.idea/.gitignore

@@ -1,8 +0,0 @@
-# 默认忽略的文件
-/shelf/
-/workspace.xml
-# 基于编辑器的 HTTP 客户端请求
-/httpRequests/
-# Datasource local storage ignored files
-/dataSources/
-/dataSources.local.xml

.idea/hand.iml

@@ -1,13 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<module type="PYTHON_MODULE" version="4">
-  <component name="NewModuleRootManager">
-    <content url="file://$MODULE_DIR$">
-      <excludeFolder url="file://$MODULE_DIR$/venv" />
-    </content>
-    <orderEntry type="inheritedJdk" />
-    <orderEntry type="sourceFolder" forTests="false" />
-  </component>
-  <component name="PyDocumentationSettings">
-    <option name="renderExternalDocumentation" value="true" />
-  </component>
-</module>

.idea/inspectionProfiles/profiles_settings.xml

@@ -1,6 +0,0 @@
-<component name="InspectionProjectProfileManager">
-  <settings>
-    <option name="USE_PROJECT_PROFILE" value="false" />
-    <version value="1.0" />
-  </settings>
-</component>

.idea/misc.xml

@@ -1,4 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.11 (hand)" project-jdk-type="Python SDK" />
-</project>

.idea/modules.xml

@@ -1,8 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="ProjectModuleManager">
-    <modules>
-      <module fileurl="file://$PROJECT_DIR$/.idea/hand.iml" filepath="$PROJECT_DIR$/.idea/hand.iml" />
-    </modules>
-  </component>
-</project>

.idea/other.xml

@@ -1,7 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="PySciProjectComponent">
-    <option name="PY_SCI_VIEW" value="true" />
-    <option name="PY_SCI_VIEW_SUGGESTED" value="true" />
-  </component>
-</project>

.idea/vcs.xml

@@ -1,6 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="VcsDirectoryMappings">
-    <mapping directory="$PROJECT_DIR$" vcs="Git" />
-  </component>
-</project>

README.md

@@ -0,0 +1,2 @@
+# hand_gesturer_ecognition
+

gesture_recognition.py

@@ -1,27 +1,26 @@
 import warnings
-import threading
+import threading  #导入多线程模块
 import cv2
 import mediapipe as mp
 import numpy as np
 from tensorflow.keras.models import load_model
-from tkinter import Tk, Canvas, Button, Label, LEFT, RIGHT, NW
-from PIL import Image, ImageTk
 
+# 禁用特定警告
 warnings.filterwarnings("ignore", category=UserWarning, message='SymbolDatabase.GetPrototype() is deprecated')
 
+# 初始化 MediaPipe 和 OpenCV
 hands = None
 mp_draw = mp.solutions.drawing_utils
 cap = None
 keep_running = False
-paused = False
-popup_open = False  # 用于标记当前是否有弹窗打开
 
+# 加载手势识别模型
 model_path = 'D:/hand/hand_gesture_model.h5'
 model = load_model(model_path)
 
+# 定义手势类别
 gesture_classes = ['00', '01', '02', '03', '04', '05', '06', '07', '08', '09']
 
-
 def start_recognition(callback=None):
     global keep_running, cap, hands
     if cap is None or not cap.isOpened():
@@ -33,9 +32,9 @@ def start_recognition(callback=None):
     keep_running = True
     threading.Thread(target=run_recognition, args=(callback,)).start()
 
-
 def run_recognition(callback=None):
-    global keep_running, paused
+    global keep_running
+    last_gesture = None
 
     while keep_running and cap.isOpened():
         ret, img = cap.read()
@@ -44,30 +43,25 @@ def run_recognition(callback=None):
 
         img = cv2.flip(img, 1)
         img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        results = hands.process(img_rgb)
 
