人脸检测——生成矫正人脸——cascade cnn的思想， 但是mtcnn的效果貌似更赞

简单粗暴，不多说，直接代码吧：（这个代码实在上篇博客的基础上：人脸检测——AFLW准备人脸）

先要生成AFLW_ann.txt。

import osfrom PIL import Imagefrom PIL import ImageFileImageFile.LOAD_TRUNCATED_IMAGES = Trueimport cv2import numpy as npdef crop_for_cal(sn, xn, yn, n):    with open('AFLW_ann.txt','r') as f:        lines = f.readlines()    save_dir1 = 'data_prepare/cal_positive_'+str(n)+'_12'    save_dir2 = 'data_prepare/cal_positive_'+str(n)+'_24'    save_dir3 = 'data_prepare/cal_positive_'+str(n)+'_48'    if os.path.exists(save_dir1)==False:        os.makedirs(save_dir1)    if os.path.exists(save_dir2)==False:        os.makedirs(save_dir2)    if os.path.exists(save_dir3)==False:        os.makedirs(save_dir3)    for idx, line in enumerate(lines):        spl1 = line.strip().split(' ')        image_path = spl1[0]        x = int(spl1[1])        y = int(spl1[2])        w = int(spl1[3])        h = int(spl1[4])        x = int(x-xn*w/sn)        y = int(y-yn*h/sn)        w = int(w/sn)        h = int(h/sn)        # image = Image.open(image_path)                image = cv2.imread(image_path)        if image is None:            continue        if x<=0 and y<=0 and w<=0 and h<=0:            continue        box = (x, y, x+w, y+h)            # patch = image.crop(box)        patch = image[box[1]:box[3], box[0]:box[2], :]        if patch is None:            continue        if patch.shape[0]<10 and patch.shape[1]<10:            continue        #patch1 = patch.resize((12, 12))        #patch2 = patch.resize((24, 24))        #patch3 = patch.resize((48, 48))        patch1 = cv2.resize(patch, (12, 12))          patch2 = cv2.resize(patch, (24, 24))          patch3 = cv2.resize(patch, (48, 48))                  spl2 = image_path.split('/')        image_name = spl2[-1]        save_path1 = save_dir1+'/'+str(idx)+image_name + '.jpg'        save_path2 = save_dir2+'/'+str(idx)+image_name + '.jpg'        save_path3 = save_dir3+'/'+str(idx)+image_name + '.jpg'        #patch1.save(save_path1, 'jpeg')        #patch2.save(save_path2, 'jpeg')        #patch3.save(save_path3, 'jpeg')        cv2.imwrite(save_path1, np.array(patch1))        cv2.imwrite(save_path2, np.array(patch2))        cv2.imwrite(save_path3, np.array(patch3))if __name__ == '__main__':        s_set = (0.83, 0.91, 1.0, 1.10, 1.21)    x_set = (-0.17, 0, 0.17)    y_set = (-0.17, 0, 0.17)    n = 0    for x in x_set:        for y in y_set:            for s in s_set:                n = n + 1                crop_for_cal(s, x, y, n)

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