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VTT1_OONZ5I.py
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VTT1_OONZ5I.py
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import csv
import argparse
import os
from sklearn import metrics
from sklearn.metrics import roc_auc_score
from matplotlib import pyplot
from sklearn.metrics import f1_score
from sklearn.metrics import auc
from sklearn.metrics import confusion_matrix
import seaborn as sns
import json
#provide inputs
parser = argparse.ArgumentParser()
parser.add_argument('--det',type=str) #give path to detection files
parser.add_argument('--gt',type=str) #give path to ground truth files
args = parser.parse_args()
#graph style
sns.set_style('whitegrid')
driver_ground_truth= []
front_ground_truth= []
back_ground_truth= []
# read ground truth file
for root, dirs, files in os.walk(args.gt):
if not files:
continue
files.sort()
for f in files:
if f.endswith('.json'):
with open(args.gt+f) as json_file:
loaded_data = json.load(json_file)
# retrieve ground truth and predict driver in images
driver_pred_res= []
vertical_limit= 240
horizontal_limit= 360
bbox_area_limit= 30000
for root, dirs, files in os.walk(args.det):
if not files:
continue
prefix = os.path.basename(root)
files.sort()
for f in files:
if f.endswith('.txt'):
for p in loaded_data['images']:
if os.path.splitext(f)[0]==p['file_name']:
driver_found= False
front_found= False
back_found= False
image_id= p['id']
for p2 in loaded_data['annotations']:
if p2['image_id']==image_id:
if p2['category_id']==1:
back_found = True
elif p2['category_id']==2:
driver_found = True
elif p2['category_id']==3:
front_found = True
if driver_found==False:
driver_ground_truth.append(0)
else:
driver_ground_truth.append(1)
if front_found==False:
front_ground_truth.append(0)
else:
front_ground_truth.append(1)
if back_found==False:
back_ground_truth.append(0)
else:
back_ground_truth.append(1)
#driver prediction
with open(os.path.join(root, f)) as txt_file:
prob= []
for line in txt_file:
type= line.split()[0]
xmin = float(line.split()[2])
ymin = float(line.split()[3])
xmax = float(line.split()[4])
ymax = float(line.split()[5])
conf = float(line.split()[1])
bbox_area= (xmax-xmin)*(ymax-ymin)
x= (xmin+xmax)/2
y= (ymin+ymax)/2
if bbox_area>bbox_area_limit and x>horizontal_limit and y>vertical_limit:
prob.append(conf)
temp = 1
for j in prob:
temp *= (1-j)
final_prob= (1-temp)
driver_pred_res.append(final_prob)
break
# confusion matrix for driver
print('driver')
limit= 0.87
yconf= []
for i in driver_pred_res:
if i<limit:
yconf.append(0)
else:
yconf.append(1)
tn, fp, fn, tp = confusion_matrix(driver_ground_truth, yconf).ravel()
acc= (tp+tn)/(tn+ fp+ fn+ tp)
print("True Positives: "+str(tp))
print("True Negatives: "+str(tn))
print("False Positives: "+str(fp))
print("False Negatives: "+str(fn))
print("Accuracy: "+str(acc))
