Update facial_diagnostics.py

facial_diagnostics.py

@@ -1,90 +1,92 @@
-# facial_diagnostics.py
-
-
-import cv2
-import numpy as np
-import mediapipe as mp
-import time
-import json
-import math
-import requests
-from statistics import median
-
-
-mp_face_mesh = mp.solutions.face_mesh
-FACE_MESH = mp_face_mesh.FaceMesh(
-    static_image_mode=False,
-    max_num_faces=1,
-    refine_landmarks=True,
-    min_detection_confidence=0.6,
-    min_tracking_confidence=0.6
-)
-
-LEFT_EYE = [33,160,158,133,153,144]
-RIGHT_EYE = [362,385,387,263,373,380]
-MOUTH = [13,14,78,308]
-SMILE_L = 61
-SMILE_R = 291
-
-
-def eye_aspect_ratio(pts, idx):
-    a = np.linalg.norm(np.array(pts[idx[1]]) - np.array(pts[idx[5]]))
-    b = np.linalg.norm(np.array(pts[idx[2]]) - np.array(pts[idx[4]]))
-    c = np.linalg.norm(np.array(pts[idx[0]]) - np.array(pts[idx[3]])) + 1e-8
-    return float((a + b) / (2.0 * c))
-
-def mouth_ratio(pts):
-    top, bottom, left, right = pts[MOUTH[0]], pts[MOUTH[1]], pts[MOUTH[2]], pts[MOUTH[3]]
-    return float(np.linalg.norm(np.array(top)-np.array(bottom)) /
-                 (np.linalg.norm(np.array(left)-np.array(right)) + 1e-8))
-
-def head_tilt_angle(pts):
-    left = np.mean([pts[33], pts[133]], axis=0)
-    right = np.mean([pts[362], pts[263]], axis=0)
-    diff = np.array(right) - np.array(left)
-    return float(math.degrees(math.atan2(diff[1], diff[0])))
-
-def smile_symmetry(pts):
-    left = np.array(pts[SMILE_L])
-    right = np.array(pts[SMILE_R])
-    center = (left + right) / 2
-    L = np.linalg.norm(left - center)
-    R = np.linalg.norm(right - center)
-    return float(min(L, R) / max(L, R)) if L+R != 0 else 1.0
-
-def analyze_frame(frame):
-    h, w, _ = frame.shape
-    rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-    res = FACE_MESH.process(rgb)
-
-    if not res.multi_face_landmarks:
-        return {"found": False}
-
-    lm = res.multi_face_landmarks[0]
-    pts = [(int(p.x * w), int(p.y * h)) for p in lm.landmark]
-
-    return {
-        "found": True,
-        "ear": (eye_aspect_ratio(pts, LEFT_EYE) + eye_aspect_ratio(pts, RIGHT_EYE))/2,
-        "mouth_ratio": mouth_ratio(pts),
-        "head_tilt": head_tilt_angle(pts),
-        "smile_sym": smile_symmetry(pts)
-    }
-
-
-def aggregate_metrics(metrics):
-    if not metrics:
-        return {}
-
-    ears = [m["ear"] for m in metrics]
-    mouths = [m["mouth_ratio"] for m in metrics]
-    tilts = [m["head_tilt"] for m in metrics]
-    smiles = [m["smile_sym"] for m in metrics]
-
-    return {
-        "frames": len(metrics),
-        "ear_median": median(ears),
-        "mouth_median": median(mouths),
-        "tilt_median": median(tilts),
-        "smile_median": median(smiles),
-    }
+# facial_diagnostics.py
+
+
+import cv2
+import numpy as np
+import math
+from statistics import median
+
+# Facial metric helpers
+LEFT_EYE_IDX = [33, 160, 158, 133, 153, 144]
+RIGHT_EYE_IDX = [362, 385, 387, 263, 373, 380]
+MOUTH_IDX = [13, 14, 78, 308]
+SMILE_LEFT = 61
+SMILE_RIGHT = 291
+
+def eye_aspect_ratio(pts, idx):
+    a = np.linalg.norm(np.array(pts[idx[1]]) - np.array(pts[idx[5]]))
+    b = np.linalg.norm(np.array(pts[idx[2]]) - np.array(pts[idx[4]]))
+    c = np.linalg.norm(np.array(pts[idx[0]]) - np.array(idx[3])) + 1e-8
+    return float((a + b) / (2.0 * c))
+
+def mouth_ratio(pts):
+    top, bottom, left, right = pts[MOUTH_IDX[0]], pts[MOUTH_IDX[1]], pts[MOUTH_IDX[2]], pts[MOUTH_IDX[3]]
+    return float(np.linalg.norm(np.array(top) - np.array(bottom)) /
+                 (np.linalg.norm(np.array(left) - np.array(right)) + 1e-8))
+
+def head_tilt_angle(pts):
+    left = np.mean([pts[33], pts[133]], axis=0)
+    right = np.mean([pts[362], pts[263]], axis=0)
+    diff = np.array(right) - np.array(left)
+    return float(math.degrees(math.atan2(diff[1], diff[0])))
+
+def smile_symmetry(pts):
+    left = np.array(pts[SMILE_LEFT])
+    right = np.array(pts[SMILE_RIGHT])
+    center = (left + right) / 2
+    L = np.linalg.norm(left - center)
+    R = np.linalg.norm(right - center)
+    if L + R == 0:
+        return 1.0
+    return float(min(L, R) / max(L, R))
+
+# Face mesh
+import mediapipe as mp
+mp_face_mesh = mp.solutions.face_mesh
+FACE_MESH = mp_face_mesh.FaceMesh(
+    static_image_mode=False,
+    max_num_faces=1,
+    refine_landmarks=True,
+    min_detection_confidence=0.6,
+    min_tracking_confidence=0.6
+)
+
+def analyze_frame(frame):
+    h, w, _ = frame.shape
+    rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+    res = FACE_MESH.process(rgb)
+
+    if not res.multi_face_landmarks:
+        return {"found": False}
+
+    lm = res.multi_face_landmarks[0]
+    pts = [(int(p.x * w), int(p.y * h)) for p in lm.landmark]
+
+    ear = (eye_aspect_ratio(pts, LEFT_EYE_IDX) + eye_aspect_ratio(pts, RIGHT_EYE_IDX)) / 2
+    mratio = mouth_ratio(pts)
+    tilt = head_tilt_angle(pts)
+    symmetry = smile_symmetry(pts)
+
+    return {
+        "found": True,
+        "ear": float(ear),
+        "mouth_ratio": float(mratio),
+        "head_tilt": float(tilt),
+        "smile_sym": float(symmetry)
+    }
+
+def aggregate_metrics(metrics):
+    if not metrics:
+        return {}
+    ears = [m["ear"] for m in metrics]
+    mouths = [m["mouth_ratio"] for m in metrics]
+    tilts = [m["head_tilt"] for m in metrics]
+    smiles = [m["smile_sym"] for m in metrics]
+    return {
+        "count": len(metrics),
+        "ear_median": median(ears),
+        "mouth_median": median(mouths),
+        "tilt_median": median(tilts),
+        "smile_median": median(smiles)
+    }
+