-        if not paused:
+        total_raised_fingers = 0
-            results = hands.process(img_rgb)
-            total_raised_fingers = 0
-
-            if results.multi_hand_landmarks:
-                for handLms in results.multi_hand_landmarks:
-                    mp_draw.draw_landmarks(img, handLms, mp.solutions.hands.HAND_CONNECTIONS)
-                    gesture, raised_fingers = detect_gesture_and_fingers(handLms)
-                    total_raised_fingers += raised_fingers
-
-                    if gesture == "OK":
-                        handle_ok_gesture()
 
-            if total_raised_fingers > 0:
+        if results.multi_hand_landmarks:
-                handle_finger_detection(total_raised_fingers)
+            for handLms in results.multi_hand_landmarks:
+                mp_draw.draw_landmarks(img, handLms, mp.solutions.hands.HAND_CONNECTIONS)
+                _, raised_fingers = detect_gesture_and_fingers(handLms)
+                total_raised_fingers += raised_fingers
 
-            cv2.putText(img, f'Total Raised Fingers: {total_raised_fingers}', (10, 30),
+        cv2.putText(img, f'Total Raised Fingers: {total_raised_fingers}', (10, 30),
-                        cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 0), 2, cv2.LINE_AA, )
+                    cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 0), 2, cv2.LINE_AA,)
 
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
         if callback:
             callback(img)
 
-
+    stop_recognition()
+#停止识别
 def stop_recognition():
     global keep_running, cap
     keep_running = False
@@ -77,30 +71,30 @@ def stop_recognition():
     cv2.destroyAllWindows()
 
 
+
+#释放摄像头资源
 def release_camera():
     global cap
     if cap is not None and cap.isOpened():
         cap.release()
         cap = None
 
-
 def detect_gesture_and_fingers(hand_landmarks):
+    # 手势识别
     gesture_image = get_hand_image(hand_landmarks)
     gesture = predict_gesture(gesture_image)
 
+    # 手指竖起数量检测
     raised_fingers = count_raised_fingers(hand_landmarks)
 
-    if is_ok_gesture(hand_landmarks):
-        gesture = "OK"
-
     return gesture, raised_fingers
 
-
 def get_hand_image(hand_landmarks):
-    img = np.zeros((150, 150, 3), dtype=np.uint8)
+    # 提取手部区域图像
+    # 示例实现，请根据你的需要进行调整
+    img = np.zeros((150, 150, 3), dtype=np.uint8)  # 示例图像
     return img
 
-
 def predict_gesture(img):
     img = cv2.resize(img, (150, 150))
     img_array = np.expand_dims(img, axis=0) / 255.0
@@ -108,19 +102,21 @@ def predict_gesture(img):
     predicted_class = gesture_classes[np.argmax(predictions)]
     return predicted_class
 
-
 def count_raised_fingers(hand_landmarks):
     fingers_status = [0, 0, 0, 0, 0]
 
+    # 拇指
     thumb_tip = hand_landmarks.landmark[mp.solutions.hands.HandLandmark.THUMB_TIP]
     thumb_ip = hand_landmarks.landmark[mp.solutions.hands.HandLandmark.THUMB_IP]
     thumb_mcp = hand_landmarks.landmark[mp.solutions.hands.HandLandmark.THUMB_MCP]
     thumb_cmc = hand_landmarks.landmark[mp.solutions.hands.HandLandmark.THUMB_CMC]
 
+    # 计算拇指的角度
     angle_thumb = calculate_angle(thumb_cmc, thumb_mcp, thumb_tip)
-    if angle_thumb > 160:
+    if angle_thumb > 160:  # 如果拇指的角度大于160度，认为拇指竖起
         fingers_status[0] = 1
 
+    # 其他手指
     for i, finger_tip_id in enumerate([mp.solutions.hands.HandLandmark.INDEX_FINGER_TIP,
                                        mp.solutions.hands.HandLandmark.MIDDLE_FINGER_TIP,
                                        mp.solutions.hands.HandLandmark.RING_FINGER_TIP,
@@ -129,203 +125,17 @@ def count_raised_fingers(hand_landmarks):
         finger_pip = hand_landmarks.landmark[finger_tip_id - 2]
         finger_mcp = hand_landmarks.landmark[finger_tip_id - 3]
 
