树莓派opencv python 实例教程二 手势识别 Hand Gesture Recognition
调整屏幕亮度会改变结果。尽量将手完全放在盒子里,避免手臂或手腕进入盒子(因为它会改变面积比)。使用范围值完成,因此可能适用于不同的人的不同颜色范围。
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import cv2 import numpy as np import math cap = cv2.VideoCapture(0) while(1): try: #an error comes if it does not find anything in window as it cannot find contour of max area #therefore this try error statement ret, frame = cap.read() frame=cv2.flip(frame,1) kernel = np.ones((3,3),np.uint8) #define region of interest roi=frame[100:300, 100:300] cv2.rectangle(frame,(100,100),(300,300),(0,255,0),0) hsv = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV) # define range of skin color in HSV lower_skin = np.array([0,20,70], dtype=np.uint8) upper_skin = np.array([20,255,255], dtype=np.uint8) #extract skin colur imagw mask = cv2.inRange(hsv, lower_skin, upper_skin) #extrapolate the hand to fill dark spots within mask = cv2.dilate(mask,kernel,iterations = 4) #blur the image mask = cv2.GaussianBlur(mask,(5,5),100) #find contours _,contours,hierarchy= cv2.findContours(mask,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE) #find contour of max area(hand) cnt = max(contours, key = lambda x: cv2.contourArea(x)) #approx the contour a little epsilon = 0.0005*cv2.arcLength(cnt,True) approx= cv2.approxPolyDP(cnt,epsilon,True) #make convex hull around hand hull = cv2.convexHull(cnt) #define area of hull and area of hand areahull = cv2.contourArea(hull) areacnt = cv2.contourArea(cnt) #find the percentage of area not covered by hand in convex hull arearatio=((areahull-areacnt)/areacnt)*100 #find the defects in convex hull with respect to hand hull = cv2.convexHull(approx, returnPoints=False) defects = cv2.convexityDefects(approx, hull) # l = no. of defects l=0 #code for finding no. of defects due to fingers for i in range(defects.shape[0]): s,e,f,d = defects[i,0] start = tuple(approx[s][0]) end = tuple(approx[e][0]) far = tuple(approx[f][0]) pt= (100,180) # find length of all sides of triangle a = math.sqrt((end[0] - start[0])**2 + (end[1] - start[1])**2) b = math.sqrt((far[0] - start[0])**2 + (far[1] - start[1])**2) c = math.sqrt((end[0] - far[0])**2 + (end[1] - far[1])**2) s = (a+b+c)/2 ar = math.sqrt(s*(s-a)*(s-b)*(s-c)) #distance between point and convex hull d=(2*ar)/a # apply cosine rule here angle = math.acos((b**2 + c**2 - a**2)/(2*b*c)) * 57 # ignore angles > 90 and ignore points very close to convex hull(they generally come due to noise) if angle <= 90 and d>30: l += 1 cv2.circle(roi, far, 3, [255,0,0], -1) #draw lines around hand cv2.line(roi,start, end, [0,255,0], 2) l+=1 #print corresponding gestures which are in their ranges font = cv2.FONT_HERSHEY_SIMPLEX if l==1: if areacnt<2000: cv2.putText(frame,'Put hand in the box',(0,50), font, 2, (0,0,255), 3, cv2.LINE_AA) else: if arearatio<12: cv2.putText(frame,'0',(0,50), font, 2, (0,0,255), 3, cv2.LINE_AA) elif arearatio<17.5: cv2.putText(frame,'Best of luck',(0,50), font, 2, (0,0,255), 3, cv2.LINE_AA) else: cv2.putText(frame,'1',(0,50), font, 2, (0,0,255), 3, cv2.LINE_AA) elif l==2: cv2.putText(frame,'2',(0,50), font, 2, (0,0,255), 3, cv2.LINE_AA) elif l==3: if arearatio<27: cv2.putText(frame,'3',(0,50), font, 2, (0,0,255), 3, cv2.LINE_AA) else: cv2.putText(frame,'ok',(0,50), font, 2, (0,0,255), 3, cv2.LINE_AA) elif l==4: cv2.putText(frame,'4',(0,50), font, 2, (0,0,255), 3, cv2.LINE_AA) elif l==5: cv2.putText(frame,'5',(0,50), font, 2, (0,0,255), 3, cv2.LINE_AA) elif l==6: cv2.putText(frame,'reposition',(0,50), font, 2, (0,0,255), 3, cv2.LINE_AA) else : cv2.putText(frame,'reposition',(10,50), font, 2, (0,0,255), 3, cv2.LINE_AA) #show the windows cv2.imshow('mask',mask) cv2.imshow('frame',frame) except: pass k = cv2.waitKey(5) & 0xFF if k == 27: break cv2.destroyAllWindows() cap.release()
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