backup old version before update

app_old.py

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+# 這是 2025-11-05 備份版本 app_v1.py
+
+
+import os, math, time, tempfile
+import cv2
+import numpy as np
+import mediapipe as mp
+import gradio as gr
+import pandas as pd
+
+# -------------------------
+# 參數（可在 UI 即時調整）
+# -------------------------
+DEFAULT_PASS_THRESHOLD = 0.50     # 總體通過門檻
+DEFAULT_DESK_Y_RATIO   = 0.75     # 桌面線（影像高度比例，愈大愈寬鬆）
+PROFILE_NAME           = "v1"
+
+# MediaPipe 基本物件
+mp_drawing = mp.solutions.drawing_utils
+mp_pose    = mp.solutions.pose
+
+# -------------------------
+# 幫助函式
+# -------------------------
+def angle_to_vertical(p1, p2):
+    """ 計算 p1->p2 相對『垂直向上』的夾角（度） """
+    vx, vy = p2[0] - p1[0], p2[1] - p1[1]
+    # 垂直向上單位向量 (0,-1)
+    dot = (vx * 0) + (vy * -1)
+    mag = (math.hypot(vx, vy) * 1.0) + 1e-6
+    cosang = max(-1.0, min(1.0, dot / mag))
+    ang = math.degrees(math.acos(cosang))
+    return ang
+
+def safe_get(lms, idx):
+    try:
+        lm = lms[idx]
+        if lm.visibility is not None and lm.visibility < 0.5:
+            return None
+        return lm
+    except Exception:
+        return None
+
+# -------------------------
+# 單支影片分析（核心）
+# -------------------------
+def analyze_one_file(
+    video_file_path: str,
+    pass_threshold: float,
+    desk_y_ratio: float,
+    ref_mode: str,              # "desk" 或 "waist"
+):
+    """
+    回傳：
+      - output_path: 標註後影片路徑
+      - single_md  : 單檔 Markdown 報告
+      - stats      : 彙整用數據（dict）
+    """
+    filename = os.path.basename(video_file_path)
+    cap = cv2.VideoCapture(video_file_path)
+    if not cap.isOpened():
+        return None, f"❌ 無法開啟影片：{filename}", None
+
+    fps          = cap.get(cv2.CAP_PROP_FPS) or 30
+    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT) or 0)
+    width        = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH) or 0)
+    height       = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT) or 0)
+    duration_s   = total_frames / fps if fps > 0 else 0.0
+
+    # 輸出影片
+    fourcc = cv2.VideoWriter_fourcc(*"mp4v")
+    output_path = os.path.join(tempfile.gettempdir(), f"annot_{int(time.time())}_{filename}")
+    out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
+
+    # 統計
+    effective = 0
+    correct_frames = 0
+    surface_ok_frames = 0     # 桌面以上/腰部以上 幀數
+    finger_ok_frames  = 0     # 指尖朝上 幀數
+
+    # 桌面線 (desk mode)
+    desk_y = int(height * desk_y_ratio)
+
+    # MediaPipe Pose
+    with mp_pose.Pose(
+        static_image_mode=False,
+        model_complexity=1,
+        enable_segmentation=False,
+        min_detection_confidence=0.5,
+        min_tracking_confidence=0.5,
+    ) as pose:
+
+        fi = 0
+        while True:
+            ret, frame = cap.read()
+            if not ret:
+                break
+            fi += 1
+
+            rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+            res = pose.process(rgb)
+            annotated = frame.copy()
+
+            # 畫桌面線或後續用的腰部線（等算出）
+            if ref_mode == "desk":
+                cv2.line(annotated, (0, desk_y), (width, desk_y), (0, 200, 255), 2)
+
+            above_surface = None
+            finger_up     = None
+
+            if res.pose_landmarks:
