双手手势识别

ai-two.py

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+# -*- 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 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=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 = 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 = 44
+        self.len_y = 4
+        self.label = ''
+        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)
+
+    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, datasets_dir="default", n=100):
+        if datasets_dir == "default":
+            return
+        if exists(datasets_dir):
+            shutil.rmtree(datasets_dir)
+        mkdir(datasets_dir)
+        if self.camera is None:
+            self.camera = cv2.VideoCapture(0, cv2.CAP_DSHOW)
+            self.camera.set(3, 1280)
+            self.camera.set(4, 720)
+        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):
+        if self.camera is None:
+            self.camera = cv2.VideoCapture(0, cv2.CAP_DSHOW)
+            self.camera.set(3, 1280)
+            self.camera.set(4, 720)
+        self.detector = HandDetector()
+        cnn = torch.load("CNN.pkl")
+        out_label = cnn.out_label
+        result = []
+        disp = ""
+        while True:
+            frame, img = self.camera.read()
+            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)
+                    result.append(torch.max(test_output, 1)[1].data.cpu().numpy()[0])
+                    if len(result) > 4:
+                        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.putText(img, disp, (x_2, y_2), 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-two"
+    solution.make_datasets(my_datasets_dir, 100)
+    solution.train(my_datasets_dir)
+    solution.gesture_recognition()