(frame_rgb) # 绘制检测到的手势 if results.multi_hand_landmarks: for hand_landmarks in results.multi_hand_landmarks: mp_drawing.draw_landmarks(frame, hand_landmarks, mp_hands.HAND_CONNECTIONS) # 显示图像 cv2.imshow('Hand Gesture Recognition', frame) # 按下'q'键退出循环 if cv2.wait...
frame=cap.read()ifnotret:break# 翻转图像frame=cv2.flip(frame,1)# 转换颜色空间frame_rgb=cv2.cvtColor(frame,cv2.COLOR_BGR2RGB)# 处理手势results=hands.process(frame_rgb)# 绘制手势ifresults.multi_hand_landmarks:forhand
mp_hands.draw_landmarks(frame, hand_landmarks, mp_hands.HAND_CONNECTIONS) cv2.imshow('Gesture Recognition', frame) if cv2.waitKey(5) & 0xFF == 27: # 按下ESC键退出 break cap.release() cv2.destroyAllWindows() 在这个循环中,我们首先读取摄像头的每一帧图像,然后将其转换为RGB格式并传递给...
("Thresholded Hand Image",thresholded)# 保存图像ifnum_imgs_taken<=300:cv2.imwrite(r"D:\\gesture\\train\\"+str(element)+"\\"+str(num_imgs_taken+300)+'.jpg',thresholded)else:breaknum_imgs_taken+=1else:# 没有检测到手部时显示文本cv2.putText(frame_copy,"没有检测到手部...",(200,...
HandGestureRecognition+start()+process_frame(frame)+recognize_gesture(hand_landmarks)GestureRecognizer+detect_number(hand_landmarks) 结论 通过使用Python的MediaPipe库,开发者能够轻松地实现手势识别功能。本文简要介绍了如何使用OpenCV捕捉图像,并利用MediaPipe进行手势识别。此外,我们展示了如何通过饼状图和类图来可视...
# import necessary packages for hand gesture recognition project using Python OpenCV import cv2 import numpy as np import mediapipe as mp import tensorflow as tf from tensorflow.keras.models import load_model #1、建立环境 #conda create --name GestureRecognition python=3.8 --channelhttps://mirrors....
for point in hand_landmarks.landmark:x = int(point.x * frame.shape[1])y = int(point.y * frame.shape[0])cv2.circle(frame, (x, y), 5, (0, 255, 0), -1) # 画出关键点cv2.imshow('Gesture Recognition', frame) # 显示结果if cv2.waitKey(1) & 0xFF == ord('q'):breakcap...
(s.split('\n')): # 根据\n来竖向排列文字 y = y0 + i*dy cv2.putText(image,txt,(50,y),cv2.FONT_HERSHEY_SIMPLEX,1,(255,0,0),3) cv2.imshow('MediaPipe Gesture Recognition',image) # 显示图像 cv2.imwrite('save/{0}.jpg'.format(idx),image) if cv2.waitKey(5) & 0xFF == 27:...
hand_mask=cv2.morphologyEx(hand_mask,cv2.MORPH_OPEN,self.str_el) hand_mask_in_frame=np.zeros(self.frame.shape[:2],np.uint8) hand_mask_in_frame[hand_y:hand_y+hand_h,hand_x:hand_x+hand_w]=hand_mask thresh_deg=80.0 # Convexity hull based gesture recognition. ...
C. A. Octavian, H. Mihaela and I. J. Catalin, "Gesture Recognition using PYTHON," 2021 International Conference on Speech Technology and Human-Computer Dialogue (SpeD), Bucharest, Romania, 2021, pp. 139-144, doi: 10.1109/SpeD53181.2021.9587341.Abstract: Hand gesture recognition is a domain ...