testPath = "G:\\test"
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
desc = Calculator(8)
data = []
#labels = []
for imageFolder in os.listdir(trainPath):
imagePath = os.path.join(trainPath, imageFolder)
for trainImg in os.listdir(imagePath):
gray = cv2.imread(os.path.join(imagePath, trainImg), 0)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in faces:
images = cv2.rectangle(gray, (x, y), (x + w, y + h), (255, 0, 0),
2)
imageFac = cv2.resize(images, (360, 480))
hist = desc.calc_hist(imageFac)
labels.append(int(imageFolder[-2:]))
data.append(hist)
print("Processed folder " + imageFolder)
if imageFolder == "Subject03":
break
# For a label L, avg of the histograms
#avgHist={#Classes}
for imageFolder in os.listdir(testPath):
imagePath = os.path.join(testPath, imageFolder)
for testImg in os.listdir(imagePath):
gray = cv2.imread(os.path.join(imagePath, testImg), 0)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
class FaceRecognition:
def __init__(self,block_size):
self.desc=Calculator(block_size)
self.detector=MTCNN()
def predictPath(self, path, model):
image=cv2.imread(path,0)
hist = self.desc.calc_hist(image[40:-70, 120:-180])
hist = np.array(hist)
prediction = model.predict(hist.reshape(1, -1))
return prediction
def predictImg(self,img,model): # Input : Gray Image; Output : Face Prediction
#image=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
hist = self.desc.calc_hist(img)
hist = np.array(hist)
prediction = model.predict(hist.reshape(1, -1))
return prediction
def find_face(self,img): # Input : BGR Image; Output : GRAY Image with bounded faces and resized to 360x480
imgRGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
faces = self.detector.detect_faces(imgRGB)
if len(faces)==0:
return np.asarray([0,0,0])
i = 0
memo = {}
for face in faces:
key = "Face" + str (i)
x1, y1, w1, h1 = face['box']
# cv2.rectangle(imgRGB, (x1, y1), (x1 + w1, y1 + h1), (0, 255, 0), 3)
memo[key] = [x1, y1, w1, h1]
#memo.add (key, [x1, y1, w1, h1])
#imgRGB = imgRGB[y1:y1 + h1, x1:x1 + w1, :]
#imgfinal = cv2.cvtColor(imgRGB, cv2.COLOR_RGB2GRAY)
i += 1
##break
#imgfinal=cv2.resize(imgfinal,(360,480))
return memo
#return imgfinal
def captureData(self,path):
counter=0
i = 0
cap = cv2.VideoCapture(0,cv2.CAP_DSHOW)
while True:
ret, img = cap.read()
if counter%7==0:
imgFinal = self.find_face(img)
if not imgFinal is None:
filename=str(i)+'.jpeg'
cv2.imwrite(os.path.join(path,filename),imgFinal)
i += 1
print('No. of images : '+ str(i))
if cv2.waitKey(1) & 0xFF == ord('q'):
break
counter+=1
def trainRecognizer(self, trainPath, printAcc, testPath = None):
data = []
labels = []
print('Extracting Features.....')
for imageFolder in os.listdir(trainPath):
imagePath = os.path.join(trainPath, imageFolder)
for trainImg in os.listdir(imagePath):
image = cv2.imread(os.path.join(imagePath, trainImg))
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
#print(gray[50:-70,130:-170].shape)
hist = self.desc.calc_hist(gray)
labels.append(int(imageFolder[-1]))
data.append(hist)
print("Processed folder " + imageFolder)
if imageFolder == "Subject09":
break
print('Completed Feature Extraction!')
print('Training Classifier.......')
temp = list(zip(data, labels))
random.shuffle(temp)
data, labels = zip(*temp)
model = KNeighborsClassifier(n_neighbors=1)
model.fit(data, labels)
print('Done Training')
if printAcc:
correct = 0
total = 0
print('Calculating accuracy....')
for imageFolder in os.listdir(testPath):
imagePath = os.path.join(testPath, imageFolder)
for testImg in os.listdir(imagePath):
image = cv2.imread(os.path.join(imagePath, testImg))
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
hist = self.desc.calc_hist(gray)
hist = np.array(hist)
prediction = model.predict(hist.reshape(1, -1))
total += 1
if prediction == int(imageFolder[-1]):
correct += 1
print("Done "+imageFolder)
tempacc = (correct/total)*100
print("Total : "+str(total)+" Correct : "+str(correct)+" Accuracy : "+str(tempacc))
if imageFolder == "Subject09":
break
acc = (correct/total)*100
print('Accuracy on test set is : '+str(acc)+'%')
return model
class FaceRecognition:
def __init__(self, block_size, stride):
self.desc = Calculator(block_size)
#self.detector = MTCNN()
self.stride = stride
self.block_size = block_size
def trainRecognizertesting(self,
trainPath,
testPath,
no_of_classes,
printAcc=True):
data = []
labels = []
acc = 0
print('Extracting Features.....')
for imageFolder in os.listdir(trainPath):
count = 0
imagePath = os.path.join(trainPath, imageFolder)
for trainImg in os.listdir(imagePath):
#count+=1
image = cv2.imread(os.path.join(imagePath, trainImg))
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
t1 = time.time()
hist = self.desc.calc_hist(gray, self.stride)
t2 = time.time()
#print(t2-t1)
label = imageFolder[-2] + imageFolder[-1]
labels.append(int(label))
data.append(hist)
#if count>5:
# break
print("Processed folder " + imageFolder)
if no_of_classes <= 9:
stopFolder = "Subject0" + str(no_of_classes)
else:
stopFolder = "Subject" + str(no_of_classes)
if imageFolder == stopFolder:
break
print('Completed Feature Extraction!')
print('Training Classifier.......')
temp = list(zip(data, labels))
random.shuffle(temp)
data, labels = zip(*temp)
model = KNeighborsClassifier(n_neighbors=1)
model.fit(data, labels)
print('Done Training KNN')
if printAcc:
correct = 0
total = 0
print('Calculating accuracy....')
for imageFolder in os.listdir(testPath):
imagePath = os.path.join(testPath, imageFolder)
for testImg in os.listdir(imagePath):
image = cv2.imread(os.path.join(imagePath, testImg))
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
hist = self.desc.calc_hist(gray, self.stride)
hist = np.array(hist)
prediction = model.predict(hist.reshape(1, -1))
total += 1
label = imageFolder[-2] + imageFolder[-1]
if prediction == int(label):
correct += 1
print("Done " + imageFolder)
tempacc = (correct / total) * 100
#print("Total : "+str(total)+" Correct : "+str(correct)+" Accuracy : "+str(tempacc))
if no_of_classes <= 9:
stopFolder = "Subject0" + str(no_of_classes)
else:
stopFolder = "Subject" + str(no_of_classes)
if imageFolder == stopFolder:
break
acc = (correct / total) * 100
#print('Accuracy on test set is : '+str(acc)+'%')
return model, acc