+        # 计算手指的角度
         angle_finger = calculate_angle(finger_mcp, finger_pip, finger_tip)
-        if angle_finger > 160:
+        if angle_finger > 160:  # 如果手指的角度大于160度，认为手指竖起
             fingers_status[i + 1] = 1
 
     return sum(fingers_status)
 
-
 def calculate_angle(point1, point2, point3):
+    # 计算三个点之间的角度
     angle = np.arctan2(point3.y - point2.y, point3.x - point2.x) - np.arctan2(point1.y - point2.y, point1.x - point2.x)
     angle = np.abs(angle)
     if angle > np.pi:
         angle = 2 * np.pi - angle
     return angle * 180 / np.pi
-
-
-def is_ok_gesture(hand_landmarks):
-    thumb_tip = hand_landmarks.landmark[mp.solutions.hands.HandLandmark.THUMB_TIP]
-    index_tip = hand_landmarks.landmark[mp.solutions.hands.HandLandmark.INDEX_FINGER_TIP]
-    distance = np.linalg.norm(np.array([thumb_tip.x, thumb_tip.y]) - np.array([index_tip.x, index_tip.y]))
-
-    # 检查其他手指是否弯曲
-    middle_tip = hand_landmarks.landmark[mp.solutions.hands.HandLandmark.MIDDLE_FINGER_TIP]
-    ring_tip = hand_landmarks.landmark[mp.solutions.hands.HandLandmark.RING_FINGER_TIP]
-    pinky_tip = hand_landmarks.landmark[mp.solutions.hands.HandLandmark.PINKY_TIP]
-    middle_pip = hand_landmarks.landmark[mp.solutions.hands.HandLandmark.MIDDLE_FINGER_PIP]
-    ring_pip = hand_landmarks.landmark[mp.solutions.hands.HandLandmark.RING_FINGER_PIP]
-    pinky_pip = hand_landmarks.landmark[mp.solutions.hands.HandLandmark.PINKY_FINGER_PIP]
-
-    middle_finger_bent = middle_tip.y > middle_pip.y
-    ring_finger_bent = ring_tip.y > ring_pip.y
-    pinky_finger_bent = pinky_tip.y > pinky_pip.y
-
-    return distance < 0.05 and middle_finger_bent and ring_finger_bent and pinky_finger_bent  # 根据实际情况调整这个阈值
-
-
-def handle_ok_gesture():
-    global paused, popup_open
-    if not popup_open:
-        paused = True
-        popup_open = True
-        show_ok_window()
-
-
-def show_ok_window():
-    def on_continue():
-        global paused, popup_open
-        paused = False
-        popup_open = False  # 关闭弹窗后将标志设置为False
-        ok_window.destroy()
-        start_recognition(show_frame)
-
-    ok_window = Tk()
-    ok_window.title("手势检测")
-
-    label = Label(ok_window, text="检测到OK手势", font=('Helvetica', 24, 'bold'))
-    label.pack(pady=20)
-
-    continue_button = Button(ok_window, text="继续识别", command=on_continue)
-    continue_button.pack(pady=10)
-
-    ok_window.protocol("WM_DELETE_WINDOW", on_continue)
-    ok_window.mainloop()
-
-
-def handle_finger_detection(finger_count):
-    global paused, popup_open
-    if not popup_open:  # 只有在没有弹窗打开的情况下才处理手指检测并显示弹窗
-        if finger_count == 1:
-            paused = True
-            popup_open = True
-            show_finger_window("您竖起了一根手指")
-        elif finger_count == 2:
-            paused = True
-            popup_open = True
-            show_finger_window("您竖起了两根手指")
-        elif finger_count == 3:
-            paused = True
-            popup_open = True
-            show_finger_window("您竖起了三根手指")
-        elif finger_count == 4:
-            paused = True
-            popup_open = True
-            show_finger_window("您竖起了四根手指")
-        elif finger_count == 5:
-            paused = True
-            popup_open = True
-            show_stop_recognition_window()
-        elif finger_count == 6:
-            paused = True
-            popup_open = True
-            show_finger_window("您竖起了六根手指")
-        elif finger_count == 7:
