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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
modified from: https://github.com/DeepLabCut/DeepLabCut-core/testscript_cli.py
by Mackenzie.
DEVELOPERS:
This script tests various functionalities in an automatic way.
It produces nothing of interest scientifically.
"""
task = "Testcore" # Enter the name of your experiment Task
scorer = "Mackenzie" # Enter the name of the experimenter/labeler
import os, subprocess, sys
#def install(package):
# subprocess.check_call([sys.executable, "-m", "pip", "install", package])
#install("tensorflow==1.13.1")
import deeplabcut 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_cli.py"))
videoname = "reachingvideo1"
video = [
os.path.join(
basepath,
"examples",
"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 = 3
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"] = [0.8]
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)
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)
print("Making plots")
dlc.plot_trajectories(path_config_file,[videotest])
print("CREATING TRAININGSET 2")
dlc.create_training_dataset(path_config_file, Shuffles=[2],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(2),'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, shuffle=2,allow_growth=True)
print("EVALUATE")
dlc.evaluate_network(path_config_file,Shuffles=[2],plotting=False)
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=1, make_tar=False)
print(
"ALL DONE!!! - default/imgaug cases of DLCcore training and evaluation are functional (no extract outlier or refinement tested)."
)