face_detect_demo.py

import tensorflow as tf
from align import detect_face
import cv2
import imutils
import numpy as np
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("--img", type = str, required=True)
args = parser.parse_args()

# some constants kept as default from facenet
minsize = 20
threshold = [0.6, 0.7, 0.7]
factor = 0.709
margin = 44
input_image_size = 160

sess = tf.Session()
# read pnet, rnet, onet models from align directory and files are det1.npy, det2.npy, det3.npy
pnet, rnet, onet = detect_face.create_mtcnn(sess, 'align')

def getFace(img):
    faces = []
    img_size = np.asarray(img.shape)[0:2]
    bounding_boxes, _ = detect_face.detect_face(img, minsize, pnet, rnet, onet, threshold, factor)
    if not len(bounding_boxes) == 0:
        for face in bounding_boxes:
            if face[4] > 0.50:
                det = np.squeeze(face[0:4])
                bb = np.zeros(4, dtype=np.int32)
                bb[0] = np.maximum(det[0] - margin / 2, 0)
                bb[1] = np.maximum(det[1] - margin / 2, 0)
                bb[2] = np.minimum(det[2] + margin / 2, img_size[1])
                bb[3] = np.minimum(det[3] + margin / 2, img_size[0])
                cropped = img[bb[1]:bb[3], bb[0]:bb[2], :]
                resized = cv2.resize(cropped, (input_image_size,input_image_size),interpolation=cv2.INTER_AREA)
                faces.append({'face':resized,'rect':[bb[0],bb[1],bb[2],bb[3]]})
    return faces

img = cv2.imread(args.img)
img = imutils.resize(img,width=1000)
faces = getFace(img)
for face in faces:
    cv2.rectangle(img, (face['rect'][0], face['rect'][1]), (face['rect'][2], face['rect'][3]), (0, 255, 0), 2)
cv2.imshow("faces", img)
cv2.waitKey(0)
cv2.destroyAllWindows()