修复bug

Tm

DM.py

@@ -0,0 +1,318 @@
+# -*- coding:utf-8 -*-
+
+"""
+信号设计课程小组设计
+
+@ by: Leaf
+@ date: 2022-05-28
+"""
+
+import mediapipe as mp
+import cv2
+# import HandDetector
+import math
+from datetime import datetime
+import time
+import numpy as np
+
+
+# 旋转函数
+def Rotate(angle, x, y, point_x, point_y):
+    px = (x - point_x) * math.cos(angle) - (y - point_y) * math.sin(angle) + point_x
+    py = (x - point_x) * math.sin(angle) + (y - point_y) * math.cos(angle) + point_y
+    return px, py
+
+
+class HandDetector:
+    """
+    使用mediapipe库查找手。导出地标像素格式。添加了额外的功能。
+    如查找方式，许多手指向上或两个手指之间的距离。而且提供找到的手的边界框信息。
+    """
+
+    def __init__(self, mode=False, max_hands=2, detection_con=0.5, min_track_con=0.5):
+        """
+        :param mode: 在静态模式下，对每个图像进行检测
+        :param max_hands: 要检测的最大手数
+        :param detection_con: 最小检测置信度
+        :param min_track_con: 最小跟踪置信度
+        """
+        self.results = None
+        self.mode = mode
+        self.max_hands = max_hands
+        self.modelComplex = 1
+        self.detection_con = detection_con
+        self.min_track_con = min_track_con
+
+        # 初始化手部的识别模型
+        self.mpHands = mp.solutions.hands
+        self.hands = self.mpHands.Hands(static_image_mode=self.mode,
+                                        max_num_hands=self.max_hands,
+                                        min_detection_confidence=self.detection_con,
+                                        min_tracking_confidence=self.min_track_con)
+        self.mpDraw = mp.solutions.drawing_utils  # 初始化绘图器
+        self.tipIds = [4, 8, 12, 16, 20]  # 指尖列表
+        # self.knuckles = {'0': [4, 3, 2, 1], "1": [8, 7, 6, 5], "2": [12, 11, 10, 9], "3": [16, 15, 14, 13],
+        #                  "4": [20, 19, 18, 17]}
+        self.fingers = []
+        self.lmList = []
+        self.re_lmList = []
+
+    def find_hands(self, img, draw=True):
+        """
+        从图像(BRG)中找到手部。
+        :param img: 用于查找手的图像。
+        :param draw: 在图像上绘制输出的标志。
+        :return: 带或不带图形的图像
+        """
+        img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  # 将传入的图像由BGR模式转标准的Opencv模式——RGB模式，
+        self.results = self.hands.process(img_rgb)
+
+        if self.results.multi_hand_landmarks:
+            for handLms in self.results.multi_hand_landmarks:
+                if draw:
+                    self.mpDraw.draw_landmarks(img, handLms,
+                                               self.mpHands.HAND_CONNECTIONS)
+        return img
+
+    def find_position(self, img, hand_no=0, draw=True):
+        """
+        查找单手的地标并将其放入列表中像素格式。还可以返回手部的周围的边界框。
+        :param img: 要查找的主图像
+        :param hand_no: 如果检测到多只手，则为手部id
+        :param draw: 在图像上绘制输出的标志。(默认绘制矩形框)
+        :return: 像素格式的手部关节位置列表；手部边界框
+        """
+
+        x_list = []
+        y_list = []
+        bbox_info = []
+        self.lmList = []
+        self.re_lmList = []
+        if self.results.multi_hand_landmarks:
+            my_hand = self.results.multi_hand_landmarks[hand_no]
+            for _, lm in enumerate(my_hand.landmark):
+                h, w, c = img.shape
+                px, py = int(lm.x * w), int(lm.y * h)
+                x_list.append(px)
+                y_list.append(py)
+                self.lmList.append([px, py])
+                if draw:
+                    cv2.circle(img, (px, py), 5, (255, 0, 255), cv2.FILLED)
+            x_min, x_max = min(x_list), max(x_list)
+            y_min, y_max = min(y_list), max(y_list)
+            box_w, box_h = x_max - x_min, y_max - y_min
+            bbox = x_min, y_min, box_w, box_h
+            cx, cy = bbox[0] + (bbox[2] // 2), bbox[1] + (bbox[3] // 2)
+            bbox_info = {"id": hand_no, "bbox": bbox, "center": (cx, cy)}
+
+            if draw:
+                cv2.rectangle(img, (bbox[0] - 20, bbox[1] - 20),
+                              (bbox[0] + bbox[2] + 20, bbox[1] + bbox[3] + 20),
+                              (0, 255, 0), 2)
+
+        return self.lmList, bbox_info
+
+    def revolve(self, img, draw=True):
+        """
+            旋转手势识别点
+            :param img: 要查找的主图像
+            :param draw: 在图像上绘制输出的标志。(默认绘制矩形框)
+            :return: 像素格式的手部关节位置列表
+        """
+        # print(self.lmList)
+        point_x = self.lmList[0][0]
+        point_y = self.lmList[0][1]
+        delta_x = self.lmList[13][0] - point_x
+        delta_y = self.lmList[13][1] - point_y
+        if delta_y == 0:
+            if delta_x < 0:
+                theta = math.pi / 2
+            else:
+                theta = -math.pi / 2
+        else:
+            theta = math.atan(delta_x / delta_y)
+            if delta_y > 0:
+                theta = theta + math.pi
+        # print(theta*180/math.pi)
+        for i in self.lmList:
+            px, py = Rotate(theta, i[0], i[1], point_x, point_y)
+            px = int(px)
+            py = int(py)
+            self.re_lmList.append([px, py])
+            if draw:
+                cv2.circle(img, (px, py), 5, (0, 0, 255), cv2.FILLED)
+        return self.re_lmList
+
+    def fingers_up(self):
+        """
+        查找列表中打开并返回的手指数。会分别考虑左手和右手
+        :return: 竖起手指的列表
+        """
+        fingers = []
+        if self.results.multi_hand_landmarks:
+            my_hand_type = self.hand_type()
+            # Thumb
+            if my_hand_type == "Right":
+                if self.lmList[self.tipIds[0]][0] > self.lmList[self.tipIds[0] - 1][0]:
+                    fingers.append(1)
+                else:
+                    fingers.append(0)
+            else:
+                if self.lmList[self.tipIds[0]][0] < self.lmList[self.tipIds[0] - 1][0]:
+                    fingers.append(1)
+                else:
+                    fingers.append(0)
+            # 4 Fingers
+            for i in range(1, 5):
+                if self.lmList[self.tipIds[i]][1] < self.lmList[self.tipIds[i] - 2][1]:
+                    fingers.append(1)
+                else:
+                    fingers.append(0)
+        return fingers
+    
+    def re_fingers_up(self):
+        """
+        查找列表中打开并返回的手指数。会分别考虑左手和右手
+        :return: 竖起手指的列表
+        """
+        fingers = []
+        if self.results.multi_hand_landmarks:
+            my_hand_type = self.hand_type()
+            # Thumb
+            if my_hand_type == "Right":
+                if self.re_lmList[self.tipIds[0]][0] > self.re_lmList[self.tipIds[0] - 1][0]:
+                    fingers.append(1)
+                else:
+                    fingers.append(0)
+            else:
+                if self.re_lmList[self.tipIds[0]][0] < self.re_lmList[self.tipIds[0] - 1][0]:
+                    fingers.append(1)
+                else:
+                    fingers.append(0)
+            # 4 Fingers
+            for i in range(1, 5):
+                if self.re_lmList[self.tipIds[i]][1] < self.re_lmList[self.tipIds[i] - 2][1]:
+                    fingers.append(1)
+                else:
+                    fingers.append(0)
+        return fingers
+
+    def knuckles_up(self):
+        """
+                查找列表中打开并返回的手指数。会分别考虑左手和右手
+                :return: 竖起手指的列表
+                """
+        knuckles = []
+        distan = 10
+        if self.results.multi_hand_landmarks:
+            my_hand_type = self.hand_type()
+            # Thumb
+            xx = self.re_lmList[self.tipIds[0]][0]
+            yy = self.re_lmList[self.tipIds[0] - 1][0]
+            if my_hand_type == "Right":
+                if -distan < xx - yy < distan:
+                    knuckles.append(2)
+                elif xx > yy:
+                    knuckles.append(1)
+                else:
+                    knuckles.append(0)
+            else:
+                if -distan < xx - yy < distan:
+                    knuckles.append(2)
+                elif xx < yy:
+                    knuckles.append(1)
+                else:
+                    knuckles.append(0)
+            # 12 knuckles
+            for i in range(1, 5):
+                for j in range(3):
+                    xx = self.re_lmList[self.tipIds[i]-j][1]
+                    yy = self.re_lmList[self.tipIds[i]-j - 1][1]
+                    if -distan < xx - yy < distan:
+                        knuckles.append(2)
+                    elif xx < yy:
+                        knuckles.append(1)
+                    else:
+                        knuckles.append(0)
+        return knuckles
+
+    def hand_type(self):
+        """
+        检查传入的手部是左还是右
+        :return: "Right" 或 "Left"
+        """
+        if self.results.multi_hand_landmarks:
+            if self.lmList[17][0] < self.lmList[5][0]:
+                return "Right"
+            else:
+                return "Left"
+
+
+class Main:
+    def __init__(self):
+        self.detector = None
+        self.camera = cv2.VideoCapture(0, cv2.CAP_DSHOW)
+        self.camera.set(3, 1280)
+        self.camera.set(4, 720)
+
+    def gesture_recognition(self):
+        self.detector = HandDetector()
+        gesture_store = {}
+        startTime = time.time()
+        stored_round = 1
+        stored_flag = 0
+        xl = np.zeros((1, 13))  # 特征值存储
+        while True:
+            frame, img = self.camera.read()
+            img = self.detector.find_hands(img)
+            lm_list, bbox = self.detector.find_position(img)
+            if lm_list:
+                re_lm_list = self.detector.revolve(img)
+                x_1, y_1 = bbox["bbox"][0], bbox["bbox"][1]
+                knucks = self.detector.knuckles_up()
+                # x1, x2, x3, x4, x5 = self.detector.re_fingers_up()
+                #
+                # if (x2 == 1 and x3 == 1) and (x4 == 0 and x5 == 0 and x1 == 0):
+                #     cv2.putText(img, "GOOD!", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
+                #                 (0, 0, 255), 3)
+
+                print(time.time() - startTime)
+                if (time.time() - startTime) < 3:  # 手势存储时间
+                    xl = np.vstack((xl, knucks))
+                    cv2.putText(img, 'Please put the gesture to be stored in 1 second', (50, 50),
+                                cv2.FONT_HERSHEY_PLAIN, 1.2, (255, 255, 255), 2)
+                else:  # 开始手势识别
+                    self.detector.fingers = xl
+                    value = ''
+                    for j in range(13):
+                        value = value + str(np.argmax(
+                            np.bincount(xl[:, j].astype(int))))   # 找出第3列最频繁出现的值
+                    gesture_store[value] = stored_round
+                    stored_flag = 1
+                    # startTime = time.time()
+                    gesture_dete = ''.join(str(knuck) for knuck in knucks)
+                    if gesture_dete in gesture_store:
+                        cv2.putText(img, str(gesture_store[gesture_dete]), (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
+                                    (0, 0, 255), 3)
+                    cv2.putText(img, 'Gesture stored, recognition started', (50, 50),
+                                cv2.FONT_HERSHEY_PLAIN, 1.2, (255, 255, 255), 2)
+            else:
+                if stored_flag:
+                    stored_round += 1
+                    stored_flag = 0
+                startTime = time.time()  # 当检测不到手势时，初始化手势存储
+                xl = np.zeros((1, 13))  # 特征值存储
+                cv2.putText(img, 'Please put the gesture to be stored in 1 second', (50, 50), cv2.FONT_HERSHEY_PLAIN,
+                            1.2, (255, 255, 255), 2)
+
+            cv2.imshow("camera", img)
+            key = cv2.waitKey(1)
+            if cv2.getWindowProperty('camera', cv2.WND_PROP_VISIBLE) < 1:
+                break
+            elif key == 27:
+                break
+
+
+if __name__ == '__main__':
+    Solution = Main()
+    Solution.gesture_recognition()

