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Updated 5 years ago
jovian.commit()[jovian] Saving notebook..
import csv
import glob
import jovian
import numpy
import pandas as pd
root_path = 'E:/segmentation_pupil/'
csv_paths = glob.glob(root_path + '*/*.csv')
csv_paths = csv_paths[:16]
csv_column = ['ori_Area', 'remove_blink', 'derivative', 'change_rate',
'nom_Area', 'Center_x', 'Center_y', 'Blink',
't_aver_change', 'c_aver_change', 'r_aver_change', 'Blink_cnt', 'Blink_rate', 'Close_cnt', 'Close_rate']
# contraction, relaxation
for csv_path in csv_paths:
file_name = csv_path.split('\\')[-1][:-4]
csv = pd.read_csv(csv_path, error_bad_lines=False)
ori_csv = list(csv['Area'])
if file_name.split('_')[-1] == '1':
mean_bright = 0
for idx in range(csv.shape[0]):
mean_bright += csv['Area'][idx]
mean_bright /= csv.shape[0]
else:
s_csv_list = []
csv['Area'] -= mean_bright
blink = False
blink_cnt = 0
close_cnt = 0
bf_lists = []
m_change = []
p_change = []
change_cnt = 0
total_change = 0
for idx in range(csv.shape[0] - 1):
c_x = csv['Center'][idx].strip('()').split(',')[0]
c_y = csv['Center'][idx].strip('()').split(',')[1].lstrip()
change_rate = ''
if blink == False:
if csv['Area'][idx] == -mean_bright:
blink = True
bf_list = [idx]
blink_start_idx = idx
blink_cnt += 1
before_area = ori_csv[idx-1]
derivative = ori_csv[idx + 1] - ori_csv[idx]
if idx == 0:
s_idx = idx
if derivative >= 0:
sign_check = True
else:
sign_check = False
else:
change_f = -1
change_a = 0
if sign_check == True:
if derivative < 0:
change_f = idx - s_idx
change_a = ori_csv[idx] - ori_csv[s_idx]
sign_check = False
s_idx = idx
else:
if derivative > 0:
change_f = idx - s_idx
change_a = ori_csv[idx] - ori_csv[s_idx]
sign_check = True
s_idx = idx
if change_f != -1:
change_cnt += 1
change_rate = change_a / change_f
total_change += abs(change_rate)
if change_rate > 0 :
p_change.append(change_rate)
else:
m_change.append(change_rate)
row = [ori_csv[idx], ori_csv[idx], derivative, change_rate, csv['Area'][idx], c_x, c_y, int(blink)]
else:
derivative = 0
row = [ori_csv[idx], before_area, derivative, change_rate, csv['Area'][idx], c_x, c_y, int(blink)]
s_csv_list.append(row)
if blink == True:
close_cnt += 1
if idx < csv.shape[0] - 1:
diff = csv['Area'][idx+1] - csv['Area'][idx]
if diff < 0:
blink = False
bf_list.append(idx)
bf_lists.append(bf_list)
if idx == csv.shape[0] - 1:
bf_list.append(idx)
bf_lists.append(bf_list)
s_csv_list[0].append(total_change / change_cnt)
s_csv_list[0].append(sum(m_change) / len(m_change))
s_csv_list[0].append(sum(p_change) / len(p_change))
s_csv_list[0].append(blink_cnt)
s_csv_list[0].append(blink_cnt / csv.shape[0])
s_csv_list[0].append(close_cnt)
s_csv_list[0].append(close_cnt / csv.shape[0])
s_csv = pd.DataFrame(s_csv_list, columns=csv_column)
csv_name = './' + file_name + '.csv'
s_csv.to_csv(csv_name)b'Skipping line 260: expected 3 fields, saw 5\nSkipping line 1583: expected 3 fields, saw 5\nSkipping line 6147: expected 3 fields, saw 5\n'
b'Skipping line 9: expected 3 fields, saw 7\nSkipping line 142: expected 3 fields, saw 5\n'
b'Skipping line 10794: expected 3 fields, saw 5\nSkipping line 11584: expected 3 fields, saw 5\nSkipping line 14503: expected 3 fields, saw 5\nSkipping line 19828: expected 3 fields, saw 5\nSkipping line 20270: expected 3 fields, saw 5\n'
