增加:MP版本兼容
（被动包含调整复杂系数为1）
This commit is contained in:
leafiber
parent
8feabea649
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351
datatest.py
351
datatest.py
@ -1,174 +1,177 @@
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import cv2
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import cv2
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import mediapipe as mp
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import mediapipe as mp
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import numpy as np
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import numpy as np
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class HandDetector:
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class HandDetector:
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"""
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"""
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使用mediapipe库查找手。导出地标像素格式。添加了额外的功能。
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使用mediapipe库查找手。导出地标像素格式。添加了额外的功能。
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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。
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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。
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"""
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"""
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def __init__(self, mode=False, max_hands=2, detection_con=0.5, min_track_con=0.5):
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def __init__(self, mode=False, max_hands=2, detection_con=0.5, min_track_con=0.5):
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"""
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"""
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:param mode: 在静态模式下，对每个图像进行检测
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:param mode: 在静态模式下，对每个图像进行检测
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:param max_hands: 要检测的最大手数
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:param max_hands: 要检测的最大手数
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:param detection_con: 最小检测置信度
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:param detection_con: 最小检测置信度
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:param min_track_con: 最小跟踪置信度
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:param min_track_con: 最小跟踪置信度
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"""
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"""
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self.results = None
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self.results = None
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self.mode = mode
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self.mode = mode
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self.max_hands = max_hands
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self.max_hands = max_hands
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self.modelComplex = False
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self.modelComplex = 1
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self.detection_con = detection_con
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self.detection_con = detection_con
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self.min_track_con = min_track_con
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self.min_track_con = min_track_con
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# 初始化手部的识别模型
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# 初始化手部的识别模型
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self.mpHands = mp.solutions.hands
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self.mpHands = mp.solutions.hands
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self.hands = self.mpHands.Hands(self.mode, self.max_hands, self.detection_con, self.min_track_con)
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self.hands = self.mpHands.Hands(static_image_mode=self.mode,
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self.mpDraw = mp.solutions.drawing_utils # 初始化绘图器
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max_num_hands=self.max_hands,
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self.tipIds = [4, 8, 12, 16, 20] # 指尖列表
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min_detection_confidence=self.detection_con,
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self.fingers = []
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min_tracking_confidence=self.min_track_con)
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self.lmList = []
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self.mpDraw = mp.solutions.drawing_utils # 初始化绘图器
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self.tipIds = [4, 8, 12, 16, 20] # 指尖列表
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def find_hands(self, img, draw=True):
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self.fingers = []
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"""
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self.lmList = []
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从图像(BRG)中找到手部。
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:param img: 用于查找手的图像。
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def find_hands(self, img, draw=True):
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:param draw: 在图像上绘制输出的标志。
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"""
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:return: 带或不带图形的图像
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从图像(BRG)中找到手部。
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"""
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:param img: 用于查找手的图像。
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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，
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:param draw: 在图像上绘制输出的标志。
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self.results = self.hands.process(img_rgb)
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:return: 带或不带图形的图像
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"""
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if self.results.multi_hand_landmarks:
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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，
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for handLms in self.results.multi_hand_landmarks:
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self.results = self.hands.process(img_rgb)
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if draw:
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self.mpDraw.draw_landmarks(img, handLms,
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if self.results.multi_hand_landmarks:
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self.mpHands.HAND_CONNECTIONS)
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for handLms in self.results.multi_hand_landmarks:
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return img
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if draw:
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self.mpDraw.draw_landmarks(img, handLms,
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def find_position(self, img, hand_no=0, draw=True):
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self.mpHands.HAND_CONNECTIONS)
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"""
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return img
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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。
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:param img: 要查找的主图像
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def find_position(self, img, hand_no=0, draw=True):
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:param hand_no: 如果检测到多只手，则为手部id
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"""
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:param draw: 在图像上绘制输出的标志。(默认绘制矩形框)
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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。
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:return: 像素格式的手部关节位置列表；手部边界框
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:param img: 要查找的主图像
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"""
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:param hand_no: 如果检测到多只手，则为手部id
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:param draw: 在图像上绘制输出的标志。(默认绘制矩形框)
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x_list = []
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:return: 像素格式的手部关节位置列表；手部边界框
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y_list = []
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"""
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onedata = np.zeros([21,3])
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zerodata = np.zeros([21,3])
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x_list = []
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h, w, c = img.shape
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y_list = []
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self.lmList = []
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onedata = np.zeros([21,3])
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zerodata = np.zeros([21,3])
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if self.results.multi_hand_landmarks:
