眨眼频率、眼动分析、心率、视频录制

reproject/geometry_utils.py

@@ -5,71 +5,115 @@ import cv2
 LEFT_EYE = [33, 160, 158, 133, 153, 144]
 # 右眼
 RIGHT_EYE = [362, 385, 387, 263, 373, 380]
+# 左眼虹膜关键点索引
+LEFT_EYE_GAZE_IDXS = [33, 133, 159, 145, 468]
+# 右眼虹膜关键点索引
+RIGHT_EYE_GAZE_IDXS = [263, 362, 386, 374, 473] 
 # 嘴唇 (内圈)
 LIPS = [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308, 415, 310, 311, 312, 13]
 
+
 def _euclidean_distance(point1, point2):
     return np.linalg.norm(point1 - point2)
 
+
 def calculate_ear(landmarks, width, height):
     """计算眼睛纵横比 EAR"""
     # 坐标转换
     points = np.array([(p.x * width, p.y * height) for p in landmarks])
-    
+
     # 垂直距离
     v1 = _euclidean_distance(points[1], points[5])
     v2 = _euclidean_distance(points[2], points[4])
     # 水平距离
     h = _euclidean_distance(points[0], points[3])
-    
+
     ear = (v1 + v2) / (2.0 * h)
     return ear
 
+
+def calculate_iris_pos(landmarks, indices, width, height):
+    p_left = np.array([landmarks[indices[0]].x * width, landmarks[indices[0]].y * height])
+    p_right = np.array([landmarks[indices[1]].x * width, landmarks[indices[1]].y * height])
+    p_top = np.array([landmarks[indices[2]].x * width, landmarks[indices[2]].y * height])
+    p_bottom = np.array([landmarks[indices[3]].x * width, landmarks[indices[3]].y * height])
+    p_iris = np.array([landmarks[indices[4]].x * width, landmarks[indices[4]].y * height])
+
+    # 修改为欧几里得距离计算
+    eye_width = _euclidean_distance(p_left, p_right)
+    iris_left = _euclidean_distance(p_iris, p_left)
+    if eye_width < 1e-3: 
+        raw_x = 0.5
+    else :
+        raw_x = iris_left / eye_width
+    eye_height = _euclidean_distance(p_bottom, p_top)
+    iris_top = _euclidean_distance(p_iris, p_top)
+    if eye_height < 1e-3:
+        raw_y = 0.5
+    else:
+        raw_y = iris_top / eye_height
+
+    # x_min = 0.4
+    # x_max = 0.6
+    # ratio_x = abs(raw_x - x_min) / (x_max - x_min)
+    # y_min = 0.2
+    # y_max = 0.8
+    # ratio_y = abs(raw_y - y_min) / (y_max - y_min)
+    ratio_x = raw_x
+    ratio_y = raw_y
+    
+    return max(0.0, min(1.0, ratio_x)), max(0.0, min(1.0, ratio_y))
+
+
 def calculate_mar(landmarks, width, height):
     """计算嘴巴纵横比 MAR"""
     points = np.array([(p.x * width, p.y * height) for p in landmarks])
-    pass 
+    pass
+
 
 def calculate_mar_simple(top, bottom, left, right):
     h = _euclidean_distance(top, bottom)
     w = _euclidean_distance(left, right)
     return h / w
 
+
 # geometry_utils.py 中的 estimate_head_pose 函数替换为以下内容
 
+
 def estimate_head_pose(landmarks, width, height):
     """
     计算头部姿态 (Pitch, Yaw, Roll)
     返回单位：角度 (Degree)
     """
     # 3D 模型点 (标准人脸模型)
-    model_points = np.array([
-        (0.0, 0.0, 0.0),             # Nose tip
-        (0.0, -330.0, -65.0),        # Chin
-        (-225.0, 170.0, -135.0),     # Left eye left corner
-        (225.0, 170.0, -135.0),      # Right eye right corner
-        (-150.0, -150.0, -125.0),    # Left Mouth corner
-        (150.0, -150.0, -125.0)      # Right mouth corner
-    ])
+    model_points = np.array(
+        [
+            (0.0, 0.0, 0.0),  # Nose tip
+            (0.0, -330.0, -65.0),  # Chin
+            (-225.0, 170.0, -135.0),  # Left eye left corner
+            (225.0, 170.0, -135.0),  # Right eye right corner
+            (-150.0, -150.0, -125.0),  # Left Mouth corner
+            (150.0, -150.0, -125.0),  # Right mouth corner
+        ]
+    )
 
     # MediaPipe 对应的关键点索引
     idx_list = [1, 152, 33, 263, 61, 291]
-    
+
     image_points = []
     for idx in idx_list:
         p = landmarks[idx]
         image_points.append((p.x * width, p.y * height))
-    
+
     image_points = np.array(image_points, dtype="double")
 
     focal_length = width
     center = (width / 2, height / 2)
     camera_matrix = np.array(
-        [[focal_length, 0, center[0]],
-         [0, focal_length, center[1]],
-         [0, 0, 1]], dtype="double"
+        [[focal_length, 0, center[0]], [0, focal_length, center[1]], [0, 0, 1]],
+        dtype="double",
     )
-    dist_coeffs = np.zeros((4, 1)) 
+    dist_coeffs = np.zeros((4, 1))
 
     # 求解PnP
     success, rotation_vector, translation_vector = cv2.solvePnP(
@@ -80,15 +124,21 @@ def estimate_head_pose(landmarks, width, height):
     angles, mtxR, mtxQ, Qx, Qy, Qz = cv2.RQDecomp3x3(rmat)
 
     pitch = angles[0]
-    yaw   = angles[1]
-    roll  = angles[2]
+    yaw = angles[1]
+    roll = angles[2]
 
-    if pitch < -180: pitch += 360
-    if pitch > 180:  pitch -= 360
+    if pitch < -180:
+        pitch += 360
+    if pitch > 180:
+        pitch -= 360
     pitch = 180 - pitch if pitch > 0 else -pitch - 180
-    if yaw < -180: yaw += 360
-    if yaw > 180:  yaw -= 360
-    if roll < -180: roll += 360
-    if roll > 180:  roll -= 360
+    if yaw < -180:
+        yaw += 360
+    if yaw > 180:
+        yaw -= 360
+    if roll < -180:
+        roll += 360
+    if roll > 180:
+        roll -= 360
 
-    return pitch, yaw, roll
+    return pitch, yaw, roll