Datasets/README.md

@@ -0,0 +1,15 @@
+## GestureData 手势数据 v1.0
+# 文件格式：
+每个数据集（npz文件）包含：
+1个标签label（手势标签，整个数据集都是这一个标签）；
+500组数据data（每组数据是21*3，即21个点的3维数据，就是demo.py-find_position()中的lm.x, lm.y, lm.z）；
+左右手区分handtype（0为左手，1为右手）；
+画布大小shape（一般都是720*1280，对应demo.py-find_position()中的w, h）。
+
+# 注意事项：
+1. 在使用之前建议先熟悉npz文件的读写与使用（很简单的）；
+2. 数据集shape类最后会保存一个[0, 0]，其他都是正常的[720, 1280]；
+3. 左右手不建议使用，因为面向屏幕的手心手背就可以导致程序的误判。
+
+# 更新说明：
+1. 保存了0~9的手势。

TM.py

@@ -1,14 +1,8 @@
 # -*- coding:utf-8 -*-
 
-"""
-信号设计课程小组设计
-
-@ by: Leaf
-@ date: 2022-05-28
-"""
-
 import cv2
 import mediapipe as mp
+import numpy as np
 
 
 class HandDetector:
@@ -33,8 +27,10 @@ class HandDetector:
 
         # 初始化手部的识别模型
         self.mpHands = mp.solutions.hands
-        self.hands = self.mpHands.Hands(self.mode, self.max_hands, self.modelComplex,
+        self.hands = self.mpHands.Hands(static_image_mode=self.mode,
-                                        self.detection_con, self.min_track_con)
+                                        max_num_hands=self.max_hands,
+                                        min_detection_confidence=self.detection_con,
+                                        min_tracking_confidence=self.min_track_con)
         self.mpDraw = mp.solutions.drawing_utils  # 初始化绘图器
         self.tipIds = [4, 8, 12, 16, 20]  # 指尖列表
         self.fingers = []
@@ -77,7 +73,7 @@ class HandDetector:
                 px, py = int(lm.x * w), int(lm.y * h)
                 x_list.append(px)
                 y_list.append(py)
-                self.lmList.append([px, py])
+                self.lmList.append(np.array([px, py]))
                 if draw:
                     cv2.circle(img, (px, py), 5, (255, 0, 255), cv2.FILLED)
             x_min, x_max = min(x_list), max(x_list)
@@ -115,7 +111,9 @@ class HandDetector:
                     fingers.append(0)
             # 4 Fingers
             for i in range(1, 5):
-                if self.lmList[self.tipIds[i]][1] < self.lmList[self.tipIds[i] - 2][1]:
+                # if self.lmList[self.tipIds[i]][1] < self.lmList[self.tipIds[i] - 2][1]:
+                if np.dot(self.lmList[self.tipIds[i]-2]-self.lmList[self.tipIds[i]-3],
+                          self.lmList[self.tipIds[i]-1]-self.lmList[self.tipIds[i]-2]) >= 0:
                     fingers.append(1)
                 else:
                     fingers.append(0)
@@ -136,52 +134,49 @@ class HandDetector:
 class Main:
     def __init__(self):
         self.detector = None
-        self.camera = cv2.VideoCapture(0, cv2.CAP_DSHOW)
+        self.camera = None
-        self.camera.set(3, 1280)
+        # self.camera = cv2.VideoCapture(0, cv2.CAP_DSHOW)
-        self.camera.set(4, 720)
+        # self.camera.set(3, 1280)
+        # self.camera.set(4, 720)
 
-    def gesture_recognition(self):
+    def gesture_recognition(self, img, detector):
-        self.detector = HandDetector()
+        self.detector = detector
-        while True:
+        img = self.detector.find_hands(img)
-            frame, img = self.camera.read()
+        lm_list, bbox = self.detector.find_position(img)
-            img = self.detector.find_hands(img)
-            lm_list, bbox = self.detector.find_position(img)
 