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h, w, c = img.shape
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my_hand = self.results.multi_hand_landmarks[hand_no]
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self.lmList = []
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for i, lm in enumerate(my_hand.landmark):
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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
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if self.results.multi_hand_landmarks:
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my_hand = self.results.multi_hand_landmarks[hand_no]
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px, py= int(lm.x * w), int(lm.y * h)
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for i, lm in enumerate(my_hand.landmark):
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x_list.append(px)
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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
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y_list.append(py)
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self.lmList.append([px, py])
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px, py= int(lm.x * w), int(lm.y * h)
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if draw:
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x_list.append(px)
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cv2.circle(img, (px, py), 5, (255, 0, 255), cv2.FILLED)
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y_list.append(py)
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self.lmList.append([px, py])
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return onedata, (h, w)
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if draw:
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cv2.circle(img, (px, py), 5, (255, 0, 255), cv2.FILLED)
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def fingers_up(self):
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"""
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return onedata, (h, w)
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查找列表中打开并返回的手指数。会分别考虑左手和右手
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:return: 竖起手指的列表
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def fingers_up(self):
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"""
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"""
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fingers = []
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查找列表中打开并返回的手指数。会分别考虑左手和右手
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if self.results.multi_hand_landmarks:
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:return: 竖起手指的列表
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my_hand_type = self.hand_type()
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"""
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# Thumb
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fingers = []
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if my_hand_type == "Right":
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if self.results.multi_hand_landmarks:
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if self.lmList[self.tipIds[0]][0] > self.lmList[self.tipIds[0] - 1][0]:
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my_hand_type = self.hand_type()
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fingers.append(1)
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# Thumb
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else:
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if my_hand_type == "Right":
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fingers.append(0)
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if self.lmList[self.tipIds[0]][0] > self.lmList[self.tipIds[0] - 1][0]:
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else:
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fingers.append(1)
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if self.lmList[self.tipIds[0]][0] < self.lmList[self.tipIds[0] - 1][0]:
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else:
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fingers.append(1)
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fingers.append(0)
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else:
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else:
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fingers.append(0)
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if self.lmList[self.tipIds[0]][0] < self.lmList[self.tipIds[0] - 1][0]:
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# 4 Fingers
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fingers.append(1)
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for i in range(1, 5):
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else:
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if self.lmList[self.tipIds[i]][1] < self.lmList[self.tipIds[i] - 2][1]:
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fingers.append(0)
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fingers.append(1)
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# 4 Fingers
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else:
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for i in range(1, 5):
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fingers.append(0)
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if self.lmList[self.tipIds[i]][1] < self.lmList[self.tipIds[i] - 2][1]:
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return fingers
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fingers.append(1)
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else:
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def hand_type(self):
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fingers.append(0)
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"""
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return fingers
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检查传入的手部是左还是右
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:return: "Right" 或 "Left"
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def hand_type(self):
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"""
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"""
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if self.results.multi_hand_landmarks:
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检查传入的手部是左还是右
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if self.lmList[17][0] < self.lmList[5][0]:
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:return: "Right" 或 "Left"
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return 1
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"""
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else:
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if self.results.multi_hand_landmarks:
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return 0
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if self.lmList[17][0] < self.lmList[5][0]:
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return 1
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else:
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class Main:
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return 0
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def __init__(self, label, N = 100):
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self.detector = None
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self.camera = cv2.VideoCapture(0, cv2.CAP_DSHOW)
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class Main:
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self.camera.set(3, 1280)
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def __init__(self, label, N = 100):
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self.camera.set(4, 720)
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self.detector = None
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self.N = N
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self.camera = cv2.VideoCapture(0, cv2.CAP_DSHOW)
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#初始化数据包
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self.camera.set(3, 1280)
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self.label = label
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self.camera.set(4, 720)
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self.data = np.zeros([N,21,3])
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self.N = N
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self.shape = np.zeros([N,2], dtype = np.int16)
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#初始化数据包
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self.handtype = np.zeros(N, dtype = np.int8)
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self.label = label
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self.data = np.zeros([N,21,3])
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def gesture_recognition(self):
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self.shape = np.zeros([N,2], dtype = np.int16)
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self.detector = HandDetector()
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self.handtype = np.zeros(N, dtype = np.int8)
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#初始化数据
