### Cant Import cv2 (Pi 3 Mobel B) Python 3.5

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**Tue Apr 24, 2018 9:09 am**Basically Im very new to linux systems and an, above average in python, I intended to make a program that detect hand gesture (high five , victory one,three etc) , I created my own test code that doeast work since then I trien different codes from many people they all are stopping at same point that is cant import cv2 , due to following:

before , it was showing , which i installed , then it asked for , which I installed , but now cant find libQtTest.

For now Im trying to remove this error on following code, but it does shows the same error in other codes too, Ive thrice updated my pi.

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`from .cv2 import * ImportError: libQtTest.so.4: cannot open shared object file: No such file or directory`

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`libQtTest `

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`libatlas `

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`libjasper`

For now Im trying to remove this error on following code, but it does shows the same error in other codes too, Ive thrice updated my pi.

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```
import cv2
import numpy as np
import time
#Open Camera object
cap = cv2.VideoCapture(0)
#Decrease frame size
cap.set(cv2.cv.CV_CAP_PROP_FRAME_WIDTH, 1000)
cap.set(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT, 600)
def nothing(x):
pass
# Function to find angle between two vectors
def Angle(v1,v2):
dot = np.dot(v1,v2)
x_modulus = np.sqrt((v1*v1).sum())
y_modulus = np.sqrt((v2*v2).sum())
cos_angle = dot / x_modulus / y_modulus
angle = np.degrees(np.arccos(cos_angle))
return angle
# Function to find distance between two points in a list of lists
def FindDistance(A,B):
return np.sqrt(np.power((A[0][0]-B[0][0]),2) + np.power((A[0][1]-B[0][1]),2))
# Creating a window for HSV track bars
cv2.namedWindow('HSV_TrackBar')
# Starting with 100's to prevent error while masking
h,s,v = 100,100,100
# Creating track bar
cv2.createTrackbar('h', 'HSV_TrackBar',0,179,nothing)
cv2.createTrackbar('s', 'HSV_TrackBar',0,255,nothing)
cv2.createTrackbar('v', 'HSV_TrackBar',0,255,nothing)
while(1):
#Measure execution time
start_time = time.time()
#Capture frames from the camera
ret, frame = cap.read()
#Blur the image
blur = cv2.blur(frame,(3,3))
#Convert to HSV color space
hsv = cv2.cvtColor(blur,cv2.COLOR_BGR2HSV)
#Create a binary image with where white will be skin colors and rest is black
mask2 = cv2.inRange(hsv,np.array([2,50,50]),np.array([15,255,255]))
#Kernel matrices for morphological transformation
kernel_square = np.ones((11,11),np.uint8)
kernel_ellipse= cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(5,5))
#Perform morphological transformations to filter out the background noise
#Dilation increase skin color area
#Erosion increase skin color area
dilation = cv2.dilate(mask2,kernel_ellipse,iterations = 1)
erosion = cv2.erode(dilation,kernel_square,iterations = 1)
dilation2 = cv2.dilate(erosion,kernel_ellipse,iterations = 1)
filtered = cv2.medianBlur(dilation2,5)
kernel_ellipse= cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(8,8))
dilation2 = cv2.dilate(filtered,kernel_ellipse,iterations = 1)
kernel_ellipse= cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(5,5))
dilation3 = cv2.dilate(filtered,kernel_ellipse,iterations = 1)
median = cv2.medianBlur(dilation2,5)
ret,thresh = cv2.threshold(median,127,255,0)
#Find contours of the filtered frame
contours, hierarchy = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
#Draw Contours
#cv2.drawContours(frame, cnt, -1, (122,122,0), 3)
#cv2.imshow('Dilation',median)
