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()
ゝ一世哀愁。
朱雀
小灰灰
Bertha 。
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