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import streamlit as st
from streamlit_webrtc import webrtc_streamer, WebRtcMode
import av
import os
from twilio.rest import Client
from streamlit_image_select import image_select
import cv2 as cv
import numpy as np
import math
from feat import Detector
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)
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