+                lms = res.pose_landmarks.landmark
+
+                # 取得關鍵點
+                lwrist = safe_get(lms, mp_pose.PoseLandmark.LEFT_WRIST.value)
+                rwrist = safe_get(lms, mp_pose.PoseLandmark.RIGHT_WRIST.value)
+                lelbow = safe_get(lms, mp_pose.PoseLandmark.LEFT_ELBOW.value)
+                relbow = safe_get(lms, mp_pose.PoseLandmark.RIGHT_ELBOW.value)
+                lindex = safe_get(lms, mp_pose.PoseLandmark.LEFT_INDEX.value)
+                rindex = safe_get(lms, mp_pose.PoseLandmark.RIGHT_INDEX.value)
+
+                need_wrist = (lwrist is not None and rwrist is not None)
+
+                # ---- 參考線：桌面 or 腰部 ----
+                if ref_mode == "desk":
+                    if need_wrist:
+                        ly, ry = int(lwrist.y * height), int(rwrist.y * height)
+                        above_surface = (ly < desk_y and ry < desk_y)
+                else:  # "waist"
+                    lhip = safe_get(lms, mp_pose.PoseLandmark.LEFT_HIP.value)
+                    rhip = safe_get(lms, mp_pose.PoseLandmark.RIGHT_HIP.value)
+                    if need_wrist and lhip is not None and rhip is not None:
+                        hip_y_pix = int(((lhip.y + rhip.y) / 2.0) * height)
+                        # 畫腰部線
+                        cv2.line(annotated, (0, hip_y_pix), (width, hip_y_pix), (255, 180, 0), 2)
+                        ly, ry = int(lwrist.y * height), int(rwrist.y * height)
+                        # 略放寬：手腕必須在腰部線之上 3% 高度
+                        margin = int(0.03 * height)
+                        above_surface = (ly < hip_y_pix - margin and ry < hip_y_pix - margin)
+
+                # ---- 指尖朝上 / 手臂近似直立 ----
+                up_count = 0
+                up_need  = 0
+
+                if lwrist is not None and lindex is not None:
+                    up_need += 1
+                    w = (lwrist.x * width, lwrist.y * height)
+                    i = (lindex.x * width, lindex.y * height)
+                    ang = angle_to_vertical(w, i)  # 越小越接近垂直向上
+                    # index 明顯在手腕上方 + 角度 < 35°
+                    if i[1] < w[1] - 0.02 * height and ang < 35:
+                        up_count += 1
+
+                if rwrist is not None and rindex is not None:
+                    up_need += 1
+                    w = (rwrist.x * width, rwrist.y * height)
+                    i = (rindex.x * width, rindex.y * height)
+                    ang = angle_to_vertical(w, i)
+                    if i[1] < w[1] - 0.02 * height and ang < 35:
+                        up_count += 1
+
+                # 兩手都有點到才算有效（你也可改成 只要一手有效）
+                if up_need == 2 and above_surface is not None:
+                    effective += 1
+                    finger_up = (up_count == 2)
+                    if finger_up:
+                        finger_ok_frames += 1
+                    if above_surface:
+                        surface_ok_frames += 1
+                    if above_surface and finger_up:
+                        correct_frames += 1
+
+                # 畫點與骨架
+                mp_drawing.draw_landmarks(
+                    annotated, res.pose_landmarks, mp_pose.POSE_CONNECTIONS,
+                    landmark_drawing_spec=mp_drawing.DrawingSpec(color=(0, 0, 255), thickness=2, circle_radius=2),
