Editing Openai/692fcfd8-4b44-800f-a184-fa098175a6d5 (section)

==== 請將整段貼到一個檔案，例如 taiwan_hand_number_1_10.py： ====

<syntaxhighlight lang="python">from cvzone.HandTrackingModule import HandDetector
import cv2
import os
import numpy as np

=== ========= 小工具：2D 距離（取代 math.dist，支援 Python 3.7） ========= ===
def euclidean(a, b):
    """
    計算 2D 歐幾里得距離
    a, b: [x, y, (z)]，這裡只用前兩個值
    """
    return ((a[0] - b[0])'''2 + (a[1] - b[1])'''2)**0.5

=== ========= 初始設定 ========= ===
cap = cv2.VideoCapture(0)  # 如有需要可改成 cv2.VideoCapture(0, cv2.CAP_DSHOW)
detector = HandDetector(detectionCon=0.7, maxHands=2)

=== 中文數字對照（只是讓畫面顯示好看一點） ===
zh_map = {
    1: "一",
    2: "二",
    3: "三",
    4: "四",
    5: "五",
    6: "六",
    7: "七",
    8: "八",
    9: "九",
    10: "十",
}

=== =========（可選）讀取手勢示意圖：gestures/1.png ~ 10.png ========= ===
gesture_images = {}
for n in range(1, 11):
    path = os.path.join("gestures", f"{n}.png")
    if os.path.exists(path):
        img = cv2.imread(path)
        if img is not None:
            gesture_images[n] = img
    else:
        # 沒有圖也沒關係，只是少了右側示意圖
        pass

=== ========= 單手台灣數字手勢 1～9 判斷 ========= ===
def classify_single_hand_number(hand):
    """
    根據單手手勢判斷 1~9（台灣數字手勢）
    使用 fingersUp + 一點點關節角度判斷（區分 1 / 9）
    回傳 (number, label, fingersPattern)
    """
    fingers = detector.fingersUp(hand)  # [thumb, index, middle, ring, pinky]
    pattern = tuple(fingers)

    # 固定 pattern 的 2~8（不含 1，因為 1 和 9 pattern 一樣，要特判）
    mapping = {
        (0, 1, 1, 0, 0): 2,      # 食 + 中
        (1, 1, 1, 0, 0): 3,      # 拇 + 食 + 中
        (0, 1, 1, 1, 1): 4,      # 四指伸出，拇指收
        (1, 1, 1, 1, 1): 5,      # 五指全伸
        (1, 0, 0, 0, 1): 6,      # 拇 + 小（台灣 6）
        (1, 1, 0, 0, 1): 7,      # 拇 + 食 + 小（台灣 7 的一種）
        (1, 1, 0, 0, 0): 8,      # 拇 + 食（手槍，台灣 8）
    }

    # 先吃 mapping 裡的（2~8）
    if pattern in mapping:
        n = mapping[pattern]
        return n, str(n), fingers

    # ---- 只伸食指 → 可能是 1 或 9 ----
    if pattern == (0, 1, 0, 0, 0):
        lm = hand["lmList"]
        # 食指：MCP = 5, PIP = 6, DIP = 7, TIP = 8
        mcp = lm[5]
        pip = lm[6]
        dip = lm[7]
        tip = lm[8]

        # 直線距離 MCP→TIP
        dist_mcp_tip = euclidean(mcp, tip)
        # 折線距離 MCP→PIP→DIP→TIP
        dist_mcp_pip = euclidean(mcp, pip)
        dist_pip_dip = euclidean(pip, dip)
        dist_dip_tip = euclidean(dip, tip)
        poly_len = dist_mcp_pip + dist_pip_dip + dist_dip_tip

        # 比例 > 1.25 → 手指彎得比較明顯 → 當作 9
        if dist_mcp_tip > 0 and poly_len / dist_mcp_tip > 1.25:
            return 9, "9", fingers
        else:
            return 1, "1", fingers

    # 其他 pattern 不判斷
    return None, "Unknown", fingers

=== ========= 判斷整體數字（考慮 10） ========= ===
def classify_number(hands):
    """
    hands: HandDetector 回傳的 hands 陣列
    回傳 (number, label, bbox_for_draw)
    """
    if not hands:
        return None, "No Hand", None

    # 若有兩手 → 有機會判斷「10」
    if len(hands) >= 2:
        results = []
        for h in hands[:2]:
            n, _, _ = classify_single_hand_number(h)
            results.append(n)

        # 這裡定義：兩隻手都比「5」 → 視為 10
        if results[0] == 5 and results[1] == 5:
            bbox = hands[0]["bbox"]
            return 10, "10", bbox

    # 只有一手，或兩手但不符合 10 → 以第一隻手為主
    main_hand = hands[0]
    bbox = main_hand["bbox"]
    number, label, _ = classify_single_hand_number(main_hand)
    return number, label, bbox

=== ========= 主迴圈 ========= ===
while True:
    success, img = cap.read()
    if not success:
        break

    hands, img = detector.findHands(img)

    number, label, bbox = classify_number(hands)

    h, w, _ = img.shape

    # 右側示意面板（可省略，不想要的話可以整塊刪掉）
    panel_width = 300
    panel = 255 * np.ones((h, panel_width, 3), dtype="uint8")

    if number in zh_map:
        # 若有圖就貼圖
        if number in gesture_images:
            g = gesture_images[number]
            gh, gw, _ = g.shape
            scale = min((h '' 0.6) / gh, (panel_width '' 0.9) / gw)
            nw = int(gw * scale)
            nh = int(gh * scale)
            g2 = cv2.resize(g, (nw, nh))

            xo = (panel_width - nw) // 2
            yo = 20
            panel[yo:yo + nh, xo:xo + nw] = g2

        # 底下寫上「阿拉伯 + 中文」
        ty = int(h * 0.8)
        cv2.putText(panel, f"{number}",
                    (20, ty),
                    cv2.FONT_HERSHEY_SIMPLEX, 1.2, (0, 0, 0), 2)
        cv2.putText(panel, f"中文：{zh_map[number]}",
                    (20, ty + 35),
                    cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 0), 2)
    else:
        cv2.putText(panel, "請比出 1~10 的手勢",
                    (20, h // 2),
                    cv2.FONT_HERSHEY_SIMPLEX, 0.8, (100, 100, 100), 2)

    # 在原影像上畫框＆數字
    if bbox and number:
        x, y, bw, bh = bbox
        cv2.rectangle(img, (x, y), (x + bw, y + bh), (0, 255, 0), 2)
        if number in zh_map:
            text = f"{number} ({zh_map[number]})"
        else:
            text = label
        cv2.putText(img, text, (x, y - 15),
                    cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)

    # 合併畫面：左邊攝影機，右邊示意
    panel = cv2.resize(panel, (panel_width, h))
    combined = cv2.hconcat([img, panel])

    cv2.imshow("Taiwan Hand Number (1-10)", combined)

    if cv2.waitKey(1) & 0xFF == ord("q"):
        break

cap.release()
cv2.destroyAllWindows()

</syntaxhighlight>