-            paused = True
-            popup_open = True
-            show_finger_window("您竖起了七根手指")
-        elif finger_count == 8:
-            paused = True
-            popup_open = True
-            show_finger_window("您竖起了八根手指")
-        elif finger_count == 9:
-            paused = True
-            popup_open = True
-            show_finger_window("您竖起了九根手指")
-        elif finger_count == 10:
-            paused = True
-            popup_open = True
-            show_finger_window("您竖起了十根手指")
-
-
-def show_finger_window(message):
-    def on_continue():
-        global paused, popup_open
-        paused = False
-        popup_open = False  # 关闭弹窗后将标志设置为False
-        finger_window.destroy()
-        start_recognition(show_frame)
-
-    finger_window = Tk()
-    finger_window.title("手指检测")
-
-    label = Label(finger_window, text=message, font=('Helvetica', 24, 'bold'))
-    label.pack(pady=20)
-
-    continue_button = Button(finger_window, text="继续识别", command=on_continue)
-    continue_button.pack(pady=10)
-
-    finger_window.protocol("WM_DELETE_WINDOW", on_continue)
-    finger_window.mainloop()
-
-
-def show_stop_recognition_window():
-    def on_continue():
-        global paused, popup_open
-        paused = False
-        popup_open = False  # 关闭弹窗后将标志设置为False
-        stop_window.destroy()
-        start_recognition(show_frame)
-
-    def on_stop():
-        global popup_open
-        stop_recognition()
-        popup_open = False  # 关闭弹窗后将标志设置为False
-        stop_window.destroy()
-
-    stop_window = Tk()
-    stop_window.title("停止识别")
-
-    label = Label(stop_window, text="您竖起了五根手指，是否停止识别？", font=('Helvetica', 24, 'bold'))
-    label.pack(pady=20)
-
-    continue_button = Button(stop_window, text="继续识别", command=on_continue)
-    continue_button.pack(side=LEFT, padx=10, pady=10)
-
-    stop_button = Button(stop_window, text="停止识别", command=on_stop)
-    stop_button.pack(side=RIGHT, padx=10, pady=10)
-
-    stop_window.protocol("WM_DELETE_WINDOW", on_continue)
-    stop_window.mainloop()
-
-
-def show_frame(img=None):
-    global paused
-    if keep_running and cap.isOpened():
-        if img is not None:
-            frame = img
-            frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-        else:
-            ret, frame = cap.read()
-            if not ret:
-                return
-
-            frame = cv2.flip(frame, 1)
-            frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-
-        img = Image.fromarray(frame_rgb)
-        imgtk = ImageTk.PhotoImage(image=img)
-        canvas.create_image(0, 0, anchor=NW, image=imgtk)
-        canvas.image = imgtk
-
-        if not paused:
-            root.after(10, show_frame)
-        else:
-            root.update_idletasks()
-            root.update()
-
-
-if __name__ == "__main__":
-    root = Tk()
-    root.title("手势识别")
-
-    canvas = Canvas(root, width=640, height=480)
-    canvas.pack()
-
-    start_button = Button(root, text="开始识别", command=lambda: start_recognition(show_frame))
-    start_button.pack(side=LEFT, padx=10, pady=10)
-
-    stop_button = Button(root, text="停止识别", command=stop_recognition)
-    stop_button.pack(side=RIGHT, padx=10, pady=10)
-
-    root.mainloop()

gui.py

@@ -4,6 +4,7 @@ from tkinter import ttk
 from PIL import Image, ImageTk
 from gesture_recognition import start_recognition, stop_recognition, release_camera, keep_running
 
