From 4e2ba65d3e49bd52f9fa74d9f8392cb4d5b90c72 Mon Sep 17 00:00:00 2001 From: leafiber Date: Sat, 28 May 2022 19:29:21 +0800 Subject: [PATCH] =?UTF-8?q?1=20bugfix:=E4=BF=AE=E5=A4=8D=E5=86=85=E5=AD=98?= =?UTF-8?q?=E6=B3=84=E9=9C=B2=E9=97=AE=E9=A2=98=202=20bugfix:=E8=A7=A3?= =?UTF-8?q?=E5=86=B3=E6=97=A0=E6=B3=95=E9=80=80=E5=87=BA=E9=97=AE=E9=A2=98?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- origin.py | 30 +++++++++++++++++------------- 1 file changed, 17 insertions(+), 13 deletions(-) diff --git a/origin.py b/origin.py index 6f98246..ee1cdfd 100644 --- a/origin.py +++ b/origin.py @@ -1,4 +1,3 @@ - # -*- coding:utf-8 -*- """ @@ -15,13 +14,13 @@ import cv2 import mediapipe as mp - 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, maxHands=2, detectionCon=0.5, minTrackCon = 0.5): + + def __init__(self, mode=False, maxHands=2, detectionCon=0.5, minTrackCon=0.5): """ :param mode: 在静态模式下，对每个图像进行检测 :param maxHands: 要检测的最大手数 @@ -34,15 +33,14 @@ class HandDetector: self.detectionCon = detectionCon self.minTrackCon = minTrackCon - # 初始化手部识别模型 + # 初始化手部识别模型 self.mpHands = mp.solutions.hands self.hands = self.mpHands.Hands(self.mode, self.maxHands, self.modelComplex, self.detectionCon, self.minTrackCon) - self.mpDraw = mp.solutions.drawing_utils # 初始化绘图器 - self.tipIds = [4, 8, 12, 16, 20] # 指尖列表 + 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 = [] - self.connection = [(1,0,5),(1,0,17),(5,0,17),(2,1,0),(3,2,1),(4,3,2),(0,5,6),(0,5,9),(6,5,9),(5,6,7),(6,7,8),(5,9,13),(5,9,10),(10,9,13),(9,10,11),(10,11,12),(9,13,14),(9,13,17),(14,13,17),(13,14,15),(14,15,16),(13,17,18),(0,17,13),(0,17,18),(17,18,19),(18,19,20)] def findHands(self, img, draw=True): """ @@ -51,7 +49,7 @@ class HandDetector: :param draw: 在图像上绘制输出的标志。 :return: 带或不带图形的图像 """ - imgRGB = 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， + imgRGB = 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(imgRGB) if self.results.multi_hand_landmarks: @@ -73,7 +71,7 @@ class HandDetector: xList = [] yList = [] bbox = [] - bboxInfo =[] + bboxInfo = [] self.lmList = [] if self.results.multi_hand_landmarks: myHand = self.results.multi_hand_landmarks[handNo] @@ -91,7 +89,7 @@ class HandDetector: bbox = xmin, ymin, boxW, boxH cx, cy = bbox[0] + (bbox[2] // 2), \ bbox[1] + (bbox[3] // 2) - bboxInfo = {"id": id, "bbox": bbox,"center": (cx, cy)} + bboxInfo = {"id": id, "bbox": bbox, "center": (cx, cy)} if draw: cv2.rectangle(img, (bbox[0] - 20, bbox[1] - 20), @@ -138,15 +136,17 @@ class HandDetector: else: return "Left" + class Main: def __init__(self): - self.camera = cv2.VideoCapture(0,cv2.CAP_DSHOW) + self.camera = cv2.VideoCapture(0, cv2.CAP_DSHOW) self.camera.set(3, 1280) self.camera.set(4, 720) def Gesture_recognition(self): + fps = cv2.CAP_PROP_FPS + self.detector = HandDetector() while True: - self.detector = HandDetector() frame, img = self.camera.read() img = self.detector.findHands(img) lmList, bbox = self.detector.findPosition(img) @@ -180,9 +180,13 @@ class Main: cv2.putText(img, "GOOD!", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3, (0, 0, 255), 3) cv2.imshow("camera", img) + + key = cv2.waitKey(1) if cv2.getWindowProperty('camera', cv2.WND_PROP_VISIBLE) < 1: break - cv2.waitKey(1) + elif key == 27: + break + if __name__ == '__main__':