Python OpenCV人脸识别戴道具眼镜

朱雀 2023-10-02 12:07 8阅读 0赞

import cv2 
    import numpy as np 
     
    face_cascade = cv2.CascadeClassifier('./cascade_files/haarcascade_frontalface_alt.xml') 
    eye_cascade = cv2.CascadeClassifier('./cascade_files/haarcascade_eye.xml') 
     
    if face_cascade.empty(): 
      raise IOError('Unable to load the face cascade classifier xml file') 
    if eye_cascade.empty(): 
      raise IOError('Unable to load the eye cascade classifier xml file') 
     
    cap = cv2.VideoCapture(0)
    sunglasses_img = cv2.imread('images/sunglasses.png')
    
    while True:
        ret, frame = cap.read() 
        frame = cv2.resize(frame, None, fx=0.5, fy=0.5, interpolation=cv2.INTER_AREA)
        vh, vw = frame.shape[:2]
        vh, vw = int(vh), int(vw)
    
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) 
        faces = face_cascade.detectMultiScale(gray, scaleFactor=1.3, minNeighbors=1)
        centers = []
    
        for (x,y,w,h) in faces: 
            roi_gray = gray[y:y+h, x:x+w] 
            roi_color = frame[y:y+h, x:x+w] 
            eyes = eye_cascade.detectMultiScale(roi_gray) 
            for (x_eye,y_eye,w_eye,h_eye) in eyes: 
                centers.append((x + int(x_eye + 0.5*w_eye), y + int(y_eye + 0.5*h_eye))) 
        
        if len(centers) > 1: # if detects both eyes
            h, w = sunglasses_img.shape[:2]
            # Extract the region of interest from the image 
            eye_distance = abs(centers[1][0] - centers[0][0])
            # Overlay sunglasses; the factor 2.12 is customizable depending on the size of the face 
            sunglasses_width = 2.12 * eye_distance
            scaling_factor = sunglasses_width / w
            print(scaling_factor, eye_distance)
            overlay_sunglasses = cv2.resize(sunglasses_img, None, fx=scaling_factor, fy=scaling_factor, interpolation=cv2.INTER_AREA)
    
            x = centers[0][0] if centers[0][0] < centers[1][0] else centers[1][0] 
         
            # customizable X and Y locations; depends on the size of the face 
            x -= int(0.26*overlay_sunglasses.shape[1])
            y += int(0.26*overlay_sunglasses.shape[0])
            
            h, w = overlay_sunglasses.shape[:2]
            h, w = int(h), int(w)
            frame_roi = frame[y:y+h, x:x+w]
            # Convert color image to grayscale and threshold it 
            gray_overlay_sunglassess = cv2.cvtColor(overlay_sunglasses, cv2.COLOR_BGR2GRAY) 
            ret, mask = cv2.threshold(gray_overlay_sunglassess, 180, 255, cv2.THRESH_BINARY_INV) 
    
            # Create an inverse mask 
            mask_inv = cv2.bitwise_not(mask) 
    
            try:
                # Use the mask to extract the face mask region of interest 
                masked_face = cv2.bitwise_and(overlay_sunglasses, overlay_sunglasses, mask=mask) 
                # Use the inverse mask to get the remaining part of the image 
                masked_frame = cv2.bitwise_and(frame_roi, frame_roi, mask=mask_inv) 
            except cv2.error as e:
                print('Ignoring arithmentic exceptions: '+ str(e))
                #raise e
    
            # add the two images to get the final output 
            frame[y:y+h, x:x+w] = cv2.add(masked_face, masked_frame)
        else:
            print('Eyes not detected')
    
        
        cv2.imshow('Eye Detector', frame) 
        c = cv2.waitKey(1) 
        if c == 27: 
            break 
    
    cap.release() 
    cv2.destroyAllWindows()