Update emotion.py

emotion.py

@@ -15,10 +15,7 @@ DOWNSAMPLE_W = 720
 EMA_ALPHA = 0.45
 CONF_TH = 40.0
 SWITCH_CONFIRM_SEC = 2.5
-IOU_TH = 0.25
 ZOOM_FACTOR = 1.3
-DETECTOR_BACKEND = "mtcnn"
-ALIGN_FACE = True
 
 # =========================
 # 狀態與映射
@@ -30,13 +27,11 @@ SCORE_CHOICES = [SCORE_LABELS[i] for i in range(1,6)]
 
 @dataclass
 class AppState:
-    mode: str = "攝影機辨識" 
     running: bool = True
     finished: bool = False
     last_analyze_t: float = 0.0
-    last_check_t: float = 0.0  # 新增：控制檢查位置的頻率
-    is_aligned: bool = False   # 新增：快取對準狀態
-    track_bbox: Optional[Tuple[int, int, int, int]] = None
+    last_check_t: float = 0.0
+    is_aligned: bool = False
     ema_scores: Optional[np.ndarray] = None
     emo_keys: List[str] = field(default_factory=list)
     stable_emo: str = "unknown"
@@ -44,29 +39,25 @@ class AppState:
     candidate_since: Optional[float] = None
     final_emo: Optional[str] = None
     final_conf: Optional[float] = None
-    final_score: int = 3
 
 # =========================
-# 介面 HTML
+# UI 介面
 # =========================
 def _hint_html(msg: str) -> str:
-    return f'<div style="border-radius:14px;padding:14px;border:1px dashed #DADADA;background:#FAFAFA;color:#000000;">{msg}</div>'
+    return f'<div style="border-radius:14px;padding:14px;border:1px dashed #DADADA;background:#FAFAFA;color:#000000;text-align:center;">{msg}</div>'
 
 def _result_card_html(emo_key: str, conf: Optional[float]) -> str:
     zh = EMO_ZH.get(emo_key, emo_key)
     score = EMO_TO_SCORE.get(emo_key, 3)
-    conf_txt = "" if conf is None else f"{conf:.1f}%"
-    return f'<div style="border-radius:16px;padding:20px;border:1px solid #E6E6E6;background:#FFFFFF;color:#000000;"><div style="font-size:14px;margin-bottom:8px;">辨識結果</div><div style="font-size:42px;font-weight:800;line-height:1.1;margin-bottom:14px;">{zh}</div><div style="font-size:16px;margin-bottom:6px;">心情分數：{score}</div><div style="font-size:14px;">信心值：{conf_txt}</div></div>'
-
-# =========================
-# 影像處理
-# =========================
-def _zoom_center(frame, zoom):
-    if frame is None or zoom <= 1.0: return frame
-    h, w = frame.shape[:2]
-    nw, nh = int(w / zoom), int(h / zoom)
-    sx, sy = (w - nw) // 2, (h - nh) // 2
-    return frame[sy:sy+nh, sx:sx+nw]
+    conf_txt = f"{conf:.1f}%" if conf else "--%"
+    return f"""
+    <div style="border-radius:16px;padding:30px;border:2px solid #4CAF50;background:#FFFFFF;color:#000000;text-align:center;box-shadow: 0 4px 10px rgba(0,0,0,0.1);">
+      <div style="font-size:18px;margin-bottom:10px;color:#666;">✨ 辨識完成 ✨</div>
+      <div style="font-size:56px;font-weight:800;color:#2E7D32;margin-bottom:15px;">{zh}</div>
+      <div style="font-size:20px;margin-bottom:10px;">心情分數：<strong>{score}</strong></div>
+      <div style="font-size:16px;color:#888;">系統信心值：{conf_txt}</div>
+    </div>
+    """
 
 def _draw_ui_overlay(frame: np.ndarray, is_aligned: bool):
     canvas = frame.copy()
@@ -74,109 +65,111 @@ def _draw_ui_overlay(frame: np.ndarray, is_aligned: bool):
     bw, bh = int(w * 0.45), int(h * 0.55)
     x1, y1 = (w - bw) // 2, (h - bh) // 2
     x2, y2 = x1 + bw, y1 + bh
-    
     color = (0, 255, 0) if is_aligned else (255, 0, 0)
     text = "SCANNING..." if is_aligned else "PLACE FACE HERE"
-
-    # 效能優化：不再使用 addWeighted 製作全畫面遮罩，改用簡單線條
     cv2.rectangle(canvas, (x1, y1), (x2, y2), color, 2)
     l = 40
-    cv2.line(canvas, (x1, y1), (x1+l, y1), color, 5)
-    cv2.line(canvas, (x1, y1), (x1, y1+l), color, 5)
-    cv2.line(canvas, (x2, y2), (x2-l, y2), color, 5)
-    cv2.line(canvas, (x2, y2), (x2, y2-l), color, 5)
+    cv2.line(canvas, (x1, y1), (x1+l, y1), color, 5); cv2.line(canvas, (x1, y1), (x1, y1+l), color, 5)
+    cv2.line(canvas, (x2, y2), (x2-l, y2), color, 5); cv2.line(canvas, (x2, y2), (x2, y2-l), color, 5)
     cv2.putText(canvas, text, (x1, y1 - 15), cv2.FONT_HERSHEY_SIMPLEX, 0.8, color, 2)
     return canvas
 