-            if lm_list:
+        if lm_list:
-                x_1, y_1 = bbox["bbox"][0], bbox["bbox"][1]
+            x_1, y_1 = bbox["bbox"][0], bbox["bbox"][1]
-                x1, x2, x3, x4, x5 = self.detector.fingers_up()
+            x1, x2, x3, x4, x5 = self.detector.fingers_up()
-
+            if (np.linalg.norm(lm_list[4]-lm_list[8]) < 50) and (np.linalg.norm(lm_list[4]-lm_list[12]) < 50):
-                if (x2 == 1 and x3 == 1) and (x4 == 0 and x5 == 0 and x1 == 0):
+                cv2.putText(img, "7_SEVEN", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
-                    cv2.putText(img, "2_TWO", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
+                            (0, 0, 255), 3)
-                                (0, 0, 255), 3)
+            elif (x2 == 1 and x3 == 1) and (x4 == 0 and x5 == 0 and x1 == 0):
-                elif (x2 == 1 and x3 == 1 and x4 == 1) and (x1 == 0 and x5 == 0):
+                cv2.putText(img, "2_TWO", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
-                    cv2.putText(img, "3_THREE", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
+                            (0, 0, 255), 3)
-                                (0, 0, 255), 3)
+            elif (x2 == 1 and x3 == 1 and x4 == 1) and (x1 == 0 and x5 == 0):
-                elif (x2 == 1 and x3 == 1 and x4 == 1 and x5 == 1) and (x1 == 0):
+                cv2.putText(img, "3_THREE", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
-                    cv2.putText(img, "4_FOUR", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
+                            (0, 0, 255), 3)
-                                (0, 0, 255), 3)
+            elif (x2 == 1 and x3 == 1 and x4 == 1 and x5 == 1) and (x1 == 0):
-                elif x1 == 1 and x2 == 1 and x3 == 1 and x4 == 1 and x5 == 1:
+                cv2.putText(img, "4_FOUR", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
-                    cv2.putText(img, "5_FIVE", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
+                            (0, 0, 255), 3)
-                                (0, 0, 255), 3)
+            elif x1 == 1 and x2 == 1 and x3 == 1 and x4 == 1 and x5 == 1:
-                elif x2 == 1 and x1 == 0 and (x3 == 0, x4 == 0, x5 == 0):
+                cv2.putText(img, "5_FIVE", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
-                    cv2.putText(img, "1_ONE", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
+                            (0, 0, 255), 3)
-                                (0, 0, 255), 3)
+            elif (x2 == 1 and x1 == 0) and (x3 == 0 and x4 == 0 and x5 == 0):
-                elif x1 == 1 and x2 == 1 and (x3 == 0, x4 == 0, x5 == 0):
+                cv2.putText(img, "1_ONE", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
-                    cv2.putText(img, "8_EIGHT", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
+                            (0, 0, 255), 3)
-                                (0, 0, 255), 3)
+            elif (x1 == 1 and x2 == 1) and (x3 == 0 and x4 == 0 and x5 == 0):
-                elif x1 == 1 and x5 == 1 and (x3 == 0, x4 == 0, x5 == 0):
+                cv2.putText(img, "8_EIGHT", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
-                    cv2.putText(img, "6_SIX", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
+                            (0, 0, 255), 3)
-                                (0, 0, 255), 3)
+            elif (x1 == 1 and x5 == 1) and (x3 == 0 and x4 == 0 and x2 == 0):
-                elif x1 and (x2 == 0, x3 == 0, x4 == 0, x5 == 0):
+                cv2.putText(img, "6_SIX", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
-                    cv2.putText(img, "GOOD!", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
+                            (0, 0, 255), 3)
-                                (0, 0, 255), 3)
+            elif x1 == 0 and x5 == 0 and x3 == 0 and x4 == 0 and x2 == 0:
-
+                cv2.putText(img, "0_ZERO", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
-            cv2.imshow("camera", img)
+                            (0, 0, 255), 3)
-            key = cv2.waitKey(1)
+            else:
-            if cv2.getWindowProperty('camera', cv2.WND_PROP_VISIBLE) < 1:
+                return 1
-                break
+        return 0
-            elif key == 27:
-                break
 
 
 if __name__ == '__main__':

