def Magnitude(self, dx, dy, Mask=None, precise=True, method="cv"):
'''Calculates the magnitude of the gradient using precise and fast approach'''
dxconv = cv.CreateImage(cv.GetSize(dx), cv.IPL_DEPTH_32F, dx.channels)
dyconv = cv.CreateImage(cv.GetSize(dx), cv.IPL_DEPTH_32F, dx.channels)
dxdest = cv.CreateImage(cv.GetSize(dx), cv.IPL_DEPTH_32F, dx.channels)
dydest = cv.CreateImage(cv.GetSize(dx), cv.IPL_DEPTH_32F, dx.channels)
magdest = cv.CreateImage(cv.GetSize(dx), cv.IPL_DEPTH_32F, dx.channels)
magnitude = cv.CreateImage(cv.GetSize(dx), cv.IPL_DEPTH_32F,
dx.channels)
magnitudetemp = cv.CreateImage(cv.GetSize(dx), cv.IPL_DEPTH_32F,
dx.channels)
zero = cv.CreateImage(cv.GetSize(dx), cv.IPL_DEPTH_32F, dx.channels)
cv.Convert(dx, dxconv)
cv.Convert(dy, dyconv)
if precise:
cv.Pow(dxconv, dxdest, 2)
cv.Pow(dyconv, dydest, 2)
cv.Add(dxdest, dydest, magdest)
cv.Pow(magdest, magnitude, 1. / 2)
else:
#Add the |dx| + |dy|
return None
if method == "slow":
size = cv.GetSize(magnitude)
for x in range(size[0]):
for y in range(size[1]):
if Mask == None:
pass
elif Mask[y, x] > 0:
pass
else:
magnitude[y, x] = 0
final = cv.CreateImage(cv.GetSize(dx), cv.IPL_DEPTH_8U,
dx.channels)
cv.ConvertScaleAbs(magnitude, final)
else:
cv.Add(zero, magnitude, magnitudetemp, Mask)
final = cv.CreateImage(cv.GetSize(dx), cv.IPL_DEPTH_8U,
dx.channels)
cv.ConvertScaleAbs(magnitudetemp, final)
if self.visualize:
magnitude2 = cv.CreateImage(cv.GetSize(dy), cv.IPL_DEPTH_8U, 1)
cv.EqualizeHist(final, magnitude2)
while True:
cv.NamedWindow("Magnitude")
cv.ShowImage("Magnitude", magnitude2)
c = cv.WaitKey(5)
if c > 0:
break
cv.DestroyAllWindows()
return final
def compute_distance_image(self, input_map):
#Create necessary matrices
dist_matrix=cv.CreateMat(input_map.height, input_map.width, cv.CV_32FC1)
dist_img_gray=cv.CreateMat(input_map.height, input_map.width, cv.CV_8UC1)
#Get the Euclidean distance from every free point in the map to the closest obstacle
cv.DistTransform(input_map, dist_matrix)
#To make the pikes of the distance function we use the square of the resulted value
square_mat=cv.CreateMat(input_map.height, input_map.width, cv.CV_32FC1)
cv.Pow(dist_matrix,square_mat, 3)
#We normalize the values of all pixels to print the image.
matrix = dist_matrix
max_val = np.max(matrix)
m=255/max_val
for r in range(0, matrix.height):
for c in range(0, matrix.width):
dist_img_gray[r,c]=np.int(m*matrix[r,c])
return(dist_img_gray, dist_matrix)
def Process(image, pos_var, pos_w, pos_phase, pos_psi):
global kernel_size
if kernel_size % 2 == 0:
kernel_size += 1
kernel = cv.CreateMat(kernel_size, kernel_size, cv.CV_32FC1)
# kernelimg = cv.CreateImage((kernel_size,kernel_size),cv.IPL_DEPTH_32F,1)
# big_kernelimg = cv.CreateImage((kernel_size*20,kernel_size*20),cv.IPL_DEPTH_32F,1)
src = cv.CreateImage((image.width, image.height), cv.IPL_DEPTH_8U, 1)
src_f = cv.CreateImage((image.width, image.height), cv.IPL_DEPTH_32F, 1)
# src = image #cv.CvtColor(image,src,cv.CV_BGR2GRAY) #no conversion is needed
if cv.GetElemType(image) == cv.CV_8UC3:
cv.CvtColor(image, src, cv.CV_BGR2GRAY)
else:
src = image
cv.ConvertScale(src, src_f, 1.0 / 255, 0)
