config_file, Shuffles=[shuffle], plotting=True)
print("Analyze Video")
videofile_path = os.path.join(
os.getcwd(), "openfield-Pranav-2018-10-30", "videos", "m3v1mp4.mp4"
)
deeplabcut.analyze_videos(
path_config_file, [videofile_path], shuffle=shuffle
) # ,videotype='.mp4')
print("Create Labeled Video")
deeplabcut.create_labeled_video(
path_config_file, [videofile_path], save_frames=False, shuffle=shuffle
def cli_create_project(project_path,
video_paths,
name='eye_video_demo',
experimenter='experimenter',
recursive=False,
format_='mp4',
exclude=[],
num_frames=20,
train=False,
analyze=False,
create_videos=False):
"""run all steps to create a DLC project"""
if len(video_paths) == 0:
# use provided example
video_files = [op.join(op.split(op.realpath(__file__))[0], 'data', 'example_eye_camera_video.mp4')]
else:
# get video files
video_files = []
for vp in list(video_paths):
video_files.extend(get_video_files(vp, recursive, exclude,
file_format=format_.lower()))
# list all video files (and convert to mp4 if required)
for i, vf in enumerate(video_files):
print("found video file: %s" % vf)
if op.splitext(vf)[1] == '.h264':
vide_files[i] = convert_h264_to_mp4(vf)
# create a new project
config_path = deeplabcut.create_new_project(name, experimenter, video_files,
working_directory=project_path,
copy_videos=False)
config = deeplabcut.utils.read_config(config_path)
config['bodyparts'] = ['pupil_center', 'nasal_corner', 'temporal_corner']
config['numframes2pick'] = num_frames
deeplabcut.utils.write_config(config_path, config)
# extract and label frames
deeplabcut.extract_frames(config_path,
mode='automatic',
algo='kmeans',
crop=True)
deeplabcut.label_frames(config_path)
deeplabcut.check_labels(config_path)
# create training dataset
deeplabcut.create_training_dataset(config_path)
if train:
# train and evaluate the network
deeplabcut.train_network(config_path)
deeplabcut.evaluate_network(config_path)
if analyze:
deeplabcut.analyze_videos(config_path, video_files)
if create_videos:
# create a video
deeplabcut.create_labeled_video(config_path, video_files)
posefile = os.path.join(
cfg['project_path'], 'dlc-models/iteration-' + str(cfg['iteration']) +
'/' + cfg['Task'] + cfg['date'] + '-trainset' +
str(int(cfg['TrainingFraction'][0] * 100)) + 'shuffle' + str(1),
'train/pose_cfg.yaml')
DLC_config = deeplabcut.auxiliaryfunctions.read_plainconfig(posefile)
DLC_config['save_iters'] = numiter
DLC_config['display_iters'] = 2
DLC_config['multi_step'] = [[0.001, numiter]]
print("CHANGING training parameters to end quickly!")
deeplabcut.auxiliaryfunctions.write_plainconfig(posefile, DLC_config)
print("TRAIN")
deeplabcut.train_network(path_config_file)
print("EVALUATE")
deeplabcut.evaluate_network(path_config_file, plotting=True)
#deeplabcut.evaluate_network(path_config_file,plotting=True,trainingsetindex=33)
print("CUT SHORT VIDEO AND ANALYZE (with dynamic cropping!)")
# Make super short video (so the analysis is quick!)
try: #you need ffmpeg command line interface
#subprocess.call(['ffmpeg','-i',video[0],'-ss','00:00:00','-to','00:00:00.4','-c','copy',newvideo])
newvideo = deeplabcut.ShortenVideo(video[0],
start='00:00:00',
stop='00:00:00.4',
outsuffix='short',
outpath=os.path.join(
import deeplabcut
import os
configpath = os.path.join(
'/home/neudata/Desktop/DeepLabCut/examples/openfield-Pranav-2018-10-30/config.yaml'
)
deeplabcut.load_demo_data(configpath)
deeplabcut.train_network(configpath, maxiters=5) #trains for 5 iterations
#manually test GUI...
