def f2():
import pgm
#
# imgs is the image to be modified
# imgt is the reference image
#
imgarray,w,h = pgm.pgmread('histo_inp_image.pgm')
#imgarray,w,h = pgm.pgmread('histo_ref_image.pgm')
#imgarray,w,h = pgm.pgmread('histo_inp_image2.pgm')
#imgarray,w,h = pgm.pgmread('histo_ref_image2.pgm')
#imgarray,w,h = pgm.pgmread('lena.pgm')
#imgarray,w,h = pgm.pgmread('fourier_transform.pgm')
#imgarray,w,h = pgm.pgmread('Fourier.pgm')
imgs = Img.Img(imgarray)
#
#imgarray,w,h = pgm.pgmread('histo_inp_image.pgm')
imgarray,w,h = pgm.pgmread('histo_ref_image.pgm')
#imgarray,w,h = pgm.pgmread('histo_inp_image2.pgm')
#imgarray,w,h = pgm.pgmread('histo_ref_image2.pgm')
#imgarray,w,h = pgm.pgmread('lena.pgm')
#imgarray,w,h = pgm.pgmread('fourier_transform.pgm')
#imgarray,w,h = pgm.pgmread('Fourier.pgm')
imgt = Img.Img(imgarray)
#
imgt1 = imgs.modify_hist(imgt)
plot_fig(imgs.pix, 'Original image')
plot_fig(imgt.pix, 'Reference image')
plot_fig(imgt1.pix, 'Modified original image')
stem_hist(imgs, 'Original image')
stem_hist(imgt, 'Reference image')
stem_hist(imgt1, 'Modified original image')
print np.sum((imgs.pix - imgt1.pix)**2)
def f2(file1, file2):
from scipy.fftpack import fftshift
(imgarr1,w,h) = pgm.pgmread(file1)
(imgarr2,w,h) = pgm.pgmread(file2)
imgarr1 = np.float32(imgarr1)
imgarr2 = np.float32(imgarr2)
print imgarr1.shape, imgarr2.shape
#
dftArr1 = dft_2d_from_1d(imgarr1)
dftArr2 = dft_2d_from_1d(imgarr2)
#
absArr1 = np.abs(dftArr1)
absArr2 = np.abs(dftArr2)
phaseArr1 = np.angle(dftArr1)
phaseArr2 = np.angle(dftArr2)
#
newArr1 = absArr1 * np.exp(1j * phaseArr2)
newArr2 = absArr2 * np.exp(1j * phaseArr1)
#print 'Error : ', np.sum((newArr1 - dftArr1)**2)
#print 'Error : ', np.sum((newArr2 - dftArr2)**2)
#
idftArr1 = idft_2d_from_1d(newArr1)
idftArr2 = idft_2d_from_1d(newArr2)
idftArr1 = np.round(idftArr1.real)
idftArr2 = np.round(idftArr2.real)
#
dftArrAbs1 = np.abs(fftshift(dftArr1))
dftArrAbs2 = np.abs(fftshift(dftArr2))
#
plt.figure()
plt.subplot(121)
plt.title(file1)
plt.imshow(imgarr1, cmap=cm.gray)
#plt.colorbar()
plt.subplot(122)
plt.title(file2)
plt.imshow(imgarr2, cmap=cm.gray)
#plt.colorbar()
#
plt.figure()
plt.subplot(121)
plt.title('DFT of ' + file1)
plt.imshow(np.int32(np.log(dftArrAbs1)), cmap=cm.gray)
plt.colorbar()
plt.subplot(122)
plt.title('DFT of ' + file2)
plt.imshow(np.int32(np.log(dftArrAbs2)), cmap=cm.gray)
plt.colorbar()
#
plt.figure()
plt.subplot(121)
plt.title('IDFT1')
plt.imshow(np.int32(idftArr1), cmap=cm.gray)
#plt.colorbar()
plt.subplot(122)
plt.title('IDFT2')
plt.imshow(np.int32(idftArr2), cmap=cm.gray)
def f2(file1, file2):
from scipy.fftpack import fftshift
(imgarr1, w, h) = pgm.pgmread(file1)
(imgarr2, w, h) = pgm.pgmread(file2)
imgarr1 = np.float32(imgarr1)
imgarr2 = np.float32(imgarr2)
print imgarr1.shape, imgarr2.shape
#
dftArr1 = dft_2d_from_1d(imgarr1)
dftArr2 = dft_2d_from_1d(imgarr2)
#
absArr1 = np.abs(dftArr1)
absArr2 = np.abs(dftArr2)