dest = cv.CloneImage(src_f)
dest_mag = cv.CloneImage(src_f)
var = pos_var / 10.0
w = pos_w / 10.0
phase = pos_phase * cv.CV_PI / 180.0
psi = cv.CV_PI * pos_psi / 180.0
cv.Zero(kernel)
for x in range(-kernel_size / 2 + 1, kernel_size / 2 + 1):
for y in range(-kernel_size / 2 + 1, kernel_size / 2 + 1):
kernel_val = math.exp(-(
(x * x) +
(y * y)) / (2 * var)) * math.cos(w * x * math.cos(phase) +
w * y * math.sin(phase) + psi)
cv.Set2D(kernel, y + kernel_size / 2, x + kernel_size / 2,
cv.Scalar(kernel_val))
# cv.Set2D(kernelimg,y+kernel_size/2,x+kernel_size/2,cv.Scalar(kernel_val/2+0.5))
cv.Filter2D(src_f, dest, kernel, (-1, -1))
# cv.Resize(kernelimg,big_kernelimg)
cv.Pow(dest, dest_mag, 2)
# return (dest_mag, big_kernelimg, dest)
return (dest_mag, dest)
# cv.ShowImage("Mag",dest_mag)
# cv.ShowImage("Kernel",big_kernelimg)
# cv.ShowImage("Process window",dest)
def on_trackbar(edge_thresh):
cv.Threshold(gray, edge, float(edge_thresh), float(edge_thresh),
cv.CV_THRESH_BINARY)
#Distance transform
cv.DistTransform(edge, dist, cv.CV_DIST_L2, cv.CV_DIST_MASK_5)
cv.ConvertScale(dist, dist, 5000.0, 0)
cv.Pow(dist, dist, 0.5)
cv.ConvertScale(dist, dist32s, 1.0, 0.5)
cv.AndS(dist32s, cv.ScalarAll(255), dist32s, None)
cv.ConvertScale(dist32s, dist8u1, 1, 0)
cv.ConvertScale(dist32s, dist32s, -1, 0)
cv.AddS(dist32s, cv.ScalarAll(255), dist32s, None)
cv.ConvertScale(dist32s, dist8u2, 1, 0)
cv.Merge(dist8u1, dist8u2, dist8u2, None, dist8u)
cv.ShowImage(wndname, dist8u)
cv.AbsDiff(im, im2, res) # Like minus for each pixel im(i) - im2(i)
cv.ShowImage("AbsDiff", res)
cv.Mul(im, im2, res) #Multiplie each pixels (almost white)
cv.ShowImage("Mult", res)
cv.Div(im, im2,
res) #Values will be low so the image will likely to be almost black
cv.ShowImage("Div", res)
cv.And(im, im2, res) #Bit and for every pixels
cv.ShowImage("And", res)
cv.Or(im, im2, res) # Bit or for every pixels
cv.ShowImage("Or", res)
cv.Not(im, res) # Bit not of an image
cv.ShowImage("Not", res)
cv.Xor(im, im2, res) #Bit Xor
cv.ShowImage("Xor", res)
cv.Pow(im, res, 2) #Pow the each pixel with the given value
cv.ShowImage("Pow", res)
cv.Max(im, im2, res) #Maximum between two pixels
#Same form Min MinS
cv.ShowImage("Max", res)
cv.WaitKey(0)
cv.Zero( tmp )
# no need to pad bottom part of dft_A with zeros because of
# use nonzero_rows parameter in cv.FT() call below
cv.DFT( dft_A, dft_A, cv.CV_DXT_FORWARD, complexInput.height )
cv.NamedWindow("win", 0)
cv.NamedWindow("magnitude", 0)
cv.ShowImage("win", im)
# Split Fourier in real and imaginary parts
cv.Split( dft_A, image_Re, image_Im, None, None )
# Compute the magnitude of the spectrum Mag = sqrt(Re^2 + Im^2)
cv.Pow( image_Re, image_Re, 2.0)
cv.Pow( image_Im, image_Im, 2.0)
cv.Add( image_Re, image_Im, image_Re, None)
cv.Pow( image_Re, image_Re, 0.5 )
# Compute log(1 + Mag)
cv.AddS( image_Re, cv.ScalarAll(1.0), image_Re, None ) # 1 + Mag
cv.Log( image_Re, image_Re ) # log(1 + Mag)
# Rearrange the quadrants of Fourier image so that the origin is at
# the image center
cvShiftDFT( image_Re, image_Re )
min, max, pt1, pt2 = cv.MinMaxLoc(image_Re)
cv.Scale(image_Re, image_Re, 1.0/(max-min), 1.0*(-min)/(max-min))
