diff --git a/datatest.py b/datatest.py index 3e5328e..037a0e4 100644 --- a/datatest.py +++ b/datatest.py @@ -1,174 +1,177 @@ -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(self.mode, self.max_hands, self.detection_con, 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 = [] - onedata = np.zeros([21,3]) - zerodata = np.zeros([21,3]) - h, w, c = img.shape - self.lmList = [] - - if self.results.multi_hand_landmarks: - my_hand = self.results.multi_hand_landmarks[hand_no] - for i, lm in enumerate(my_hand.landmark): - onedata[i] = np.array([lm.x,lm.y,lm.z]) #将三维坐标添加到单次截屏的数据中 - - 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) - - return onedata, (h, w) - - 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 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 1 - else: - return 0 - - -class Main: - def __init__(self, label, N = 100): - self.detector = None - self.camera = cv2.VideoCapture(0, cv2.CAP_DSHOW) - self.camera.set(3, 1280) - self.camera.set(4, 720) - self.N = N - #初始化数据包 - self.label = label - self.data = np.zeros([N,21,3]) - self.shape = np.zeros([N,2], dtype = np.int16) - self.handtype = np.zeros(N, dtype = np.int8) - - def gesture_recognition(self): - self.detector = HandDetector() - #初始化数据 - - zerodata = np.zeros([21,3]) - rezult = np.zeros([21,3]) - count = 0 - - while True: - frame, img = self.camera.read() - img = self.detector.find_hands(img) - - rezult,shape = self.detector.find_position(img) - if rezult.all() != zerodata.all(): #假设矩阵不为0，即捕捉到手部时 - self.data[count] = rezult - self.handtype[count] = self.detector.hand_type() - self.shape[count] = np.array(shape) - count += 1 - - cv2.imshow("camera", img) - key = cv2.waitKey(1) - if cv2.getWindowProperty('camera', cv2.WND_PROP_VISIBLE) < 1: - break - elif key == 27: - break - elif count == self.N - 1: - break - - np.savez('firstdata', label = self.label, data = self.data, - handtype = self.handtype, shape = self.shape) - - -if __name__ == '__main__': - Solution = Main(label = "five") - Solution.gesture_recognition() - npzfile = np.load('firstdata.npz') - - #print(npzfile['data'][0]) - #print(" ") - #print(npzfile['handtype']) - #print(npzfile['label']) - #print(npzfile['shape']) +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 = 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.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 = [] + onedata = np.zeros([21,3]) + zerodata = np.zeros([21,3]) + h, w, c = img.shape + self.lmList = [] + + if self.results.multi_hand_landmarks: + my_hand = self.results.multi_hand_landmarks[hand_no] + for i, lm in enumerate(my_hand.landmark): + onedata[i] = np.array([lm.x,lm.y,lm.z]) #将三维坐标添加到单次截屏的数据中 + + 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) + + return onedata, (h, w) + + 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 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 1 + else: + return 0 + + +class Main: + def __init__(self, label, N = 100): + self.detector = None + self.camera = cv2.VideoCapture(0, cv2.CAP_DSHOW) + self.camera.set(3, 1280) + self.camera.set(4, 720) + self.N = N + #初始化数据包 + self.label = label + self.data = np.zeros([N,21,3]) + self.shape = np.zeros([N,2], dtype = np.int16) + self.handtype = np.zeros(N, dtype = np.int8) + + def gesture_recognition(self): + self.detector = HandDetector() + #初始化数据 + + zerodata = np.zeros([21,3]) + rezult = np.zeros([21,3]) + count = 0 + + while True: + frame, img = self.camera.read() + img = self.detector.find_hands(img) + + rezult,shape = self.detector.find_position(img) + if rezult.all() != zerodata.all(): #假设矩阵不为0，即捕捉到手部时 + self.data[count] = rezult + self.handtype[count] = self.detector.hand_type() + self.shape[count] = np.array(shape) + count += 1 + + cv2.imshow("camera", img) + key = cv2.waitKey(1) + if cv2.getWindowProperty('camera', cv2.WND_PROP_VISIBLE) < 1: + break + elif key == 27: + break + elif count == self.N - 1: + break + + np.savez('firstdata', label = self.label, data = self.data, + handtype = self.handtype, shape = self.shape) + + +if __name__ == '__main__': + Solution = Main(label = "five") + Solution.gesture_recognition() + npzfile = np.load('firstdata.npz') + + #print(npzfile['data'][0]) + #print(" ") + #print(npzfile['handtype']) + #print(npzfile['label']) + #print(npzfile['shape']) diff --git a/demo.py b/demo.py index a411694..0ac1a8e 100644 --- a/demo.py +++ b/demo.py @@ -27,14 +27,16 @@ class HandDetector: self.results = None self.mode = mode self.max_hands = max_hands - self.modelComplex = False + 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(self.mode, self.max_hands, self.modelComplex, - self.detection_con, self.min_track_con) + 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 = [] @@ -154,6 +156,9 @@ class Main: if (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 x3 and x1 == 0 and x2 == 0 and (x4 == 0, x5 == 0): + cv2.putText(img, "FUCK YOU!!", (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)