image_preprocess.py

import dlib
import cv2
def compute_aspect_preserved_bbox(bbox, increase_area, h, w):
    left, top, right, bot = bbox
    width = right - left
    height = bot - top

    width_increase = max(increase_area, ((1 + 2 * increase_area) * height - width) / (2 * width))
    height_increase = max(increase_area, ((1 + 2 * increase_area) * width - height) / (2 * height))

    left_t = int(left - width_increase * width)
    top_t = int(top - height_increase * height)
    right_t = int(right + width_increase * width)
    bot_t = int(bot + height_increase * height)

    left_oob = -min(0, left_t)
    right_oob = right - min(right_t, w)
    top_oob = -min(0, top_t)
    bot_oob = bot - min(bot_t, h)

    if max(left_oob, right_oob, top_oob, bot_oob) > 0:
        max_w = max(left_oob, right_oob)
        max_h = max(top_oob, bot_oob)
        if max_w > max_h:
            return left_t + max_w, top_t + max_w, right_t - max_w, bot_t - max_w
        else:
            return left_t + max_h, top_t + max_h, right_t - max_h, bot_t - max_h

    else:
        return (left_t, top_t, right_t, bot_t)

def crop_src_image(src_img,save_img, detector=None):
    if  detector is None:
        detector = dlib.get_frontal_face_detector()

    img = cv2.imread(src_img)
    faces = detector(img, 0)
    h, width, _ = img.shape
    if len(faces) > 0:
        bbox = [faces[0].left(), faces[0].top(),faces[0].right(), faces[0].bottom()]
        l = bbox[3]-bbox[1]
        bbox[1]= bbox[1]-l*0.1
        bbox[3]= bbox[3]-l*0.1
        bbox[1] = max(0,bbox[1])
        bbox[3] = min(h,bbox[3])
        bbox = compute_aspect_preserved_bbox(tuple(bbox), 0.5, img.shape[0], img.shape[1])
        img = img[bbox[1] :bbox[3] , bbox[0]:bbox[2]]
        img = cv2.resize(img, (256, 256))
        cv2.imwrite(save_img,img)
    else:
        img = cv2.resize(img,(256,256))
        cv2.imwrite(save_img, img)

if __name__ == '__main__':
    src_img = ""
    out_img = ""
    crop_src_image(src_img,out_img)