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TASSCore.py
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TASSCore.py
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# *****************************************************************************
# Copyright (c) 2016 and other Contributors.
#
# All rights reserved. This program and the accompanying materials
# are made available under the terms of the Eclipse Public License v1.0
# which accompanies this distribution, and is available at
# http://www.eclipse.org/legal/epl-v10.html
#
# Contributors:
# Adam Milton-Barker - Limited
# Andrej Petelin - Limited
# *****************************************************************************
import numpy as np
import struct
import cv2
import os
import fnmatch
import json
from datetime import datetime
class TassCore():
def __init__(self):
with open('required/config.json') as configs:
self._configs = json.loads(configs.read())
def captureAndDetect(self,frame):
faceCascade = cv2.CascadeClassifier( os.getcwd()+'/'+self._configs["ClassifierSettings"]["HAAR_FACES"])
gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
faces = faceCascade.detectMultiScale(gray,
scaleFactor=self._configs["ClassifierSettings"]["HAAR_SCALE_FACTOR"],
minNeighbors=self._configs["ClassifierSettings"]["HAAR_MIN_NEIGHBORS"],
minSize=(30,30)
)
if not len(faces):
faceCascade = cv2.CascadeClassifier( os.getcwd()+'/'+self._configs["ClassifierSettings"]["HAAR_FACES2"])
faces = faceCascade.detectMultiScale(gray,
scaleFactor=self._configs["ClassifierSettings"]["HAAR_SCALE_FACTOR"],
minNeighbors=self._configs["ClassifierSettings"]["HAAR_MIN_NEIGHBORS"],
minSize=(30,30)
)
if not len(faces):
faceCascade = cv2.CascadeClassifier( os.getcwd()+'/'+self._configs["ClassifierSettings"]["HAAR_FACES3"])
faces = faceCascade.detectMultiScale(gray,
scaleFactor=self._configs["ClassifierSettings"]["HAAR_SCALE_FACTOR"],
minNeighbors=self._configs["ClassifierSettings"]["HAAR_MIN_NEIGHBORS"],
minSize=(30,30)
)
if not len(faces):
faceCascade = cv2.CascadeClassifier( os.getcwd()+'/'+self._configs["ClassifierSettings"]["HAAR_PROFILES"])
faces = faceCascade.detectMultiScale(gray,
scaleFactor=self._configs["ClassifierSettings"]["HAAR_SCALE_FACTOR"],
minNeighbors=self._configs["ClassifierSettings"]["HAAR_MIN_NEIGHBORS"],
minSize=(30,30)
)
print( "Found " + str(len(faces)) + " face(s)")
if len(faces):
return frame, faces[0]
else:
return frame, None
def resize(self,image):
return cv2.resize(image,(self._configs["ClassifierSettings"]["width"], self._configs["ClassifierSettings"]["height"]),interpolation=cv2.INTER_LANCZOS4)
def crop(self,image, x, y, w, h):
crop_height = int((self._configs["ClassifierSettings"]["width"] / float(self._configs["ClassifierSettings"]["height"]))*w)
midy = y + h/2
y1 = max(0, midy-crop_height/2)
y2 = min(image.shape[0]-1, midy+crop_height/2)
return image[int(y1):int(y2), int(x):int(x)+int(w)]
def prepareImage(self,filename):
return self.resize(cv2.imread(filename, cv2.IMREAD_GRAYSCALE))
def normalize(self,X, low, high, dtype=None):
X = np.asarray(X)
minX, maxX = np.min(X), np.max(X)
X = X - float(minX)
X = X / float((maxX - minX))
X = X * (high-low)
X = X + low
if dtype is None:
return np.asarray(X)
return np.asarray(X, dtype=dtype)
def processTrainingData(self):
print("Processing Training Data")
rootdir=os.getcwd()+"/training/"