 # =========================
-# 串流核心
+# 核心串流邏輯
 # =========================
 def on_stream(frame_rgb: np.ndarray, st: AppState):
-    if frame_rgb is None or st.finished or (not st.running):
-        return (None, gr.update(), gr.update(), gr.update(), st, gr.update())
+    if frame_rgb is None or st.finished or not st.running:
+        # 當 finished 為 True 時，這裡會被觸發，返回關閉攝影機的指令
+        return (gr.update(visible=False), gr.update(), gr.update(), gr.update(), st)
 
     now = time.time()
-    frame_rgb = _zoom_center(frame_rgb, ZOOM_FACTOR)
+    # 數位變焦
+    h, w = frame_rgb.shape[:2]
+    nw, nh = int(w / ZOOM_FACTOR), int(h / ZOOM_FACTOR)
+    sx, sy = (w - nw) // 2, (h - nh) // 2
+    frame_rgb = frame_rgb[sy:sy+nh, sx:sx+nw]
 
-    # --- 效能優化點：降低檢查頻率 (每 0.3 秒才檢查一次人臉位置) ---
+    # 每 0.3 秒檢查一次臉部位置
     if now - st.last_check_t > 0.3:
         st.last_check_t = now
-        # 極���化影像以利快速檢查
-        small_check = np.array(Image.fromarray(frame_rgb).resize((250, 250)))
         try:
-            faces = DeepFace.extract_faces(img_path=small_check, detector_backend="opencv", enforce_detection=False)
+            small = np.array(Image.fromarray(frame_rgb).resize((200, 200)))
+            faces = DeepFace.extract_faces(img_path=small, detector_backend="opencv", enforce_detection=False)
             if faces and faces[0]['face'] is not None:
                 fa = faces[0]['facial_area']
-                # 將座標還原到原圖比例
-                fx, fy = fa['x'] * (frame_rgb.shape[1]/250), fa['y'] * (frame_rgb.shape[0]/250)
-                fw, fh = fa['w'] * (frame_rgb.shape[1]/250), fa['h'] * (frame_rgb.shape[0]/250)
-                
-                # 檢查中心點是否在導引框內 (中央 45%x55%)
-                h, w = frame_rgb.shape[:2]
-                gx1, gy1 = (w - int(w*0.45))//2, (h - int(h*0.55))//2
-                gx2, gy2 = gx1 + int(w*0.45), gy1 + int(h*0.55)
+                fx, fy = fa['x'] * (frame_rgb.shape[1]/200), fa['y'] * (frame_rgb.shape[0]/200)
+                fw, fh = fa['w'] * (frame_rgb.shape[1]/200), fa['h'] * (frame_rgb.shape[0]/200)
+                gx1, gy1 = (frame_rgb.shape[1] - int(frame_rgb.shape[1]*0.45))//2, (frame_rgb.shape[0] - int(frame_rgb.shape[0]*0.55))//2
+                gx2, gy2 = gx1 + int(frame_rgb.shape[1]*0.45), gy1 + int(frame_rgb.shape[0]*0.55)
                 st.is_aligned = (gx1 < fx + fw/2 < gx2) and (gy1 < fy + fh/2 < gy2)
             else:
                 st.is_aligned = False
         except:
             st.is_aligned = False
 
-    # 繪製畫面 (這個步驟很快)
     frame_with_ui = _draw_ui_overlay(frame_rgb, st.is_aligned)
 
