testscript.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Oct  2 13:56:11 2018
@author: alex

DEVELOPERS:
This script tests various functionalities in an automatic way.

It should take about 3:30 minutes to run this in a CPU.
It should take about 1:30 minutes on a GPU (incl. downloading the ResNet weights)

It produces nothing of interest scientifically.
"""

task='Testcore' # Enter the name of your experiment Task
scorer='Alex' # Enter the name of the experimenter/labeler

import os,  subprocess

import deeplabcutcore as dlc

from pathlib import Path
import pandas as pd
import numpy as np
import platform

print("Imported DLC!")

basepath=os.path.dirname(os.path.abspath('testscript.py'))
videoname='reachingvideo1'
#video=[os.path.join(Path(basepath).parents[0],'DLCreleases/DeepLabCut/examples/Reaching-Mackenzie-2018-08-30','videos',videoname+'.avi')]

video = [
    os.path.join(
        basepath, "Reaching-Mackenzie-2018-08-30", "videos", videoname + ".avi"
    )
]
# For testing a color video:
#videoname='baby4hin2min'
#video=[os.path.join('/home/alex/Desktop/Data',videoname+'.mp4')]
#to test destination folder:
#dfolder=basepath
print(video)

dfolder=None
net_type='resnet_50' #'mobilenet_v2_0.35' #'resnet_50'
augmenter_type='default'
augmenter_type2='imgaug'

if platform.system() == 'Darwin' or platform.system()=='Windows':
    print("On Windows/OSX tensorpack is not tested by default.")
    augmenter_type3='imgaug'
else:
    augmenter_type3='tensorpack' #Does not work on WINDOWS

numiter=5

print("CREATING PROJECT")
path_config_file=dlc.create_new_project(task,scorer,video,copy_videos=True)

cfg=dlc.auxiliaryfunctions.read_config(path_config_file)
cfg['numframes2pick']=5
cfg['pcutoff']=0.01
cfg['TrainingFraction']=[.8]
cfg['skeleton']=[['bodypart1','bodypart2'],['bodypart1','bodypart3']]

dlc.auxiliaryfunctions.write_config(path_config_file,cfg)

print("EXTRACTING FRAMES")
dlc.extract_frames(path_config_file,mode='automatic',userfeedback=False)

print("CREATING-SOME LABELS FOR THE FRAMES")
frames=os.listdir(os.path.join(cfg['project_path'],'labeled-data',videoname))
#As this next step is manual, we update the labels by putting them on the diagonal (fixed for all frames)
for index,bodypart in enumerate(cfg['bodyparts']):
        columnindex = pd.MultiIndex.from_product([[scorer], [bodypart], ['x', 'y']],names=['scorer', 'bodyparts', 'coords'])
        frame = pd.DataFrame(100+np.ones((len(frames),2))*50*index, columns = columnindex, index = [os.path.join('labeled-data',videoname,fn) for fn in frames])
        if index==0:
            dataFrame=frame
        else:
            dataFrame = pd.concat([dataFrame, frame],axis=1)

dataFrame.to_csv(os.path.join(cfg['project_path'],'labeled-data',videoname,"CollectedData_" + scorer + ".csv"))
dataFrame.to_hdf(os.path.join(cfg['project_path'],'labeled-data',videoname,"CollectedData_" + scorer + '.h5'),'df_with_missing',format='table', mode='w')

print("Plot labels...")

dlc.check_labels(path_config_file)

print("CREATING TRAININGSET")
dlc.create_training_dataset(path_config_file,net_type=net_type,augmenter_type=augmenter_type)

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)
#dlc.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=dlc.ShortenVideo(video[0],start='00:00:00',stop='00:00:00.4',outsuffix='short',outpath=os.path.join(cfg['project_path'],'videos'))
    vname=Path(newvideo).stem
except: # if ffmpeg is broken
    vname='brief'
    newvideo=os.path.join(cfg['project_path'],'videos',vname+'.mp4')
    from moviepy.editor import VideoFileClip,VideoClip
    clip = VideoFileClip(video[0])
    clip.reader.initialize()
    def make_frame(t):
        return clip.get_frame(1)

