def generatetempyaml(yamlfile,videolist):
try:
#copy yaml and rename
tempyaml = os.path.dirname(yamlfile) +'\\temp.yaml'
shutil.copy(yamlfile,tempyaml)
#adding new videos to tempyaml
deeplabcut.add_new_videos(tempyaml,[videolist],copy_videos=True)
with open(tempyaml) as f:
read_yaml = yaml.load(f, Loader=yaml.FullLoader)
original_videosets = read_yaml['video_sets'].keys()
keys=[]
for i in original_videosets:
keys.append(i)
#remove the original video to get only newly added videos
read_yaml['video_sets'].pop(keys[0],None)
with open(tempyaml, 'w') as outfile:
yaml.dump(read_yaml, outfile, default_flow_style=False)
print('temp.yaml generated.')
except FileNotFoundError:
print('Please select a video file.')
def add_videos(self, event):
print("adding new videos to be able to label ...")
self.cfg = self.sel_config.GetPath()
if len(self.filelistnew) > 0:
self.filelistnew = self.filelistnew + self.addvids
deeplabcut.add_new_videos(self.cfg, self.filelistnew)
else:
print(
"Please select videos to add first. Click 'Load New Videos'..."
)
def video_upload(projectId):
config_path = projectRepository.find_one({'_id': ObjectId(projectId)
})['config_path']
if 'file' not in request.files:
return "No file was sent"
project_path = projectRepository.find_one(
{'_id': ObjectId(projectId)})['config_path'][0:-11] + 'videos/'
file = request.files['file']
filename = secure_filename(file.filename)
video_path = os.path.join(project_path, filename)
file.save(video_path)
deeplabcut.add_new_videos(config_path, [video_path])
return "Video uploaded" #TODO: Return real response
def clicked_add():
self.load_an_exsiting_project(window)
window.destroy()
self.select_body_parts()
self.add_new_videos()
# for item in self.video_list:
# if len(item) < 2:
# self.video_list.remove(item)
print(self.video_list)
if len(self.video_list) > 0:
deeplabcut.add_new_videos(self.path_config_file, self.video_list, copy_videos=True)
deeplabcut.extract_frames(self.path_config_file,'automatic', 'kmeans', checkcropping=False, crop=True)
self.working_window_for_deeplabcut()
def clicked_create():
self.select_working_directory(window)
window.destroy()
self.get_window_for_project_name()
self.path_config_file = deeplabcut.create_new_project(self.name_of_project, self.experimenter, self.video_list,
working_directory=self.working_directory,
copy_videos=True)
self.select_body_parts()
self.add_new_videos()
print(self.video_list)
if len(self.video_list) > 0:
deeplabcut.add_new_videos(self.path_config_file, self.video_list, copy_videos=True)
deeplabcut.extract_frames(self.path_config_file, 'automatic', 'kmeans', checkcropping=False, crop=True)
self.working_window_for_deeplabcut()
def generatetempyaml_multi(yamlfile, videolist):
#copy yaml and rename
tempyaml = os.path.dirname(yamlfile) + '\\temp.yaml'
shutil.copy(yamlfile, tempyaml)
deeplabcut.add_new_videos(tempyaml, videolist, copy_videos=True)
with open(tempyaml) as f:
read_yaml = yaml.load(f, Loader=yaml.FullLoader)
original_videosets = read_yaml['video_sets'].keys()
keys = []
for i in original_videosets:
keys.append(i)
read_yaml['video_sets'].pop(keys[0], None)
with open(tempyaml, 'w') as outfile:
yaml.dump(read_yaml, outfile, default_flow_style=False)
import deeplabcut as dlc
import json
import helpers
with open('config.json', 'r') as fp:
config = json.load(fp)
path_config_file = config["path_config_file"]
# get full path here
video_full_paths = helpers.get_video_full_paths()
dlc.add_new_videos(path_config_file, video_full_paths)
def usingDeep(path_config, project):
exit = False
while exit == False:
video_path = path_config.split("/")
video_path = '/' + video_path[1] + '/' + video_path[
2] + '/' + video_path[3] + '/' + video_path[4] + '/videos/'
print_usage("project")
action = input()
while action not in [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]:
try:
action = int(action)
if action not in [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]:
print("please type number between 0 and 12")
action = input()
except ValueError:
print("Please enter number")
action = input()
print("been here")
if action == 0:
return False
elif action == 1:
print("do you want to crop the video? yes/no")
if input() == "yes" or "y":
print(
"how many videos you you want to crop? (use number: 1,2,3 etc)"
)
crops = int(input())
print("only crop all the video's and than exit")
for loop in range(0, crops):
deeplabcut.extract_frames(path_config,
'manual',
'kmeans',
crop=True)
deeplabcut.extract_frames(path_config,
'automatic',
'kmeans',
crop=True)
elif action == 2:
deeplabcut.label_frames(path_config)
elif action == 3:
deeplabcut.check_labels(path_config)
elif action == 4:
deeplabcut.create_training_dataset(path_config)
elif action == 5:
with open("training_network.py") as fp:
lines = fp.readlines()
lines[3] = lines[3].split("=")
lines[3] = lines[3][0] + "= '" + path_config + "'\n"