ai.py

@@ -0,0 +1,472 @@
+# -*- coding:utf-8 -*-
+
+"""
+信号设计课程小组设计
+
+@ by: Leaf
+@ date: 2022-05-28
+"""
+import tkinter as tk
+import cv2
+import mediapipe as mp
+import torch
+import torch.nn as nn
+import numpy as np
+import shutil
+import math
+from scipy import stats
+from os.path import exists
+from os import mkdir
+from pathlib import Path
+from torch.utils.data import DataLoader, TensorDataset
+
+
+# 旋转函数
+def rotate(angle, x, y, point_x, point_y):
+    px = (x - point_x) * math.cos(angle) - (y - point_y) * math.sin(angle) + point_x
+    py = (x - point_x) * math.sin(angle) + (y - point_y) * math.cos(angle) + point_y
+    return px, py
+
+
+# 归一化
+def normalize(x):
+    max_x = np.max(x)
+    min_x = np.min(x)
+    return (x - min_x) / (max_x - min_x)
+
+
+class CNN(nn.Module):
+    def __init__(self, m):
+        super(CNN, self).__init__()
+        self.out_label = []
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(
+                in_channels=1,
+                out_channels=16,
+                kernel_size=5,
+                stride=1,
+                padding=2,
+            ),
+            nn.ReLU(),
+            nn.MaxPool2d(kernel_size=1),
+        )
+        self.conv2 = nn.Sequential(
+            nn.Conv2d(16, 32, 5, 1, 2),
+            nn.ReLU(),
+            nn.MaxPool2d(2),
+        )
+        self.med = nn.Linear(32 * 11 * 2, 500)
+        self.med2 = nn.Linear(1 * 21 * 3, 100)
+        self.med3 = nn.Linear(100, 500)
+        self.out = nn.Linear(500, m)  # fully connected layer, output 10 classes
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.conv2(x)
+        x = x.view(x.size(0), -1)  # 展平多维的卷积图成 (batch_size, 32 * 7 * 7)
+        x = self.med(x)
+        # x = self.med2(x)
+        # x = self.med3(x)
+        output = self.out(x)
+        return output
+
+
+class HandDetector:
+    """
+    使用mediapipe库查找手。导出地标像素格式。添加了额外的功能。
+    如查找方式，许多手指向上或两个手指之间的距离。而且提供找到的手的边界框信息。
+    """
+
+    def __init__(self, mode=False, max_hands=2, detection_con=0.5, min_track_con=0.5):
+        """
+        :param mode: 在静态模式下，对每个图像进行检测
+        :param max_hands: 要检测的最大手数
+        :param detection_con: 最小检测置信度
+        :param min_track_con: 最小跟踪置信度
+        """
+        self.results = None
+        self.mode = mode
+        self.max_hands = max_hands
+        self.modelComplex = 1
+        self.detection_con = detection_con
+        self.min_track_con = min_track_con
+
+        # 初始化手部的识别模型
+        self.mpHands = mp.solutions.hands
+        self.hands = self.mpHands.Hands(static_image_mode=self.mode,
+                                        max_num_hands=self.max_hands,
+                                        min_detection_confidence=self.detection_con,
+                                        min_tracking_confidence=self.min_track_con)
+        self.mpDraw = mp.solutions.drawing_utils  # 初始化绘图器
+        self.tipIds = [4, 8, 12, 16, 20]  # 指尖列表
+        self.fingers = []
+        self.lmList = []
+        self.re_lmList = []
+
+    def find_hands(self, img, draw=True):
+        """
+        从图像(BRG)中找到手部。
+        :param img: 用于查找手的图像。
+        :param draw: 在图像上绘制输出的标志。
+        :return: 带或不带图形的图像
+        """
+        img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  # 将传入的图像由BGR模式转标准的Opencv模式——RGB模式，
+        self.results = self.hands.process(img_rgb)
+
+        if self.results.multi_hand_landmarks:
+            for handLms in self.results.multi_hand_landmarks:
+                if draw:
+                    self.mpDraw.draw_landmarks(img, handLms,
+                                               self.mpHands.HAND_CONNECTIONS)
+        return img
+
+    def find_position(self, img, hand_no=0, draw=True):
+        """
+        查找单手的地标并将其放入列表中像素格式。还可以返回手部的周围的边界框。
+        :param img: 要查找的主图像
+        :param hand_no: 如果检测到多只手，则为手部id
+        :param draw: 在图像上绘制输出的标志。(默认绘制矩形框)
+        :return: 像素格式的手部关节位置列表；手部边界框
+        """
+        x_list = []
+        y_list = []
+        bbox_info = []
+        self.lmList = []
+        h, w, c = img.shape
+        if self.results.multi_hand_landmarks:
+            my_hand = self.results.multi_hand_landmarks[hand_no]
+            for i, lm in enumerate(my_hand.landmark):
+                px, py = int(lm.x * w), int(lm.y * h)
+                x_list.append(px)
+                y_list.append(py)
+                self.lmList.append([lm.x, lm.y, 0])
+                if draw:
+                    cv2.circle(img, (px, py), 5, (255, 0, 255), cv2.FILLED)
+            x_min, x_max = min(x_list), max(x_list)
+            y_min, y_max = min(y_list), max(y_list)
+            box_w, box_h = x_max - x_min, y_max - y_min
+            bbox = x_min, y_min, box_w, box_h
+            cx, cy = bbox[0] + (bbox[2] // 2), bbox[1] + (bbox[3] // 2)
+            bbox_info = {"id": hand_no, "bbox": bbox, "center": (cx, cy), "shape": (h, w)}
+
+            if draw:
+                cv2.rectangle(img, (bbox[0] - 20, bbox[1] - 20),
+                              (bbox[0] + bbox[2] + 20, bbox[1] + bbox[3] + 20),
+                              (0, 255, 0), 2)
+
+        self.revolve(img)
+        self.re_lmList = np.array(self.re_lmList)
+        if self.re_lmList.any():
+            self.re_lmList = np.concatenate((np.zeros((21, 1)), self.re_lmList), axis=1)
+            self.re_lmList = np.concatenate((self.re_lmList, np.zeros((1, 4))), axis=0)
+
+        return self.re_lmList, bbox_info
+
+    def revolve(self, img, draw=True):
+        """
+            旋转手势识别点
+            :param img: 要查找的主图像
+            :param draw: 在图像上绘制输出的标志。(默认绘制矩形框)
+            :return: 像素格式的手部关节位置列表
+        """
+        h, w, c = img.shape
+        if len(self.lmList) >= 21:
+            # print(self.lmList)
+            self.re_lmList = []
+            point_x = self.lmList[0][0]
+            point_y = self.lmList[0][1]
+            delta_x = self.lmList[13][0] - point_x
+            delta_y = self.lmList[13][1] - point_y
+            if delta_y == 0:
+                if delta_x < 0:
+                    theta = math.pi / 2
+                else:
+                    theta = -math.pi / 2
+            else:
+                theta = math.atan(delta_x / delta_y)
+                if delta_y > 0:
+                    theta = theta + math.pi
+            # print(theta*180/math.pi)
+            for i in self.lmList:
+                px, py = rotate(theta, i[0] * w, i[1] * h, point_x * w, point_y * h)
+                self.re_lmList.append([px, py, 0])
+                if draw:
+                    cv2.circle(img, (int(px), int(py)), 5, (0, 0, 255), cv2.FILLED)
+            # 归一化
+            x_array = normalize(np.array(self.re_lmList)[:, 0])
+            # print(x_array)
+            for i in range(len(x_array)):
+                self.re_lmList[i][0] = x_array[i]
+            y_array = normalize(np.array(self.re_lmList)[:, 1])
+            for i in range(len(y_array)):
+                self.re_lmList[i][1] = x_array[i]
+        else:
+            self.re_lmList = self.lmList
+        return self.re_lmList
+
+    def hand_type(self):
+        """
+        检查传入的手部 是左还是右
+        :return: 1 或 0
+        """
+        if self.results.multi_hand_landmarks:
+            if self.lmList[17][0] < self.lmList[5][0]:
+                return 1
+            else:
+                return 0
+
+
+class AI:
+    def __init__(self, datasets_dir):
+        self.EPOCH = 20
+        self.BATCH_SIZE = 2
+        self.LR = 10e-5
+        self.DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        self.datasets_dir = datasets_dir
+        self.train_loader = None
+        self.m = 0
+        self.out_label = []  # CNN网络输出后数字标签转和字符串标签的映射关系
+
+    def load_datasets(self):
+        train_data = []
+        train_label = []
+        self.m = 0
+        for file in Path(self.datasets_dir).rglob("*.npz"):
+            data = np.load(str(file))
+            train_data.append(data["data"])
+            label_number = np.ones(len(data["data"])) * len(self.out_label)
+            train_label.append(label_number)
+            self.out_label.append(data["label"])
+            self.m += 1
+        train_data = torch.Tensor(np.concatenate(train_data, axis=0))
+        train_data = train_data.unsqueeze(1)
+        train_label = torch.tensor(np.concatenate(train_label, axis=0)).long()
+
+        dataset = TensorDataset(train_data, train_label)
+        self.train_loader = DataLoader(dataset, batch_size=self.BATCH_SIZE, shuffle=True)
+        return self.m
+
+    def train_cnn(self):
+        cnn = CNN(self.m).to(self.DEVICE)
