# -*- coding:utf-8 -*- import cv2 import mediapipe as mp import numpy as np 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 = False 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(np.array([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]: if np.dot(self.lmList[self.tipIds[i]-2]-self.lmList[self.tipIds[i]-3], self.lmList[self.tipIds[i]-1]-self.lmList[self.tipIds[i]-2]) >= 0: 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.detector = None self.camera = None # self.camera = cv2.VideoCapture(0, cv2.CAP_DSHOW) # self.camera.set(3, 1280) # self.camera.set(4, 720) def gesture_recognition(self, img, detector): self.detector = detector 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 (np.linalg.norm(lm_list[4]-lm_list[8]) < 50) and (np.linalg.norm(lm_list[4]-lm_list[12]) < 50): cv2.putText(img, "7_SEVEN", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3, (0, 0, 255), 3) elif (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 and x4 == 0 and 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 and x4 == 0 and 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 and x4 == 0 and x2 == 0): cv2.putText(img, "6_SIX", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3, (0, 0, 255), 3) elif x1 == 0 and x5 == 0 and x3 == 0 and x4 == 0 and x2 == 0: cv2.putText(img, "0_ZERO", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3, (0, 0, 255), 3) else: return 1 return 0 if __name__ == '__main__': Solution = Main() Solution.gesture_recognition()