diff --git a/demo.py b/demo.py deleted file mode 100644 index 4a48ab9..0000000 --- a/demo.py +++ /dev/null @@ -1,318 +0,0 @@ -# -*- 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()