    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, destfolder=dfolder)

file=os.path.join(cfg['project_path'],'labeled-data',vname,"machinelabels-iter"+ str(cfg['iteration']) + '.h5')

print("RELABELING")
DF=pd.read_hdf(file,'df_with_missing')
DLCscorer=np.unique(DF.columns.get_level_values(0))[0]
DF.columns.set_levels([scorer.replace(DLCscorer,scorer)],level=0,inplace=True)
DF =DF.drop('likelihood',axis=1,level=2)
DF.to_csv(os.path.join(cfg['project_path'],'labeled-data',vname,"CollectedData_" + scorer + ".csv"))
DF.to_hdf(os.path.join(cfg['project_path'],'labeled-data',vname,"CollectedData_" + scorer + '.h5'),'df_with_missing',format='table', mode='w')

print("MERGING")
dlc.merge_datasets(path_config_file)

print("CREATING TRAININGSET")
dlc.create_training_dataset(path_config_file,net_type=net_type,augmenter_type=augmenter_type2)

cfg=dlc.auxiliaryfunctions.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=dlc.auxiliaryfunctions.read_plainconfig(posefile)
DLC_config['save_iters']=numiter
DLC_config['display_iters']=1
DLC_config['multi_step']=[[0.001,numiter]]

print("CHANGING training parameters to end quickly!")
dlc.auxiliaryfunctions.write_config(posefile,DLC_config)

print("TRAIN")
dlc.train_network(path_config_file)

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])
    newvideo2=dlc.ShortenVideo(video[0],start='00:00:00',stop='00:00:00.4',outsuffix='short2',outpath=os.path.join(cfg['project_path'],'videos'))

    vname=Path(newvideo2).stem
except: # if ffmpeg is broken
    vname='brief'
    newvideo2=os.path.join(cfg['project_path'],'videos',vname+'.mp4')
    from moviepy.editor import VideoFileClip,VideoClip
    clip = VideoFileClip(video[0])
    clip.reader.initialize()
    def make_frame(t):
        return clip.get_frame(1)

    newclip = VideoClip(make_frame, duration=1)
    newclip.write_videofile(newvideo2,fps=30)


print("Inference with direct cropping")
dlc.analyze_videos(path_config_file, [newvideo2], save_as_csv=True, destfolder=dfolder, crop=[0, 50, 0, 50])

print("Extracting skeleton distances, filter and plot filtered output")
dlc.analyzeskeleton(path_config_file, [newvideo], save_as_csv=True, destfolder=dfolder)
dlc.filterpredictions(path_config_file,[newvideo])

#dlc.create_labeled_video(path_config_file,[newvideo], destfolder=dfolder,filtered=True)
dlc.create_labeled_video(path_config_file,[newvideo2], destfolder=dfolder,displaycropped=True,filtered=True)
dlc.plot_trajectories(path_config_file,[newvideo2], destfolder=dfolder,filtered=True)

print("ALL DONE!!! - default cases without Tensorpack loader are functional.")

print("CREATING TRAININGSET for shuffle 2")
print("will be used for 3D testscript...")
# TENSORPACK could fail in WINDOWS...
dlc.create_training_dataset(path_config_file,Shuffles=[2],net_type=net_type,augmenter_type=augmenter_type3)

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(2),'train/pose_cfg.yaml')

DLC_config=dlc.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!")
dlc.auxiliaryfunctions.write_plainconfig(posefile,DLC_config)

print("TRAINING shuffle 2, with smaller allocated memory")
dlc.train_network(path_config_file,shuffle=2,allow_growth=True)

print("ANALYZING some individual frames")
dlc.analyze_time_lapse_frames(path_config_file,os.path.join(cfg['project_path'],'labeled-data/reachingvideo1/'))

print("Export model...")
dlc.export_model(path_config_file,shuffle=2,make_tar=False)

print("ALL DONE!!! - default cases of DLCcore are functional.")