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face_detect.py
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face_detect.py
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import cv2
import matplotlib.pyplot as plt
import numpy as np
from pathlib import Path
faceDetect=cv2.CascadeClassifier('data/haarcascade_frontalface_alt2.xml');
faceDetect2=cv2.CascadeClassifier('data/haarcascade_profileface.xml');
pathlist = Path('img').glob('**/*.jpg')
good = []
bad = []
cropped_path='cropped/'
for path in pathlist:
img_path = str(path)
test_img = cv2.imread(img_path)
gray_img = cv2.cvtColor(test_img, cv2.COLOR_BGR2GRAY)
cropped_file=cropped_path+img_path.split('/')[-1]
#detect face
(x,y,w,h) = (0,0,0,0)
faces = []
faces=faceDetect.detectMultiScale(gray_img);
if (len(faces)): (x,y,w,h)=faces[0]
else:
faces=faceDetect2.detectMultiScale(gray_img);
if (len(faces)): (x,y,w,h)=faces[0]
else:
faces=faceDetect2.detectMultiScale(cv2.flip(gray_img,1))
if(len(faces)): (x,y,w,h)=faces[0]
x = gray_img.shape[1]-x-w
if (len(faces) and w>=100):
good.append(img_path)
cv2.imwrite(cropped_file, gray_img[y:y+h,x:x+w])
else:
bad.append(img_path)
print(len(good), len(bad))