Update app.py

app.py

@@ -12,134 +12,4 @@ from feat.utils import FEAT_EMOTION_COLUMNS
 import torch
 from PIL import Image
 
-os.environ["TWILIO_ACCOUNT_SID"] = "ACf1e76f3fd6e9cbca940decc4ed443c20"
-os.environ["TWILIO_AUTH_TOKEN"] = "56a1d1ee494933269fe042706392ac9f"
-
-
-def get_ice_servers():
-    try:
-        account_sid = os.environ["TWILIO_ACCOUNT_SID"]
-        auth_token = os.environ["TWILIO_AUTH_TOKEN"]
-    except KeyError:
-        logger.warning("TURN credentials are not set. Fallback to a free STUN server from Google.")
-        return [{"urls": ["stun:stun.l.google.com:19302"]}]
-
-    client = Client(account_sid, auth_token)
-
-    token = client.tokens.create()
-
-    return token.ice_servers
-
-def eye_aspect_ratio(eye):
-
-	A = math.dist(eye[1], eye[5])
-	B = math.dist(eye[2], eye[4])
-
-	C = math.dist(eye[0], eye[3])
-
-	ear = (A + B) / (2.0 * C)
-
-	return ear
-
-def detect_eyes(landmarks, img, threshold):
-    lm = landmarks
-    eyes = np.array(lm[36:48], np.int32)
-
-    left_eye = eyes[0:6]
-    right_eye = eyes[6:12]
-    ear = max(eye_aspect_ratio(left_eye), eye_aspect_ratio(right_eye))
-    left_eye = left_eye.reshape((-1,1,2))
-    right_eye = right_eye.reshape((-1,1,2))
-    cv.polylines(img, [left_eye], True, (0, 255, 255))
-    cv.polylines(img, [right_eye], True, (255, 0, 255))
-
-    if (ear > threshold):
-         return True
-    else:
-         return False
-
-def proc_image(img, detector):
-    detected_faces = detector.detect_faces(img)
-    faces_detected = len(detected_faces[0])
-    if ( faces_detected < 1):
-        return img
-    
-    detected_landmarks = detector.detect_landmarks(img, detected_faces)
-    assert len(detected_landmarks[0]) == faces_detected, "Number of faces and landsmarks are mismatched!"
-
-    is_eye_open = [detect_eyes(face, img, 0.20) for face in detected_landmarks[0]]
-    eye_dict = {True: "eyes open", False: "eyes closed"}
-
-    detected_emotions = detector.detect_emotions(img, detected_faces, detected_landmarks)
-    assert len(detected_emotions[0]) == faces_detected, "Number of faces and emotions are mismatched!"
-
-    em = detected_emotions[0]
-    em_labels = em.argmax(axis=1)
-
-
-
-    for face, has_open_eyes, label in zip(detected_faces[0], (eye_dict[eyes] for eyes in is_eye_open), em_labels):
-        (x0, y0, x1, y1, p) = face
-        res_scale = img.shape[0]/704
-        cv.rectangle(img, (int(x0), int(y0)), (int(x1), int(y1)), color = (0, 0, 255), thickness = 3)
-        cv.putText(img, FEAT_EMOTION_COLUMNS[label], (int(x0)-10, int(y1+25*res_scale*1.5)), fontFace = 0, color = (0, 255, 0), thickness = 2, fontScale = res_scale)
-        cv.putText(img, f"{faces_detected } face(s) found", (0, int(25*res_scale*1.5)), fontFace = 0, color = (0, 255, 0), thickness = 2, fontScale = res_scale)
-        cv.putText(img, has_open_eyes, (int(x0)-10, int(y0)-10), fontFace = 0, color = (0, 255, 0), thickness = 2, fontScale = res_scale)
-    return img
-    
-def extract_feat():
-    return [1,2,3,4,5]
-    
-def image_processing(frame):
-    return proc_image(img, detector) if recog else img
-
-def video_frame_callback(frame):
-    img = frame.to_ndarray(format="bgr24")
-
-    ann = proc_image(img, detector) if recog else img
-
-    return av.VideoFrame.from_ndarray(ann, format="bgr24")
-
-detector = Detector(face_model="retinaface", landmark_model= "pfld", au_model = "xgb", emotion_model="resmasknet")
-source = "Webcam"
-recog = True
-
-source  = st.radio(
-    label = "Image source for emotion recognition",
-    options = ["Webcam", "Images"],
-    horizontal = True,
-    label_visibility = "collapsed",
-    args = (source, )
-    )
-
-has_cam = True if (source == "Webcam") else False
-
-stream = st.container()
-with stream:
-    if has_cam:
-        webrtc_streamer(
-            key="example",
-            mode=WebRtcMode.SENDRECV,
-            video_frame_callback=video_frame_callback,
-            rtc_configuration={ "iceServers": get_ice_servers() },
-            media_stream_constraints={"video": True, "audio": False},
-            async_processing=True,
-        )
-    else:
-        pic = st.container()
-        frame = image_select(
-        label="Try the classifier on one of the provided examples!",
-        images=[
-            "ex1.jpg",
-            "ex4.jpg",
-            "ex5.jpg",
-            "ex6.jpg",
-        ],
-        use_container_width= False
-        )
-        img = np.array(Image.open(frame))
-        pic.image(image_processing(img), width = 704)
-
-
-recog = st.toggle(":green[Emotion recogntion]", key = "stream", value = True)
-
+st.text(f"{os.getcwt()}")