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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+shape_predictor_68_face_landmarks.dat filter=lfs diff=lfs merge=lfs -text

finalbgtt.py

@@ -0,0 +1,120 @@
+import streamlit as st
+import cv2
+import numpy as np
+import dlib
+from scipy.spatial import distance as dist
+from imutils import face_utils
+
+# Constants 
+EYE_AR_THRESH = 0.3
+EYE_AR_CONSEC_FRAMES = 30
+YAWN_THRESH = 20 
+
+# Global variables
+COUNTER = 0
+
+# Functions
+def eye_aspect_ratio(eye):
+    A = dist.euclidean(eye[1], eye[5])
+    B = dist.euclidean(eye[2], eye[4])
+    C = dist.euclidean(eye[0], eye[3])
+    ear = (A + B) / (2.0 * C)
+    return ear
+
+def final_ear(shape):
+    (lStart, lEnd) = face_utils.FACIAL_LANDMARKS_IDXS["left_eye"]
+    (rStart, rEnd) = face_utils.FACIAL_LANDMARKS_IDXS["right_eye"]
+    leftEye = shape[lStart:lEnd]
+    rightEye = shape[rStart:rEnd]
+    leftEAR = eye_aspect_ratio(leftEye)
+    rightEAR = eye_aspect_ratio(rightEye)
+    ear = (leftEAR + rightEAR) / 2.0
+    return (ear, leftEye, rightEye)
+
+def lip_distance(shape):
+    top_lip = shape[50:53]
+    top_lip = np.concatenate((top_lip, shape[61:64]))
+    low_lip = shape[56:59]
+    low_lip = np.concatenate((low_lip, shape[65:68]))
+    top_mean = np.mean(top_lip, axis=0)
+    low_mean = np.mean(low_lip, axis=0)
+    distance = abs(top_mean[1] - low_mean[1])
+    return distance
+
+# Load detector and predictor
+detector = cv2.CascadeClassifier('./haarcascade_frontalface_default.xml')
+predictor = dlib.shape_predictor('./shape_predictor_68_face_landmarks.dat') 
+
+
+# Streamlit UI
+st.title("Sleep Detection using OpenCV")
+st.markdown("**Check the box below to start the camera:**")
+
+FRAME_WINDOW = st.image([])
+run = st.checkbox("Run Camera", key="run_camera")
+
+# Video capture
+if "cap" not in st.session_state:
+    st.session_state.cap = None
+
+if run:
+    if st.session_state.cap is None:
+        st.session_state.cap = cv2.VideoCapture(0)
+        st.success("Camera Started!")
+
+    while run:
+        ret, frame = st.session_state.cap.read()
+        if not ret:
+            st.error("Failed to open webcam.")
+            break
+
+        frame = cv2.resize(frame, (450, 300))
+        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+
+        rects = detector.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30), flags=cv2.CASCADE_SCALE_IMAGE)
+
+        for (x, y, w, h) in rects:
+            rect = dlib.rectangle(int(x), int(y), int(x + w), int(y + h))
+            shape = predictor(gray, rect)
+            shape = face_utils.shape_to_np(shape) 
+
+            ear, leftEye, rightEye = final_ear(shape)
+            distance = lip_distance(shape)
+
+            leftEyeHull = cv2.convexHull(leftEye)
+            rightEyeHull = cv2.convexHull(rightEye)
+            cv2.drawContours(frame, [leftEyeHull], -1, (0, 255, 0), 1)
+            cv2.drawContours(frame, [rightEyeHull], -1, (0, 255, 0), 1)
+
+            lip = shape[48:60]
+            cv2.drawContours(frame, [lip], -1, (0, 255, 0), 1)
+
+            if ear < EYE_AR_THRESH:
+                COUNTER += 1
+                if COUNTER >= EYE_AR_CONSEC_FRAMES:
+                    cv2.putText(frame, "DROWSINESS", (10, 30),
+                                cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
+            else:
+                COUNTER = 0
+
+            if distance > YAWN_THRESH:
+                cv2.putText(frame, "YAWN", (10, 60),
+                            cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
+
+            cv2.putText(frame, f"EAR: {ear:.2f}", (300, 30),
+                        cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
+            cv2.putText(frame, f"YAWN: {distance:.2f}", (300, 60),
+                        cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
+
+        FRAME_WINDOW.image(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
+
+    st.session_state.cap.release()
+    st.session_state.cap = None
+    FRAME_WINDOW.image([])
+    st.checkbox("Run Camera", value=False, key="run_camera")  # Uncheck the checkbox automatically
+else:
+    if st.session_state.cap is not None:
+        st.session_state.cap.release()
+        st.session_state.cap = None
+        FRAME_WINDOW.image([])
+    st.info("Check 'Run Camera' to start detection.") 

requirements.txt.txt

@@ -0,0 +1,6 @@
+streamlit
+opencv-python-headless
+numpy
+dlib
+imutils
+scipy

shape_predictor_68_face_landmarks.dat

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fbdc2cb80eb9aa7a758672cbfdda32ba6300efe9b6e6c7a299ff7e736b11b92f
+size 99693937