+                    connection_drawing_spec=mp_drawing.DrawingSpec(color=(255, 255, 255), thickness=2, circle_radius=2),
+                )
+
+            # 視覺化狀態條
+            status_text = []
+            if above_surface is True:
+                status_text.append("表面✓")
+            elif above_surface is False:
+                status_text.append("表面✗")
+            if finger_up is True:
+                status_text.append("指尖↑✓")
+            elif finger_up is False:
+                status_text.append("指尖↑✗")
+
+            if status_text:
+                cv2.rectangle(annotated, (10, 10), (190, 46), (0, 0, 0), -1)
+                cv2.putText(annotated, " ".join(status_text), (16, 36),
+                            cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 255), 2, cv2.LINE_AA)
+
+            out.write(annotated)
+
+    cap.release()
+    out.release()
+
+    # ---- 統計輸出 ----
+    surface_rate = (surface_ok_frames / effective) if effective > 0 else 0.0
+    finger_rate  = (finger_ok_frames / effective)  if effective > 0 else 0.0
+    overall_rate = (correct_frames / effective)    if effective > 0 else 0.0
+    passed = (overall_rate >= pass_threshold)
+
+    ref_label = "桌面以上" if ref_mode == "desk" else "腰部以上"
+    single_md = (
+        f"📁 檔案名稱：{filename}\n"
+        f"{'✅ 通過（姿勢正確）' if passed else '❌ 未通過（姿勢不正確）'}\n"
+        f"逐幀正確率：{overall_rate*100:.2f}%\n"
+        f"（有效幀數：{effective}/{total_frames}）\n"
+        f"{ref_label} 幀率：{surface_rate*100:.2f}%、指尖朝上幀率：{finger_rate*100:.2f}%\n"
+        f"🔧 FPS：{int(fps)}，🧮 總幀數：{total_frames}，⏱️ 長度：約 {duration_s:.2f} 秒\n"
+        f"⚙️ 設定檔：{PROFILE_NAME}（門檻 {pass_threshold*100:.0f}%、桌面線 {desk_y_ratio:.2f}、參考線 {ref_label}）\n"
+    )
+
+    stats = {
+        "filename": filename,
+        "passed": passed,
+        "overall_rate": overall_rate,
+        "surface_rate": surface_rate,
+        "finger_rate": finger_rate,
+        "effective": effective,
+        "total": total_frames,
+        "fps": int(fps),
+        "duration_s": duration_s,
+        "profile": PROFILE_NAME,
+        "pass_threshold": pass_threshold,
+        "desk_y_ratio": desk_y_ratio,
+        "ref_mode": ref_mode,
+    }
+    return output_path, single_md, stats
+
+# -------------------------
+# 單一檔包裝
+# -------------------------
+def run_single(file, pass_threshold, desk_y_ratio, ref_choice):
+    if not file:
+        return None, "請先上傳一支 MP4。"
+    ref_mode = "desk" if ref_choice == "桌面以上" else "waist"
+    fp = file if isinstance(file, str) else file.name
+    return analyze_one_file(fp, pass_threshold, desk_y_ratio, ref_mode)[:2]
+
+# -------------------------
+# 多檔包裝（含彙整表 & CSV）
+# -------------------------
+def run_batch(files, pass_threshold, desk_y_ratio, ref_choice):
+    if not files:
+        return [], "尚未上傳影片。", None
+
+    ref_mode = "desk" if ref_choice == "桌面以上" else "waist"
+    rows = []
+    gallery_items = []
+
+    for f in files:
+        fp = f if isinstance(f, str) else f.name
+        out_path, single_md, st = analyze_one_file(fp, pass_threshold, desk_y_ratio, ref_mode)
+
+        caption = f"{st['filename']}｜{'通過' if st['passed'] else '未通過'}｜{st['overall_rate']*100:.1f}%"
+        gallery_items.append((out_path, caption))
+
+        rows.append({
