From 7103d4a7aad7302704384d5a224e47844e202fc3 Mon Sep 17 00:00:00 2001 From: leaf <48828021+leafliber@users.noreply.github.com> Date: Wed, 29 Jun 2022 19:14:04 +0800 Subject: [PATCH] =?UTF-8?q?=E9=87=8D=E5=91=BD=E5=90=8D?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- DM.py | 318 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 318 insertions(+) create mode 100644 DM.py diff --git a/DM.py b/DM.py new file mode 100644 index 0000000..4a48ab9 --- /dev/null +++ b/DM.py @@ -0,0 +1,318 @@ +# -*- 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()