def main():
imgname1='imgs/porta1pb.pnm'
imgname2='imgs/larapiopb.pnm'
if len(argv)>2:
imgname1=argv[1]
imgname2=argv[2]
thresh_factor = 65
min_size = 10 #(minimum w and h to be not considered noise)
print 'Subtracting...'
subtract(imgname1, imgname2)
print 'Thresholding...'
threshold('subtracted_image.png', 65)
print 'Eroding...'
k=open('kernel', 'w')
k.write('''1 1 1 1 1
1 1 1 1 1
1 1 1 1 1''')
k.close()
erode('thresholded_image.png')
print 'Dilating...'
dilate('eroded_image.png')
#Dilating to merge pieces, then erode to get back to the original size
print 'Merging pieces...',
k=open('kernel', 'w')
for i in range(10):
k.write('1 1 1 1 1 1 1 1 1 1\n')
k.close()
dilate('dilated_image.png', outfile='result0.png')
print 'halfway done...'
erode('result0.png', outfile = 'result.png')
#Using opencv to calculate the bounding box to figure out what we found
img = cv2.imread('result.png', cv2.CV_LOAD_IMAGE_GRAYSCALE)
contours, hierarchy = cv2.findContours(img,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)
noise=0
for cnt in contours:
b_box = cv2.boundingRect(cnt)
w,h = b_box[2], b_box[3]
#print w,h
if w > min_size and h > min_size:
#Found something...
if w > h:
print 'Found a dog!'
else:
print 'Found an intruder!'
else:
noise+=1
print 'Found',str(noise),'noise(s).'
def blurshift(img_blur, sigmai, rhoi, phii):
alpha = 0.1167
sigma0 = 0.5
rows, cols = img_blur.shape
sigma1 = sigma0 + alpha * (rhoi)
filter_gaussian = gauss2D((rows, cols), sigma1)
delta_xi = -1 * rhoi * np.cos(phii)
delta_yi = -1 * rhoi * np.sin(phii)
rows1, cols1 = img_blur.shape
M = np.float32([[1, 0, delta_xi], [0, 1, delta_yi]])
Dogresp_trans = cv2.warpAffine(img_blur, M, (cols1, rows1))
blurshift_img = dilate(Dogresp_trans, filter_gaussian)
return blurshift_img
def blurshift(img_blur,sigmai,rhoi,phii,alpha):
#alpha = 0.7
#sigma0 = 0.5
rows,cols = img_blur.shape
sigma1 = sigmai+alpha*(rhoi)
filter_gaussian = gauss2D((rows,cols),sigma1)
delta_xi = -1*rhoi*np.cos(phii)
delta_yi = -1*rhoi*np.sin(phii)
rows1,cols1 =img_blur.shape
M = np.float32([[1,0,delta_xi],[0,1,delta_yi]])
Dogresp_trans = cv2.warpAffine(img_blur,M,(cols1,rows1))
blurshift_img = dilate(Dogresp_trans,filter_gaussian)
return blurshift_img
