def update_brightcont(self):
# The algorithm is by Werner D. Streidt
# (http://visca.com/ffactory/archives/5-99/msg00021.html)
if self.contrast > 0:
delta = 127. * self.contrast / 100
a = 255. / (255. - delta * 2)
b = a * (self.brightness - delta)
else:
delta = -128. * self.contrast / 100
a = (256. - delta * 2) / 255.
b = a * self.brightness + delta
cv.ConvertScale(self.src_image, self.dst_image, a, b)
cv.ShowImage("image", self.dst_image)
cv.CalcArrHist([self.dst_image], self.hist)
(min_value, max_value, _, _) = cv.GetMinMaxHistValue(self.hist)
cv.Scale(self.hist.bins, self.hist.bins, float(self.hist_image.height) / max_value, 0)
cv.Set(self.hist_image, cv.ScalarAll(255))
bin_w = round(float(self.hist_image.width) / hist_size)
for i in range(hist_size):
cv.Rectangle(self.hist_image, (int(i * bin_w), self.hist_image.height),
(int((i + 1) * bin_w), self.hist_image.height - cv.Round(self.hist.bins[i])),
cv.ScalarAll(0), -1, 8, 0)
cv.ShowImage("histogram", self.hist_image)
def gray_swap(image):
new_image = cv.CreateImage((image.width, image.height), image.depth,
image.nChannels)
minVal, maxVal, minLoc, maxLoc = cv.MinMaxLoc(image)
scale = -1
shift = 255
cv.Scale(image, new_image, scale, shift)
return new_image
def linear_translate(image):
linear_image = cv.CreateImage((image.width, image.height), image.depth,
image.nChannels)
minVal, maxVal, minLoc, maxLoc = cv.MinMaxLoc(image)
scale = 255.0 / (maxVal - minVal)
shift = -(scale * minVal)
cv.Scale(image, linear_image, scale, shift)
return linear_image
def scaleTo8U(bldIm, counts):
w = bldIm.shape[1]
h = bldIm.shape[0]
mat = np.zeros((h, w, bldIm.shape[2]), dtype=np.int32)
mat[:, :, 0] = np.where(counts == 0, 1, counts)
mat[:, :, 1] = np.where(counts == 0, 1, counts)
mat[:, :, 2] = np.where(counts == 0, 1, counts)
mat = cv.fromarray(mat)
cvbldIm = cv.fromarray(bldIm)
bldIm8U = cv.CreateMat(h, w, cv.CV_8UC3)
cv.Div(cvbldIm, mat, cvbldIm)
cv.Scale(cvbldIm, bldIm8U, 1.0, 0)
return np.asarray(bldIm8U)
if __name__ == "__main__":
if len(sys.argv) > 1:
im = cv.LoadImage( sys.argv[1], cv.CV_LOAD_IMAGE_GRAYSCALE)
else:
url = 'https://raw.github.com/opencv/opencv/master/samples/c/baboon.jpg'
filedata = urllib2.urlopen(url).read()
imagefiledata = cv.CreateMatHeader(1, len(filedata), cv.CV_8UC1)
cv.SetData(imagefiledata, filedata, len(filedata))
im = cv.DecodeImageM(imagefiledata, cv.CV_LOAD_IMAGE_GRAYSCALE)
realInput = cv.CreateImage( cv.GetSize(im), cv.IPL_DEPTH_64F, 1)
imaginaryInput = cv.CreateImage( cv.GetSize(im), cv.IPL_DEPTH_64F, 1)
complexInput = cv.CreateImage( cv.GetSize(im), cv.IPL_DEPTH_64F, 2)
cv.Scale(im, realInput, 1.0, 0.0)
cv.Zero(imaginaryInput)
cv.Merge(realInput, imaginaryInput, None, None, complexInput)
dft_M = cv.GetOptimalDFTSize( im.height - 1 )
dft_N = cv.GetOptimalDFTSize( im.width - 1 )
dft_A = cv.CreateMat( dft_M, dft_N, cv.CV_64FC2 )
image_Re = cv.CreateImage( (dft_N, dft_M), cv.IPL_DEPTH_64F, 1)
image_Im = cv.CreateImage( (dft_N, dft_M), cv.IPL_DEPTH_64F, 1)
# copy A to dft_A and pad dft_A with zeros
tmp = cv.GetSubRect( dft_A, (0,0, im.width, im.height))
cv.Copy( complexInput, tmp, None )
if(dft_A.width > im.width):
tmp = cv.GetSubRect( dft_A, (im.width,0, dft_N - im.width, im.height))
