Update emotion.py

emotion.py

@@ -5,7 +5,6 @@ from typing import Optional, Dict, Tuple, List
 import gradio as gr
 import numpy as np
 import cv2
-from PIL import Image
 import mediapipe as mp
 from mediapipe.tasks import python
 from mediapipe.tasks.vision import FaceDetector, FaceDetectorOptions, RunningMode
@@ -15,7 +14,7 @@ from deepface import DeepFace
 # =========================
 # 參數與 MediaPipe 初始化
 # =========================
-ANALYZE_EVERY_SEC = 0.6  # MediaPipe 很快，可以稍微縮短間隔
+ANALYZE_EVERY_SEC = 0.6  
 DOWNSAMPLE_W = 720
 EMA_ALPHA = 0.45
 CONF_TH = 40.0
@@ -23,7 +22,7 @@ SWITCH_CONFIRM_SEC = 2.0
 IOU_TH = 0.25
 ZOOM_FACTOR = 1.3
 
-# MediaPipe 設定
+# MediaPipe 設定 (請確保 model 資料夾內有此檔案)
 MODEL_PATH = 'model/blaze_face_short_range.tflite'
 base_options = python.BaseOptions(model_asset_path=MODEL_PATH)
 options = FaceDetectorOptions(
@@ -109,36 +108,49 @@ def _zoom_center(frame: np.ndarray, zoom: float) -> np.ndarray:
     return frame[sy:sy+nh, sx:sx+nw]
 
 # =========================
-# MediaPipe 偵測邏輯
+# MediaPipe 偵測邏輯 (整合繪圖)
 # =========================
 def get_face_mediapipe(frame: np.ndarray, prev_bbox: Optional[Tuple[int, int, int, int]]):
     h, w = frame.shape[:2]
+    draw_frame = frame.copy() # 建立一個複本用來畫圖
     mp_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=frame)
     res = mp_detector.detect(mp_image)
     
     if not res.detections:
-        return None, None
+        return None, None, draw_frame
 
     candidates = []
     for det in res.detections:
         b = det.bounding_box
-        # 數值校正，避免超出邊界導致 DeepFace 報錯
         nx, ny = max(0, b.origin_x), max(0, b.origin_y)
         nw, nh = min(b.width, w - nx), min(b.height, h - ny)
+        
+        # 繪製白色正方形外框 (Thickness=5)
+        cv2.rectangle(draw_frame, (nx, ny), (nx + nw, ny + nh), (255, 255, 255), 5)
+        
+        # 繪製紅色五官小圓點
+        if det.keypoints:
+            for kp in det.keypoints:
+                kx, ky = int(kp.x * w), int(kp.y * h)
+                cv2.circle(draw_frame, (kx, ky), 6, (0, 0, 255), -1)
+
         if nw > 10 and nh > 10:
             candidates.append(((nx, ny, nw, nh), frame[ny:ny+nh, nx:nx+nw]))
 
     if not candidates:
-        return None, None
+        return None, None, draw_frame
 
-    # 優先匹配上一幀的位置
+    # 優先匹配上一幀位置
+    best_match = None
     if prev_bbox:
         best_match = max(candidates, key=lambda c: _iou(prev_bbox, c[0]), default=(None, None))
-        if best_match[0] and _iou(prev_bbox, best_match[0]) > IOU_TH:
-            return best_match
+        if best_match[0] and _iou(prev_bbox, best_match[0]) < IOU_TH:
+            best_match = None
 
-    # 否則回傳面積最大者
-    return max(candidates, key=lambda c: c[0][2] * c[0][3])
+    if best_match is None:
+        best_match = max(candidates, key=lambda c: c[0][2] * c[0][3])
+
+    return best_match[0], best_match[1], draw_frame
 
 # =========================
 # Gradio 事件處理
@@ -160,20 +172,20 @@ def on_stream(frame_rgb: np.ndarray, st: AppState):
     
     st.last_analyze_t = now
 
-    # 使用 MediaPipe 找臉
-    bbox, face_crop = get_face_mediapipe(frame_rgb, st.track_bbox)
+    # 使用 MediaPipe 找臉並繪圖
+    bbox, face_crop, frame_with_drawings = get_face_mediapipe(frame_rgb, st.track_bbox)
     
     if face_crop is None:
-        return gr.update(), gr.update(), gr.update(), _hint_html("未偵測到人臉"), st, gr.update()
+        return gr.update(value=frame_with_drawings), gr.update(), gr.update(), _hint_html("未偵測到人臉"), st, gr.update()
 
     st.track_bbox = bbox
 
-    # DeepFace 情緒分析 (傳入已裁切的人臉)
+    # DeepFace 情緒分析
     try:
         res = DeepFace.analyze(face_crop, actions=['emotion'], enforce_detection=False, silent=True)
         emo_dict = res[0]['emotion'] if isinstance(res, list) else res['emotion']
     except:
-        return gr.update(), gr.update(), gr.update(), gr.update(), st, gr.update()
+        return gr.update(value=frame_with_drawings), gr.update(), gr.update(), gr.update(), st, gr.update()
 
     top_key = max(emo_dict, key=lambda k: float(emo_dict[k]))
     top_conf = float(emo_dict[top_key])
@@ -198,7 +210,7 @@ def on_stream(frame_rgb: np.ndarray, st: AppState):
             st.finished, st.running = True, False
 
     return (
-        gr.update(visible=not st.finished),
+        gr.update(value=frame_with_drawings, visible=not st.finished),
         gr.update(value=_result_card_html(st.stable_emo, top_conf)),
         gr.update(value=_score_radio_value(EMO_TO_SCORE.get(st.stable_emo, 3))),
         gr.update(value=_hint_html("正在辨識中，請保持表情穩定...")),