File size: 8,026 Bytes
9825f94 697963f 1ef230c 9825f94 46335ce 9825f94 46335ce 3525ad6 022af52 a549554 bb922d4 022af52 697963f 26321f9 697963f 37bed39 697963f 9825f94 1ef230c 9825f94 1ef230c e916c8e 1ef230c e916c8e 1ef230c e916c8e 1ef230c e916c8e 1ef230c e916c8e 1ef230c e916c8e 1ef230c ec56169 1ef230c 9825f94 1ef230c 3f28202 1ef230c e916c8e 1ef230c e916c8e 1ef230c 3a66698 3525ad6 1ef230c 3525ad6 1ef230c ec56169 9825f94 1ef230c e916c8e 1ef230c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 |
import os
import traceback
os.environ["HOME"] = "/tmp"
os.environ["STREAMLIT_HOME"] = "/tmp"
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["TF_FORCE_GPU_ALLOW_GROWTH"] = "true"
import cv2
import numpy as np
from PIL import Image
import streamlit as st
from streamlit_webrtc import VideoProcessorBase, webrtc_streamer, RTCConfiguration
from twilio.rest import Client
account_sid = os.environ.get("ACCOUNT_SID")
auth_token = os.environ.get("AUTH_TOKEN")
ICE_SERVERS = [{"urls": ["stun:stun.l.google.com:19302"]}]
if account_sid and auth_token:
try:
twilio_client = Client(account_sid, auth_token)
token = twilio_client.tokens.create()
try:
ICE_SERVERS = [
server for server in token.ice_servers
if any("udp" in url for url in ([server["urls"]] if isinstance(server["urls"], str) else server["urls"]))
]
st.success("✅ Using Twilio TURN/STUN servers with UDP")
except Exception as e:
ICE_SERVERS = token.ice_servers
st.success("✅ Using Twilio TURN/STUN servers")
except Exception as e:
st.error(f"❌ Failed to get ICE servers from Twilio: {e}")
st.text(traceback.format_exc())
else:
st.warning("⚠️ Twilio credentials not set. Falling back to STUN-only.")
import tensorflow as tf
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
try:
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
except Exception as e:
print(e)
from collections import deque
shared_emotion_history = deque(maxlen=20)
import logging
logging.getLogger("streamlit.runtime.scriptrunner.script_run_context").setLevel(logging.ERROR)
logger = logging.getLogger(__name__)
from classification import Classification
# --- Main Streamlit App ---
if __name__ == '__main__':
st.title("Personal Video Logger")
st.write("Turn on your camera and talk about anything that worries you or just about your day.")
model_choice = st.selectbox(
"Choose a model:",
options=["mobilenet", "vgg16"],
index=0,
help="Select the model used for emotion classification."
)
@st.cache_resource
def get_model(model):
return Classification(model)
classificator = get_model(model_choice)
face_cascade = cv2.CascadeClassifier(
cv2.data.haarcascades + 'haarcascade_frontalface_alt.xml'
)
def face_detect(img):
try:
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(
img_gray,
scaleFactor=1.1,
minNeighbors=3,
minSize=(30, 30)
)
return img, img_gray, faces
except Exception as e:
st.error(f"OpenCV face detection error: {e}")
return img, np.zeros_like(img), []
def map_emotion_to_class(emotion):
positive = ['happiness', 'happy']
negative = ['disgust', 'sadness', 'fear', 'sad', 'angry', 'disgusted']
surprise = ['surprise']
others = ['repression', 'tense', 'neutral', 'others']
e = emotion.lower()
if any(p in e for p in positive):
return 'Positive'
elif any(n in e for n in negative):
return 'Negative'
elif any(s in e for s in surprise):
return 'Surprise'
else:
return 'Others'
if 'emotion_history' not in st.session_state:
st.session_state['emotion_history'] = []
class EmotionRecognitionProcessor(VideoProcessorBase):
def __init__(self):
self.last_class = None
self.rapid_change_count = 0
self.frame_count = 0
self.last_faces = []
self.last_img_gray = None
self.last_results = []
def recv(self, frame):
border_color = (255, 0, 0)
font_color = (0, 0, 255)
try:
img = frame.to_ndarray(format="bgr24")
self.frame_count += 1
if self.frame_count % 2 == 0:
img_disp, img_gray, faces = face_detect(img)
self.last_faces = faces
self.last_img_gray = img_gray
self.last_results = []
current_class = None
if len(faces) == 0:
cv2.putText(
img_disp, 'No Face Detect.', (2, 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.4, (0, 0, 255), 1
)
for (x, y, w, h) in faces:
x1, y1 = max(x - 10, 0), max(y - 10, 0)
x2 = min(x + w + 10, img_disp.shape[1])
y2 = min(y + h + 10, img_disp.shape[0])
face_img_gray = img_gray[y1:y2, x1:x2]
if face_img_gray.size == 0:
continue
face_img_pil = Image.fromarray(face_img_gray)
emotion, probability = classificator.detect_image(face_img_pil)
emotion_class = map_emotion_to_class(emotion)
self.last_results.append((x1, y1, x2, y2, emotion, probability, emotion_class))
current_class = emotion_class
if current_class:
shared_emotion_history.append(current_class)
if len(shared_emotion_history) >= 3 and len(set(list(shared_emotion_history)[-3:])) > 1:
self.rapid_change_count += 1
else:
self.rapid_change_count = 0
else:
img_disp = img.copy()
img_gray = self.last_img_gray
faces = self.last_faces
for (x1, y1, x2, y2, emotion, probability, emotion_class) in self.last_results:
cv2.rectangle(
img_disp,
(x1, y1),
(x2, y2),
border_color,
thickness=2
)
cv2.putText(
img_disp, emotion, (x1 + 30, y1 - 30),
cv2.FONT_HERSHEY_SIMPLEX, 1, font_color, 1
)
cv2.putText(
img_disp, str(round(probability, 3)), (x1 + 30, y1 - 50),
cv2.FONT_HERSHEY_SIMPLEX, 0.3, font_color, 1
)
if len(faces) == 0:
cv2.putText(
img_disp, 'No Face Detect.', (2, 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.4, (0, 0, 255), 1
)
return frame.from_ndarray(img_disp, format="bgr24")
except Exception as e:
logger.exception("Video processing error", e)
return frame
RTC_CONFIGURATION = RTCConfiguration({"iceServers": ICE_SERVERS})
webrtc_streamer(
key="emotion-detection",
video_processor_factory=EmotionRecognitionProcessor,
rtc_configuration=RTC_CONFIGURATION,
media_stream_constraints={"video": True, "audio": False},
async_processing=True,
)
history = list(shared_emotion_history)
if len(history) >= 3 and len(set(history[-3:])) > 1:
st.warning(
"⚠️ Rapid changes in your detected emotional state were observed. "
"Micro-expressions may not always reflect your true feelings. "
"If you feel emotionally unstable or distressed, "
"consider reaching out to a mental health professional, "
"talking it over with a close person or taking a break."
)
|