+            "檔案": st["filename"],
+            "結果": "✅" if st["passed"] else "❌",
+            "整體正確率(%)": round(st["overall_rate"]*100, 2),
+            f"{ref_choice}幀率(%)": round(st["surface_rate"]*100, 2),
+            "指尖朝上幀率(%)": round(st["finger_rate"]*100, 2),
+            "有效幀/總幀": f"{st['effective']}/{st['total']}",
+            "FPS": st["fps"],
+            "長度(秒)": round(st["duration_s"], 2),
+            "設定檔": st["profile"],
+            "門檻(%)": round(st["pass_threshold"]*100, 0),
+            "桌面線y": round(st["desk_y_ratio"], 2),
+            "參考線": ref_choice,
+        })
+
+    df = pd.DataFrame(rows)
+    md_header = "| 檔案 | 結果 | 整體正確率(%) | " + ref_choice + "幀率(%) | 指尖朝上幀率(%) | 有效幀/總幀 | FPS | 長度(秒) | 設定檔 | 門檻(%) | 桌面線y | 參考線 |\n"
+    md_header += "|---|:--:|---:|---:|---:|---:|---:|---:|:--:|---:|---:|:--:|\n"
+    md_body = "\n".join(
+        f"| {r['檔案']} | {r['結果']} | {r['整體正確率(%)']:.2f} | {r[f'{ref_choice}幀率(%)']:.2f} | "
+        f"{r['指尖朝上幀率(%)']:.2f} | {r['有效幀/總幀']} | {r['FPS']} | {r['長度(秒)']:.2f} | "
+        f"{r['設定檔']} | {int(r['門檻(%)'])} | {r['桌面線y']:.2f} | {r['參考線']} |"
+        for _, r in df.iterrows()
+    )
+    summary_md = "### 📊 動作通過率彙整表\n" + md_header + md_body
+
+    csv_path = os.path.join(tempfile.gettempdir(), f"glove_summary_{int(time.time())}.csv")
+    df.to_csv(csv_path, index=False, encoding="utf-8-sig")
+
+    return gallery_items, summary_md, csv_path
+
+# -------------------------
+# Gradio 介面
+# -------------------------
+with gr.Blocks(title="無菌手套姿勢檢測系統") as demo:
+    gr.Markdown("上傳 MP4，由 AI 檢測：① **手在桌面/腰部以上**（可切換）② **雙手直立、指尖朝上**。")
+
+    with gr.Row():
+        mode = gr.Radio(choices=["單一影片", "多部影片"], value="單一影片", label="模式")
+        ref_choice = gr.Radio(choices=["桌面以上", "腰部以上"], value="桌面以上", label="參考線")
+
+    with gr.Accordion("檢測參數（可即時調整）", open=False):
+        pass_slider = gr.Slider(0.10, 0.95, value=DEFAULT_PASS_THRESHOLD, step=0.01, label="通過門檻（整體正確率）")
+        desk_slider = gr.Slider(0.60, 0.90, value=DEFAULT_DESK_Y_RATIO, step=0.01, label="桌面線 y 比例（愈大愈寬鬆；只在「桌面以上」時生效）")
+
+    # 單一
+    single_box = gr.Group(visible=True)
+    with single_box:
+        in_file = gr.File(label="上傳 1 支 MP4", file_types=[".mp4"])
+        btn_single = gr.Button("執行單檔分析", variant="primary")
+        out_video = gr.Video(label="分析結果影片")
+        out_md = gr.Markdown(label="單檔報告")
+
+    # 多部
+    batch_box = gr.Group(visible=False)
+    with batch_box:
+        in_files = gr.Files(label="上傳多支 MP4", file_types=[".mp4"])
+        btn_batch = gr.Button("執行多檔分析（產生彙整表）", variant="primary")
+        out_gallery = gr.Gallery(label="分析結果影片（可逐支播放）", columns=2, height=480)
+        out_summary_md = gr.Markdown(label="彙整表")
+        out_csv = gr.File(label="下載彙整 CSV")
+
+    def switch_mode(m):
+        return (gr.update(visible=(m == "單一影片")),
+                gr.update(visible=(m == "多部影片")))
+    mode.change(switch_mode, inputs=mode, outputs=[single_box, batch_box])
+
+    btn_single.click(run_single,
+        inputs=[in_file, pass_slider, desk_slider, ref_choice],
+        outputs=[out_video, out_md])
+
+    btn_batch.click(run_batch,
+        inputs=[in_files, pass_slider, desk_slider, ref_choice],
+        outputs=[out_gallery, out_summary_md, out_csv])
+
+demo.launch()