-    # --- 深度分析邏輯 (只有對準時執行) ---
+    # 分析邏輯
     if st.is_aligned and (now - st.last_analyze_t >= ANALYZE_EVERY_SEC):
         st.last_analyze_t = now
-        # 進行高品質偵測與分析
-        res = DeepFace.analyze(img_path=frame_rgb, actions=["emotion"], enforce_detection=False, detector_backend="opencv")
-        emo_dict = res[0].get("emotion") if isinstance(res, list) else res.get("emotion")
-        
-        if emo_dict:
-            top_key = max(emo_dict, key=lambda k: float(emo_dict[k]))
-            keys = list(emo_dict.keys())
-            vec = np.array([float(emo_dict[k]) for k in keys])
-            if st.ema_scores is None: st.ema_scores, st.emo_keys = vec, keys
-            else: st.ema_scores = EMA_ALPHA * vec + (1.0 - EMA_ALPHA) * st.ema_scores
-            
-            stable_key = st.emo_keys[np.argmax(st.ema_scores)]
-            st.stable_emo = stable_key
-            
-            if st.candidate_emo != stable_key:
-                st.candidate_emo, st.candidate_since = stable_key, now
-            elif st.candidate_since and (now - st.candidate_since >= SWITCH_CONFIRM_SEC):
-                st.final_emo, st.final_conf, st.running, st.finished = stable_key, float(emo_dict[top_key]), False, True
-                return (frame_with_ui, _result_card_html(stable_key, st.final_conf), SCORE_LABELS[EMO_TO_SCORE.get(stable_key, 3)], "辨識完成", st, gr.update(value="重新攝影機辨識"))
-
-    return (frame_with_ui, gr.update(value=_result_card_html(st.stable_emo, st.final_conf)), gr.update(), gr.update(), st, gr.update())
+        try:
+            res = DeepFace.analyze(img_path=frame_rgb, actions=["emotion"], enforce_detection=False, detector_backend="opencv")
+            emo_dict = res[0].get("emotion") if isinstance(res, list) else res.get("emotion")
+            if emo_dict:
+                keys = list(emo_dict.keys())
+                vec = np.array([float(emo_dict[k]) for k in keys])
+                if st.ema_scores is None: st.ema_scores, st.emo_keys = vec, keys
+                else: st.ema_scores = EMA_ALPHA * vec + (1.0 - EMA_ALPHA) * st.ema_scores
+                
+                stable_key = st.emo_keys[np.argmax(st.ema_scores)]
+                st.stable_emo = stable_key
+                
+                if st.candidate_emo != stable_key:
+                    st.candidate_emo, st.candidate_since = stable_key, now
+                elif st.candidate_since and (now - st.candidate_since >= SWITCH_CONFIRM_SEC):
+                    # 【關鍵點】辨識成功，設定 finished 並關閉攝像頭
+                    st.final_emo, st.final_conf, st.running, st.finished = stable_key, float(emo_dict[stable_key]), False, True
+                    return (
+                        gr.update(visible=False), # 隱藏攝像頭
+                        _result_card_html(stable_key, st.final_conf), # 跳出大卡片
+                        SCORE_LABELS[EMO_TO_SCORE.get(stable_key, 3)], # 更新分數按鈕
+                        _hint_html("辨識完成！"), # 更新提示文字
+                        st
+                    )
+        except:
+            pass
+
+    return (frame_with_ui, gr.update(value=_result_card_html(st.stable_emo, None)), gr.update(), gr.update(), st)
 
 # =========================
-# 其他 Button 函式 (簡化版以維持穩定)
+# 按鈕動作
 # =========================
 def on_restart(st: AppState):
-    st.running, st.finished, st.ema_scores, st.stable_emo, st.final_emo = True, False, None, "unknown", None
-    return (gr.update(visible=True), _hint_html("重新開始..."), SCORE_LABELS[3], "請對準方框", st, "重新攝影機辨識")
-
-def on_stop(st: AppState):
-    st.running, st.finished = False, True
-    return (gr.update(visible=False), _hint_html("已停止"), SCORE_LABELS[3], "停止", st, "重新攝影機辨識")
+    st.running, st.finished, st.ema_scores, st.stable_emo = True, False, None, "unknown"
+    return (gr.update(visible=True), _hint_html("請將臉部對準方框"), SCORE_LABELS[3], st)
 
 # =========================
-# UI
+# UI 配置
 # =========================
 with gr.Blocks(title="Emotion Detector") as demo:
     state = gr.State(AppState())
-    gr.Markdown("## 情緒辨識系統 (效能優化版)")
-    hint = gr.HTML("請將臉部對準螢幕中央方框")
-    cam = gr.Image(sources=["webcam"], streaming=True, type="numpy", label="攝影機預覽")
+    gr.Markdown("## 情緒辨識系統")
+    
+    hint = gr.HTML(_hint_html("請點擊下方錄製按鈕開始辨識"))
+    
+    with gr.Column(elem_id="main-container"):
+        cam = gr.Image(sources=["webcam"], streaming=True, type="numpy", label="攝影機預覽")
+        result_area = gr.HTML(_hint_html("辨識結果將在此顯示"))
+    
     with gr.Row():
-        btn_restart = gr.Button("重新攝影機辨識", variant="primary")
+        btn_restart = gr.Button("重新辨識 / 開啟攝影機", variant="primary")
         btn_stop = gr.Button("停止", variant="secondary")
-    result = gr.HTML(_hint_html("等待串流..."))
+        
     mood = gr.Radio(choices=SCORE_CHOICES, value=SCORE_LABELS[3], label="判定心情", interactive=False)
 
-    cam.stream(on_stream, [cam, state], [cam, result, mood, hint, state, btn_restart], show_progress="minimal")
-    btn_restart.click(on_restart, [state], [cam, result, mood, hint, state, btn_restart])
-    btn_stop.click(on_stop, [state], [cam, result, mood, hint, state, btn_restart])
+    cam.stream(on_stream, [cam, state], [cam, result_area, mood, hint, state], show_progress="minimal")
+    btn_restart.click(on_restart, [state], [cam, hint, mood, state])
+    btn_stop.click(lambda st: (gr.update(visible=False), _hint_html("已停止"), st), [state], [cam, hint, state])
 
 if __name__ == "__main__":
     demo.launch(server_name="0.0.0.0", server_port=7860)