Update face_analyzer.py

face_analyzer.py

@@ -1,60 +1,60 @@
-import cv2
-import numpy as np
-
-class FaceAnalyzer:
-    def __init__(self):
-        # 加載OpenCV的人臉檢測器和眼睛檢測器
-        self.face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
-        self.eye_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_eye.xml')
-        
-    def _get_eye_aspect_ratio(self, eye_region):
-        """
-        計算眼睛縱橫比（EAR）
-        :param eye_region: 眼睛區域的圖像
-        :return: EAR值
-        """
-        # 將眼睛區域轉換為灰度圖
-        gray_eye = cv2.cvtColor(eye_region, cv2.COLOR_BGR2GRAY)
-        
-        # 檢測眼睛
-        eyes = self.eye_cascade.detectMultiScale(gray_eye)
-        
-        if len(eyes) != 2:  # 如果沒有檢測到兩個眼睛
-            return 0.0
-            
-        # 獲取眼睛的寬度和高度
-        eye1 = eyes[0]
-        eye2 = eyes[1]
-        
-        # 計算眼睛的寬高比
-        ear1 = eye1[2] / eye1[3]
-        ear2 = eye2[2] / eye2[3]
-        
-        # 返回平均EAR
-        return (ear1 + ear2) / 2.0
-        
-    def is_drowsy(self, face_image):
-        """
-        檢測是否犯困
-        :param face_image: 人臉圖片
-        :return: 是否犯困（True/False）
-        """
-        # 將圖片轉換為灰度圖
-        gray = cv2.cvtColor(face_image, cv2.COLOR_BGR2GRAY)
-        
-        # 檢測人臉
-        faces = self.face_cascade.detectMultiScale(gray, 1.3, 5)
-        
-        if len(faces) == 0:
-            return False
-            
-        # 獲取最大的人臉區域
-        (x, y, w, h) = faces[0]
-        face_roi = face_image[y:y+h, x:x+w]
-        
-        # 計算眼睛縱橫比
-        ear = self._get_eye_aspect_ratio(face_roi)
-        
-        # 如果EAR小於閾值，認為是犯困
-        EAR_THRESHOLD = 0.25
-        return ear < EAR_THRESHOLD 
+import cv2
+import numpy as np
+
+class FaceAnalyzer:
+    def __init__(self):
+        # Load OpenCV's face detector and eye detector
+        self.face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+        self.eye_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_eye.xml')
+        
+    def _get_eye_aspect_ratio(self, eye_region):
+        """
+        Calculate eye aspect ratio (EAR)
+        :param eye_region: Image of eye region
+        :return: EAR value
+        """
+        # Convert eye region to grayscale
+        gray_eye = cv2.cvtColor(eye_region, cv2.COLOR_BGR2GRAY)
+        
+        # Detect eyes
+        eyes = self.eye_cascade.detectMultiScale(gray_eye)
+        
+        if len(eyes) != 2:  # If not detected two eyes
+            return 0.0
+            
+        # Get eye width and height
+        eye1 = eyes[0]
+        eye2 = eyes[1]
+        
+        # Calculate eye width-height ratio
+        ear1 = eye1[2] / eye1[3]
+        ear2 = eye2[2] / eye2[3]
+        
+        # Return average EAR
+        return (ear1 + ear2) / 2.0
+        
+    def is_drowsy(self, face_image):
+        """
+        Detect drowsiness
+        :param face_image: Face image
+        :return: Whether drowsy (True/False)
+        """
+        # Convert image to grayscale
+        gray = cv2.cvtColor(face_image, cv2.COLOR_BGR2GRAY)
+        
+        # Detect faces
+        faces = self.face_cascade.detectMultiScale(gray, 1.3, 5)
+        
+        if len(faces) == 0:
+            return False
+            
+        # Get the largest face region
+        (x, y, w, h) = faces[0]
+        face_roi = face_image[y:y+h, x:x+w]
+        
+        # Calculate eye aspect ratio
+        ear = self._get_eye_aspect_ratio(face_roi)
+        
+        # If EAR is less than the threshold, consider it drowsy
+        EAR_THRESHOLD = 0.25
+        return ear < EAR_THRESHOLD