gesture-recognition/demo.py

# -*- 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
from pathlib import Path
from torch.utils.data import DataLoader, TensorDataset


class CNN(nn.Module):
    def __init__(self):
        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(3),
        )
        self.med = nn.Linear(32 * 7 * 1, 500)
        self.out = nn.Linear(500, 10)  # 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)
        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 = []

    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 _, 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, lm.z])
                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 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 "Right"
            else:
                return "Left"


class Main:
    def __init__(self):
        self.EPOCH = 50
        self.BATCH_SIZE = 5
        self.LR = 10e-5
        self.DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")

        self.camera = cv2.VideoCapture(0, cv2.CAP_DSHOW)
        self.camera.set(3, 1280)
        self.camera.set(4, 720)

        self.datasets_dir = "Datasets"
        self.train_loader = None
        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

        self.detector = None

    def load_datasets(self):
        train_data = []
        train_label = []

        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"])
        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)

    def train_cnn(self):
        cnn = CNN().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) % 100 == 0:  # 输出结果
                    if (step + 1) % 100 == 0:  # 输出结果
                        print(
                            "\r[Epoch: %d] [%d/%d (%0.f %%)][Loss: %f]"
                            % (
                                epoch,
                                step * len(data),
                                len(self.train_loader.dataset),
                                100. * step / len(self.train_loader),
                                loss.item()
                            ), end="")

        cnn.out_label = self.out_label
        torch.save(cnn, 'CNN.pkl')
        print("训练结束")

    def gesture_recognition(self):
        self.detector = HandDetector()
        cnn = torch.load("CNN.pkl")
        out_label = cnn.out_label
        while True:
            frame, img = self.camera.read()
            img = self.detector.find_hands(img)
            lm_list, bbox = self.detector.find_position(img)

            if lm_list:
                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 = torch.max(test_output, 1)[1].data.cpu().numpy()[0]
                cv2.putText(img, str(out_label[result]), (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()
    Solution.load_datasets()
    Solution.train_cnn()
    Solution.gesture_recognition()