with open("training_network.py", "w") as fp:
for line in lines:
fp.writelines(line)
print("run: sbatch slurm.sh")
return True
elif action == 6:
try:
deeplabcut.evaluate_network(path_config,
Shuffles=[1],
trainingsetindex=0,
plotting=None,
show_errors=True,
comparisonbodyparts='all',
gputouse=None,
rescale=False)
except OSError as e:
print("file does not exist")
elif action == 7:
print("\nType video name in project/videos you want to analyze")
video_path = video_path + create_dict_list(
path_config[:-11] + "videos/", 1)[0]
with open("training_network.py") as fp:
lines = fp.readlines()
lines[3] = lines[3].split("=")
lines[3] = lines[3][0] + "= '" + path_config + "'\n"
lines[4] = lines[4].split("=")
lines[4] = lines[4][0] + "= '" + video_path + "'\n"
with open("training_network.py", "w") as fp:
for line in lines:
fp.writelines(line)
print(
"run: sbatch slurm.sh after changing the command in training_network.py"
)
return True
elif action == 8:
print(
"\nChoose the video in project/videos you want to plot trajectories from"
)
video_path = video_path + create_dict_list(
path_config[:-11] + "videos/", 1)[0]
print(video_path)
deeplabcut.plot_trajectories(path_config, [video_path],
filtered=True)
elif action == 9:
print(
"\nChoose the video in project/videos you want to make a labeled video from"
)
video_path = video_path + create_dict_list(
path_config[:-11] + "videos/", 1)[0]
deeplabcut.create_labeled_video(path_config, [video_path],
videotype='.mp4',
draw_skeleton=True)
elif action == 10:
print("\nChoose where to upload the video from")
video_path = '/data/11012579/videos/' + create_dict_list(
'/data/11012579/videos/', 0)[0]
print("\nChoose which video to upload")
video_path_list = [
video_path + "/" + create_dict_list(video_path, 1)[0]
]
while True:
print("\nDo you want to add more videos?\nType yes or no")
if input() == 'yes':
video_path_list.append(video_path + "/" +
create_dict_list(video_path, 1)[0])
else:
deeplabcut.add_new_videos(path_config,
video_path_list,
copy_videos=False)
break
elif action == 11:
print("also here")
Dlc_results2 = pd.read_hdf(
'/data/11012579/videos/vidDLC_resnet50_demo_project_grab2Feb7shuffle1_11500.h5'
)
Dlc_results2.plot()
else:
print_usage("error")
print("klaar")
# "step_15" or "refine_labels"
# "step_16" or "merge_datasets"
stages_to_run = ["analyzeskeleton"]
# ------------------------------------------------------------------
# STAGE I: Stage I: opening DeepLabCut and creation of a new project
# Steps 1, 2
# ------------------------------------------------------------------
if "step_2" in stages_to_run or "create_new_project" in stages_to_run:
config_path = deeplabcut.create_new_project(project="test_valentin", experimenter="Robin", videos=[movie_file],
copy_videos=True,
videotype='.avi',
working_directory=working_dir)
if "step_2_bis" in stages_to_run or "add_new_videos" in stages_to_run:
deeplabcut.add_new_videos(config=config_path, videos=new_videos_to_add, copy_videos=True)
# ------------------------------------------------------------------
# STAGE II: configuration of the project, open the config.yaml file
# Step 3
# ------------------------------------------------------------------
# start stop indicate which portion of the movie to extract the frames from, ex 0 & 0.5 means first 50%
"""
Exemple of config:
bodyparts:
- forepaw
- foreleg_joint
- foreleg_body_jonction
- hindlepaw
- hindleleg_joint
##basic code to run deeplabcut##
#https://github.com/AlexEMG/DeepLabCut/blob/master/docs/functionDetails.md#g-train-the-network#
import deeplabcut
import os, yaml
from pathlib import Path
##once#create the project, set working directory for videos, and find videos
deeplabcut.create_new_project('name_of_project','author', ['path_to_video.avi' ], working_directory='path_to_folder', copy_videos=False)
#specify path to config.yaml
####change yaml for the project
config_path = 'copy the path to the created yaml file here.yaml'
##opt# add more videos
deeplabcut.add_new_videos(config_path, [video_directory], copy_videos=False)
#data selection (auto)
deeplabcut.extract_frames(config_path,'automatic','uniform', crop=False, checkcropping=False)
##opt#extract data frames by hand
deeplabcut.extract_frames(config_path,'manual')
#label frames
deeplabcut.label_frames(config_path)
##opt#check annotated frames
deeplabcut.check_labels(config_path)
#create training dataset
deeplabcut.create_training_dataset(config_path,num_shuffles=1)
# %%
import os
import deeplabcut as dlc
# %%
dlc_config_file = "D:\\Dropbox (UCL - SWC)\\Project_vgatPAG\\code\\DLC\\vgatPAG-federico-2020-02-25\\config.yaml"
dlc_fld = "D:\\Dropbox (UCL - SWC)\\Project_vgatPAG\\code\\DLC"
# %%
clips = [
os.path.join(dlc_fld, v) for v in os.listdir(dlc_fld)
if 'clip' in v and 'cam' not in v
]
dlc.add_new_videos(dlc_config_file, clips, copy_videos=True)