# -*- coding:utf-8 -*- """ 信号设计课程小组设计 @ by: Leaf @ date: 2022-05-28 """ import mediapipe as mp import cv2 # import HandDetector import math from datetime import datetime import time import numpy as np # 旋转函数 def Rotate(angle, x, y, point_x, point_y): px = (x - point_x) * math.cos(angle) - (y - point_y) * math.sin(angle) + point_x py = (x - point_x) * math.sin(angle) + (y - point_y) * math.cos(angle) + point_y return px, py 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.knuckles = {'0': [4, 3, 2, 1], "1": [8, 7, 6, 5], "2": [12, 11, 10, 9], "3": [16, 15, 14, 13], # "4": [20, 19, 18, 17]} self.fingers = [] self.lmList = [] self.re_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 = [] self.re_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 revolve(self, img, draw=True): """ 旋转手势识别点 :param img: 要查找的主图像 :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 """ # print(self.lmList) point_x = self.lmList[0][0] point_y = self.lmList[0][1] delta_x = self.lmList[13][0] - point_x delta_y = self.lmList[13][1] - point_y if delta_y == 0: if delta_x < 0: theta = math.pi / 2 else: theta = -math.pi / 2 else: theta = math.atan(delta_x / delta_y) if delta_y > 0: theta = theta + math.pi # print(theta*180/math.pi) for i in self.lmList: px, py = Rotate(theta, i[0], i[1], point_x, point_y) px = int(px) py = int(py) self.re_lmList.append([px, py]) if draw: cv2.circle(img, (px, py), 5, (0, 0, 255), cv2.FILLED) return self.re_lmList 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 re_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.re_lmList[self.tipIds[0]][0] > self.re_lmList[self.tipIds[0] - 1][0]: fingers.append(1) else: fingers.append(0) else: if self.re_lmList[self.tipIds[0]][0] < self.re_lmList[self.tipIds[0] - 1][0]: fingers.append(1) else: fingers.append(0) # 4 Fingers for i in range(1, 5): if self.re_lmList[self.tipIds[i]][1] < self.re_lmList[self.tipIds[i] - 2][1]: fingers.append(1) else: fingers.append(0) return fingers def knuckles_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 """ knuckles = [] distan = 10 if self.results.multi_hand_landmarks: my_hand_type = self.hand_type() # Thumb xx = self.re_lmList[self.tipIds[0]][0] yy = self.re_lmList[self.tipIds[0] - 1][0] if my_hand_type == "Right": if -distan < xx - yy < distan: knuckles.append(2) elif xx > yy: knuckles.append(1) else: knuckles.append(0) else: if -distan < xx - yy < distan: knuckles.append(2) elif xx < yy: knuckles.append(1) else: knuckles.append(0) # 12 knuckles for i in range(1, 5): for j in range(3): xx = self.re_lmList[self.tipIds[i]-j][1] yy = self.re_lmList[self.tipIds[i]-j - 1][1] if -distan < xx - yy < distan: knuckles.append(2) elif xx < yy: knuckles.append(1) else: knuckles.append(0) return knuckles 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.detector = None self.camera = cv2.VideoCapture(0, cv2.CAP_DSHOW) self.camera.set(3, 1280) self.camera.set(4, 720) def gesture_recognition(self): self.detector = HandDetector() gesture_store = {} startTime = time.time() stored_round = 1 stored_flag = 0 xl = np.zeros((1, 13)) # 特征值存储 while True: frame, img = self.camera.read() img = self.detector.find_hands(img) lm_list, bbox = self.detector.find_position(img) if lm_list: re_lm_list = self.detector.revolve(img) x_1, y_1 = bbox["bbox"][0], bbox["bbox"][1] knucks = self.detector.knuckles_up() # x1, x2, x3, x4, x5 = self.detector.re_fingers_up() # # if (x2 == 1 and x3 == 1) and (x4 == 0 and x5 == 0 and x1 == 0): # cv2.putText(img, "GOOD!", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3, # (0, 0, 255), 3) print(time.time() - startTime) if (time.time() - startTime) < 3: # 手势存储时间 xl = np.vstack((xl, knucks)) cv2.putText(img, 'Please put the gesture to be stored in 1 second', (50, 50), cv2.FONT_HERSHEY_PLAIN, 1.2, (255, 255, 255), 2) else: # 开始手势识别 self.detector.fingers = xl value = '' for j in range(13): value = value + str(np.argmax( np.bincount(xl[:, j].astype(int)))) # 找出第3列最频繁出现的值 gesture_store[value] = stored_round stored_flag = 1 # startTime = time.time() gesture_dete = ''.join(str(knuck) for knuck in knucks) if gesture_dete in gesture_store: cv2.putText(img, str(gesture_store[gesture_dete]), (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3, (0, 0, 255), 3) cv2.putText(img, 'Gesture stored, recognition started', (50, 50), cv2.FONT_HERSHEY_PLAIN, 1.2, (255, 255, 255), 2) else: if stored_flag: stored_round += 1 stored_flag = 0 startTime = time.time() # 当检测不到手势时，初始化手势存储 xl = np.zeros((1, 13)) # 特征值存储 cv2.putText(img, 'Please put the gesture to be stored in 1 second', (50, 50), cv2.FONT_HERSHEY_PLAIN, 1.2, (255, 255, 255), 2) 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()