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def gesture_recognition(self):
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zerodata = np.zeros([21,3])
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self.detector = HandDetector()
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rezult = np.zeros([21,3])
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#初始化数据
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count = 0
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zerodata = np.zeros([21,3])
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while True:
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rezult = np.zeros([21,3])
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frame, img = self.camera.read()
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count = 0
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img = self.detector.find_hands(img)
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while True:
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rezult,shape = self.detector.find_position(img)
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frame, img = self.camera.read()
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if rezult.all() != zerodata.all(): #假设矩阵不为0，即捕捉到手部时
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img = self.detector.find_hands(img)
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self.data[count] = rezult
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self.handtype[count] = self.detector.hand_type()
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rezult,shape = self.detector.find_position(img)
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self.shape[count] = np.array(shape)
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if rezult.all() != zerodata.all(): #假设矩阵不为0，即捕捉到手部时
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count += 1
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self.data[count] = rezult
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self.handtype[count] = self.detector.hand_type()
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cv2.imshow("camera", img)
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self.shape[count] = np.array(shape)
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key = cv2.waitKey(1)
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count += 1
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if cv2.getWindowProperty('camera', cv2.WND_PROP_VISIBLE) < 1:
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break
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cv2.imshow("camera", img)
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elif key == 27:
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key = cv2.waitKey(1)
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break
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if cv2.getWindowProperty('camera', cv2.WND_PROP_VISIBLE) < 1:
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elif count == self.N - 1:
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break
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break
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elif key == 27:
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break
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np.savez('firstdata', label = self.label, data = self.data,
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elif count == self.N - 1:
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handtype = self.handtype, shape = self.shape)
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break
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np.savez('firstdata', label = self.label, data = self.data,
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if __name__ == '__main__':
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handtype = self.handtype, shape = self.shape)
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Solution = Main(label = "five")
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Solution.gesture_recognition()
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npzfile = np.load('firstdata.npz')
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if __name__ == '__main__':
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Solution = Main(label = "five")
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#print(npzfile['data'][0])
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Solution.gesture_recognition()
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#print(" ")
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npzfile = np.load('firstdata.npz')
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#print(npzfile['handtype'])
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#print(npzfile['label'])
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#print(npzfile['data'][0])
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#print(npzfile['shape'])
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#print(" ")
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#print(npzfile['handtype'])
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#print(npzfile['label'])
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#print(npzfile['shape'])
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11
demo.py
11
demo.py
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self.results = None
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self.results = None
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self.mode = mode
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self.mode = mode
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self.max_hands = max_hands
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self.max_hands = max_hands
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self.modelComplex = False
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self.modelComplex = 1
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self.detection_con = detection_con
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self.detection_con = detection_con
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self.min_track_con = min_track_con
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self.min_track_con = min_track_con
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# 初始化手部的识别模型
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# 初始化手部的识别模型
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self.mpHands = mp.solutions.hands
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self.mpHands = mp.solutions.hands
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self.hands = self.mpHands.Hands(self.mode, self.max_hands, self.modelComplex,
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self.hands = self.mpHands.Hands(static_image_mode=self.mode,
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self.detection_con, self.min_track_con)
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max_num_hands=self.max_hands,
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min_detection_confidence=self.detection_con,
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min_tracking_confidence=self.min_track_con)
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self.mpDraw = mp.solutions.drawing_utils # 初始化绘图器
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self.mpDraw = mp.solutions.drawing_utils # 初始化绘图器
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self.tipIds = [4, 8, 12, 16, 20] # 指尖列表
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self.tipIds = [4, 8, 12, 16, 20] # 指尖列表
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self.fingers = []
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self.fingers = []
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@ -154,6 +156,9 @@ class Main:
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if (x2 == 1 and x3 == 1) and (x4 == 0 and x5 == 0 and x1 == 0):
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if (x2 == 1 and x3 == 1) and (x4 == 0 and x5 == 0 and x1 == 0):
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cv2.putText(img, "2_TWO", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
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cv2.putText(img, "2_TWO", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
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(0, 0, 255), 3)
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(0, 0, 255), 3)
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elif x3 and x1 == 0 and x2 == 0 and (x4 == 0, x5 == 0):
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cv2.putText(img, "FUCK YOU!!", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
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(0, 0, 255), 3)
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elif (x2 == 1 and x3 == 1 and x4 == 1) and (x1 == 0 and x5 == 0):
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elif (x2 == 1 and x3 == 1 and x4 == 1) and (x1 == 0 and x5 == 0):
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cv2.putText(img, "3_THREE", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
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cv2.putText(img, "3_THREE", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
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(0, 0, 255), 3)
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(0, 0, 255), 3)
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