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generateBoxPlot.py
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##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Jitesh Joshi
## Department of Computer Science, University College London
## jitesh.joshi.20@ucl.ac.uk
## Copyright (c) 2022
## This source code is licensed under the MIT-style license found in the
## LICENSE file in the root directory of this source tree
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
from turtle import color
import numpy as np
import argparse
import os
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
def main(args):
base_dir = args.base_dir
data_dirs = []
dir_names = []
base_dir_list = os.listdir(base_dir)
for ff in range(len(base_dir_list)):
dir_name = base_dir_list[ff]
if os.path.isdir(os.path.join(base_dir, dir_name)):
dir_names.append(dir_name)
data_dirs.append(os.path.join(base_dir, dir_name))
data_dict_min = {}
data_dict_min['x'] = []
data_dict_min['y'] = []
data_dict_avg = {}
data_dict_avg['x'] = []
data_dict_avg['y'] = []
data_dict_max = {}
data_dict_max['x'] = []
data_dict_max['y'] = []
data_dict_std = {}
data_dict_std['x'] = []
data_dict_std['y'] = []
data_dict_fg_avg = {}
data_dict_fg_avg['x'] = []
data_dict_fg_avg['y'] = []
data_dict_bg_avg = {}
data_dict_bg_avg['x'] = []
data_dict_bg_avg['y'] = []
data_dict_fg_max_diff = {}
data_dict_fg_max_diff['x'] = []
data_dict_fg_max_diff['y'] = []
for i, fn in enumerate(data_dirs):
min_array = None
avg_array = None
max_array = None
std_array = None
fg_avg_array = None
bg_avg_array = None
fs_min = os.path.join(data_dirs[i], 'min_array.npy')
if os.path.exists(fs_min):
min_array = np.load(fs_min)
fs_avg = os.path.join(data_dirs[i], 'avg_array.npy')
if os.path.exists(fs_avg):
avg_array = np.load(fs_avg)
fs_max_ = os.path.join(data_dirs[i], 'max_array.npy')
if os.path.exists(fs_max_):
max_array = np.load(fs_max_)
fs_std = os.path.join(data_dirs[i], 'std_array.npy')
if os.path.exists(fs_std):
std_array = np.load(fs_std)
fs_fg_avg = os.path.join(data_dirs[i], 'fg_avg_array.npy')
if os.path.exists(fs_fg_avg):
fg_avg_array = np.load(fs_fg_avg)
fs_bg_avg = os.path.join(data_dirs[i], 'bg_avg_array.npy')
if os.path.exists(fs_bg_avg):
bg_avg_array = np.load(fs_bg_avg)
fs_fg_max_diff = os.path.join(data_dirs[i], 'fg_max_diff_array.npy')
if os.path.exists(fs_fg_max_diff):
fg_max_diff_array = np.load(fs_fg_max_diff)
instance_count = 0
if np.all(min_array) != None:
instance_count = len(min_array)
elif np.all(avg_array) != None:
instance_count = len(avg_array)
elif np.all(std_array) != None:
instance_count = len(std_array)
elif np.all(max_array) != None:
instance_count = len(max_array)
elif np.all(fg_avg_array) != None:
instance_count = len(fg_avg_array)
elif np.all(bg_avg_array) != None:
instance_count = len(bg_avg_array)
elif np.all(fg_max_diff_array) != None:
instance_count = len(fg_max_diff_array)
for j in range(instance_count):
if np.all(min_array) != None:
data_dict_min['x'].append(dir_names[i])
data_dict_min['y'].append(min_array[j])
if np.all(avg_array) != None:
data_dict_avg['x'].append(dir_names[i])
data_dict_avg['y'].append(avg_array[j])
if np.all(max_array) != None:
data_dict_max['x'].append(dir_names[i])
data_dict_max['y'].append(max_array[j])
if np.all(std_array) != None:
data_dict_std['x'].append(dir_names[i])
data_dict_std['y'].append(std_array[j])
if np.all(fg_avg_array) != None:
data_dict_fg_avg['x'].append(dir_names[i])
data_dict_fg_avg['y'].append(fg_avg_array[j])
if np.all(bg_avg_array) != None:
data_dict_bg_avg['x'].append(dir_names[i])
data_dict_bg_avg['y'].append(bg_avg_array[j])
if np.all(fg_max_diff_array) != None:
data_dict_fg_max_diff['x'].append(dir_names[i])