phaseArr1 = np.angle(dftArr1)
phaseArr2 = np.angle(dftArr2)
#
newArr1 = absArr1 * np.exp(1j * phaseArr2)
newArr2 = absArr2 * np.exp(1j * phaseArr1)
#print 'Error : ', np.sum((newArr1 - dftArr1)**2)
#print 'Error : ', np.sum((newArr2 - dftArr2)**2)
#
idftArr1 = idft_2d_from_1d(newArr1)
idftArr2 = idft_2d_from_1d(newArr2)
idftArr1 = np.round(idftArr1.real)
idftArr2 = np.round(idftArr2.real)
#
dftArrAbs1 = np.abs(fftshift(dftArr1))
dftArrAbs2 = np.abs(fftshift(dftArr2))
#
plt.figure()
plt.subplot(121)
plt.title(file1)
plt.imshow(imgarr1, cmap=cm.gray)
#plt.colorbar()
plt.subplot(122)
plt.title(file2)
plt.imshow(imgarr2, cmap=cm.gray)
#plt.colorbar()
#
plt.figure()
plt.subplot(121)
plt.title('DFT of ' + file1)
plt.imshow(np.int32(np.log(dftArrAbs1)), cmap=cm.gray)
plt.colorbar()
plt.subplot(122)
plt.title('DFT of ' + file2)
plt.imshow(np.int32(np.log(dftArrAbs2)), cmap=cm.gray)
plt.colorbar()
#
plt.figure()
plt.subplot(121)
plt.title('IDFT1')
plt.imshow(np.int32(idftArr1), cmap=cm.gray)
#plt.colorbar()
plt.subplot(122)
plt.title('IDFT2')
plt.imshow(np.int32(idftArr2), cmap=cm.gray)
def f1(filename):
from scipy.fftpack import fftshift
(imgarr, w, h) = pgm.pgmread(filename)
imgarr = np.float32(imgarr)
#
dftArr = dft_2d_from_1d(imgarr)
#
idftArr = idft_2d_from_1d(dftArr)
idftArr = np.round(idftArr.real)
print 'Error : ', np.sum((imgarr - idftArr)**2)
#
dftArrAbs = np.abs(fftshift(dftArr))
#
plt.figure()
plt.subplot(131)
plt.title(filename)
plt.imshow(imgarr, cmap=cm.gray)
plt.colorbar()
plt.subplot(132)
plt.title('DFT of ' + filename)
plt.imshow(np.int32(np.log(dftArrAbs)), cmap=cm.gray)
plt.colorbar()
plt.subplot(133)
plt.title('IDFT')
plt.imshow(np.int32(idftArr), cmap=cm.gray)
plt.colorbar()
def f1(filename):
from scipy.fftpack import fftshift
(imgarr,w,h) = pgm.pgmread(filename)
imgarr = np.float32(imgarr)
#
dftArr = dft_2d_from_1d(imgarr)
#
idftArr = idft_2d_from_1d(dftArr)
idftArr = np.round(idftArr.real)
print 'Error : ', np.sum((imgarr - idftArr)**2)
#
dftArrAbs = np.abs(fftshift(dftArr))
#
plt.figure()
plt.subplot(131)
plt.title(filename)
plt.imshow(imgarr, cmap=cm.gray)
plt.colorbar()
plt.subplot(132)
plt.title('DFT of ' + filename)
plt.imshow(np.int32(np.log(dftArrAbs)), cmap=cm.gray)
plt.colorbar()
plt.subplot(133)
plt.title('IDFT')
plt.imshow(np.int32(idftArr), cmap=cm.gray)
plt.colorbar()
def f1():
""" Convolves image and kernel """
(imgarr,w,h) = pgm.pgmread('cameraman.pgm')
imgs = Img.Img(imgarr)
k1 = Img.create_gauss_kernel(Img.find_gauss_support(0.8))
k2 = Img.create_gauss_kernel(Img.find_gauss_support(1.2))
k3 = Img.create_gauss_kernel(Img.find_gauss_support(1.6))
imgt1 = imgs.conv(k1)
imgt2 = imgs.conv(k2)
imgt3 = imgs.conv(k3)
# print imgt1.col, imgt1.row
# print imgt2.col, imgt2.row
# print imgt3.col, imgt3.row
plt.figure(1)
plt.imshow(imgs.pix, cmap=cm.gray)
plt.title('Original image')
plt.figure(2)
plt.imshow(imgt1.pix, cmap=cm.gray)
plt.title('Img convolved with Gaussian kernel' + str(shape(k1)))
