adc.py

#Calculate the average detection confidence(ADC)

import os
import sys
import numpy as np

# Open the original annotation file of WIDER FACE training set.
fin = open('wider_face_train_bbx_gt.txt','r')
# The path of detection results on WIDER FACE training set, which is generated by TinaFace predicting detector.
tinaface_train_predict = './formulation/'

image_num = 0

# The sum of detection confidence
total_detection_confidence = np.float64(0.0)
total_gt_num = np.int64(0)

# The statistic number from predicted detection results, based on min(predict_num,gt_num) of each image.
# Here gt_num is the number of original annotations, while predict_num represents predicted detection results on their corresponding image.
total_statistic_num = np.int64(0)

while True:
    im_path = fin.readline().replace('\r','').replace('\n','').replace('\t','')
    if not im_path:
        break

    image_num = image_num + 1
    print("Processing image %d" % image_num)

    gt_num = int(fin.readline().replace('\r','').replace('\n','').replace('\t',''))

#   Training image has no annotated bbox
    if gt_num == 0:
        continue

    total_gt_num = total_gt_num + gt_num

#   Construct the path of predict annotation
    im_path = os.path.splitext(im_path)[0]
    predict_anno_path = os.path.join(tinaface_train_predict, im_path + '.txt')

#   Open predict annotation txt
    fin_predict = open(predict_anno_path,'r')

#   Read image path from predicted detection results
    fin_predict.readline()
#   Read the number of predict detection results
    predict_num = int(fin_predict.readline().replace('\r','').replace('\n','').replace('\t',''))

#   There are no predicted detection results
    if predict_num == 0:
        fin_predict.close()
        for index in range(gt_num):
            fin.readline()
        continue

#   Sum top_k predict detection confidence.
#   Avoid predict_num < gt_num
#   That is why we need a high performance predicting face detector
    predict_index = min(predict_num,gt_num)

    total_statistic_num = total_statistic_num + predict_index

    detection_confidence_score = 0.0

#   Obtain detection confidence of the top min(predict_num,gt_num)
    while predict_index > 0:
        fin.readline()
        
        predict_anno_line = fin_predict.readline().split(" ")

        detection_confidence_score = float(predict_anno_line[4])

        total_detection_confidence = total_detection_confidence + detection_confidence_score

        predict_index = predict_index - 1

 #  When predict_num < gt_num, scan the rest gt annotations to prepare for dealing with the next image annotation.
    if predict_num < gt_num:
        temp = gt_num - predict_num
        while temp > 0:
            fin.readline()
            temp = temp -1

    fin_predict.close()
    pass

print("Total gt num: %d" % total_gt_num)
print("Total statistic num: %d" % total_statistic_num)
print("Average detection confidence score (ADC): %f" % (total_detection_confidence/total_statistic_num))

fin.close()