477 lines
17 KiB
Python
477 lines
17 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 tkinter as tk
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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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import shutil
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import math
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from scipy import stats
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from os.path import exists
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from os import mkdir
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from pathlib import Path
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from torch.utils.data import DataLoader, TensorDataset
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# 旋转函数
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def rotate(angle, x, y, point_x, point_y):
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px = (x - point_x) * math.cos(angle) - (y - point_y) * math.sin(angle) + point_x
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py = (x - point_x) * math.sin(angle) + (y - point_y) * math.cos(angle) + point_y
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return px, py
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# 归一化
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def normalize(x):
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max_x = np.max(x)
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min_x = np.min(x)
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return (x - min_x) / (max_x - min_x)
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class CNN(nn.Module):
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def __init__(self, m):
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super(CNN, self).__init__()
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self.out_label = []
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self.conv1 = nn.Sequential(
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nn.Conv2d(
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in_channels=1,
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out_channels=16,
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kernel_size=5,
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stride=1,
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padding=2,
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),
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nn.ReLU(),
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nn.MaxPool2d(kernel_size=1),
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)
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self.conv2 = nn.Sequential(
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nn.Conv2d(16, 32, 5, 1, 2),
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nn.ReLU(),
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nn.MaxPool2d(2),
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)
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self.med = nn.Linear(32 * 11 * 2, 500)
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self.med2 = nn.Linear(1 * 21 * 3, 100)
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self.med3 = nn.Linear(100, 500)
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self.out = nn.Linear(500, m) # 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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# x = self.med2(x)
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# x = self.med3(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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self.re_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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h, w, c = img.shape
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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 i, lm in enumerate(my_hand.landmark):
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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([lm.x, lm.y, 0])
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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), "shape": (h, w)}
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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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self.revolve(img)
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self.re_lmList = np.array(self.re_lmList)
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if self.re_lmList.any():
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self.re_lmList = np.concatenate((np.zeros((21, 1)), self.re_lmList), axis=1)
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self.re_lmList = np.concatenate((self.re_lmList, np.zeros((1, 4))), axis=0)
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return self.re_lmList, bbox_info
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def revolve(self, img, draw=True):
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"""
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旋转手势识别点
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:param img: 要查找的主图像
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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
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"""
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h, w, c = img.shape
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if len(self.lmList) >= 21:
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# print(self.lmList)
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self.re_lmList = []
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point_x = self.lmList[0][0]
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point_y = self.lmList[0][1]
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delta_x = self.lmList[13][0] - point_x
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delta_y = self.lmList[13][1] - point_y
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if delta_y == 0:
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if delta_x < 0:
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theta = math.pi / 2
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else:
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theta = -math.pi / 2
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else:
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theta = math.atan(delta_x / delta_y)
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if delta_y > 0:
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theta = theta + math.pi
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# print(theta*180/math.pi)
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for i in self.lmList:
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px, py = rotate(theta, i[0] * w, i[1] * h, point_x * w, point_y * h)
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self.re_lmList.append([px, py, 0])
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if draw:
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cv2.circle(img, (int(px), int(py)), 5, (0, 0, 255), cv2.FILLED)
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# 归一化
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x_array = normalize(np.array(self.re_lmList)[:, 0])
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# print(x_array)
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for i in range(len(x_array)):
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self.re_lmList[i][0] = x_array[i]
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y_array = normalize(np.array(self.re_lmList)[:, 1])
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for i in range(len(y_array)):
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self.re_lmList[i][1] = x_array[i]
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else:
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self.re_lmList = self.lmList
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return self.re_lmList
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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: 1 或 0
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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 1
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else:
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return 0
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class AI:
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def __init__(self, datasets_dir):
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self.EPOCH = 20
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self.BATCH_SIZE = 2
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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.datasets_dir = datasets_dir
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self.train_loader = None
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self.m = 0
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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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def load_datasets(self):
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train_data = []
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train_label = []
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self.m = 0
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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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self.m += 1
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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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return self.m
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def train_cnn(self):
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cnn = CNN(self.m).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) % 50 == 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 + 1,
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(step + 1) * len(data),