b'Skipping line 2352: expected 3 fields, saw 5\nSkipping line 2895: expected 3 fields, saw 5\nSkipping line 7709: expected 3 fields, saw 5\nSkipping line 9006: expected 3 fields, saw 5\nSkipping line 9892: expected 3 fields, saw 5\nSkipping line 15145: expected 3 fields, saw 5\nSkipping line 15261: expected 3 fields, saw 5\nSkipping line 15601: expected 3 fields, saw 5\nSkipping line 15828: expected 3 fields, saw 5\nSkipping line 15853: expected 3 fields, saw 5\nSkipping line 15893: expected 3 fields, saw 5\nSkipping line 20393: expected 3 fields, saw 5\nSkipping line 21258: expected 3 fields, saw 5\nSkipping line 21457: expected 3 fields, saw 5\nSkipping line 21671: expected 3 fields, saw 5\nSkipping line 21672: expected 3 fields, saw 5\nSkipping line 21709: expected 3 fields, saw 5\nSkipping line 21710: expected 3 fields, saw 5\nSkipping line 21757: expected 3 fields, saw 5\nSkipping line 21866: expected 3 fields, saw 5\nSkipping line 22771: expected 3 fields, saw 5\n'
csv_paths['E:/segmentation_pupil\\1_after_b\\1_after_b_1.csv',
'E:/segmentation_pupil\\1_after_b\\1_after_b_15.csv',
'E:/segmentation_pupil\\1_after_g\\1_after_g_1.csv',
'E:/segmentation_pupil\\1_after_g\\1_after_g_15.csv',
'E:/segmentation_pupil\\1_after_t\\1_after_t_1.csv',
'E:/segmentation_pupil\\1_after_t\\1_after_t_15.csv',
'E:/segmentation_pupil\\1_before\\1_before_1.csv',
'E:/segmentation_pupil\\1_before\\1_before_15.csv',
'E:/segmentation_pupil\\2_after_b\\2_after_b_1.csv',
'E:/segmentation_pupil\\2_after_b\\2_after_b_15.csv',
'E:/segmentation_pupil\\2_after_g\\2_after_g_1.csv',
'E:/segmentation_pupil\\2_after_g\\2_after_g_15.csv',
'E:/segmentation_pupil\\2_after_t\\2_after_t_1.csv',
'E:/segmentation_pupil\\2_after_t\\2_after_t_15.csv',
'E:/segmentation_pupil\\2_before\\2_before_1.csv',
'E:/segmentation_pupil\\2_before\\2_before_15.csv']Version 2
import csv
import glob
import jovian
import numpy
import pandas as pd
root_path = 'E:/segmentation_pupil/'
csv_paths = glob.glob(root_path + '*/*.csv')
csv_paths = csv_paths[:2]
for csv_path in csv_paths:
file_name = csv_path.split('\\')[-1][:-4]
csv = pd.read_csv(csv_path)
ori_csv = list(csv['Area'])
if file_name.split('_')[-1] == '1':
mean_bright = 0
for idx in range(csv.shape[0]):
mean_bright += csv['Area'][idx]
mean_bright /= csv.shape[0]
else:
s_csv_list = []
csv['Area'] -= mean_bright
blink = False
blink_cnt = 0
close_cnt = 0
bf_lists = []
for idx in range(csv.shape[0]):
c_x = csv['Center'][idx].strip('()').split(',')[0]
c_y = csv['Center'][idx].strip('()').split(',')[1].lstrip()
if blink == False:
if csv['Area'][idx] == -mean_bright:
blink = True
bf_list = [idx]
blink_start_idx = idx
blink_cnt += 1
before_area = ori_csv[idx-1]
if blink == False:
s_csv_list.append([ori_csv[idx], ori_csv[idx], csv['Area'][idx], c_x, c_y, int(blink)])
else:
s_csv_list.append([ori_csv[idx], before_area, csv['Area'][idx], c_x, c_y, int(blink)])
if blink == True:
close_cnt += 1
if idx < csv.shape[0] - 1:
diff = csv['Area'][idx+1] - csv['Area'][idx]
if diff < 0:
blink = False
bf_list.append(idx)
bf_lists.append(bf_list)
if idx == csv.shape[0] - 1:
bf_list.append(idx)
bf_lists.append(bf_list)
s_csv_list[0].append(blink_cnt)
s_csv_list[0].append(blink_cnt / csv.shape[0])
s_csv_list[0].append(close_cnt)
s_csv_list[0].append(close_cnt / csv.shape[0])
s_csv = pd.DataFrame(s_csv_list, columns=['ori_Area', 'remove_blink', 'nom_Area', 'Center_x', 'Center_y', 'Blink', 'Blink_cnt', 'Blink_rate', 'Close_cnt', 'Close_rate'])
s_csv.to_csv('./result.csv')