288 lines
12 KiB
Python
288 lines
12 KiB
Python
# -*- coding:utf-8 -*-
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"""
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信号设计课程小组设计
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@ by: Leaf
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@ date: 2022-05-28
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"""
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import cv2
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import mediapipe as mp
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import torch
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import torch.nn as nn
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import numpy as np
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from pathlib import Path
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from torch.utils.data import DataLoader, TensorDataset
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class CNN(nn.Module):
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def __init__(self):
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super(CNN, self).__init__()
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self.out_label = []
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self.conv1 = nn.Sequential( # input shape (1, 21, 3)
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nn.Conv2d(
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in_channels=1, # input height
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out_channels=16, # n_filters
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kernel_size=5, # filter size
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stride=1, # filter movement/step
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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
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), # output shape (16, 28, 28)
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nn.ReLU(), # activation
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nn.MaxPool2d(kernel_size=1), # 在 2x2 空间里向下采样, output shape (16, 14, 14)
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)
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self.conv2 = nn.Sequential( # input shape (16, 14, 14)
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nn.Conv2d(16, 32, 5, 1, 2), # output shape (32, 14, 14)
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nn.ReLU(), # activation
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nn.MaxPool2d(3), # output shape (32, 7, 7)
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)
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self.med = nn.Linear(32 * 7 * 1, 500)
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self.out = nn.Linear(500, 10) # fully connected layer, output 10 classes
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def forward(self, x):
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x = self.conv1(x)
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x = self.conv2(x)
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x = x.view(x.size(0), -1) # 展平多维的卷积图成 (batch_size, 32 * 7 * 7)
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x = self.med(x)
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output = self.out(x)
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return output
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class HandDetector:
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"""
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使用mediapipe库查找手。导出地标像素格式。添加了额外的功能。
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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。
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"""
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def __init__(self, mode=False, max_hands=2, detection_con=0.5, min_track_con=0.5):
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"""
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:param mode: 在静态模式下，对每个图像进行检测
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:param max_hands: 要检测的最大手数
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:param detection_con: 最小检测置信度
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:param min_track_con: 最小跟踪置信度
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"""
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self.results = None
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self.mode = mode
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self.max_hands = max_hands
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self.modelComplex = 1
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self.detection_con = detection_con
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self.min_track_con = min_track_con
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# 初始化手部的识别模型
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self.mpHands = mp.solutions.hands
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self.hands = self.mpHands.Hands(static_image_mode=self.mode,
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max_num_hands=self.max_hands,
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min_detection_confidence=self.detection_con,
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min_tracking_confidence=self.min_track_con)
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self.mpDraw = mp.solutions.drawing_utils # 初始化绘图器
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self.tipIds = [4, 8, 12, 16, 20] # 指尖列表
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self.fingers = []
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self.lmList = []
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def find_hands(self, img, draw=True):
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"""
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从图像(BRG)中找到手部。
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:param img: 用于查找手的图像。
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:param draw: 在图像上绘制输出的标志。
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:return: 带或不带图形的图像
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"""
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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，
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self.results = self.hands.process(img_rgb)
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if self.results.multi_hand_landmarks:
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for handLms in self.results.multi_hand_landmarks:
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if draw:
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self.mpDraw.draw_landmarks(img, handLms,
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self.mpHands.HAND_CONNECTIONS)
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return img
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def find_position(self, img, hand_no=0, draw=True):
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"""
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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。
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:param img: 要查找的主图像
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:param hand_no: 如果检测到多只手，则为手部id
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:param draw: 在图像上绘制输出的标志。(默认绘制矩形框)
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:return: 像素格式的手部关节位置列表；手部边界框
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"""
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x_list = []
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y_list = []
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bbox_info = []
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self.lmList = []
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if self.results.multi_hand_landmarks:
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my_hand = self.results.multi_hand_landmarks[hand_no]
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for _, lm in enumerate(my_hand.landmark):
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h, w, c = img.shape
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px, py = int(lm.x * w), int(lm.y * h)
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x_list.append(px)
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y_list.append(py)
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self.lmList.append([px, py])
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if draw:
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cv2.circle(img, (px, py), 5, (255, 0, 255), cv2.FILLED)
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x_min, x_max = min(x_list), max(x_list)
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y_min, y_max = min(y_list), max(y_list)
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box_w, box_h = x_max - x_min, y_max - y_min
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bbox = x_min, y_min, box_w, box_h
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cx, cy = bbox[0] + (bbox[2] // 2), bbox[1] + (bbox[3] // 2)
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bbox_info = {"id": hand_no, "bbox": bbox, "center": (cx, cy)}
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if draw:
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cv2.rectangle(img, (bbox[0] - 20, bbox[1] - 20),
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(bbox[0] + bbox[2] + 20, bbox[1] + bbox[3] + 20),
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(0, 255, 0), 2)
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return self.lmList, bbox_info
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def fingers_up(self):
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"""
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查找列表中打开并返回的手指数。会分别考虑左手和右手
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:return: 竖起手指的列表
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"""
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fingers = []
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if self.results.multi_hand_landmarks:
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my_hand_type = self.hand_type()
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# Thumb
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if my_hand_type == "Right":
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if self.lmList[self.tipIds[0]][0] > self.lmList[self.tipIds[0] - 1][0]:
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fingers.append(1)
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else:
