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create_database.py
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#!/usr/bin/env python3
# Author: huangh12 <he.huang1@outlook.com>
import cv2
from PyQt5.QtCore import QTimer, QRegExp, pyqtSignal
from PyQt5.QtGui import QImage, QPixmap, QIcon, QIntValidator, QRegExpValidator, QTextCursor
from PyQt5.QtWidgets import QDialog, QApplication, QWidget, QMessageBox
from PyQt5.uic import loadUi
import queue
import threading
import sqlite3
import os
import sys
import numpy as np
from datetime import datetime
import tensorflow as tf
if tf.__version__.startswith('2'):
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
from process import alignImages
class CreateDatabase(QWidget):
receiveLogSignal = pyqtSignal(str)
def __init__(self,
det_model='./models/mtcnn',
recog_model='./models/arcface',
database='./database/feat.libsvm',):
super(CreateDatabase, self).__init__()
loadUi('./ui/CreateDatabase.ui', self)
self.setFixedSize(1011, 601)
# OpenCV
self.cap = cv2.VideoCapture()
# start webcam
self.startWebcamButton.toggled.connect(self.startWebcam)
self.startWebcamButton.setCheckable(True)
# timer
self.timer = QTimer(self)
self.timer.timeout.connect(self.updateFrame)
# load the model
config = tf.ConfigProto(
allow_soft_placement=True,
intra_op_parallelism_threads=4,
inter_op_parallelism_threads=4)
config.gpu_options.allow_growth = True
det_graph = tf.Graph()
recog_graph = tf.Graph()
self.det_sess = tf.Session(graph=det_graph, config=config)
self.recog_sess = tf.Session(graph=recog_graph, config=config)
# load det model
with self.det_sess.as_default():
with det_graph.as_default():
meta_graph_def = tf.saved_model.loader.load(
self.det_sess,
[tf.saved_model.tag_constants.SERVING],
det_model)
signature = meta_graph_def.signature_def
inputs_name = signature['predict'].inputs['inputs'].name
boxes_name = signature['predict'].outputs['boxes'].name
lmks_name = signature['predict'].outputs['landmarks'].name
scores_name = signature['predict'].outputs['scores'].name
self.det_inputs = self.det_sess.graph.get_tensor_by_name(inputs_name)
self.det_boxes = self.det_sess.graph.get_tensor_by_name(boxes_name)
self.det_lmks = self.det_sess.graph.get_tensor_by_name(lmks_name)
self.det_scores = self.det_sess.graph.get_tensor_by_name(scores_name)
# load recog model
with self.recog_sess.as_default():
with recog_graph.as_default():
meta_graph_def = tf.saved_model.loader.load(
self.recog_sess,
[tf.saved_model.tag_constants.SERVING],
recog_model)
signature = meta_graph_def.signature_def
inputs_name = signature['predict'].inputs['inputs'].name
outputs_name = signature['predict'].outputs['outputs'].name
self.recog_inputs = self.recog_sess.graph.get_tensor_by_name(inputs_name)
self.recog_outputs = self.recog_sess.graph.get_tensor_by_name(outputs_name)
self.isFaceDetectEnabled = False
self.enableFaceDetectButton.toggled.connect(self.enableFaceDetect)
self.enableFaceDetectButton.setCheckable(True)
# capture frame
self.isCaptureFrameEnabled = False
self.captureFrameButton.clicked.connect(self.enableCaptureFrame)
self.releaseFrameButton.clicked.connect(self.disableCaptureFrame)
self.captureFrameButton.setEnabled(True)
self.releaseFrameButton.setEnabled(False)
# data entry
self.BoxIDLineEdit.textChanged.connect(self.setBoxID)
self.PersonNameLineEdit.textChanged.connect(self.setPersonName)
self.BoxIDLineEdit.setEnabled(False)
self.PersonNameLineEdit.setEnabled(False)
self.BoxIDLineEdit.setValidator(QIntValidator(0, 65535))
# import
self.ImportToDatabaseButton.clicked.connect(self.importToDatabase)
self.ImportToDatabaseButton.setEnabled(False)
self.database = database
def importToDatabase(self):
self.ImportToDatabaseButton.setEnabled(False)
try:
if int(self.BoxID) < len(self.pred_boxes):
idx = int(self.BoxID)
box = self.pred_boxes[idx]
lmk = self.pred_lmks[idx]
aligned_img = alignImages(self.current_frame, box, lmk)
aligned_img = aligned_img.reshape((1, 112, 112, 3))
res = self.recog_sess.run(self.recog_outputs, feed_dict={self.recog_inputs: aligned_img})
res = res[0]
write_str_i = [self.PersonName]
for j in range(1, 513):
write_str_i.append('%d:%f' %(j, res[j-1]))
with open(self.database, 'a', encoding="utf-8") as f1:
f1.write(' '.join(write_str_i)+'\n')
self.isCaptureFrameEnabled = False
self.captureFrameButton.setEnabled(True)
self.releaseFrameButton.setEnabled(False)
self.BoxIDLineEdit.setEnabled(False)
self.PersonNameLineEdit.setEnabled(False)
else:
text = 'Fail to import'
informativeText = '<b>Please check the validity of Box id and Person name.</b>'
CreateDatabase.callDialog(QMessageBox.Information, text, informativeText, QMessageBox.Ok)
self.ImportToDatabaseButton.setEnabled(True)
except:
text = 'Fail to import'
informativeText = '<b>Please check the validity of Box id and Person name.</b>'
