widerface筛选数据

import cv2

from utils.not_good import is_ok

file_names =[  ]# get_image_list(ini_name,360000,list_path)

txt_path =r"WIDER_train\train_label.txt"
f = open(txt_path, 'r')
lines = f.readlines()
isFirst = True
labels = []
words = []
for line in lines:
    line = line.rstrip()
    if line.startswith('#'):
        if isFirst is True:
            isFirst = False
        else:
            labels_copy = labels.copy()
            words.append(labels_copy)
            labels.clear()

        path = line[2:]
        path = txt_path.replace('train_label.txt', 'images/') + path
        file_names.append(path)
    else:
        line = line.split(' ')
        label = [float(x) for x in line]
        labels.append(label)
words.append(labels)
datas =[]
a_len =len(file_names)
for index, i in enumerate(file_names[::-1]):

    res,datas =is_ok(i)
    if not res:
        file_names.remove(i)
        words.remove(words[a_len -index -1])

for index, file in enumerate(file_names):
    img_raw = cv2.imread(file)

    labels = words[index]
    face_num = 0
    has_small = False
    has_max = False
    for label in labels:
        x1 = int(label[0])
        y1 = int(label[1])
        x2 = int(label[0] + label[2])
        y2 = int(label[1] + label[3])

        print((x1, y1), (x2, y2), label[2] * label[3])
        cv2.rectangle(img_raw, (x1, y1), (x2, y2), (0, 255, 0), 1)
    cv2.imshow("sdf",img_raw)
    cv2.waitKey()