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len(self.train_loader.dataset),
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100. * (step + 1) / 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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class Main:
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def __init__(self):
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self.camera = None
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self.detector = HandDetector()
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self.default_datasets = "Datasets"
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self.len_x = 22
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self.len_y = 4
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self.label = ''
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self.top1 = tk.Tk()
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self.top1.geometry('300x50')
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self.top1.title('请输入标签')
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tk.Label(self.top1, text='Label:').place(x=27, y=10)
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self.entry = tk.Entry(self.top1, width=15)
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self.entry.place(x=80, y=10)
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tk.Button(self.top1, text='确定', command=self.change_state).place(x=235, y=5)
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def change_state(self):
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self.label = self.entry.get() # 调用get()方法，将Entry中的内容获取出来
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self.top1.quit()
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if self.label == "":
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self.top1.destroy()
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def make_datasets(self, datasets_dir="default", n=100):
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if datasets_dir == "default":
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return
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if exists(datasets_dir):
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shutil.rmtree(datasets_dir)
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mkdir(datasets_dir)
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if self.camera is None:
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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.top1.mainloop()
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while not self.label == "":
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data = np.zeros([n, self.len_x, self.len_y])
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shape_list = np.zeros([n, 2], dtype=np.int16)
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hand_type = np.zeros(n, dtype=np.int8)
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count = 0
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cv2.startWindowThread()
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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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result = np.zeros((self.len_x, self.len_y))
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lm_list, bbox = self.detector.find_position(img)
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for i in range(len(lm_list)):
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result[i] = np.array(lm_list[i])
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if result.sum() > 0: # 假设矩阵不为0，即捕捉到手部时
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shape = bbox["shape"]
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x_1, y_1 = bbox["bbox"][0], bbox["bbox"][1]
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data[count] = result
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hand_type[count] = self.detector.hand_type()
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shape_list[count] = np.array(shape)
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count += 1
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cv2.putText(img, str("{}/{}".format(count, n)), (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
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(0, 255, 0), 3)
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cv2.imshow("camera", img)
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key = cv2.waitKey(100)
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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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elif count == n - 1:
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break
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cv2.destroyAllWindows()
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open(datasets_dir + "/" + self.label + ".npz", "w")
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np.savez(datasets_dir + "/" + self.label + ".npz", label=self.label, data=data,
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handtype=hand_type, shape=shape_list)
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self.top1.mainloop()
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def train(self, datasets_dir="default"):
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if datasets_dir == "default":
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datasets_dir = self.default_datasets
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ai = AI(datasets_dir)
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ai.load_datasets()
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ai.train_cnn()
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def gesture_recognition_camera(self):
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if self.camera is None:
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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.detector = HandDetector()
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cnn = torch.load("CNN.pkl")
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out_label = cnn.out_label
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result = []
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disp = ""
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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.any():
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x_1, y_1 = bbox["bbox"][0], bbox["bbox"][1]
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data = torch.Tensor(lm_list)
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data = data.unsqueeze(0)
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data = data.unsqueeze(0)
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test_output = cnn(data)
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result.append(torch.max(test_output, 1)[1].data.cpu().numpy()[0])
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if len(result) > 5:
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disp = str(out_label[stats.mode(result)[0][0]])
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result = []
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cv2.putText(img, disp, (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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def gesture_recognition_video(self, filedir):
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self.detector = HandDetector()
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cnn = torch.load("CNN.pkl")
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out_label = cnn.out_label
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result = []
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disp = ""
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cap = cv2.VideoCapture(filedir)
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while True:
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ret, img = cap.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.any():
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x_1, y_1 = bbox["bbox"][0], bbox["bbox"][1]
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data = torch.Tensor(lm_list)
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data = data.unsqueeze(0)
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data = data.unsqueeze(0)
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test_output = cnn(data)
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result.append(torch.max(test_output, 1)[1].data.cpu().numpy()[0])
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if len(result) > 5:
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disp = str(out_label[stats.mode(result)[0][0]])
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result = []
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cv2.putText(img, disp, (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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cap.release()
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def gesture_recognition_img(self, filedir):
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self.detector = HandDetector()
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cnn = torch.load("CNN.pkl")
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out_label = cnn.out_label
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result = []
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disp = ""
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img = cv2.imread(filedir)
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img = self.detector.find_hands(img)
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while True:
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lm_list, bbox = self.detector.find_position(img)
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if lm_list.any():
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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)
|