+        optimizer = torch.optim.Adam(cnn.parameters(), self.LR)  # optimize all cnn parameters
+        loss_func = nn.CrossEntropyLoss()  # the target label is not one-hotted
+
+        for epoch in range(self.EPOCH):
+            for step, (data, target) in enumerate(self.train_loader):
+                # 分配 batch data, normalize x when iterate train_loader
+                data, target = data.to(self.DEVICE), target.to(self.DEVICE)
+                output = cnn(data)  # cnn output
+                loss = loss_func(output, target)  # cross entropy loss
+                optimizer.zero_grad()  # clear gradients for this training step
+                loss.backward()  # backpropagation, compute gradients
+                optimizer.step()  # apply gradients
+                if (step + 1) % 50 == 0:  # 输出结果
+                    print(
+                        "\r[Epoch: %d] [%d/%d (%0.f %%)][Loss: %f]"
+                        % (
+                            epoch + 1,
+                            (step + 1) * len(data),
+                            len(self.train_loader.dataset),
+                            100. * (step + 1) / len(self.train_loader),
+                            loss.item()
+                        ), end="")
+
+        cnn.out_label = self.out_label
+        torch.save(cnn, 'CNN.pkl')
+        print("训练结束")
+
+
+class Main:
+    def __init__(self):
+        self.camera = None
+        self.detector = HandDetector()
+        self.default_datasets = "Datasets"
+        self.len_x = 22
+        self.len_y = 4
+        self.label = ''
+        self.result = []
+        self.disp = ""
+
+    def change_state(self):
+        self.label = self.entry.get()  # 调用get()方法，将Entry中的内容获取出来
+        self.top1.quit()
+        if self.label == "":
+            self.top1.destroy()
+
+    def make_datasets(self, camera, datasets_dir="default", n=100):
+        if datasets_dir == "default":
+            return
+        if exists(datasets_dir):
+            shutil.rmtree(datasets_dir)
+        mkdir(datasets_dir)
+        self.camera = camera
+
+        self.top1 = tk.Tk()
+        self.top1.geometry('300x50')
+        self.top1.title('请输入标签')
+        tk.Label(self.top1, text='Label:').place(x=27, y=10)
+        self.entry = tk.Entry(self.top1, width=15)
+        self.entry.place(x=80, y=10)
+        tk.Button(self.top1, text='确定', command=self.change_state).place(x=235, y=5)
+
+        self.top1.mainloop()
+        while not self.label == "":
+            data = np.zeros([n, self.len_x, self.len_y])
+            shape_list = np.zeros([n, 2], dtype=np.int16)
+            hand_type = np.zeros(n, dtype=np.int8)
+
+            count = 0
+            cv2.startWindowThread()
+            while True:
+                frame, img = self.camera.read()
+                img = self.detector.find_hands(img)
+                result = np.zeros((self.len_x, self.len_y))
+
+                lm_list, bbox = self.detector.find_position(img)
+                for i in range(len(lm_list)):
+                    result[i] = np.array(lm_list[i])
+                if result.sum() > 0:  # 假设矩阵不为0，即捕捉到手部时
+
+                    shape = bbox["shape"]
+                    x_1, y_1 = bbox["bbox"][0], bbox["bbox"][1]
+                    data[count] = result
+                    hand_type[count] = self.detector.hand_type()
+                    shape_list[count] = np.array(shape)
+                    count += 1
+                    cv2.putText(img, str("{}/{}".format(count, n)), (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
+                                (0, 255, 0), 3)
+
+                cv2.imshow("camera", img)
+                key = cv2.waitKey(100)
+                if cv2.getWindowProperty('camera', cv2.WND_PROP_VISIBLE) < 1:
+                    break
+                elif key == 27:
+                    break
+                elif count == n - 1:
+                    break
+            cv2.destroyAllWindows()
+
+            open(datasets_dir + "/" + self.label + ".npz", "w")
+            np.savez(datasets_dir + "/" + self.label + ".npz", label=self.label, data=data,
+                     handtype=hand_type, shape=shape_list)
+            self.top1.mainloop()
+
+    def train(self, datasets_dir="default"):
+        if datasets_dir == "default":
+            datasets_dir = self.default_datasets
+        ai = AI(datasets_dir)
+        ai.load_datasets()
+        ai.train_cnn()
+
+    def gesture_recognition_camera(self, detector, img, cnn):
+        self.detector = detector
+        out_label = cnn.out_label
+        img = self.detector.find_hands(img)
+        lm_list, bbox = self.detector.find_position(img)
+
+        if lm_list.any():
+            x_1, y_1 = bbox["bbox"][0], bbox["bbox"][1]
+            data = torch.Tensor(lm_list)
+            data = data.unsqueeze(0)
+            data = data.unsqueeze(0)
+
+            test_output = cnn(data)
+
+            test_np = test_output.detach().numpy()[0]
+            # normal_temp = normalize(test_np)
+            # temp = normal_temp[np.argpartition(normal_temp, -2)[-2:]]
+            temp = test_np[np.argpartition(test_np, -2)[-2:]]
+            print(temp[1]-temp[0])
+            if temp[1]-temp[0] < 5.5:
+                return 1
+
+            self.result.append(torch.max(test_output, 1)[1].data.cpu().numpy()[0])
+            if len(self.result) > 4:
+                self.disp = str(out_label[stats.mode(self.result)[0][0]])
+                self.result = []
+
+            cv2.putText(img, self.disp, (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
+                        (0, 0, 255), 3)
+        return 0
+
+    def gesture_recognition_video(self, filedir):
+        self.detector = HandDetector()
+        cnn = torch.load("CNN.pkl")
+        out_label = cnn.out_label
+        result = []
+        disp = ""
+        cap = cv2.VideoCapture(filedir)
+        while True:
+            ret, img = cap.read()
+            img = self.detector.find_hands(img)
+            lm_list, bbox = self.detector.find_position(img)
+
+            if lm_list.any():
+                x_1, y_1 = bbox["bbox"][0], bbox["bbox"][1]
+                data = torch.Tensor(lm_list)
+                data = data.unsqueeze(0)
+                data = data.unsqueeze(0)
+
+                test_output = cnn(data)
+                result.append(torch.max(test_output, 1)[1].data.cpu().numpy()[0])
+
+                if len(result) > 5:
+                    disp = str(out_label[stats.mode(result)[0][0]])
+                    result = []
+
+                cv2.putText(img, disp, (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
+                            (0, 0, 255), 3)
+
+            cv2.imshow("camera", img)
+            key = cv2.waitKey(1)
+            if cv2.getWindowProperty('camera', cv2.WND_PROP_VISIBLE) < 1:
+                break
+            elif key == 27:
+                break
+        cap.release()
+
+    def gesture_recognition_img(self, filedir):
+        self.detector = HandDetector()
+        cnn = torch.load("CNN.pkl")
+        out_label = cnn.out_label
+        result = []
+        disp = ""
+        img = cv2.imread(filedir)
+        img = self.detector.find_hands(img)
+        while True:
+
+            lm_list, bbox = self.detector.find_position(img)
+
+            if lm_list.any():
+                x_1, y_1 = bbox["bbox"][0], bbox["bbox"][1]
+                data = torch.Tensor(lm_list)
+                data = data.unsqueeze(0)
+                data = data.unsqueeze(0)
+
+                test_output = cnn(data)
+                result.append(torch.max(test_output, 1)[1].data.cpu().numpy()[0])
+                if len(result) > 5:
+                    disp = str(out_label[stats.mode(result)[0][0]])
+                    result = []
+
+                cv2.putText(img, disp, (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
+                            (0, 0, 255), 3)
+
+            cv2.imshow("camera", img)
+            key = cv2.waitKey(1)
+            if cv2.getWindowProperty('camera', cv2.WND_PROP_VISIBLE) < 1:
+                break
+            elif key == 27:
+                break
+
+
+if __name__ == '__main__':
+    solution = Main()
+    my_datasets_dir = "test"
+    solution.make_datasets(my_datasets_dir, 100)
+    solution.train(my_datasets_dir)
+    dir_video = "C:/Users/Liar/Pictures/Camera Roll/WIN_20220630_20_11_47_Pro.mp4"
+    dir_img = "C:/Users/Liar/Pictures/Camera Roll/WIN_20220630_20_01_22_Pro.jpg"
+    # solution.gesture_recognition_camera()
+    # solution.gesture_recognition_video(dir_video)
+    # solution.gesture_recognition_img(dir_img)

