def predict(self, *grays):
res = []
for gray in grays:
faces = face_cascade.detectMultiScale(gray)
for x, y, w, h in faces:
if self.lbph_rec is not None:
try:
res.append(self.lbph_rec.predict(gray[y: y + h, x: x + w])[0])
except:
res.append(None)
for recognizer in (self.eigenface_rec, self.fisherface_rec):
faces = face_cascade.detectMultiScale(gray)
for x, y, w, h in faces:
img = gray[y: y + h, x: x + w].copy()
img = cv2.resize(img, (self.max_width, self.max_height))
if recognizer is not None:
try:
res.append(recognizer.predict(img)[0])
except:
res.append(None)
if not res: return None, None
if len(res) < len(grays) * 3:
for i in range(len(grays) * 3 - len(res)):
res.append(None)
top, occur = Counter(res).most_common(1)[0]
percent = int((occur / len(res)) * 100)
return top, percent
def predict(self, *grays):
with open(r'static/trained.yaml') as file:
documents = yaml.load(file, Loader=yaml.Loader)
self.known_faces=np.array(documents)
print(self.known_faces)
face_locations = []
face_encodings = []
match=[]
for gray in grays:
faces = face_cascade.detectMultiScale(gray)
for (x, y, w, h) in faces:
img = gray.copy()
img = Image.fromarray(img).save('static/temp/User.png')
img1 = face_recognition.load_image_file("static/temp/User.png") #load image that need to be processed
face_locations = face_recognition.face_locations(img1, model='hog')
face_encodings = face_recognition.face_encodings(img1, face_locations)
for face_encoding in face_encodings: #face recognition start
match = face_recognition.compare_faces(self.known_faces, face_encoding, tolerance=0.30)
print(match)
id=0
for i in range(len(match)):
if match[i]==True:
id = i+1
else:
pass
print(id)
percent=100
return int(id), int(percent)
def get_images_and_labels(self, same_size=False):
image_paths = [
os.path.join(self.photos, f) for f in os.listdir(self.photos)
]
images, labels = [], []
if same_size:
width, height = self.get_photo_size()
for image_path in image_paths:
image_pil = cv2.imread(image_path)
try:
nbr = int(os.path.split(image_path)[1].split("_")[0])
except:
continue
gray = cv2.cvtColor(image_pil, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
image = np.array(gray, 'uint8')
for x, y, w, h in faces:
if same_size:
resized = cv2.resize(image[y:y + h, x:x + w],
(width, height))
images.append(resized)
else:
images.append(image[y:y + h, x:x + w])
labels.append(nbr)
return images, labels
def crop_photos(paths):
for image in paths:
img = cv2.imread(image)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = detector.detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in faces:
cv2.imwrite(image, gray[y:y + h, x:x + w])
def predict(self, *grays):
res = []
#print("Start Predict")
for gray in grays:
#print("Stage 1")
faces = face_cascade.detectMultiScale(gray)
#print("Face Detected")
#print(faces)
for (x, y, w, h) in faces:
if self.lbph_rec is not None:
try:
#print("OMG")
output = self.lbph_rec.predict(gray[y:y + h, x:x + w])
#print(output)
res.append(output)
#print("haha")
#print(res)
except:
#print("No")
res.append(None)
for recognizer in (self.eigenface_rec, self.fisherface_rec):
#print("stage2")
faces = face_cascade.detectMultiScale(gray)
for (x, y, w, h) in faces:
img = gray[y:y + h, x:x + w].copy()
img = cv2.resize(img, (self.max_width, self.max_height))
if recognizer is not None:
try:
#print("Stage3")
out = recognizer.predict(img)
res.append(out)
#print("hahaha")
#print(res)
except:
#print("NONO")
res.append(None)
if not res: return None, None
if len(res) < len(grays) * 3:
for i in range(len(grays) * 3 - len(res)):
res.append(None)
top, occur = Counter(res).most_common(1)[0]
#print(occur)
percent = int((top[1]))
#print(percent)
#print(top)
return int(top[0]), int(percent)
def get_photo_size(self):
return 100,100
if len(os.listdir(self.photos)) == 0:
return None, None
image_paths = [os.path.join(self.photos, f) for f in os.listdir(self.photos)]
image_pil = cv2.imread(image_paths[0])
gray = cv2.cvtColor(image_pil, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
width, height = faces[0][2:4]
for image_path in image_paths:
image_pil = cv2.imread(image_path)
gray = cv2.cvtColor(image_pil, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray,scaleFactor = 1.3,minNeighbors = 4)
try:
x, y, w, h = faces[0]
if w < width: width = w
if h < height: height = h
except:
continue
return width, height
def save_and_get_label(self):
for i in range(5):
self.read_image()
grays, paths = [], []
for i in range(5):
grays.append(self.read_image()[1])
for i, gray in enumerate(grays):
faces = face_cascade.detectMultiScale(gray,scaleFactor = 1.3,minNeighbors = 4)
for (x, y, w, h) in faces:
paths.append('img' + str(i) + '.jpg')
cv2.imwrite(paths[-1], gray[y:y + h, x:x + w])
return self.get_image_label(*paths)