+# 设置窗口宽高
 WINDOW_WIDTH = 800
 WINDOW_HEIGHT = 705
 
@@ -22,6 +23,7 @@ def show_welcome_screen():
     welcome.title("欢迎使用")
     set_window_position(welcome, WINDOW_WIDTH, WINDOW_HEIGHT)
 
+    # 加载背景图片
     bg_image = Image.open("1.png")
     bg_image = bg_image.resize((WINDOW_WIDTH, WINDOW_HEIGHT), Image.Resampling.LANCZOS)
     bg_image = ImageTk.PhotoImage(bg_image)
@@ -39,7 +41,7 @@ def show_welcome_screen():
     welcome.mainloop()
 
 def show_main_screen():
-    global window, canvas
+    global window
     window = Tk()
     window.title("手势识别")
     set_window_position(window, WINDOW_WIDTH, WINDOW_HEIGHT)
@@ -67,6 +69,7 @@ def show_main_screen():
     btn_exit = ttk.Button(frame_controls, text="退出", command=lambda: exit_program())
     btn_exit.pack(side=LEFT, padx=10)
 
+    # 创建一个样式对象并设置按钮的样式
     style = ttk.Style(window)
     style.configure('TButton', font=('Helvetica', 14))
 

load.py

@@ -1,14 +0,0 @@
-import os
-
-from tensorflow.keras.preprocessing.image import ImageDataGenerator
-
-# 设置数据目录
-data_dir = 'D:/hand/archive'
-train_dir = os.path.join(data_dir, 'leapGestRecog')
-validation_dir = os.path.join(data_dir, 'validation')
-
-# 简单的标准化处理
-datagen = ImageDataGenerator(rescale=1./255)
-
-train_generator = datagen.flow_from_directory(train_dir, target_size=(150, 150), batch_size=32, class_mode='categorical')
-validation_generator = datagen.flow_from_directory(validation_dir, target_size=(150, 150), batch_size=32, class_mode='categorical')

