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face-alignment.py
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face-alignment.py
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import os
import cv2
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import math
from PIL import Image
#------------------------
def euclidean_distance(a, b):
x1 = a[0]; y1 = a[1]
x2 = b[0]; y2 = b[1]
return math.sqrt(((x2 - x1) * (x2 - x1)) + ((y2 - y1) * (y2 - y1)))
def detectFace(img):
faces = face_detector.detectMultiScale(img, 1.3, 5)
#print("found faces: ", len(faces))
if len(faces) > 0:
face = faces[0]
face_x, face_y, face_w, face_h = face
img = img[int(face_y):int(face_y+face_h), int(face_x):int(face_x+face_w)]
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
return img, img_gray
else:
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
return img, img_gray
#raise ValueError("No face found in the passed image ")
def alignFace(img_path):
img = cv2.imread(img_path)
plt.imshow(img[:, :, ::-1])
plt.show()
img_raw = img.copy()
img, gray_img = detectFace(img)
eyes = eye_detector.detectMultiScale(gray_img)
#print("found eyes: ",len(eyes))
if len(eyes) >= 2:
#find the largest 2 eye
base_eyes = eyes[:, 2]
#print(base_eyes)
items = []
for i in range(0, len(base_eyes)):
item = (base_eyes[i], i)
items.append(item)
df = pd.DataFrame(items, columns = ["length", "idx"]).sort_values(by=['length'], ascending=False)
eyes = eyes[df.idx.values[0:2]]
#--------------------
#decide left and right eye
eye_1 = eyes[0]; eye_2 = eyes[1]
if eye_1[0] < eye_2[0]:
left_eye = eye_1
right_eye = eye_2
else:
left_eye = eye_2
right_eye = eye_1
#--------------------
#center of eyes
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]
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]
#center_of_eyes = (int((left_eye_x+right_eye_x)/2), int((left_eye_y+right_eye_y)/2))
cv2.circle(img, left_eye_center, 2, (255, 0, 0) , 2)
cv2.circle(img, right_eye_center, 2, (255, 0, 0) , 2)
#cv2.circle(img, center_of_eyes, 2, (255, 0, 0) , 2)
#----------------------
#find rotation direction
if left_eye_y > right_eye_y:
point_3rd = (right_eye_x, left_eye_y)
direction = -1 #rotate same direction to clock
print("rotate to clock direction")
else:
point_3rd = (left_eye_x, right_eye_y)
direction = 1 #rotate inverse direction of clock
print("rotate to inverse clock direction")
#----------------------
cv2.circle(img, point_3rd, 2, (255, 0, 0) , 2)
cv2.line(img,right_eye_center, left_eye_center,(67,67,67),1)
cv2.line(img,left_eye_center, point_3rd,(67,67,67),1)
cv2.line(img,right_eye_center, point_3rd,(67,67,67),1)
a = euclidean_distance(left_eye_center, point_3rd)
b = euclidean_distance(right_eye_center, point_3rd)
c = euclidean_distance(right_eye_center, left_eye_center)
#print("left eye: ", left_eye_center)
#print("right eye: ", right_eye_center)
#print("additional point: ", point_3rd)
#print("triangle lengths: ",a, b, c)
cos_a = (b*b + c*c - a*a)/(2*b*c)
#print("cos(a) = ", cos_a)
angle = np.arccos(cos_a)
#print("angle: ", angle," in radian")
angle = (angle * 180) / math.pi
print("angle: ", angle," in degree")
if direction == -1:
angle = 90 - angle
print("angle: ", angle," in degree")
#--------------------
#rotate image
new_img = Image.fromarray(img_raw)
new_img = np.array(new_img.rotate(direction * angle))
return new_img
#------------------------
#opencv path
opencv_home = cv2.__file__
folders = opencv_home.split(os.path.sep)[0:-1]
path = folders[0]
for folder in folders[1:]:
path = path + "/" + folder
face_detector_path = path+"/data/haarcascade_frontalface_default.xml"
eye_detector_path = path+"/data/haarcascade_eye.xml"
nose_detector_path = path+"/data/haarcascade_mcs_nose.xml"
if os.path.isfile(face_detector_path) != True:
raise ValueError("Confirm that opencv is installed on your environment! Expected path ",detector_path," violated.")
face_detector = cv2.CascadeClassifier(face_detector_path)
eye_detector = cv2.CascadeClassifier(eye_detector_path)
nose_detector = cv2.CascadeClassifier(nose_detector_path)
#------------------------
#test_set = ["angelina.jpg", "angelina2.jpg", "angelina3.jpg"]
test_set = ["angelina.jpg"]
for instance in test_set:
alignedFace = alignFace(instance)
plt.imshow(alignedFace[:, :, ::-1])
plt.show()
img, gray_img = detectFace(alignedFace)
plt.imshow(img[:, :, ::-1])
plt.show()