# import cv2

# img1 = cv2.imread('face1.jpeg')

# gray = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)

# face_detector = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")

# faces = face_detector.detectMultiScale(gray, 1.1, 3,cv2.CASCADE_SCALE_IMAGE,(50,50),(100,100))

# for (x, y, w, h) in faces:

#     cv2.rectangle(img1, (x, y), (x + w, y + w), (0, 255, 0),2)

# # cv2.imshow("img",img1)


# cv2.waitKey(0)



# import cv2
# import os
# import matplotlib.pyplot as plt


# def detect(filename=r"\static\img\face1.jpeg"):# 定位识别
#     save_file=r"\static\img\detect_result.png"
#     face_cascade=cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

#     img = cv2.imread(filename)
#     gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
         
#     faces = face_cascade.detectMultiScale(gray,1.3,5)
    
#     for(x,y,w,h)in faces:
#         cv2.rectangle(img,(x,y),(x + w,y + h),(255,0,0),2)
#     cv2.imwrite(filename=save_file,img=img)
#     return save_file
         
# import face_recognition
# import cv2
# import matplotlib.pyplot as plt

# image=face_recognition.load_image_file("mctz.jfif")
# face_landmarks_list = face_recognition.face_landmarks(image)
# #print(faces_landmarks_list)

# for points in face_landmarks_list:
#         #print(points)
#         for pp in points:
#             print(pp)
#             img=cv2.circle(image,(100,100),60,(255,0,0),2)
            
            
# plt.imshow(img)
# plt.axis('off')
# plt.show()

from tools.face import get_face_res

get_face_res()