Opencv+dlib realizes "matching" glasses for Mona Lisa

opencv+dlib To Mona Lisa “ with ” glasses

This case uses opencv+dlib To achieve the Mona Lisa wearing glasses .

The main principle is to use dlib Feature point extraction effect of face recognition , And use feature points to add glasses to the face .

Match Mona Lisa with glasses

Import toolkit

import cv2
import numpy as np
import dlib
from PIL import Image, ImageDraw, ImageFont
from imutils import face_utils, translate, rotate, resize
# Import python mapping matplotlib
import matplotlib.pyplot as plt
# Use ipython The magic method of , Embed the drawn image directly in notebook In the cell 
%matplotlib inline

# Define visual image functions 
def look_img(img):
    '''opencv The format of the read image is BGR,matplotlib The visualization format is RGB, So we need to BGR turn RGB'''
    img_RGB = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
    plt.imshow(img_RGB)
    plt.show()

Import model

#  Create a face detector 
det_face = dlib.get_frontal_face_detector()
#  Load the marker detector 
det_landmarks = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")  # 68 spot 

Single picture processing

max_width = 500
img=cv2.imread('mnls.jpg')
img=resize(img,width=max_width)
deal = Image.open("0.png")      # Glasses pictures 
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
rects = det_face(img_gray, 0)
img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))

for rect in rects:
    face = {
    }
    shades_width = rect.right() - rect.left()
    
    #  A predictor used to detect the position and direction of the current face 
    shape = det_landmarks(img_gray, rect)
    shape = face_utils.shape_to_np(shape)
    
    #  Capture the outline of each eye from the input image 
    leftEye = shape[36:42]
    rightEye = shape[42:48]
    
    #  Calculate the center of each eye 
    leftEyeCenter = leftEye.mean(axis=0).astype("int")
    rightEyeCenter = rightEye.mean(axis=0).astype("int")
    
    #  Calculate the included angle between the eyes 
    dY = leftEyeCenter[1] - rightEyeCenter[1]
    dX = leftEyeCenter[0] - rightEyeCenter[0]
    angle = np.rad2deg(np.arctan2(dY, dX)) 
    
    #  Picture rewriting 
    current_deal = deal.resize((shades_width, int(shades_width * deal.size[1] / deal.size[0])),
                           resample=Image.Resampling.LANCZOS)
    current_deal = current_deal.rotate(angle, expand=True)
    current_deal = current_deal.transpose(Image.Transpose.FLIP_TOP_BOTTOM)
    face['glasses_image'] = current_deal
    left_eye_x = leftEye[0,0] - shades_width // 4
    left_eye_y = leftEye[0,1] - shades_width // 6
    face['final_pos'] = (left_eye_x, left_eye_y)
    current_animation=1   # Parameter adjustment 
    glasses_on=1         # Parameter adjustment 
    current_y = int(current_animation / glasses_on * left_eye_y)
    img.paste(current_deal, (left_eye_x, current_y-20), current_deal)   # Adjust the position of glasses 
    
display(img)

​

Complete code

#  Complete code ：
import cv2
import numpy as np
import dlib
from PIL import Image, ImageDraw, ImageFont
from imutils import face_utils, translate, rotate, resize
# Import python mapping matplotlib
import matplotlib.pyplot as plt
# Use ipython The magic method of , Embed the drawn image directly in notebook In the cell 
%matplotlib inline

# Define visual image functions 
def look_img(img):
    '''opencv The format of the read image is BGR,matplotlib The visualization format is RGB, So we need to BGR turn RGB'''
    img_RGB = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
    plt.imshow(img_RGB)
    plt.show()

#  Create a face detector 
det_face = dlib.get_frontal_face_detector()
#  Load the marker detector 
det_landmarks = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")  # 68 spot 

max_width = 500
img=cv2.imread('mnls.jpg')     # Face photos 
img=resize(img,width=max_width)
deal = Image.open("./Glasses/1.png")      # Glasses pictures 
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
rects = det_face(img_gray, 0)
img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))

for rect in rects:
    face = {
    }
    shades_width = rect.right() - rect.left()
    
    #  A predictor used to detect the position and direction of the current face 
    shape = det_landmarks(img_gray, rect)
    shape = face_utils.shape_to_np(shape)
    
    #  Capture the outline of each eye from the input image 
    leftEye = shape[36:42]
    rightEye = shape[42:48]
    
    #  Calculate the center of each eye 
    leftEyeCenter = leftEye.mean(axis=0).astype("int")
    rightEyeCenter = rightEye.mean(axis=0).astype("int")
    
    #  Calculate the included angle between the eyes 
    dY = leftEyeCenter[1] - rightEyeCenter[1]
    dX = leftEyeCenter[0] - rightEyeCenter[0]
    angle = np.rad2deg(np.arctan2(dY, dX)) 
    
    #  Picture rewriting 
    current_deal = deal.resize((shades_width, int(shades_width * deal.size[1] / deal.size[0])),
                           resample=Image.Resampling.LANCZOS)
    current_deal = current_deal.rotate(angle, expand=True)
    current_deal = current_deal.transpose(Image.Transpose.FLIP_TOP_BOTTOM)
    face['glasses_image'] = current_deal
    left_eye_x = leftEye[0,0] - shades_width // 4
    left_eye_y = leftEye[0,1] - shades_width // 6
    face['final_pos'] = (left_eye_x, left_eye_y)
    current_animation=1   # Parameter adjustment 
    glasses_on=0.8          # Parameter adjustment 
    current_y = int(current_animation / glasses_on * left_eye_y)
    img.paste(current_deal, (left_eye_x, current_y-20), current_deal)   # Adjust the position of glasses 

#PIL Image to CV2 Images 
cv2_img = cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR)
look_img(cv2_img)

Picture synthesis gif

import imageio

def compose_gif():
    gif_images = []
    for path in img_paths:
        gif_images.append(imageio.imread(path))
    imageio.mimsave("test.gif",gif_images,fps=1)

data_path='./output'   # Data folder 
images=os.listdir(data_path)
img_paths=[]
for i in images:
    img_paths+=[os.path.join(data_path,i)]  
compose_gif()

Effect display ：