ai_two.py

@@ -0,0 +1,414 @@
+# -*- coding:utf-8 -*-
+
+"""
+信号设计课程小组设计
+
+@ by: Leaf
+@ date: 2022-05-28
+"""
+
+import cv2
+import mediapipe as mp
+import torch
+import torch.nn as nn
+import numpy as np
+import tkinter as tk
+import shutil
+import math
+from scipy import stats
+from os.path import exists
+from os import mkdir
+from pathlib import Path
+from torch.utils.data import DataLoader, TensorDataset
+
+
+# 旋转函数
+def rotate(angle, x, y, point_x, point_y):
+    px = (x - point_x) * math.cos(angle) - (y - point_y) * math.sin(angle) + point_x
+    py = (x - point_x) * math.sin(angle) + (y - point_y) * math.cos(angle) + point_y
+    return px, py
+
+
+# 归一化
+def normalize(x):
+    max_x = np.max(x)
+    min_x = np.min(x)
+    return (x-min_x)/(max_x-min_x)
+
+
+class CNNTwo(nn.Module):
+    def __init__(self, m):
+        super(CNNTwo, self).__init__()
+        self.out_label = []
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(
+                in_channels=1,
+                out_channels=16,
+                kernel_size=5,
+                stride=1,
+                padding=2,
+            ),
+            nn.ReLU(),
+            nn.MaxPool2d(kernel_size=2),
+        )
+        self.conv2 = nn.Sequential(
+            nn.Conv2d(16, 32, 5, 1, 2),
+            nn.ReLU(),
+            nn.MaxPool2d(2),
+        )
+        self.med = nn.Linear(32 * 11 * 1, 500)
+        self.med2 = nn.Linear(1*21*3, 100)
+        self.med3 = nn.Linear(100, 500)
+        self.out = nn.Linear(500, m)  # fully connected layer, output 10 classes
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.conv2(x)
+        x = x.view(x.size(0), -1)  # 展平多维的卷积图成 (batch_size, 32 * 7 * 7)
+        x = self.med(x)
+        # x = self.med2(x)
+        # x = self.med3(x)
+        output = self.out(x)
+        return output
+
+
+class HandDetector:
+    """
+    使用mediapipe库查找手。导出地标像素格式。添加了额外的功能。
+    如查找方式，许多手指向上或两个手指之间的距离。而且提供找到的手的边界框信息。
+    """
+
+    def __init__(self, mode=False, max_hands=2, detection_con=0.5, min_track_con=0.5):
+        """
+        :param mode: 在静态模式下，对每个图像进行检测
+        :param max_hands: 要检测的最大手数
+        :param detection_con: 最小检测置信度
+        :param min_track_con: 最小跟踪置信度
+        """
+        self.results = None
+        self.mode = mode
+        self.max_hands = max_hands
+        self.modelComplex = 1
+        self.detection_con = detection_con
+        self.min_track_con = min_track_con
+
+        # 初始化手部的识别模型
+        self.mpHands = mp.solutions.hands
+        self.hands = self.mpHands.Hands(static_image_mode=self.mode,
+                                        max_num_hands=self.max_hands,
+                                        min_detection_confidence=self.detection_con,
+                                        min_tracking_confidence=self.min_track_con)
+        self.mpDraw = mp.solutions.drawing_utils  # 初始化绘图器
+        self.tipIds = [4, 8, 12, 16, 20]  # 指尖列表
+        self.fingers = []
+        self.lmList = []
+        self.re_lmList = []
+
+    def find_hands(self, img, draw=True):
+        """
+        从图像(BRG)中找到手部。
+        :param img: 用于查找手的图像。
+        :param draw: 在图像上绘制输出的标志。
+        :return: 带或不带图形的图像
+        """
+        img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  # 将传入的图像由BGR模式转标准的Opencv模式——RGB模式，
+        self.results = self.hands.process(img_rgb)
+
+        is_two_hand = False
+        if self.results.multi_hand_landmarks is not None and len(self.results.multi_hand_landmarks) >= 2:
+            is_two_hand = True
+            for handLms in self.results.multi_hand_landmarks:
+                if draw:
+                    self.mpDraw.draw_landmarks(img, handLms,
+                                               self.mpHands.HAND_CONNECTIONS)
+        return img, is_two_hand
+
+    def find_position(self, img, hand_no=0, draw=True):
+        """
+        查找单手的地标并将其放入列表中像素格式。还可以返回手部的周围的边界框。
+        :param img: 要查找的主图像
+        :param hand_no: 如果检测到多只手，则为手部id
+        :param draw: 在图像上绘制输出的标志。(默认绘制矩形框)
+        :return: 像素格式的手部关节位置列表；手部边界框
+        """
+        x_list = []
+        y_list = []
+        bbox_info = []
+        self.lmList = []
+        h, w, c = img.shape
+        if self.results.multi_hand_landmarks:
+            my_hand = self.results.multi_hand_landmarks[hand_no]
+            for i, lm in enumerate(my_hand.landmark):
+                px, py = int(lm.x * w), int(lm.y * h)
+                x_list.append(px)
+                y_list.append(py)
+                self.lmList.append([lm.x, lm.y, 0])
+                if draw:
+                    cv2.circle(img, (px, py), 5, (255, 0, 255), cv2.FILLED)
+            x_min, x_max = min(x_list), max(x_list)
+            y_min, y_max = min(y_list), max(y_list)
+            box_w, box_h = x_max - x_min, y_max - y_min
+            bbox = x_min, y_min, box_w, box_h
+            cx, cy = bbox[0] + (bbox[2] // 2), bbox[1] + (bbox[3] // 2)
+            bbox_info = {"id": hand_no, "bbox": bbox, "center": (cx, cy), "shape": (h, w)}
+
+            if draw:
+                cv2.rectangle(img, (bbox[0] - 20, bbox[1] - 20),
+                              (bbox[0] + bbox[2] + 20, bbox[1] + bbox[3] + 20),
+                              (0, 255, 0), 2)
+
+        self.revolve(img)
+        self.re_lmList = np.array(self.re_lmList)
+        if self.re_lmList.any():
+            self.re_lmList = np.concatenate((np.zeros((21, 1)), self.re_lmList), axis=1)
+            self.re_lmList = np.concatenate((self.re_lmList, np.zeros((1, 4))), axis=0)
+
+        return self.re_lmList, bbox_info
+
+    def revolve(self, img, draw=True):
+        """
+            旋转手势识别点
+            :param img: 要查找的主图像
+            :param draw: 在图像上绘制输出的标志。(默认绘制矩形框)
+            :return: 像素格式的手部关节位置列表
+        """
+        h, w, c = img.shape
+        if len(self.lmList) >= 21:
+            # print(self.lmList)
+            self.re_lmList = []
+            point_x = self.lmList[0][0]
+            point_y = self.lmList[0][1]
+            delta_x = self.lmList[13][0] - point_x
+            delta_y = self.lmList[13][1] - point_y
+            if delta_y == 0:
+                if delta_x < 0:
+                    theta = math.pi / 2
+                else:
+                    theta = -math.pi / 2
+            else:
+                theta = math.atan(delta_x / delta_y)
+                if delta_y > 0:
+                    theta = theta + math.pi
+            # print(theta*180/math.pi)
+            for i in self.lmList:
+                px, py = rotate(theta, i[0] * w, i[1] * h, point_x * w, point_y * h)
+                self.re_lmList.append([px, py, 0])
+                if draw:
+                    cv2.circle(img, (int(px), int(py)), 5, (0, 0, 255), cv2.FILLED)
+            # 归一化
+            x_array = normalize(np.array(self.re_lmList)[:, 0])
+            # print(x_array)
+            for i in range(len(x_array)):
+                self.re_lmList[i][0] = x_array[i]
+            y_array = normalize(np.array(self.re_lmList)[:, 1])
+            for i in range(len(y_array)):
+                self.re_lmList[i][1] = x_array[i]
+        else:
+            self.re_lmList = self.lmList
+        return self.re_lmList
+
+    def hand_type(self):
+        """
+        检查传入的手部 是左还是右
+        :return: 1 或 0
+        """
+        if self.results.multi_hand_landmarks:
+            if self.lmList[17][0] < self.lmList[5][0]:
+                return 1
+            else:
+                return 0
+
+
+class AI:
+    def __init__(self, datasets_dir):
+        self.EPOCH = 100
+        self.BATCH_SIZE = 4
+        self.LR = 10e-5
+        self.DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        self.datasets_dir = datasets_dir
+        self.train_loader = None
+        self.m = 0
+        self.out_label = []  # CNN网络输出后数字标签转和字符串标签的映射关系
+
+    def load_datasets(self):
+        train_data = []
+        train_label = []
+        self.m = 0
+        for file in Path(self.datasets_dir).rglob("*.npz"):
+            data = np.load(str(file))
+            train_data.append(data["data"])
+            label_number = np.ones(len(data["data"])) * len(self.out_label)
+            train_label.append(label_number)
+            self.out_label.append(data["label"])
+            self.m += 1
+        train_data = torch.Tensor(np.concatenate(train_data, axis=0))
+        train_data = train_data.unsqueeze(1)
+        train_label = torch.tensor(np.concatenate(train_label, axis=0)).long()
+
+        dataset = TensorDataset(train_data, train_label)
+        self.train_loader = DataLoader(dataset, batch_size=self.BATCH_SIZE, shuffle=True)
+        return self.m
+
+    def train_cnn(self):
+        cnn = CNNTwo(self.m).to(self.DEVICE)
+        optimizer = torch.optim.Adam(cnn.parameters(), self.LR)  # optimize all cnn parameters
+        loss_func = nn.CrossEntropyLoss()  # the target label is not one-hotted
+
+        for epoch in range(self.EPOCH):
+            for step, (data, target) in enumerate(self.train_loader):
+                # 分配 batch data, normalize x when iterate train_loader
+                data, target = data.to(self.DEVICE), target.to(self.DEVICE)
+                output = cnn(data)  # cnn output
+                loss = loss_func(output, target)  # cross entropy loss
+                optimizer.zero_grad()  # clear gradients for this training step
+                loss.backward()  # backpropagation, compute gradients
+                optimizer.step()  # apply gradients
+                if (step + 1) % 50 == 0:  # 输出结果
+                    print(
+                        "\r[Epoch: %d] [%d/%d (%0.f %%)][Loss: %f]"
+                        % (
+                            epoch + 1,
+                            (step + 1) * len(data),
+                            len(self.train_loader.dataset),
+                            100. * (step + 1) / len(self.train_loader),
+                            loss.item()
+                        ), end="")
+
+        cnn.out_label = self.out_label
+        torch.save(cnn, 'CNN_two.pkl')
+        print("训练结束")
+
+
+class Main:
+    def __init__(self):
+        self.camera = None
+        self.detector = HandDetector()
+        self.default_datasets = "Datasets"
+        self.len_x = 44
+        self.len_y = 4
+        self.label = ''
+
+        self.result = []
+        self.disp = ""
+
+    def change_state(self):
+        self.label = self.entry.get()  # 调用get()方法，将Entry中的内容获取出来
+        self.top1.quit()
+        if self.label == "":
+            self.top1.destroy()
+
+    def on_closing(self):
+        self.label = ""
+        self.top1.destroy()
+
+    def make_datasets(self, camera, datasets_dir="default", n=100):
+        if datasets_dir == "default":
+            return
+        if exists(datasets_dir):
+            shutil.rmtree(datasets_dir)
+        mkdir(datasets_dir)
+        self.camera = camera
+
+        self.top1 = tk.Tk()
+        self.top1.geometry('300x50')
+        self.top1.title('请输入标签')
+        self.top1.protocol("WM_DELETE_WINDOW", self.on_closing)
+        tk.Label(self.top1, text='Label:').place(x=27, y=10)
+        self.entry = tk.Entry(self.top1, width=15)
+        self.entry.place(x=80, y=10)
+        tk.Button(self.top1, text='确定', command=self.change_state).place(x=235, y=5)
+
+        self.top1.mainloop()
+        while not self.label == "":
+            data = np.zeros([n, self.len_x, self.len_y])
+            shape_list = np.zeros([n, 2], dtype=np.int16)
+            hand_type = np.zeros(n, dtype=np.int8)
+
+            count = 0
+            cv2.startWindowThread()
+            while True:
+                frame, img = self.camera.read()
+                img, is_two_hand = self.detector.find_hands(img)
+                result = np.zeros((self.len_x, self.len_y))
+
+                if is_two_hand:
+                    lm_list1, bbox1 = self.detector.find_position(img, 0)
+                    lm_list2, bbox2 = self.detector.find_position(img, 1)
+                    for i in range(len(lm_list1)):
+                        result[i] = np.array(lm_list1[i])
+                    for i in range(len(lm_list1), len(lm_list1)+len(lm_list2)):
+                        result[i] = np.array(lm_list2[i-len(lm_list1)])
+                    if result.sum() > 0:  # 假设矩阵不为0，即捕捉到手部时
+
+                        shape1 = bbox1["shape"]
+                        x_1, y_1 = bbox1["bbox"][0], bbox1["bbox"][1]
+                        shape2 = bbox2["shape"]
+                        x_2, y_2 = bbox2["bbox"][0], bbox2["bbox"][1]
+                        data[count] = result
+                        hand_type[count] = self.detector.hand_type()
+                        shape_list[count] = np.array(shape1)
+                        count += 1
+                        cv2.putText(img, str("{}/{}".format(count, n)), (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
+                                    (0, 255, 0), 3)
+                        cv2.putText(img, str("{}/{}".format(count, n)), (x_2, y_2), cv2.FONT_HERSHEY_PLAIN, 3,
+                                    (0, 255, 0), 3)
+
+                cv2.imshow("camera", img)
+                key = cv2.waitKey(100)
+                if cv2.getWindowProperty('camera', cv2.WND_PROP_VISIBLE) < 1:
+                    break
+                elif key == 27:
+                    break
+                elif count == n - 1:
+                    break
+            cv2.destroyAllWindows()
+
+            open(datasets_dir + "/" + self.label + ".npz", "w")
+            np.savez(datasets_dir + "/" + self.label + ".npz", label=self.label, data=data,
+                     handtype=hand_type, shape=shape_list)
+
+            self.top1.mainloop()
+
+    def train(self, datasets_dir="default"):
+        if datasets_dir == "default":
+            datasets_dir = self.default_datasets
+        ai = AI(datasets_dir)
+        ai.load_datasets()
+        ai.train_cnn()
+
+    def gesture_recognition(self, detector, img, cnn):
+        self.detector = detector
+        out_label = cnn.out_label
+        img, is_two_hand = self.detector.find_hands(img)
+        if is_two_hand:
+            lm_list1, bbox1 = self.detector.find_position(img, 0)
+            lm_list2, bbox2 = self.detector.find_position(img, 1)
+            if lm_list1.any() and lm_list2.any():
+                x_1, y_1 = bbox1["bbox"][0], bbox1["bbox"][1]
+                x_2, y_2 = bbox2["bbox"][0], bbox2["bbox"][1]
+                lm_list = np.concatenate((lm_list1, lm_list2), axis=0)
+                data = torch.Tensor(lm_list)
+                data = data.unsqueeze(0)
+                data = data.unsqueeze(0)
+
+                test_output = cnn(data)
+                self.result.append(torch.max(test_output, 1)[1].data.cpu().numpy()[0])
+                if len(self.result) > 4:
+                    self.disp = str(out_label[stats.mode(self.result)[0][0]])
+                    self.result = []
+
+                cv2.putText(img, self.disp, (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
+                            (0, 0, 255), 3)
+                cv2.putText(img, self.disp, (x_2, y_2), cv2.FONT_HERSHEY_PLAIN, 3,
+                            (0, 0, 255), 3)
+        else:
+            return 1
+        return 0
+
+
+if __name__ == '__main__':
+    solution = Main()
+    my_datasets_dir = "test-two"
+    solution.make_datasets(my_datasets_dir, 100)
+    solution.train(my_datasets_dir)
+    solution.gesture_recognition()