# # Optional
# # f1 score
# pr_f1 = f1_score(driver_ground_truth, yconf)
# print('PR curve max F1: %.3f' %(pr_f1))
# # calculate AUC
# roc_auc = roc_auc_score(driver_ground_truth, driver_pred_res)
# print('ROC curve AUC, driver: %.3f' % roc_auc)
# # roc curve
# fpr1, tpr1, thresholds1 = metrics.roc_curve(driver_ground_truth, driver_pred_res, pos_label=1)
# pyplot.plot(fpr1, tpr1, marker='.')
# pyplot.xlabel('False Positive Rate', **{'size':'14'})
# pyplot.ylabel('True Positive Rate', **{'size':'14'})
# pyplot.savefig('ROC_driver.png')
# pyplot.close()
#prediction of front passenger in images
front_pred_res= []
vertical_limit= 240
horizontal_limit= 360
bbox_area_limit= 30000
for root, dirs, files in os.walk(args.det):
if not files:
continue
prefix = os.path.basename(root)
files.sort()
for f in files:
if f.endswith('.txt'):
for p in loaded_data['images']:
if os.path.splitext(f)[0]==p['file_name']:
with open(os.path.join(root, f)) as txt_file:
prob= []
for line in txt_file:
type= line.split()[0]
xmin = float(line.split()[2])
ymin = float(line.split()[3])
xmax = float(line.split()[4])
ymax = float(line.split()[5])
conf = float(line.split()[1])
bbox_area= (xmax-xmin)*(ymax-ymin)
x= (xmin+xmax)/2
y= (ymin+ymax)/2
if bbox_area>bbox_area_limit and x<horizontal_limit and y>vertical_limit:
prob.append(conf)
temp = 1
for j in prob:
temp *= (1-j)
final_prob= (1-temp)
front_pred_res.append(final_prob)
break
# confusion matrix front seat passenger
print("front")
limit= 0.89
yconf= []
for i in front_pred_res:
if i<limit:
yconf.append(0)
else:
yconf.append(1)
tn, fp, fn, tp = confusion_matrix(front_ground_truth, yconf).ravel()
acc= (tp+tn)/(tn+ fp+ fn+ tp)
print("True Positives: "+str(tp))
print("True Negatives: "+str(tn))
print("False Positives: "+str(fp))
print("False Negatives: "+str(fn))
print("Accuracy: "+str(acc))
# # Optional
# # f1 score
# pr_f1 = f1_score(front_ground_truth, yconf)
# print('PR curve max F1: %.3f' %(pr_f1))
# # auc score
# roc_auc = roc_auc_score(front_ground_truth, front_pred_res)
# print('ROC curve AUC, front: %.3f' % roc_auc)
# #roc curve
# fpr2, tpr2, thresholds2 = metrics.roc_curve(front_ground_truth, front_pred_res, pos_label=1)
# pyplot.plot(fpr2, tpr2, marker='.')
# pyplot.xlabel('False Positive Rate', **{'size':'14'})
# pyplot.ylabel('True Positive Rate', **{'size':'14'})
# pyplot.savefig('ROC_front.png')
# pyplot.close()
#prediction of back passenger in images
back_pred_res= []
horizontal_limit1= 250
horizontal_limit2= 720-horizontal_limit1
bbox_area_limit= 0
for root, dirs, files in os.walk(args.det):
if not files:
continue
prefix = os.path.basename(root)
files.sort()
for f in files:
if f.endswith('.txt'):
for p in loaded_data['images']:
if os.path.splitext(f)[0]==p['file_name']:
with open(os.path.join(root, f)) as txt_file:
prob= []
for line in txt_file:
type= line.split()[0]
xmin = float(line.split()[2])
ymin = float(line.split()[3])
xmax = float(line.split()[4])
ymax = float(line.split()[5])
conf = float(line.split()[1])
bbox_area= (xmax-xmin)*(ymax-ymin)
x= (xmin+xmax)/2
y= (ymin+ymax)/2
if bbox_area>bbox_area_limit and x>horizontal_limit1 and x<horizontal_limit2:
prob.append(conf)
temp = 1
for j in prob:
temp *= (1-j)
final_prob= (1-temp)
back_pred_res.append(final_prob)
break
# confusion matrix back seat passenger
print("back")
limit= 0.11
yconf= []
for i in back_pred_res:
if i<limit:
yconf.append(0)
else:
yconf.append(1)
tn, fp, fn, tp = confusion_matrix(back_ground_truth, yconf).ravel()
acc= (tp+tn)/(tn+ fp+ fn+ tp)
print("True Positives: "+str(tp))
print("True Negatives: "+str(tn))
print("False Positives: "+str(fp))
print("False Negatives: "+str(fn))
print("Accuracy: "+str(acc))
# # Optional
# # f1 score
# pr_f1 = f1_score(back_ground_truth, yconf)
# print('PR curve max F1: %.3f' %(pr_f1))
# # calculate AUC
# roc_auc = roc_auc_score(back_ground_truth, back_pred_res)
# print('ROC curve AUC, back: %.3f' % roc_auc)
# # roc curve
# fpr3, tpr3, thresholds3 = metrics.roc_curve(back_ground_truth, back_pred_res, pos_label=1)
# pyplot.plot(fpr3, tpr3, marker='.')
# pyplot.xlabel('False Positive Rate', **{'size':'14'})
# pyplot.ylabel('True Positive Rate', **{'size':'14'})
# pyplot.savefig('ROC_back.png')
# pyplot.close()