CreateDatabase.callDialog(QMessageBox.Information, text, informativeText, QMessageBox.Ok)
self.ImportToDatabaseButton.setEnabled(True)
def setBoxID(self):
self.BoxID = self.BoxIDLineEdit.text()
def setPersonName(self):
self.PersonName = self.PersonNameLineEdit.text()
def useExternalCamera(self, useExternalCameraCheckBox):
if useExternalCameraCheckBox.isChecked():
self.isExternalCameraUsed = True
else:
self.isExternalCameraUsed = False
def startWebcam(self, status):
if status:
camID = 0
self.cap.open(camID)
self.cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
self.cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)
ret, frame = self.cap.read()
if not ret:
self.cap.release()
self.startWebcamButton.setIcon(QIcon('./icons/error.png'))
self.startWebcamButton.setChecked(False)
else:
self.startWebcamButton.setText('Close the camera')
self.enableFaceDetectButton.setEnabled(True)
self.timer.start(5)
self.startWebcamButton.setIcon(QIcon('./icons/success.png'))
else:
if self.cap.isOpened():
if self.timer.isActive():
self.timer.stop()
self.cap.release()
self.faceDetectCaptureLabel.clear()
self.faceDetectCaptureLabel.setText('<font color=red>The camera is closed.</font>')
self.startWebcamButton.setText('Open the camera')
self.enableFaceDetectButton.setEnabled(False)
self.startWebcamButton.setIcon(QIcon())
def enableFaceDetect(self, status):
if self.cap.isOpened():
if status:
self.enableFaceDetectButton.setText('Disable face detection')
self.isFaceDetectEnabled = True
else:
self.enableFaceDetectButton.setText('Enable face detection')
self.isFaceDetectEnabled = False
# capture frame
def enableCaptureFrame(self):
if not self.isFaceDetectEnabled:
text = 'Please enable face detection before capturing current frame.'
informativeText = '<b></b>'
CreateDatabase.callDialog(QMessageBox.Information, text, informativeText, QMessageBox.Ok)
return
self.isCaptureFrameEnabled = True
self.captureFrameButton.setEnabled(False)
self.releaseFrameButton.setEnabled(True)
self.BoxIDLineEdit.setEnabled(True)
self.PersonNameLineEdit.setEnabled(True)
self.ImportToDatabaseButton.setEnabled(True)
# release frame
def disableCaptureFrame(self):
self.isCaptureFrameEnabled = False
self.captureFrameButton.setEnabled(True)
self.releaseFrameButton.setEnabled(False)
self.BoxIDLineEdit.setEnabled(False)
self.PersonNameLineEdit.setEnabled(False)
self.ImportToDatabaseButton.setEnabled(False)
# timer
def updateFrame(self):
if self.isCaptureFrameEnabled:
ret, frame = self.current_ret, self.current_frame
else:
ret, frame = self.cap.read()
if ret:
self.displayImage(frame)
if self.isFaceDetectEnabled:
detected_frame = self.detectFace(frame.copy())
self.displayImage(detected_frame)
else:
self.displayImage(frame)
self.current_frame = frame
self.current_ret = ret
def detectFace(self, frame):
self.pred_boxes = np.zeros((0, 4))
self.pred_lmks = np.zeros((0, 5, 2))
pred_boxes, pred_lmks = self.det_sess.run([self.det_boxes, self.det_lmks], {self.det_inputs: frame[None,:,:]})
if pred_boxes.shape[0] == 0:
return frame
# y1,x1,y2,x2 -> x1,y1,x2,y2
pred_boxes[:, [0,1]], pred_boxes[:, [2,3]] = \
pred_boxes[:, [1,0]], pred_boxes[:, [3,2]]
# y1,...,y5,x1,...,x5 -> x1,y1,x2,y2...x5,y5
pred_lmks = pred_lmks.reshape((-1, 2, 5)).transpose((0, 2, 1))
pred_lmks[..., [0]], pred_lmks[..., [1]] = \
pred_lmks[..., [1]], pred_lmks[..., [0]]
self.pred_boxes = pred_boxes
self.pred_lmks = pred_lmks
idx = 0
for box, lmk in zip(pred_boxes, pred_lmks):
box = box.astype('int32')
frame = cv2.rectangle(frame, (box[0], box[1]), (box[2], box[3]), (255, 0, 0), 3)
img = cv2.putText(frame, 'Box id: %d' %idx, (box[0], box[1]), 0, 1, (0,255,255), 1)
idx += 1
return frame
def displayImage(self, img):
# BGR -> RGB
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# default:The image is stored using 8-bit indexes into a colormap, for example:a gray image
qformat = QImage.Format_Indexed8
if len(img.shape) == 3: # rows[0], cols[1], channels[2]
if img.shape[2] == 4:
qformat = QImage.Format_RGBA8888
else:
qformat = QImage.Format_RGB888
outImage = QImage(img, img.shape[1], img.shape[0], img.strides[0], qformat)
self.faceDetectCaptureLabel.setPixmap(QPixmap.fromImage(outImage))
self.faceDetectCaptureLabel.setScaledContents(True)
@staticmethod
def callDialog(icon, text, informativeText, standardButtons, defaultButton=None):
msg = QMessageBox()
msg.setWindowTitle('py_face_recognition DataRecord')
msg.setIcon(icon)
msg.setText(text)
msg.setInformativeText(informativeText)
msg.setStandardButtons(standardButtons)
if defaultButton:
msg.setDefaultButton(defaultButton)
return msg.exec()
def closeEvent(self, event):
if self.timer.isActive():
self.timer.stop()
if self.cap.isOpened():
self.cap.release()
event.accept()
if __name__ == '__main__':
app = QApplication(sys.argv)
window = CreateDatabase()
window.show()
sys.exit(app.exec())