datatest.py

@@ -0,0 +1,177 @@
+import cv2
+import mediapipe as mp
+import numpy as np
+
+
+class HandDetector:
+    """
+    使用mediapipe库查找手。导出地标像素格式。添加了额外的功能。
+    如查找方式，许多手指向上或两个手指之间的距离。而且提供找到的手的边界框信息。
+    """
+
+    def __init__(self, mode=False, max_hands=2, detection_con=0.5, min_track_con=0.5):
+        """
+        :param mode: 在静态模式下，对每个图像进行检测
+        :param max_hands: 要检测的最大手数
+        :param detection_con: 最小检测置信度
+        :param min_track_con: 最小跟踪置信度
+        """
+        self.results = None
+        self.mode = mode
+        self.max_hands = max_hands
+        self.modelComplex = 1
+        self.detection_con = detection_con
+        self.min_track_con = min_track_con
+
+        # 初始化手部的识别模型
+        self.mpHands = mp.solutions.hands
+        self.hands = self.mpHands.Hands(static_image_mode=self.mode,
+                                        max_num_hands=self.max_hands,
+                                        min_detection_confidence=self.detection_con,
+                                        min_tracking_confidence=self.min_track_con)
+        self.mpDraw = mp.solutions.drawing_utils  # 初始化绘图器
+        self.tipIds = [4, 8, 12, 16, 20]  # 指尖列表
+        self.fingers = []
+        self.lmList = []
+
+    def find_hands(self, img, draw=True):
+        """
+        从图像(BRG)中找到手部。
+        :param img: 用于查找手的图像。
+        :param draw: 在图像上绘制输出的标志。
+        :return: 带或不带图形的图像
+        """
+        img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  # 将传入的图像由BGR模式转标准的Opencv模式——RGB模式，
+        self.results = self.hands.process(img_rgb)
+
+        if self.results.multi_hand_landmarks:
+            for handLms in self.results.multi_hand_landmarks:
+                if draw:
+                    self.mpDraw.draw_landmarks(img, handLms,
+                                               self.mpHands.HAND_CONNECTIONS)
+        return img
+
+    def find_position(self, img, hand_no=0, draw=True):
+        """
+        查找单手的地标并将其放入列表中像素格式。还可以返回手部的周围的边界框。
+        :param img: 要查找的主图像
+        :param hand_no: 如果检测到多只手，则为手部id
+        :param draw: 在图像上绘制输出的标志。(默认绘制矩形框)
+        :return: 像素格式的手部关节位置列表；手部边界框
+        """
+
+        x_list = []
+        y_list = []
+        onedata = np.zeros([21,3])
+        zerodata = np.zeros([21,3])
+        h, w, c = img.shape
+        self.lmList = []
+
+        if self.results.multi_hand_landmarks:
+            my_hand = self.results.multi_hand_landmarks[hand_no]
+            for i, lm in enumerate(my_hand.landmark):
+                onedata[i] = np.array([lm.x,lm.y,lm.z])     #将三维坐标添加到单次截屏的数据中
+
+                px, py= int(lm.x * w), int(lm.y * h)
+                x_list.append(px)
+                y_list.append(py)
+                self.lmList.append([px, py])
+                if draw:
+                    cv2.circle(img, (px, py), 5, (255, 0, 255), cv2.FILLED)
+
+        return onedata, (h, w)
+
+    def fingers_up(self):
+        """
+        查找列表中打开并返回的手指数。会分别考虑左手和右手
+        :return: 竖起手指的列表
+        """
+        fingers = []
+        if self.results.multi_hand_landmarks:
+            my_hand_type = self.hand_type()
+            # Thumb
+            if my_hand_type == "Right":
+                if self.lmList[self.tipIds[0]][0] > self.lmList[self.tipIds[0] - 1][0]:
+                    fingers.append(1)
+                else:
+                    fingers.append(0)
+            else:
+                if self.lmList[self.tipIds[0]][0] < self.lmList[self.tipIds[0] - 1][0]:
+                    fingers.append(1)
+                else:
+                    fingers.append(0)
+            # 4 Fingers
+            for i in range(1, 5):
+                if self.lmList[self.tipIds[i]][1] < self.lmList[self.tipIds[i] - 2][1]:
+                    fingers.append(1)
+                else:
+                    fingers.append(0)
+        return fingers
+
+    def hand_type(self):
+        """
+        检查传入的手部是左还是右
+        :return: "Right" 或 "Left"
+        """
+        if self.results.multi_hand_landmarks:
+            if self.lmList[17][0] < self.lmList[5][0]:
+                return 1
+            else:
+                return 0
+
+
+class Main:
+    def __init__(self, label, N = 100):
+        self.detector = None
+        self.camera = cv2.VideoCapture(0, cv2.CAP_DSHOW)
+        self.camera.set(3, 1280)
+        self.camera.set(4, 720)
+        self.N = N
+        #初始化数据包
+        self.label = label
+        self.data = np.zeros([N,21,3])
+        self.shape = np.zeros([N,2], dtype = np.int16)
+        self.handtype = np.zeros(N, dtype = np.int8)
+
+    def gesture_recognition(self):
+        self.detector = HandDetector()
+        #初始化数据
+        
+        zerodata = np.zeros([21,3])
+        rezult = np.zeros([21,3])
+        count = 0
+
+        while True:
+            frame, img = self.camera.read()
+            img = self.detector.find_hands(img)
+            
+            rezult,shape = self.detector.find_position(img)
+            if rezult.all() != zerodata.all():              #假设矩阵不为0，即捕捉到手部时
+                self.data[count] = rezult
+                self.handtype[count] = self.detector.hand_type()
+                self.shape[count] = np.array(shape)
+                count += 1
+
+            cv2.imshow("camera", img)
+            key = cv2.waitKey(1)
+            if cv2.getWindowProperty('camera', cv2.WND_PROP_VISIBLE) < 1:
+                break
+            elif key == 27:
+                break
+            elif count == self.N - 1:
+                break
+        
+        np.savez('firstdata', label = self.label, data = self.data, 
+                                handtype = self.handtype, shape = self.shape)
+
+
+if __name__ == '__main__':
+    Solution = Main(label = "five")
+    Solution.gesture_recognition()
+    npzfile = np.load('firstdata.npz')
+    
+    #print(npzfile['data'][0])
+    #print(" ")
+    #print(npzfile['handtype'])
+    #print(npzfile['label'])
+    #print(npzfile['shape'])

