leaf
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DM.py
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318
DM.py
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# -*- 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 mediapipe as mp
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import cv2
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# import HandDetector
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import math
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from datetime import datetime
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import time
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import numpy as np
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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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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.knuckles = {'0': [4, 3, 2, 1], "1": [8, 7, 6, 5], "2": [12, 11, 10, 9], "3": [16, 15, 14, 13],
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# "4": [20, 19, 18, 17]}
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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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self.re_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 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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# print(self.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], i[1], point_x, point_y)
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px = int(px)
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py = int(py)
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self.re_lmList.append([px, py])
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if draw:
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cv2.circle(img, (px, py), 5, (0, 0, 255), cv2.FILLED)
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return self.re_lmList
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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 re_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.re_lmList[self.tipIds[0]][0] > self.re_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.re_lmList[self.tipIds[0]][0] < self.re_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.re_lmList[self.tipIds[i]][1] < self.re_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 knuckles_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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knuckles = []
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distan = 10
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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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xx = self.re_lmList[self.tipIds[0]][0]
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yy = self.re_lmList[self.tipIds[0] - 1][0]
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if my_hand_type == "Right":
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if -distan < xx - yy < distan:
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knuckles.append(2)
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elif xx > yy:
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knuckles.append(1)
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else:
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knuckles.append(0)
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else:
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if -distan < xx - yy < distan:
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knuckles.append(2)
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elif xx < yy:
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knuckles.append(1)
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else:
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knuckles.append(0)
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# 12 knuckles
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for i in range(1, 5):
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for j in range(3):
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xx = self.re_lmList[self.tipIds[i]-j][1]
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yy = self.re_lmList[self.tipIds[i]-j - 1][1]
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if -distan < xx - yy < distan:
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knuckles.append(2)
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elif xx < yy:
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knuckles.append(1)
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else:
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knuckles.append(0)
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return knuckles
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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.detector = 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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def gesture_recognition(self):
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self.detector = HandDetector()
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gesture_store = {}
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startTime = time.time()
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stored_round = 1
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stored_flag = 0
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xl = np.zeros((1, 13)) # 特征值存储
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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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re_lm_list = self.detector.revolve(img)
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x_1, y_1 = bbox["bbox"][0], bbox["bbox"][1]
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knucks = self.detector.knuckles_up()
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# x1, x2, x3, x4, x5 = self.detector.re_fingers_up()
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#
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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, "GOOD!", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
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# (0, 0, 255), 3)
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print(time.time() - startTime)
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if (time.time() - startTime) < 3: # 手势存储时间
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xl = np.vstack((xl, knucks))
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cv2.putText(img, 'Please put the gesture to be stored in 1 second', (50, 50),
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cv2.FONT_HERSHEY_PLAIN, 1.2, (255, 255, 255), 2)
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else: # 开始手势识别
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self.detector.fingers = xl
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value = ''
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for j in range(13):
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value = value + str(np.argmax(
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np.bincount(xl[:, j].astype(int)))) # 找出第3列最频繁出现的值
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gesture_store[value] = stored_round
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stored_flag = 1
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# startTime = time.time()
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gesture_dete = ''.join(str(knuck) for knuck in knucks)
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if gesture_dete in gesture_store:
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cv2.putText(img, str(gesture_store[gesture_dete]), (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
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(0, 0, 255), 3)
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cv2.putText(img, 'Gesture stored, recognition started', (50, 50),
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cv2.FONT_HERSHEY_PLAIN, 1.2, (255, 255, 255), 2)
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else:
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if stored_flag:
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stored_round += 1
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stored_flag = 0
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startTime = time.time() # 当检测不到手势时，初始化手势存储
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xl = np.zeros((1, 13)) # 特征值存储
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cv2.putText(img, 'Please put the gesture to be stored in 1 second', (50, 50), cv2.FONT_HERSHEY_PLAIN,
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1.2, (255, 255, 255), 2)
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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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