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Face Alignment with OpenCV and Python

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import os

import cv2

import math

import matplotlib.pyplot as pl

import pandas as pd

from PIL import Picture

import numpy as np

  

def face_detection(img):

    faces = face_detector.detectMultiScale(img, 1.1, 4)

    if (len(faces) <= 0):

        img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

        return img, img_gray

    else:

        X, Y, W, H = faces[0]

        img = img[int(Y):int(Y+H), int(X):int(X+W)]

        return img, cv2.cvtColor(img, cv2.COLOR_BGR2BGRA)

  

  

def trignometry_for_distance(a, b):

    return math.sqrt(((b[0] - a[0]) * (b[0] - a[0])) +

                     ((b[1] - a[1]) * (b[1] - a[1])))

  

def Face_Alignement(img_path):

    pl.imshow(cv2.imread(img_path)[:, :, ::-1])

    pl.present()

    img_raw = cv2.imread(img_path).copy()

    img, gray_img = face_detection(cv2.imread(img_path))

    eyes = eye_detector.detectMultiScale(gray_img)

  

    

    

    if len(eyes) >= 2:

        eye = eyes[:, 2]

        container1 = []

        for i in vary(0, len(eye)):

            container = (eye[i], i)

            container1.append(container)

        df = pd.DataFrame(container1, columns=[

                          "length", "idx"]).sort_values(by=['length'])

        eyes = eyes[df.idx.values[0:2]]

  

        

        eye_1 = eyes[0]

        eye_2 = eyes[1]

        if eye_1[0] > eye_2[0]:

            left_eye = eye_2

            right_eye = eye_1

        else:

            left_eye = eye_1

            right_eye = eye_2

  

        

        

        right_eye_center = (

            int(right_eye[0] + (right_eye[2]/2)), 

          int(right_eye[1] + (right_eye[3]/2)))

        right_eye_x = right_eye_center[0]

        right_eye_y = right_eye_center[1]

        cv2.circle(img, right_eye_center, 2, (255, 0, 0), 3)

  

        

        left_eye_center = (

            int(left_eye[0] + (left_eye[2] / 2)), 

          int(left_eye[1] + (left_eye[3] / 2)))

        left_eye_x = left_eye_center[0]

        left_eye_y = left_eye_center[1]

        cv2.circle(img, left_eye_center, 2, (255, 0, 0), 3)

  

        

        if left_eye_y > right_eye_y:

            print("Rotate picture to clock route")

            point_3rd = (right_eye_x, left_eye_y)

            route = -1 

        else:

            print("Rotate to inverse clock route")

            point_3rd = (left_eye_x, right_eye_y)

            route = 1 

  

        cv2.circle(img, point_3rd, 2, (255, 0, 0), 2)

        a = trignometry_for_distance(left_eye_center, 

                                     point_3rd)

        b = trignometry_for_distance(right_eye_center, 

                                     point_3rd)

        c = trignometry_for_distance(right_eye_center, 

                                     left_eye_center)

        cos_a = (b*b + c*c - a*a)/(2*b*c)

        angle = (np.arccos(cos_a) * 180) / math.pi

  

        if route == -1:

            angle = 90 - angle

        else:

            angle = -(90-angle)

  

        

        new_img = Picture.fromarray(img_raw)

        new_img = np.array(new_img.rotate(route * angle))

  

    return new_img

  

  

opencv_home = cv2.__file__

folders = opencv_home.break up(os.path.sep)[0:-1]

path = folders[0]

for folder in folders[1:]:

    path = path + "/" + folder

path_for_face = path+"/information/haarcascade_frontalface_default.xml"

path_for_eyes = path+"/information/haarcascade_eye.xml"

path_for_nose = path+"/information/haarcascade_mcs_nose.xml"

  

if os.path.isfile(path_for_face) != True:

    increase ValueError(

        "opencv isn't put in pls set up utilizing pip set up opencv "

      detector_path, " violated.")

  

face_detector = cv2.CascadeClassifier(path_for_face)

eye_detector = cv2.CascadeClassifier(path_for_eyes)

nose_detector = cv2.CascadeClassifier(path_for_nose)

  

test_set = ["pic.png"]

for i in test_set:

    alignedFace = Face_Alignement(i)

    pl.imshow(alignedFace[:, :, ::-1])

    pl.present()

    img, gray_img = face_detection(alignedFace)

    pl.imshow(img[:, :, ::-1])

    pl.present()

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