# -*- 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( # input shape (1, 21, 3) nn.Conv2d( in_channels=1, # input height out_channels=16, # n_filters kernel_size=5, # filter size stride=1, # filter movement/step padding=2, # 如果想要 con2d 出来的图片长宽没有变化, padding=(kernel_size-1)/2 当 stride=1 ), # output shape (16, 28, 28) nn.ReLU(), # activation nn.MaxPool2d(kernel_size=1), # 在 2x2 空间里向下采样, output shape (16, 14, 14) ) self.conv2 = nn.Sequential( # input shape (16, 14, 14) nn.Conv2d(16, 32, 5, 1, 2), # output shape (32, 14, 14) nn.ReLU(), # activation nn.MaxPool2d(3), # output shape (32, 7, 7) ) 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 = [] 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 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 = 20 self.BATCH_SIZE = 10 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() 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] x1, x2, x3, x4, x5 = self.detector.fingers_up() if (x2 == 1 and x3 == 1) and (x4 == 0 and x5 == 0 and x1 == 0): cv2.putText(img, "2_TWO", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3, (0, 0, 255), 3) elif (x2 == 1 and x3 == 1 and x4 == 1) and (x1 == 0 and x5 == 0): cv2.putText(img, "3_THREE", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3, (0, 0, 255), 3) elif (x2 == 1 and x3 == 1 and x4 == 1 and x5 == 1) and (x1 == 0): cv2.putText(img, "4_FOUR", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3, (0, 0, 255), 3) elif x1 == 1 and x2 == 1 and x3 == 1 and x4 == 1 and x5 == 1: cv2.putText(img, "5_FIVE", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3, (0, 0, 255), 3) elif x2 == 1 and x1 == 0 and (x3 == 0, x4 == 0, x5 == 0): cv2.putText(img, "1_ONE", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3, (0, 0, 255), 3) elif x1 == 1 and x2 == 1 and (x3 == 0, x4 == 0, x5 == 0): cv2.putText(img, "8_EIGHT", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3, (0, 0, 255), 3) elif x1 == 1 and x5 == 1 and (x3 == 0, x4 == 0, x5 == 0): cv2.putText(img, "6_SIX", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3, (0, 0, 255), 3) elif x1 and (x2 == 0, x3 == 0, x4 == 0, x5 == 0): cv2.putText(img, "GOOD!", (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.gesture_recognition() Solution.load_datasets() Solution.train_cnn()