plt.figure(3)
plt.imshow(imgt2.pix, cmap=cm.gray)
plt.title('Img convolved with Gaussian kernel' + str(shape(k2)))
plt.figure(4)
plt.imshow(imgt3.pix, cmap=cm.gray)
plt.title('Img convolved with Gaussian kernel' + str(shape(k3)))
plt.show()
def f1():
import pgm
#imgarray,w,h = pgm.pgmread('histo_inp_image.pgm')
#imgarray,w,h = pgm.pgmread('histo_ref_image.pgm')
#imgarray,w,h = pgm.pgmread('histo_inp_image2.pgm')
#imgarray,w,h = pgm.pgmread('histo_ref_image2.pgm')
imgarray,w,h = pgm.pgmread('lena.pgm')
#imgarray,w,h = pgm.pgmread('fourier_transform.pgm')
imgs = Img.Img(imgarray)
imgt = imgs.add_salt_pepper(0.65)
imgt1 = imgt.median_filter(np.ones((3,3)))
imgt1 = imgt1.median_filter(np.ones((5,5)))
#imgt1 = imgt.median_filter(np.ones((7,7)))
#imgt1 = imgt.median_filter(np.array([[2,2,2],[2,1,2],[2,2,2]]))
k = Img.create_gauss_kernel(Img.find_gauss_support(0.75), 0.75)
imgt2 = imgt.conv(k)
plot_fig(imgs.pix, 'Original image')
plot_fig(imgt.pix, 'Added salt and pepper noise')
plot_fig(imgt1.pix, 'Median filtered image')
plot_fig(imgt2.pix, 'Gaussian LP filtered image')
#plot_hist(imgs, 'Original image')
#plot_hist(imgt, 'Added salt and pepper noise')
#plot_hist(imgt1, 'Median filtered image')
#plot_hist(imgt2, 'Gaussian LP filtered image')
print np.sum((imgs.pix - imgt1.pix)**2)
def f1():
""" Convolves image and kernel """
(imgarr, w, h) = pgm.pgmread('cameraman.pgm')
imgs = Img.Img(imgarr)
k1 = Img.create_gauss_kernel(Img.find_gauss_support(0.8))
k2 = Img.create_gauss_kernel(Img.find_gauss_support(1.2))
k3 = Img.create_gauss_kernel(Img.find_gauss_support(1.6))
imgt1 = imgs.conv(k1)
imgt2 = imgs.conv(k2)
imgt3 = imgs.conv(k3)
# print imgt1.col, imgt1.row
# print imgt2.col, imgt2.row
# print imgt3.col, imgt3.row
plt.figure(1)
plt.imshow(imgs.pix, cmap=cm.gray)
plt.title('Original image')
plt.figure(2)
plt.imshow(imgt1.pix, cmap=cm.gray)
plt.title('Img convolved with Gaussian kernel' + str(shape(k1)))
plt.figure(3)
plt.imshow(imgt2.pix, cmap=cm.gray)
plt.title('Img convolved with Gaussian kernel' + str(shape(k2)))
plt.figure(4)
plt.imshow(imgt3.pix, cmap=cm.gray)
plt.title('Img convolved with Gaussian kernel' + str(shape(k3)))
plt.show()
def f1():
(imgarr,w,h) = pgm.pgmread('lena.pgm')
imgs = Image.Image(imgarr)
imgt1 = imgs.translate(3.75,4.3)
imgt2 = imgs.translate(3.75,4.3,'bilinear')
plt.figure(1)
plt.imshow(imgs.pix,cmap=cm.gray)
plt.figure(2)
plt.imshow(imgt1.pix,cmap=cm.gray)
plt.figure(3)
plt.imshow(imgt2.pix,cmap=cm.gray)
plt.show()
def f1():
(imgarr, w, h) = pgm.pgmread('lena.pgm')
imgs = Image.Image(imgarr)
imgt1 = imgs.translate(3.75, 4.3)
imgt2 = imgs.translate(3.75, 4.3, 'bilinear')
plt.figure(1)
plt.imshow(imgs.pix, cmap=cm.gray)
plt.figure(2)
plt.imshow(imgt1.pix, cmap=cm.gray)
plt.figure(3)
plt.imshow(imgt2.pix, cmap=cm.gray)
plt.show()
def f2():
(imgarr,w,h) = pgm.pgmread('pisa.pgm')
imgs = Image.Image(imgarr)
imgt1 = imgs.rotate(2.5)
imgt2 = imgt1.rotate(-2.5)
