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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) |