main.py

@@ -1,188 +0,0 @@
-import random
-from math import sin, cos, pi, log
-from tkinter import *
-
-CANVAS_WIDTH = 500  # 画布的宽
-CANVAS_HEIGHT = 400  # 画布的高
-CANVAS_CENTER_X = CANVAS_WIDTH / 2  # 画布中心的X轴坐标
-CANVAS_CENTER_Y = CANVAS_HEIGHT / 2  # 画布中心的Y轴坐标
-IMAGE_ENLARGE = 11  # 放大比例
-HEART_COLOR = "#ffb6c1"
-
-
-def heart_function(t, shrink_ratio: float = IMAGE_ENLARGE):
-    """
-    “爱心函数生成器”
-    :param shrink_ratio: 放大比例
-    :param t: 参数
-    :return: 坐标
-    """
-    # 基础函数
-    x = 16 * (sin(t) ** 3)
-    y = -(13 * cos(t) - 5 * cos(2 * t) - 2 * cos(3 * t) - cos(4 * t))
-
-    # 放大
-    x *= shrink_ratio
-    y *= shrink_ratio
-
-    # 移到画布中央
-    x += CANVAS_CENTER_X
-    y += CANVAS_CENTER_Y
-
-    return int(x), int(y)
-
-
-def scatter_inside(x, y, beta=0.15):
-    """
-    随机内部扩散
-    :param x: 原x
-    :param y: 原y
-    :param beta: 强度
-    :return: 新坐标
-    """
-    ratio_x = -beta * log(random.random())
-    ratio_y = -beta * log(random.random())
-
-    dx = ratio_x * (x - CANVAS_CENTER_X)
-    dy = ratio_y * (y - CANVAS_CENTER_Y)
-
-    return x - dx, y - dy
-
-
-def shrink(x, y, ratio):
-    """
-    抖动
-    :param x: 原x
-    :param y: 原y
-    :param ratio: 比例
-    :return: 新坐标
-    """
-    force = -1 / (
-            ((x - CANVAS_CENTER_X) ** 2 + (y - CANVAS_CENTER_Y) ** 2) ** 0.6
-    )  # 这个参数...
-    dx = ratio * force * (x - CANVAS_CENTER_X)
-    dy = ratio * force * (y - CANVAS_CENTER_Y)
-    return x - dx, y - dy
-
-
-def curve(p):
-    """
-    自定义曲线函数，调整跳动周期
-    :param p: 参数
-    :return: 正弦
-    """
-    # 可以尝试换其他的动态函数，达到更有力量的效果（贝塞尔？）
-    return 2 * (2 * sin(4 * p)) / (2 * pi)
-
-
-class Heart:
-    """
-    爱心类
-    """
-
-    def __init__(self, generate_frame=20):
-        self._points = set()  # 原始爱心坐标集合
-        self._edge_diffusion_points = set()  # 边缘扩散效果点坐标集合
-        self._center_diffusion_points = set()  # 中心扩散效果点坐标集合
-        self.all_points = {}  # 每帧动态点坐标
-        self.build(2000)
-
-        self.random_halo = 1000
-
-        self.generate_frame = generate_frame
-        for frame in range(generate_frame):
-            self.calc(frame)
-
-    def build(self, number):
-        # 爱心
-        for _ in range(number):
-            t = random.uniform(0, 2 * pi)  # 随机不到的地方造成爱心有缺口
-            x, y = heart_function(t)
-            self._points.add((x, y))
-
-        # 爱心内扩散
-        for _x, _y in list(self._points):
-            for _ in range(3):
-                x, y = scatter_inside(_x, _y, 0.05)
-                self._edge_diffusion_points.add((x, y))
-
-        # 爱心内再次扩散
-        point_list = list(self._points)
-        for _ in range(4000):
-            x, y = random.choice(point_list)
-            x, y = scatter_inside(x, y, 0.17)
-            self._center_diffusion_points.add((x, y))
-
-    @staticmethod
-    def calc_position(x, y, ratio):
-        # 调整缩放比例
-        force = 1 / (
-                ((x - CANVAS_CENTER_X) ** 2 + (y - CANVAS_CENTER_Y) ** 2) ** 0.520
-        )  # 魔法参数
-
-        dx = ratio * force * (x - CANVAS_CENTER_X) + random.randint(-1, 1)
-        dy = ratio * force * (y - CANVAS_CENTER_Y) + random.randint(-1, 1)
-
-        return x - dx, y - dy
-
-    def calc(self, generate_frame):
-        ratio = 10 * curve(generate_frame / 10 * pi)  # 圆滑的周期的缩放比例
-
-        halo_radius = int(4 + 6 * (1 + curve(generate_frame / 10 * pi)))
-        halo_number = int(3000 + 4000 * abs(curve(generate_frame / 10 * pi) ** 2))
-
-        all_points = []
-
-        # 光环
-        heart_halo_point = set()  # 光环的点坐标集合
-        for _ in range(halo_number):
-            t = random.uniform(0, 2 * pi)  # 随机不到的地方造成爱心有缺口
-            x, y = heart_function(t, shrink_ratio=11.6)  # 魔法参数
-            x, y = shrink(x, y, halo_radius)
-            if (x, y) not in heart_halo_point:
-                # 处理新的点
-                heart_halo_point.add((x, y))
-                x += random.randint(-14, 14)
-                y += random.randint(-14, 14)
-                size = random.choice((1, 2, 2))
-                all_points.append((x, y, size))
-
-        # 轮廓
-        for x, y in self._points:
-            x, y = self.calc_position(x, y, ratio)
-            size = random.randint(1, 3)
-            all_points.append((x, y, size))
-
-        # 内容
-        for x, y in self._edge_diffusion_points:
-            x, y = self.calc_position(x, y, ratio)
-            size = random.randint(1, 2)
-            all_points.append((x, y, size))
-
-        for x, y in self._center_diffusion_points:
-            x, y = self.calc_position(x, y, ratio)
-            size = random.randint(1, 2)
-            all_points.append((x, y, size))
-
-        self.all_points[generate_frame] = all_points
-
-    def render(self, render_canvas, render_frame):
-        for x, y, size in self.all_points[render_frame % self.generate_frame]:
-            render_canvas.create_rectangle(
-                x, y, x + size, y + size, width=0, fill=HEART_COLOR
-            )
-
-
-def draw(main: Tk, render_canvas: Canvas, render_heart: Heart, render_frame=0):
-    render_canvas.delete("all")
-    render_heart.render(render_canvas, render_frame)
-    main.after(160, draw, main, render_canvas, render_heart, render_frame + 1)
-
-
-if __name__ == "__main__":
-    root = Tk()  # 一个Tk
-    canvas = Canvas(root, bg="black", height=CANVAS_HEIGHT, width=CANVAS_WIDTH)
-    canvas.pack()
-    heart = Heart()  # 心
-    draw(root, canvas, heart)  # 开始画画~
-    root.mainloop()

