hello, I have been working on gesture recognition using raspberry pi and opencv, I am able to run the following code on my linux and windows machines but not on the raspberry pi, note that I am able to run other scripts on the raspberry pi
import cv2 import numpy as np import math import imutils import time from collections import deque from imutils.video import VideoStream import argparse # construct the argument parse ap = argparse.ArgumentParser() ap.add_argument("-v", "--video", help="path to the (optional) video file") ap.add_argument("-b", "--buffer", type=int, default=32, help="max buffer size") args = vars(ap.parse_args())
cap = cv2.VideoCapture(0)
pts = deque(maxlen=args["buffer"])
counter = 0
(dX, dY) = (0, 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()
ret2, frame2 = cap.read()
frame=cv2.flip(frame,1)
frame2=cv2.flip(frame2,1)
kernel = np.ones((3,3),np.uint8)
frame2 = imutils.resize(frame2, width=600)
blurred = cv2.GaussianBlur(frame2, (11, 11), 0)
hsv2 = cv2.cvtColor(blurred, cv2.COLOR_BGR2HSV)
#define region of interest
roi=frame[100:300, 100:300]
cv2.rectangle(frame,(50,50),(500,500),(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)
mask2 = cv2.inRange(hsv2, lower_skin, upper_skin)
mask2 = cv2.erode(mask2, None, iterations=2)
#extrapolate the hand to fill dark spots within
mask = cv2.dilate(mask,kernel,iterations = 4)
mask2 = cv2.dilate(mask2,None, iterations=2)
#blur the image
mask = cv2.GaussianBlur(mask,(5,5),100)
#find contours
cnts,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
cnts2 = cv2.findContours(mask2.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
cnts2 = imutils.grab_contours(cnts2)
center = None
c = max(cnts2, key=cv2.contourArea)
((x, y), radius) = cv2.minEnclosingCircle(c)
M = cv2.moments(c)
center = (int(M["m10"] / M["m00"]), int(M["m01"] / M["m00"]))
cv2.circle(frame, (int(x), int(y)), 10, [255,125,0], -1)
pts.appendleft(center)
for i in np.arange(1, len(pts)):
if pts[i - 1] is None or pts[i] is None:
continue
if counter >= 5 and i == 1 and pts[-5] is not None:
# compute the difference between the x and y
# coordinates and re-initialize the direction
# text variables
dX = pts[-10][0] - pts[i][0]
dY = pts[-10][1] - pts[i][1]
print(areacnt)
#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)
cv2.imshow('frame2',frame)
except:
pass
counter += 1
k = cv2.waitKey(5) & 0xFF
if k == 27:
break
cv2.destroyAllWindows() cap.release()
