def get_frame(self):
conn = sqlite3.connect('database.db')
c = conn.cursor()
fname = "recognizer/trainingData.yml"
if not os.path.isfile(fname):
print("\nPlease train the data first\n")
return None
# Model For Facial Expression Recognition
model = FacialExpressionModel("model.json", "model_weights.h5")
# Model For Face Recognizer
face_detector = mtcnn.MTCNN()
recognizer = cv2.face.LBPHFaceRecognizer_create()
recognizer.read(fname)
_, img = self.video.read()
faces = face_detector.detect_faces(img)
for res in faces:
x, y, w, h = res["box"]
x, y = abs(x), abs(y)
x1, y1 = x+w, y+h
image = img[y:y1, x:x1]
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Recognizing Face
ids, conf = recognizer.predict(image)
c.execute("select roll_no from users where id = (?);", (ids,))
result = c.fetchall()
try:
roll_no = result[0][0]
except:
roll_no = 'Error'
if conf > 50:
roll_no = "No Match"
image2 = cv2.resize(image, (48, 48))
# Predicting Expression
pred = model.predict_emotion(image2[np.newaxis, :, :, np.newaxis])
msg = pred + " " + roll_no
# Mark the Expression if Face is detected
if roll_no != "Error" and roll_no != "No Match" :
marked = count(roll_no, pred)
if(marked):
Attendance(roll_no)
msg = "MARKED"
cv2.putText(img, msg, (x, y), cv2.FONT_HERSHEY_SIMPLEX,
1, (255, 255, 0), 2)
cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2)
_, jpeg = cv2.imencode('.jpg', img)
return jpeg.tobytes()
def get_frame(our_image):
model = FacialExpressionModel("model.json", "model_weights.h5")
image = np.array(our_image.convert("RGB"))
new_image = cv2.cvtColor(image, 1)
gray_fr = cv2.cvtColor(new_image, cv2.COLOR_BGR2GRAY)
faces = facec.detectMultiScale(gray_fr, 1.3, 5)
for (x, y, w, h) in faces:
fc = gray_fr[y:y + h, x:x + w]
roi = cv2.resize(fc, (48, 48))
pred, probs = model.predict_emotion(roi[np.newaxis, :, :, np.newaxis])
cv2.putText(new_image, pred, (x, y), font, 1, (255, 255, 0), 2)
cv2.rectangle(new_image, (x, y), (x + w, y + h), (255, 0, 0), 2)
if len(faces) == 0:
probs = []
# _, jpeg = cv2.imencode('.jpg', fr)
return new_image, faces, probs
# loop over the face detections
for rect in rectangles:
# face detection with emotion label
cv2.rectangle(frame, (rect.left(), rect.top()),
(rect.right(), rect.bottom()), (255, 0, 0), 2)
roi_gray = gray[rect.top():rect.bottom(), rect.left():rect.right()]
# rescale needed to 48x48 for model input
try:
roi = cv2.resize(roi_gray, (48, 48))
except Exception as e:
print(str(e))
# our prediction
pred = model.predict_emotion(roi[np.newaxis, :, :, np.newaxis])
# facial expression label
cv2.putText(frame, pred, (rect.left(), rect.top()),
cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 0), 2)
# determine the facial landmarks for the face region
shape = predictor(gray, rect)
shape = face_utils.shape_to_np(shape)
# extract the left and right eye coordinates, then use the
# coordinates to compute the eye aspect ratio for both eyes
leftEye = shape[lStart:lEnd]
rightEye = shape[rStart:rEnd]
leftEAR = eye_aspect_ratio(leftEye)
rightEAR = eye_aspect_ratio(rightEye)