imgt3 = imgs - imgt2
plt.figure(1)
plt.imshow(imgs.pix,cmap=cm.gray)
plt.figure(2)
plt.imshow(imgt1.pix,cmap=cm.gray)
plt.figure(3)
plt.imshow(imgt2.pix,cmap=cm.gray)
plt.figure(4)
plt.imshow(imgt3.pix,cmap=cm.gray)
plt.show()
def f2():
(imgarr, w, h) = pgm.pgmread('pisa.pgm')
imgs = Image.Image(imgarr)
imgt1 = imgs.rotate(2.5)
imgt2 = imgt1.rotate(-2.5)
imgt3 = imgs - imgt2
plt.figure(1)
plt.imshow(imgs.pix, cmap=cm.gray)
plt.figure(2)
plt.imshow(imgt1.pix, cmap=cm.gray)
plt.figure(3)
plt.imshow(imgt2.pix, cmap=cm.gray)
plt.figure(4)
plt.imshow(imgt3.pix, cmap=cm.gray)
plt.show()
def f3():
import pgm
imgarray,w,h = pgm.pgmread('histo_inp_image.pgm')
#imgarray,w,h = pgm.pgmread('histo_ref_image.pgm')
#imgarray,w,h = pgm.pgmread('histo_inp_image2.pgm')
#imgarray,w,h = pgm.pgmread('histo_ref_image2.pgm')
#imgarray,w,h = pgm.pgmread('lena.pgm')
#imgarray,w,h = pgm.pgmread('fourier_transform.pgm')
#imgarray,w,h = pgm.pgmread('Fourier.pgm')
#imgarray,w,h = pgm.pgmread('academy.pgm')
imgs = Img.Img(imgarray)
imgt = imgs.hist_eq()
plot_fig(imgs.pix, 'Original image')
plot_fig(imgt.pix, 'Histogram equalized image')
stem_hist(imgs, 'Original image')
stem_hist(imgt, 'Histogram equalized image')
print np.sum((imgs.pix - imgt.pix)**2)
def f2():
""" Scale image """
factor = (0.8, 0.8)
(imgarr, w, h) = pgm.pgmread('cameraman.pgm')
imgs = Img.Img(imgarr)
imgt1 = imgs.scale(factor)
imgt2 = imgs.scale(factor, type='bilinear')
plt.figure(1)
plt.imshow(imgs.pix, cmap=cm.gray)
plt.title('Original image')
plt.figure(2)
plt.imshow(imgt1.pix, cmap=cm.gray)
plt.title('Scale factor = ' + str(factor))
plt.figure(3)
plt.imshow(imgt2.pix, cmap=cm.gray)
plt.title('Scale factor = ' + str(factor) +
'\nwith bilinear interpolation')
plt.show()
def f2():
""" Scale image """
factor = (0.8, 0.8)
(imgarr,w,h) = pgm.pgmread('cameraman.pgm')
imgs = Img.Img(imgarr)
imgt1 = imgs.scale(factor)
imgt2 = imgs.scale(factor, type='bilinear')
plt.figure(1)
plt.imshow(imgs.pix, cmap=cm.gray)
plt.title('Original image')
plt.figure(2)
plt.imshow(imgt1.pix, cmap=cm.gray)
plt.title('Scale factor = ' + str(factor))
plt.figure(3)
plt.imshow(imgt2.pix, cmap=cm.gray)
plt.title('Scale factor = ' + str(factor) +
'\nwith bilinear interpolation')
plt.show()
def f2(sigma):
import pgm
(imgarray,w,h) = pgm.pgmread('cameraman.pgm')
imgs = Image.Image(imgarray)
suppMat, imgt = imgs.lsv_blur(sigma)
if show_plot == True:
plt.figure()
plt.imshow(suppMat)
plt.title('Kernel size variation')
plt.colorbar()
plt.figure()
plt.imshow(imgs.pix, cmap=cm.gray)
plt.title('Original image')
plt.figure()
plt.imshow(imgt.pix, cmap=cm.gray)
plt.title('Space variant blur')
pgm.pgmwrite(imgt.pix, 'a3_lsv_blur.pgm')
#
imgt1 = f3(imgs)
print 'Kernel size: ', suppMat[0][0]
print 'Error : ', np.sum((imgt1.pix - imgt.pix)**2) / 256
def g1():
import pgm
imgarray,w,h = pgm.pgmread('test.pgm')
imgs = Img.Img(imgarray)
imgt1 = imgs.median_filter(np.ones((3,3)))
