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datatest.py

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+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'])