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register_face.py
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register_face.py
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#! /usr/bin/env python
# coding=utf-8
#================================================================
# Copyright (C) 2020 * Ltd. All rights reserved.
#
# Editor : VIM
# File name : register_face.py
# Author : YunYang1994
# Created date: 2020-02-23 22:09:11
# Description :
#
#================================================================
import os
import cv2
import glob
import argparse
import numpy as np
import tensorflow as tf
from mtcnn import pnet, rnet, onet
from MobileFaceNet import MobileFaceNet
from utils import detect_face, align_face
def extract_oneface(image, marigin=16):
# detecting faces
image = cv2.cvtColor(image ,cv2.COLOR_BGR2RGB)
h, w, c = image.shape
total_boxes, points = detect_face(image, 20, pnet, rnet, onet, [0.6, 0.7, 0.7], 0.709)
for idx, (bounding_box, keypoints) in enumerate(zip(total_boxes, points.T)):
bounding_boxes = {
'box': [int(bounding_box[0]), int(bounding_box[1]),
int(bounding_box[2]-bounding_box[0]), int(bounding_box[3]-bounding_box[1])],
'confidence': bounding_box[-1],
'keypoints': {
'left_eye': (int(keypoints[0]), int(keypoints[5])),
'right_eye': (int(keypoints[1]), int(keypoints[6])),
'nose': (int(keypoints[2]), int(keypoints[7])),
'mouth_left': (int(keypoints[3]), int(keypoints[8])),
'mouth_right': (int(keypoints[4]), int(keypoints[9])),
}
}
bounding_box = bounding_boxes['box']
keypoints = bounding_boxes['keypoints']
# align face and extract it out
align_image = align_face(image, keypoints)
align_image = cv2.cvtColor(align_image ,cv2.COLOR_RGB2BGR)
xmin = max(bounding_box[0] - marigin, 0)
ymin = max(bounding_box[1] - marigin, 0)
xmax = min(bounding_box[0] + bounding_box[2] + marigin, w)
ymax = min(bounding_box[1] + bounding_box[3] + marigin, h)
crop_image = align_image[ymin:ymax, xmin:xmax, :]
# "just need only one face"
return crop_image
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("-person", type=str)
parser.add_argument('-camera', action='store_true', default=False)
args = parser.parse_args()
model = MobileFaceNet()
person_path = "./database/%s" %(args.person)
if not os.path.exists(person_path):
os.makedirs(person_path)
if args.camera:
img_idx = 0
cv2.namedWindow("detecting face")
cap = cv2.VideoCapture(0)
while(cap.isOpened()):
ret,image = cap.read()
if ret == True:
# resize image
image_h, image_w, _ = image.shape
new_h, new_w = int(0.5*image_h), int(0.5*image_w)
image = cv2.resize(image, (new_w, new_h))
face = extract_oneface(image)
if face is None: continue
h, w, _ = face.shape
image[:h, :w, :] = face
cv2.putText(image, "%d/10" %img_idx, (new_w-100, 50),
cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0,0,255), 2)
cv2.imshow('detecting face', image)
key = cv2.waitKey(30)
if key == ord('q') or img_idx == 10:
break
elif key == ord('s'):
cv2.imwrite("./database/%s/%d.jpg" %(args.person, img_idx), face)
img_idx += 1
cap.release()
cv2.destroyAllWindows()
else:
for img_path in glob.glob(person_path+"/*.jpg"):
print(img_path)
image = cv2.imread(img_path)
face = extract_oneface(image)
cv2.imwrite(img_path, face)
image_path = "./database/%s" %(args.person)
image_list = glob.glob(image_path + "/*.jpg")
embeddings = []
for im_path in image_list:
image = cv2.imread(im_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
embeddings.append(model(image))
embedding = np.concatenate(embeddings, 0).mean(0).flatten()
np.save("./database/%s/%s" %(args.person, args.person), embedding)