modul test.py

@@ -1,42 +0,0 @@
-import os
-import numpy as np
-import matplotlib.pyplot as plt     #绘画
-from tensorflow.keras.preprocessing import image  #图片预处理
-from tensorflow.keras.models import load_model   #加载模型
-#生成图像数据
-from tensorflow.keras.preprocessing.image import ImageDataGenerator
-
-# 设置数据目录
-test_dir = 'D:/hand/archive/leapGestRecog'
-
-# 加载模型
-model = load_model('hand_gesture_model.h5')
-print("模型已从 hand_gesture_model.h5 加载")
-
-# 使用 ImageDataGenerator 加载测试数据
-test_datagen = ImageDataGenerator(rescale=1./255)
-test_generator = test_datagen.flow_from_directory(test_dir, target_size=(150, 150), batch_size=32, class_mode='categorical')
-
-# 评估模型在测试数据集上的性能
-test_loss, test_accuracy = model.evaluate(test_generator)
-print(f"Test Accuracy: {test_accuracy * 100:.2f}%")
-
-# 加载单个图像并进行预测
-img_path = 'D:/hand/archive/leapGestRecog/00/01_palm/frame_00_01_0001.png'  # 修改为实际图像路径
-img = image.load_img(img_path, target_size=(150, 150))
-img_array = image.img_to_array(img)
-img_array = np.expand_dims(img_array, axis=0) / 255.0  # 标准化图像数据
-
-# 进行预测
-predictions = model.predict(img_array)
-predicted_class = np.argmax(predictions, axis=1)
-
-# 输出预测结果
-class_labels = {v: k for k, v in test_generator.class_indices.items()}
-print(f"预测类别: {class_labels[predicted_class[0]]}")
-
-# 显示图像和预测结果
-plt.imshow(img)
-plt.title(f"预测类别: {class_labels[predicted_class[0]]}")
-plt.axis('off')
-plt.show()