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fingers.append(0)
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else:
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if self.lmList[self.tipIds[0]][0] < self.lmList[self.tipIds[0] - 1][0]:
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fingers.append(1)
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else:
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fingers.append(0)
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# 4 Fingers
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for i in range(1, 5):
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if self.lmList[self.tipIds[i]][1] < self.lmList[self.tipIds[i] - 2][1]:
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fingers.append(1)
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else:
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fingers.append(0)
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return fingers
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def hand_type(self):
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"""
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检查传入的手部是左还是右
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:return: "Right" 或 "Left"
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"""
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if self.results.multi_hand_landmarks:
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if self.lmList[17][0] < self.lmList[5][0]:
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return "Right"
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else:
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return "Left"
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class Main:
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def __init__(self):
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self.EPOCH = 20
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self.BATCH_SIZE = 10
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self.LR = 10e-5
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self.DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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self.camera = cv2.VideoCapture(0, cv2.CAP_DSHOW)
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self.camera.set(3, 1280)
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self.camera.set(4, 720)
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self.datasets_dir = "Datasets"
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self.train_loader = None
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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
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self.detector = None
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def load_datasets(self):
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train_data = []
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train_label = []
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for file in Path(self.datasets_dir).rglob("*.npz"):
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data = np.load(str(file))
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train_data.append(data["data"])
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label_number = np.ones(len(data["data"]))*len(self.out_label)
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train_label.append(label_number)
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self.out_label.append(data["label"])
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train_data = torch.Tensor(np.concatenate(train_data, axis=0))
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train_data = train_data.unsqueeze(1)
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train_label = torch.tensor(np.concatenate(train_label, axis=0)).long()
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dataset = TensorDataset(train_data, train_label)
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self.train_loader = DataLoader(dataset, batch_size=self.BATCH_SIZE, shuffle=True)
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def train_cnn(self):
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cnn = CNN().to(self.DEVICE)
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optimizer = torch.optim.Adam(cnn.parameters(), self.LR) # optimize all cnn parameters
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loss_func = nn.CrossEntropyLoss() # the target label is not one-hotted
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for epoch in range(self.EPOCH):
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for step, (data, target) in enumerate(self.train_loader):
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# 分配 batch data, normalize x when iterate train_loader
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data, target = data.to(self.DEVICE), target.to(self.DEVICE)
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output = cnn(data) # cnn output
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loss = loss_func(output, target) # cross entropy loss
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optimizer.zero_grad() # clear gradients for this training step
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loss.backward() # backpropagation, compute gradients
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optimizer.step() # apply gradients
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if (step + 1) % 100 == 0: # 输出结果
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if (step + 1) % 100 == 0: # 输出结果
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print(
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"\r[Epoch: %d] [%d/%d (%0.f %%)][Loss: %f]"
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% (
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epoch,
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step * len(data),
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len(self.train_loader.dataset),
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100. * step / len(self.train_loader),
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loss.item()
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), end="")
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cnn.out_label = self.out_label
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torch.save(cnn, 'CNN.pkl')
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print("训练结束")
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def gesture_recognition(self):
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self.detector = HandDetector()
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while True:
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frame, img = self.camera.read()
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img = self.detector.find_hands(img)
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lm_list, bbox = self.detector.find_position(img)
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if lm_list:
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x_1, y_1 = bbox["bbox"][0], bbox["bbox"][1]
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x1, x2, x3, x4, x5 = self.detector.fingers_up()
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if (x2 == 1 and x3 == 1) and (x4 == 0 and x5 == 0 and x1 == 0):
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cv2.putText(img, "2_TWO", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
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(0, 0, 255), 3)
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elif (x2 == 1 and x3 == 1 and x4 == 1) and (x1 == 0 and x5 == 0):
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cv2.putText(img, "3_THREE", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
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(0, 0, 255), 3)
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elif (x2 == 1 and x3 == 1 and x4 == 1 and x5 == 1) and (x1 == 0):
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cv2.putText(img, "4_FOUR", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
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(0, 0, 255), 3)
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elif x1 == 1 and x2 == 1 and x3 == 1 and x4 == 1 and x5 == 1:
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cv2.putText(img, "5_FIVE", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
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(0, 0, 255), 3)
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elif x2 == 1 and x1 == 0 and (x3 == 0, x4 == 0, x5 == 0):
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cv2.putText(img, "1_ONE", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
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(0, 0, 255), 3)
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elif x1 == 1 and x2 == 1 and (x3 == 0, x4 == 0, x5 == 0):
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cv2.putText(img, "8_EIGHT", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
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(0, 0, 255), 3)
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elif x1 == 1 and x5 == 1 and (x3 == 0, x4 == 0, x5 == 0):
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cv2.putText(img, "6_SIX", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
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(0, 0, 255), 3)
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elif x1 and (x2 == 0, x3 == 0, x4 == 0, x5 == 0):
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cv2.putText(img, "GOOD!", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
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(0, 0, 255), 3)
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cv2.imshow("camera", img)
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key = cv2.waitKey(1)
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if cv2.getWindowProperty('camera', cv2.WND_PROP_VISIBLE) < 1:
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break
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elif key == 27:
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break
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if __name__ == '__main__':
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Solution = Main()
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# Solution.gesture_recognition()
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Solution.load_datasets()
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Solution.train_cnn()
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