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mouth_open.py
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mouth_open.py
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'''
* @author [daweihao]
* @version [2021-08-03]
* <检测嘴巴是否张开>
'''
import math
from PIL import Image, ImageDraw
import face_recognition
import os
import shutil
import cv2
def get_lip_height(lip):
sum = 0
for i in [2, 3, 4]:
# distance between two near points up and down
distance = math.sqrt((lip[i][0] - lip[12 - i][0]) ** 2 +
(lip[i][1] - lip[12 - i][1]) ** 2)
sum += distance
return sum / 3
def get_mouth_height(top_lip, bottom_lip):
sum = 0
for i in [8, 9, 10]:
# distance between two near points up and down
distance = math.sqrt((top_lip[i][0] - bottom_lip[18 - i][0]) ** 2 +
(top_lip[i][1] - bottom_lip[18 - i][1]) ** 2)
sum += distance
return sum / 3
def check_mouth_open(top_lip, bottom_lip):
top_lip_height = get_lip_height(top_lip)
bottom_lip_height = get_lip_height(bottom_lip)
mouth_height = get_mouth_height(top_lip, bottom_lip)
# if mouth is open more than lip height * ratio, return true.
ratio = 0.5
if mouth_height > min(top_lip_height, bottom_lip_height) * ratio:
return True
else:
return False
if __name__ == "__main__":
# ffhq_path = '/data/data_hao/detect_mouth_open/images1024x1024'
# for image_file in os.listdir(ffhq_path):
# try:
# image = face_recognition.load_image_file(ffhq_path + str('/') + image_file)
# face_landmarks_list = face_recognition.face_landmarks(image)
# pil_image = Image.fromarray(image)
# d = ImageDraw.Draw(pil_image)
#
# top_lip = face_landmarks_list[0]['top_lip']
# bottom_lip = face_landmarks_list[0]['bottom_lip']
# print(top_lip)
# print(bottom_lip)
#
#
#
# top_lip_height = get_lip_height(top_lip)
# bottom_lip_height = get_lip_height(bottom_lip)
# mouth_height = get_mouth_height(top_lip, bottom_lip)
#
# # if mouth is open more than lip height * ratio, return true.
# ratio = 0.5
# if mouth_height > min(top_lip_height, bottom_lip_height) * ratio:
# # print("open")
# shutil.move(ffhq_path + str('/') + image_file, '/data/data_hao/detect_mouth_open/ffhq_open_mouth')
# else:
# pass
# except Exception:
# print('IndexError: list index out of range')
# continue
# /data/data_hao/PFLD-pytorch/03.png
# img = cv2.imread('/data/data_hao/PFLD-pytorch/woman_face/woman04.png')
# img = cv2.resize(img, (512, 512))
# cv2.imwrite('/data/data_hao/PFLD-pytorch/woman_crop04.jpg', img)
# image = face_recognition.load_image_file('/data/data_hao/PFLD-pytorch/0000_006.jpg')
image = face_recognition.load_image_file('/data/data_hao/PFLD-pytorch/woman_face/woman04.png')
face_landmarks_list = face_recognition.face_landmarks(image)
pil_image = Image.fromarray(image)
d = ImageDraw.Draw(pil_image)
print(face_landmarks_list)
top_lip = face_landmarks_list[0]['top_lip']
bottom_lip = face_landmarks_list[0]['bottom_lip']
print(top_lip)
print(bottom_lip)
for tup in top_lip:
bottom_lip.append(tup)
# print(set(bottom_lip)) # 去重
# landmark = list(set(bottom_lip))
# print(landmark)
# 将得到的关键点位置信息按行写进txt文件中，并将文件名以图像名字命名
fw = open('04.txt', 'w')
for line in list(set(bottom_lip)):
for a in line:
a = str(a)
fw.write(a)
fw.write('\t')
fw.write('\n')
fw.close()
# fw.writelines(["%s\n" % item for item in list(set(bottom_lip))])