#Find Max contour area (Assume that hand is in the frame)
max_area=100
ci=0
for i in range(len(contours)):
cnt=contours[i]
area = cv2.contourArea(cnt)
if(area>max_area):
max_area=area
ci=i
#Largest area contour
cnts = contours[ci]
#Find convex hull
hull = cv2.convexHull(cnts)
#Find convex defects
hull2 = cv2.convexHull(cnts,returnPoints = False)
defects = cv2.convexityDefects(cnts,hull2)
#Get defect points and draw them in the original image
FarDefect = []
for i in range(defects.shape[0]):
s,e,f,d = defects[i,0]
start = tuple(cnts[s][0])
end = tuple(cnts[e][0])
far = tuple(cnts[f][0])
FarDefect.append(far)
cv2.line(frame,start,end,[0,255,0],1)
cv2.circle(frame,far,10,[100,255,255],3)
#Find moments of the largest contour
moments = cv2.moments(cnts)
#Central mass of first order moments
if moments['m00']!=0:
cx = int(moments['m10']/moments['m00']) # cx = M10/M00
cy = int(moments['m01']/moments['m00']) # cy = M01/M00
centerMass=(cx,cy)
#Draw center mass
cv2.circle(frame,centerMass,7,[100,0,255],2)
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(frame,'Center',tuple(centerMass),font,2,(255,255,255),2)
#Distance from each finger defect(finger webbing) to the center mass
distanceBetweenDefectsToCenter = []
for i in range(0,len(FarDefect)):
x = np.array(FarDefect[i])
centerMass = np.array(centerMass)
distance = np.sqrt(np.power(x[0]-centerMass[0],2)+np.power(x[1]-centerMass[1],2))
distanceBetweenDefectsToCenter.append(distance)
#Get an average of three shortest distances from finger webbing to center mass
sortedDefectsDistances = sorted(distanceBetweenDefectsToCenter)
AverageDefectDistance = np.mean(sortedDefectsDistances[0:2])
#Get fingertip points from contour hull
#If points are in proximity of 80 pixels, consider as a single point in the group
finger = []
for i in range(0,len(hull)-1):
if (np.absolute(hull[i][0][0] - hull[i+1][0][0]) > 80) or ( np.absolute(hull[i][0][1] - hull[i+1][0][1]) > 80):
if hull[i][0][1] <500 :
finger.append(hull[i][0])
#The fingertip points are 5 hull points with largest y coordinates
finger = sorted(finger,key=lambda x: x[1])
fingers = finger[0:5]
#Calculate distance of each finger tip to the center mass
fingerDistance = []
for i in range(0,len(fingers)):
distance = np.sqrt(np.power(fingers[i][0]-centerMass[0],2)+np.power(fingers[i][1]-centerMass[0],2))
fingerDistance.append(distance)
#Finger is pointed/raised if the distance of between fingertip to the center mass is larger
#than the distance of average finger webbing to center mass by 130 pixels
result = 0
for i in range(0,len(fingers)):
if fingerDistance[i] > AverageDefectDistance+130:
result = result +1
#Print number of pointed fingers
cv2.putText(frame,str(result),(100,100),font,2,(255,255,255),2)
#show height raised fingers
#cv2.putText(frame,'finger1',tuple(finger[0]),font,2,(255,255,255),2)
#cv2.putText(frame,'finger2',tuple(finger[1]),font,2,(255,255,255),2)
#cv2.putText(frame,'finger3',tuple(finger[2]),font,2,(255,255,255),2)
#cv2.putText(frame,'finger4',tuple(finger[3]),font,2,(255,255,255),2)
#cv2.putText(frame,'finger5',tuple(finger[4]),font,2,(255,255,255),2)
#cv2.putText(frame,'finger6',tuple(finger[5]),font,2,(255,255,255),2)
#cv2.putText(frame,'finger7',tuple(finger[6]),font,2,(255,255,255),2)
#cv2.putText(frame,'finger8',tuple(finger[7]),font,2,(255,255,255),2)
#Print bounding rectangle
x,y,w,h = cv2.boundingRect(cnts)
img = cv2.rectangle(frame,(x,y),(x+w,y+h),(0,255,0),2)
cv2.drawContours(frame,[hull],-1,(255,255,255),2)
##### Show final image ########
cv2.imshow('Dilation',frame)
###############################
#Print execution time
#print time.time()-start_time
#close the output video by pressing 'ESC'
k = cv2.waitKey(5) & 0xFF
if k == 27:
break
cap.release()
cv2.destroyAllWindows()
```