data_dict_fg_max_diff['y'].append(fg_max_diff_array[j])
# df_min = pd.DataFrame.from_dict(data_dict_min)
# df_max = pd.DataFrame.from_dict(data_dict_max)
df_avg = pd.DataFrame.from_dict(data_dict_avg)
df_std = pd.DataFrame.from_dict(data_dict_std)
df_fg_avg = pd.DataFrame.from_dict(data_dict_fg_avg)
df_bg_avg = pd.DataFrame.from_dict(data_dict_bg_avg)
data_dict_fgbg_diff = {}
data_dict_fgbg_diff['x'] = []
data_dict_fgbg_diff['y'] = []
for i in range(len(data_dict_fg_avg['y'])):
data_dict_fgbg_diff['x'].append(data_dict_fg_avg['x'][i])
data_dict_fgbg_diff['y'].append(data_dict_fg_avg['y'][i] - data_dict_bg_avg['y'][i] - 0.75)
df_fgbg_diff = pd.DataFrame.from_dict(data_dict_fgbg_diff)
# df_fgbg_diff = pd.DataFrame.from_dict(data_dict_fg_max_diff)
if len(data_dict_avg['y']) > 0:
sns.boxplot(x='x', y='y', data=df_avg, whis=4.0, fliersize=20)
plt.xlabel('Different Datasets')
plt.ylabel('Average Value')
plt.title('Box Plot Analysis')
plt.savefig(os.path.join(base_dir, 'boxplot_avg.jpg'), bbox_inches=0)
plt.close()
if len(data_dict_std['y']) > 0:
sns.boxplot(x='x', y='y', data=df_std, whis=4.0, fliersize=20)
plt.xlabel('Different Datasets')
plt.ylabel('Standard Deviation')
plt.title('Box Plot Analysis')
plt.savefig(os.path.join(base_dir, 'boxplot_std.jpg'), bbox_inches=0)
plt.close()
if len(data_dict_fg_avg['y']) > 0:
sns.boxplot(x='x', y='y', data=df_fg_avg, whis=4.0, fliersize=20)
if len(data_dict_bg_avg['y']) > 0:
sns.boxplot(x='x', y='y', data=df_bg_avg, whis=4.0, fliersize=20)
plt.xlabel('Average Foreground and Background Temperature')
plt.ylabel('Average Value')
plt.title('Box Plot Analysis')
plt.savefig(os.path.join(base_dir, 'boxplot_foreground_background_avg.jpg'), bbox_inches=0)
plt.close()
if len(data_dict_fg_max_diff['y']) > 0:
sns.boxplot(x='x', y='y', data=df_fgbg_diff, whis=4.0, fliersize=20)
plt.xlabel('Use of Augmentation Technique - TiAug')
plt.ylabel('Difference in Foreground-Background Temperature')
plt.title('Box Plot Analysis')
plt.savefig(os.path.join(base_dir, 'boxplot_foreground_background_diff.jpg'), bbox_inches=0)
plt.close()
if len(data_dict_avg['y']) > 0:
sns.scatterplot(x='x', y='y', data=df_avg)
plt.xlabel('Different Datasets')
plt.ylabel('Average Value')
plt.title('Scatter Plot Analysis')
plt.savefig(os.path.join(base_dir, 'scatterplot_avg.jpg'), bbox_inches=0)
plt.close()
if len(data_dict_std['y']) > 0:
sns.scatterplot(x='x', y='y', data=df_std)
plt.xlabel('Different Datasets')
plt.ylabel('Standard Deviation')
plt.title('Scatter Plot Analysis')
plt.savefig(os.path.join(base_dir, 'scatterplot_std.jpg'), bbox_inches=0)
plt.close()
if len(data_dict_fg_avg['y']) > 0:
sns.scatterplot(x='x', y='y', data=df_fg_avg)
if len(data_dict_bg_avg['y']) > 0:
sns.scatterplot(x='x', y='y', data=df_bg_avg)
plt.xlabel('Average Foreground and Background Temperature')
plt.ylabel('Average Value')
plt.title('Scatter Plot Analysis')
plt.savefig(os.path.join(base_dir, 'scatterplot_foreground_background_avg.jpg'), bbox_inches=0)
plt.close()
if len(data_dict_fg_max_diff['y']) > 0:
sns.scatterplot(x='x', y='y', data=df_fgbg_diff)
plt.xlabel('Use of Augmentation Technique - TiAug')
plt.ylabel('Difference in Foreground-Background Temperature')
plt.title('Box Plot Analysis')
plt.savefig(os.path.join(base_dir, 'scatterplot_foreground_background_diff.jpg'), bbox_inches=0)
plt.close()
# plt.show()
def get_args():
parser = argparse.ArgumentParser(description='Generate the box plot for temperature ranges in multiple datasets',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('-d', '--analysis_dir', type=str,
help='Analysis Directory', dest='base_dir')
return parser.parse_args()
if __name__ == '__main__':
args = get_args()
main(args)