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Copy pathtest-openfielddata.py
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62 lines (45 loc) · 2.2 KB
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Nov 5 18:06:13 2018
@author: alex
This is a test script that should achieve a certain performance on your system.
See values on our system, the DLC 2.0 docker with TF 1.8 on a NVIDIA GTX 1080Ti.
https://github.com/MMathisLab/Docker4DeepLabCut2.0
This test trains on the open field data set for about 30 minutes (15k iterations).
The results will be something like this:
Results for 15001 training iterations: 95 1 train error: 2.89 pixels. Test error: 2.81 pixels.
With pcutoff of 0.1 train error: 2.89 pixels. Test error: 2.81 pixels
The analysis of the video takes 41 seconds (batch size 32) and creating the frames 8 seconds (+ a few seconds for ffmpeg) to create the video.
"""
# Importing the toolbox (takes several seconds)
import deeplabcut
import os, yaml
from pathlib import Path
def read_config(configname):
""" Reads config file """
with open(str(configname), 'r') as ymlfile:
cfg = yaml.load(ymlfile)
return(cfg)
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)