deeplabcut.label_frames(configpath)
posefile = os.path.join(
cfg['project_path'], 'dlc-models/iteration-' + str(cfg['iteration']) +
'/' + cfg['Task'] + cfg['date'] + '-trainset' +
str(int(cfg['TrainingFraction'][0] * 100)) + 'shuffle' + str(1),
'train/pose_cfg.yaml')
DLC_config = dlc.auxiliaryfunctions.read_plainconfig(posefile)
DLC_config['save_iters'] = numiter
DLC_config['display_iters'] = 2
DLC_config['multi_step'] = [[0.001, numiter]]
print("CHANGING training parameters to end quickly!")
dlc.auxiliaryfunctions.write_plainconfig(posefile, DLC_config)
print("TRAIN")
dlc.train_network(path_config_file)
print("EVALUATE")
dlc.evaluate_network(path_config_file, plotting=True)
videotest = os.path.join(cfg['project_path'], 'videos', videoname + ".avi")
print(videotest)
# quicker variant
'''
print("VIDEO ANALYSIS")
dlc.analyze_videos(path_config_file, [videotest], save_as_csv=True)
print("CREATE VIDEO")
dlc.create_labeled_video(path_config_file,[videotest], save_frames=False)
import os
os.environ["DLClight"] = "True"
import deeplabcut
os.system('echo $$')
os.system('echo imported')
config_path = '/dartfs-hpc/rc/home/7/f002qw7/thesis/ir_test-markt-2020-02-28/config.yaml'
os.system('echo config_path')
deeplabcut.train_network(config_path)
os.system('echo done')
# changed the cropping dimensions in the config.yaml file
%gui wx
deeplabcut.label_frames(path_config_file)
# Lables have now been created
deeplabcut.check_labels(path_config_file) #this creates a subdirectory with the frames + your labels
# Reviewed the labels, the seem to be ok
# Downloading the ResNets dataset:
deeplabcut.create_training_dataset(path_config_file)
# Training the dataset
deeplabcut.train_network(path_config_file)
# Evaluating the results
deeplabcut.evaluate_network(path_config_file)
# Analyzing video
videofile_path = ['dlc-blinking/an3_vid2_full/eyes_only-Guy-2019-01-25/videos/animal_3_video_2_150fps_correct.mp4',
'dlc-blinking/an3_vid2_full/eyes_only-Guy-2019-01-25/videos/march_8_animal_1_video_150fps_correct.mp4'] #Enter the list of videos to analyze.
videofile_path = ['dlc-blinking/whisk/whisk_only-Guy-2019-02-01/videos/crush_19_01_07_animal_3.mp4',
'dlc-blinking/whisk/whisk_only-Guy-2019-02-01/videos/animal_3_video_2_150fps_correct.mp4']
deeplabcut.analyze_videos(path_config_file,videofile_path,save_as_csv=True)
deeplabcut.create_labeled_video(path_config_file, ['D:\\dlc-blinking\\an3_vid2_full\\whisk_only-Guy-2019-02-01\\videos\\crush_19_01_07_animal_3.mp4'], save_frames=True)
deeplabcut.create_labeled_video(path_config_file, ['D:\\dlc-blinking\\whisk\\whisk_only-Guy-2019-02-01\\videos\\animal_3_video_2_150fps_correct.mp4'], save_frames=True)
if not traindatasetexist: # create training dataset if not exist
deeplabcut.check_labels(path_config_file)
deeplabcut.create_training_dataset(path_config_file, num_shuffles=1)
else:
print("Training dataset exists")
if tag_train:
# train data argments, saveiters, displayiters, maxiters are the same as in pose_cfg.yaml, here are for visualization
saveiters = 10000
displayiters = 1000
maxiters = 100000
train_start = time.time()
deeplabcut.train_network(path_config_file,
shuffle=1,
saveiters=saveiters,
displayiters=displayiters,
maxiters=maxiters)
train_end = time.time()
train_time = train_end - train_start
print('maxiters is %d' % maxiters)
print('training time is %f' % train_time)
with open(os.path.join(path_prj, 'traintime.csv'), 'w') as csv_file:
csv_writer = csv.writer(csv_file, delimiter=',')
csv_writer.writerow(['maxiters', 'training time'])
csv_writer.writerow([maxiters, train_time])
else:
print("No need to train!")
if tag_evaluation:
deeplabcut.evaluate_network(path_config_file, plotting=True) # evaluation