gr.py

@@ -0,0 +1,180 @@
+import TM
+import ai
+import ai_two
+import cv2
+import copy
+import torch
+import torch.nn as nn
+
+
+class CNN(nn.Module):
+    def __init__(self, m):
+        super(CNN, self).__init__()
+        self.out_label = []
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(
+                in_channels=1,
+                out_channels=16,
+                kernel_size=5,
+                stride=1,
+                padding=2,
+            ),
+            nn.ReLU(),
+            nn.MaxPool2d(kernel_size=1),
+        )
+        self.conv2 = nn.Sequential(
+            nn.Conv2d(16, 32, 5, 1, 2),
+            nn.ReLU(),
+            nn.MaxPool2d(2),
+        )
+        self.med = nn.Linear(32 * 11 * 2, 500)
+        self.med2 = nn.Linear(1 * 21 * 3, 100)
+        self.med3 = nn.Linear(100, 500)
+        self.out = nn.Linear(500, m)  # fully connected layer, output 10 classes
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.conv2(x)
+        x = x.view(x.size(0), -1)  # 展平多维的卷积图成 (batch_size, 32 * 7 * 7)
+        x = self.med(x)
+        # x = self.med2(x)
+        # x = self.med3(x)
+        output = self.out(x)
+        return output
+
+
+class CNNTwo(nn.Module):
+    def __init__(self, m):
+        super(CNNTwo, self).__init__()
+        self.out_label = []
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(
+                in_channels=1,
+                out_channels=16,
+                kernel_size=5,
+                stride=1,
+                padding=2,
+            ),
+            nn.ReLU(),
+            nn.MaxPool2d(kernel_size=2),
+        )
+        self.conv2 = nn.Sequential(
+            nn.Conv2d(16, 32, 5, 1, 2),
+            nn.ReLU(),
+            nn.MaxPool2d(2),
+        )
+        self.med = nn.Linear(32 * 11 * 1, 500)
+        self.med2 = nn.Linear(1*21*3, 100)
+        self.med3 = nn.Linear(100, 500)
+        self.out = nn.Linear(500, m)  # fully connected layer, output 10 classes
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.conv2(x)
+        x = x.view(x.size(0), -1)  # 展平多维的卷积图成 (batch_size, 32 * 7 * 7)
+        x = self.med(x)
+        # x = self.med2(x)
+        # x = self.med3(x)
+        output = self.out(x)
+        return output
+
+
+class Main:
+    def __init__(self):
+        self.camera = cv2.VideoCapture(0, cv2.CAP_DSHOW)
+        self.camera.set(3, 1280)
+        self.camera.set(4, 720)
+
+        self.tm_detector = TM.HandDetector()
+        self.ai_detector = ai.HandDetector()
+        self.at_detector = ai_two.HandDetector()
+
+        self.tm_main = TM.Main()
+        self.ai_main = ai.Main()
+        self.at_main = ai_two.Main()
+
+    def gr_img(self, filedir, diy):
+        print(filedir)
+        if diy:
+            cnn = torch.load("CNN.pkl")
+            cnn_two = torch.load("CNN_two.pkl")
+        while True:
+            not_match = 0
+            img = cv2.imread(filedir)
+            img_tm = copy.deepcopy(img)
+            is_one_hand = self.at_main.gesture_recognition(self.at_detector, img, cnn_two)
+            if is_one_hand:
+                not_match = self.ai_main.gesture_recognition_camera(self.ai_detector, img, cnn)
+                if not_match:
+                    self.tm_main.gesture_recognition(img_tm, self.tm_detector)
+
+            if not_match:
+                cv2.imshow("camera", img_tm)
+            else:
+                cv2.imshow("camera", img)
+            key = cv2.waitKey(1)
+            if cv2.getWindowProperty('camera', cv2.WND_PROP_VISIBLE) < 1:
+                break
+            elif key == 27:
+                break
+
+    def gr_video(self, filedir, diy):
+        cap = cv2.VideoCapture(filedir)
+        if diy:
+            cnn = torch.load("CNN.pkl")
+            cnn_two = torch.load("CNN_two.pkl")
+        while True:
+            ret, img = cap.read()
+            not_match = 0
+            img_tm = copy.deepcopy(img)
+            is_one_hand = self.at_main.gesture_recognition(self.at_detector, img, cnn_two)
+            if is_one_hand:
+                not_match = self.ai_main.gesture_recognition_camera(self.ai_detector, img, cnn)
+                if not_match:
+                    self.tm_main.gesture_recognition(img_tm, self.tm_detector)
+
+            if not_match:
+                cv2.imshow("camera", img_tm)
+            else:
+                cv2.imshow("camera", img)
+            key = cv2.waitKey(1)
+            if cv2.getWindowProperty('camera', cv2.WND_PROP_VISIBLE) < 1:
+                break
+            elif key == 27:
+                break
+        cap.release()
+
+    def gr_realtime(self, diy):
+        if diy:
+            cnn = torch.load("CNN.pkl")
+            cnn_two = torch.load("CNN_two.pkl")
+        while True:
+            frame, img = self.camera.read()
+            not_match = 0
+            img_tm = copy.deepcopy(img)
+            is_one_hand = self.at_main.gesture_recognition(self.at_detector, img, cnn_two)
+            if is_one_hand:
+                not_match = self.ai_main.gesture_recognition_camera(self.ai_detector, img, cnn)
+                if not_match:
+                    self.tm_main.gesture_recognition(img_tm, self.tm_detector)
+
+            if not_match:
+                cv2.imshow("camera", img_tm)
+            else:
+                cv2.imshow("camera", img)
+            key = cv2.waitKey(1)
+            if cv2.getWindowProperty('camera', cv2.WND_PROP_VISIBLE) < 1:
+                break
+            elif key == 27:
+                break
+
+    def ai_input(self):
+        self.ai_main.make_datasets(self.camera, "ai_datasets", 100)
+        self.ai_main.train("ai_datasets")
+        self.at_main.make_datasets(self.camera, "ai_two_datasets", 100)
+        self.at_main.train("ai_two_datasets")
+
+
+if __name__ == '__main__':
+    main = Main()
+    main.gr_img("", 0)

main.py

@@ -0,0 +1,112 @@
+# -*- coding:utf-8 -*-
+
+"""
+信号设计课程小组设计
+
+@ by: Leaf
+@ date: 2022-05-28
+"""
+import gr
+
+import tkinter as tk
+from tkinter import filedialog, Button, Label, Frame, ACTIVE, LEFT
+from PIL import Image, ImageTk
+
+
+class DisplayImage:
+    """用于展示选择的图片"""
+    def __init__(self, master):
+        self.master = master
+        master.title("GUI")
+        self.Text_lab0 = Label(master, text='已加载图像/视频')
+        self.Text_lab0.pack(pady=10)
+        self.image_frame = Frame(master, bd=0, height=300, width=300, bg='white', highlightthickness=2,
+                                 highlightbackground='gray', highlightcolor='black')
+        self.image_frame.pack()
+
+        self.Text_label = Label(master, text='加载待识别影像/视频')
+        self.Text_label.place(x=60, y=410)
+        self.Choose_image = Button(master, command=self.choose_img, text="图像",
+                                   width=7, default=ACTIVE, borderwidth=0)
+        self.Choose_image.place(x=50, y=450)
+        self.Choose_image = Button(master, command=self.choose_video, text="视频",
+                                   width=7, default=ACTIVE, borderwidth=0)
+        self.Choose_image.place(x=120, y=450)
+        self.Text_label2 = Label(master, text='运行手势识别程序')
+        self.Text_label2.place(x=60, y=500)
+        self.image_mosaic = Button(master, command=self.gesture_recognition, text="Gesture recognition",
+                                   width=17, default=ACTIVE, borderwidth=0)
+        self.image_mosaic.place(x=50, y=540)
+        self.Text_label3 = Label(master, text='运行实时手势识别程序')
+        self.Text_label3.place(x=300, y=410)
+        self.realtime = Button(master, command=self.realtime_gr, text="Realtime\n gesture recognition",
+                               width=17, height=6, default=ACTIVE, borderwidth=0)
+        self.realtime.place(x=300, y=450)
+        self.Text_label4 = Label(master, text='录入自定义手势')
+        self.Text_label4.place(x=180, y=610)
+        self.input = Button(master, command=self.input_image, text="Input gesture",
+                            width=42, default=ACTIVE, borderwidth=0)
+        self.input.place(x=60, y=650)
+
+        self.gr = gr.Main()
+        self.temp_dir = "temp"
+        self.mode = 0
+        self.directory = ""
+        self.diy = 1
+
+    def choose_img(self):
+        self.mode = 1
+        # 清空框架中的内容
+        for widget in self.image_frame.winfo_children():
+            widget.destroy()
+
+        self.directory = filedialog.askopenfilename()
+
+        # 布局所选图片
+        img = Image.open(self.directory).resize((300, 300))
+        img.save(self.temp_dir + "/photo.png")
+        image = ImageTk.PhotoImage(image=img)
+        label = Label(self.image_frame, highlightthickness=0, borderwidth=0)
+        label.configure(image=image)
+        label.pack(side=LEFT, expand=True)
+
+    def choose_video(self):
+        # 清空框架中的内容
+        self.mode = 2
+        for widget in self.image_frame.winfo_children():
+            widget.destroy()
+
+        self.directory = filedialog.askopenfilename()
+
+        # 布局所选图片
+        img = Image.open(self.temp_dir+"/video.jpg").resize((300, 300))
+        img.save(self.temp_dir + "/photo.png")
+        image = ImageTk.PhotoImage(image=img)
+        label = Label(self.image_frame, highlightthickness=0, borderwidth=0)
+        label.configure(image=image)
+        label.pack(side=LEFT, expand=True)
+
+    def gesture_recognition(self):
+        if self.mode == 1:
+            self.gr.gr_img(self.directory, self.diy)
+        elif self.mode == 2:
+            self.gr.gr_video(self.directory, self.diy)
+
+    def realtime_gr(self):
+        self.gr.gr_realtime(self.diy)
+
+    def input_image(self):
+        self.diy = 1
+        self.gr.ai_input()
+
+
+def main():
+    window = tk.Tk()
+    DisplayImage(window)
+    window.title('手势识别')
+    window.geometry('500x720')
+    window.mainloop()
+
+
+if __name__ == '__main__':
+    main()