app_old.py

# 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()