processeddir=os.getcwd()+"/processed/"
count = 0
for subdir, dirs, files in os.walk(rootdir):
dirname = os.path.basename(os.path.normpath(subdir))
for file in files:
newPayload = cv2.imread(os.getcwd()+'/training/'+dirname+"/"+file,1)
faceCascade = cv2.CascadeClassifier( os.getcwd()+'/'+self._configs["ClassifierSettings"]["HAAR_FACES"])
faces = faceCascade.detectMultiScale(newPayload,
scaleFactor=self._configs["ClassifierSettings"]["HAAR_SCALE_FACTOR"],
minNeighbors=self._configs["ClassifierSettings"]["HAAR_MIN_NEIGHBORS"],
minSize=(30,30),
flags=cv2.CASCADE_SCALE_IMAGE
)
if not len(faces):
faceCascade = cv2.CascadeClassifier( os.getcwd()+'/'+self._configs["ClassifierSettings"]["HAAR_FACES2"])
faces = faceCascade.detectMultiScale(newPayload,
scaleFactor=self._configs["ClassifierSettings"]["HAAR_SCALE_FACTOR"],
minNeighbors=self._configs["ClassifierSettings"]["HAAR_MIN_NEIGHBORS"],
minSize=(30,30),
flags=cv2.CASCADE_SCALE_IMAGE
)
if not len(faces):
faceCascade = cv2.CascadeClassifier( os.getcwd()+'/'+self._configs["ClassifierSettings"]["HAAR_FACES3"])
faces = faceCascade.detectMultiScale(newPayload,
scaleFactor=self._configs["ClassifierSettings"]["HAAR_SCALE_FACTOR"],
minNeighbors=self._configs["ClassifierSettings"]["HAAR_MIN_NEIGHBORS"],
minSize=(30,30),
flags=cv2.CASCADE_SCALE_IMAGE
)
if not len(faces):
faceCascade = cv2.CascadeClassifier( os.getcwd()+'/'+self._configs["ClassifierSettings"]["HAAR_PROFILES"])
faces = faceCascade.detectMultiScale(newPayload,
scaleFactor=self._configs["ClassifierSettings"]["HAAR_SCALE_FACTOR"],
minNeighbors=self._configs["ClassifierSettings"]["HAAR_MIN_NEIGHBORS"],
minSize=(30,30),
flags=cv2.CASCADE_SCALE_IMAGE
)
print(os.getcwd()+'/training/'+dirname+"/"+file)
if len(faces):
x, y, w, h = faces[0]
print("Cropping image")
cropped = self.crop(newPayload, x, y, w, h)
print("Writing image " + dirname+"/"+file)
if not os.path.exists(processeddir+'/'+dirname):
os.makedirs(processeddir+'/'+dirname)
newFile=datetime.now().strftime('%Y-%m-%d-%H-%M-%S')+'.pgm'
cv2.imwrite(processeddir+'/'+dirname+"/"+newFile, cropped)
os.remove(os.getcwd()+'/training/'+dirname+"/"+file)
else:
os.remove(os.getcwd()+'/training/'+dirname+"/"+file)
print('REMOVED FILE')
print("Finished Processing Training Data")
def trainModel(self):
print("Training")
rootdir=os.getcwd()+"/processed/"
faceArray=[]
labelArray=[]
count = 0
MEAN_FILE = 'model/mean.png'
POSITIVE_EIGENFACE_FILE = 'model/modelEigenvector.png'
for subdir, dirs, files in os.walk(rootdir):
dirname = os.path.basename(os.path.normpath(subdir))
print(dirname)
for file in files:
print (file)
faceArray.append(self.prepareImage(rootdir+'/'+dirname+'/'+file))
labelArray.append(int(dirname))
print (file)
count += 1
print('Collected '+str(count)+' training images')
print('Training model....')
model = cv2.face.createEigenFaceRecognizer()
model.train(np.asarray(faceArray), np.asarray(labelArray))
model.save(self._configs["ClassifierSettings"]["Model"])
print("Model saved to "+self._configs["ClassifierSettings"]["Model"])
mean = model.getMean().reshape(faceArray[0].shape)
cv2.imwrite(MEAN_FILE,self.normalize(mean, 0, 255, dtype=np.uint8))
eigenvectors = model.getEigenVectors()
eigenvector = eigenvectors[:,0].reshape(faceArray[0].shape)
cv2.imwrite(POSITIVE_EIGENFACE_FILE,self.normalize(eigenvector, 0, 255, dtype=np.uint8))
print("Finished Training")