training.py

@@ -1,110 +0,0 @@
-import os
-import matplotlib.pyplot as plt
-import matplotlib.font_manager as fm
-from tensorflow.keras.preprocessing.image import ImageDataGenerator
-from tensorflow.keras import layers, models
-import tensorflow as tf
-
-# 设置字体
-font_path = 'C:/Windows/Fonts/msyh.ttc'
-prop = fm.FontProperties(fname=font_path)
-plt.rcParams['font.family'] = prop.get_name()
-
-# 设置数据目录
-data_dir = 'D:/hand/archive'
-train_dir = os.path.join(data_dir, 'leapGestRecog')
-validation_dir = os.path.join(data_dir, 'validation')
-
-# 检查目录路径是否存在
-print("训练数据目录存在:", os.path.exists(train_dir))
-print("验证数据目录存在:", os.path.exists(validation_dir))
-
-if os.path.exists(train_dir) and os.path.exists(validation_dir):
-    # 简单的标准化处理
-    datagen = ImageDataGenerator(rescale=1./255)
-
-    train_generator = datagen.flow_from_directory(train_dir, target_size=(150, 150), batch_size=32, class_mode='categorical')
-    validation_generator = datagen.flow_from_directory(validation_dir, target_size=(150, 150), batch_size=32, class_mode='categorical')
-
-    train_dataset = tf.data.Dataset.from_generator(
-        lambda: train_generator,
-        output_signature=(
-            tf.TensorSpec(shape=(None, 150, 150, 3), dtype=tf.float32),
-            tf.TensorSpec(shape=(None, 10), dtype=tf.float32)
-        )
-    ).repeat()
-
-    validation_dataset = tf.data.Dataset.from_generator(
-        lambda: validation_generator,
-        output_signature=(
-            tf.TensorSpec(shape=(None, 150, 150, 3), dtype=tf.float32),
-            tf.TensorSpec(shape=(None, 10), dtype=tf.float32)
-        )
-    ).repeat()
-
-    # 检查类别名称和数量
-    train_classes = train_generator.class_indices
-    validation_classes = validation_generator.class_indices
-
-    print("训练数据集类别:", train_classes)
-    print("验证数据集类别:", validation_classes)
-
-    # 构建模型
-    model = models.Sequential([
-        layers.Input(shape=(150, 150, 3)),
-        layers.Conv2D(32, (3, 3), activation='relu'),
-        layers.MaxPooling2D((2, 2)),
-        layers.Conv2D(64, (3, 3), activation='relu'),
-        layers.MaxPooling2D((2, 2)),
-        layers.Conv2D(128, (3, 3), activation='relu'),
-        layers.MaxPooling2D((2, 2)),
-        layers.Conv2D(128, (3, 3), activation='relu'),
-        layers.MaxPooling2D((2, 2)),
-        layers.Flatten(),
-        layers.Dense(512, activation='relu'),
-        layers.Dense(train_generator.num_classes, activation='softmax')
-    ])
-
-    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
-
-    steps_per_epoch = train_generator.samples // train_generator.batch_size
-    validation_steps = validation_generator.samples // validation_generator.batch_size
-
-    history = model.fit(train_dataset, steps_per_epoch=steps_per_epoch,
-                        epochs=30, validation_data=validation_dataset,
-                        validation_steps=validation_steps)
-
-    validation_loss, validation_accuracy = model.evaluate(validation_dataset, steps=validation_steps)
-    print(f"Validation Accuracy: {validation_accuracy * 100:.2f}%")
-
-    # 保存模型
-    model.save('hand_gesture_model.h5')
-    print("模型已保存到 hand_gesture_model.h5")
-
-    # 从文件加载模型
-    loaded_model = load_model('hand_gesture_model.h5')
-    print("模型已从 hand_gesture_model.h5 加载")
-
-    # 可视化训练过程
-    acc = history.history['accuracy']
-    val_acc = history.history['val_accuracy']
-    loss = history.history['loss']
-    val_loss = history.history['val_loss']
-
-    epochs = range(len(acc))
-
-    plt.plot(epochs, acc, 'r', label='训练准确度')
-    plt.plot(epochs, val_acc, 'b', label='验证准确度')
-    plt.title('训练和验证准确度')
-    plt.legend()
-
-    plt.figure()
-
-    plt.plot(epochs, loss, 'r', label='训练损失')
-    plt.plot(epochs, val_loss, 'b', label='验证损失')
-    plt.title('训练和验证损失')
-    plt.legend()
-
-    plt.show()
-else:
-    print(f"路径错误，请检查以下路径是否正确:\n训练数据目录: {train_dir}\n验证数据目录: {validation_dir}")