pgm.pgmwrite(imgt1.pix, 'test2.pgm')
return imgarr
def plot_hist(img, plotTitle):
imgarray = img.calc_hist()
plt.figure()
plt.plot(imgarray)
plt.title(plotTitle)
def plot_fig(imgarr, plotTitle):
plt.figure()
plt.imshow(imgarr, cmap=cm.gray)
plt.title(plotTitle)
if __name__ == '__main__':
import pgm
(imgarray,w,h) = pgm.pgmread('academy.pgm')
img = Img.Img(imgarray)
# plot_fig(np.int32(img.pix), 'Original mage')
# plot_hist(img, 'Original image')
imgarray = add_noise(imgarray)
imgs = Img.Img(imgarray)
plot_fig(imgs.pix, 'Image with additive Gaussian with sigma=5')
pgm.pgmwrite(np.int32(imgs.pix), 'academy_with_noise.pgm')
# plot_hist(img, 'Noise image')
##
# f1(imgs, 1, 1)
# f1(imgs, 1, 10)
# f1(imgs, 1, 100)
# f1(imgs, 1, 300)
##
# f1(imgs, 3, 1)
def equalizacao_histograma(imagem):
print("Executando a thread: {}".format(threading.current_thread().name))
h = __calcular_histograma(imagem)
p = __calcular_probabilidade_ocorrencia(h)
q = __calcular_probabilidade_acumulada(p)
nova_imagem = np.zeros((480, 640))
for x in range(len(imagem[0])):
for y in range(len(imagem[0][x])):
nova_imagem[x][y] = round(255 * q[imagem[0][x][y].astype(np.int)])
pgmwrite(nova_imagem, 'equalizacao_histograma.pgm')
if __name__ == "__main__":
img_original = pgmread('balloons.ascii.pgm')
# deepcopy para evitar condição de corrida
img_tratamento_1 = copy.deepcopy(img_original)
img_tratamento_2 = copy.deepcopy(img_original)
# criando threads
t1 = threading.Thread(target=alargamento_contraste,
name='alargamento',
args=(img_tratamento_1, ))
t2 = threading.Thread(target=equalizacao_histograma,
name='equalização',
args=(img_tratamento_2, ))
# inicio das execuções
t1.start()
t2.start()
def read_pgm(filename, byteorder='>'):
"""Return image data from a raw PGM file as numpy array.
Format specification: http://netpbm.sourceforge.net/doc/pgm.html
"""
with open(filename, 'rb') as f:
buffer = f.read()
try:
header, width, height, maxval = re.search(
b"(^P5\s(?:\s*#.*[\r\n])*"
b"(\d+)\s(?:\s*#.*[\r\n])*"
b"(\d+)\s(?:\s*#.*[\r\n])*"
b"(\d+)\s(?:\s*#.*[\r\n]\s)*)", buffer).groups()
except AttributeError:
raise ValueError("Not a raw PGM file: '%s'" % filename)
return numpy.frombuffer(
buffer,
dtype='u1' if int(maxval) < 256 else byteorder + 'u2',
count=int(width) * int(height),
offset=len(header)).reshape((int(height), int(width)))
if __name__ == "__main__":
from matplotlib import pyplot
image = read_pgm("seg_example_results.pgm", byteorder='<')
pyplot.imshow(image, pyplot.cm.gray)
pyplot.show()
image1 = pgm.pgmread("seg_example_results.pgm")
print(image1)
outImage[i:i + Lw, j:j +
Lw] = (flatwindow[0:Lw, 0:Lw] +
outImage[i:i + Lw, j - 1:j + Lw - 1]) / 2.0
#outImage[i:i+Lw,j:j+Lw] = (flatwindow[0:Lw,0:Lw] + np.mean(inImage[i:i+Lw,j+Lw]) + outImage[i:i+Lw,j-1:j+Lw-1]) /2.0
outImage[i:i + Lw, j + Lw] = (flatwindow[:, Lw - 1])
if j == 0:
outImage[i:i + Lw, j:j + Lw] = flatwindow[
0:Lw, 0:Lw] ##+ np.mean(inImage[i:i+Lw,j+Lw])
#outImage[i:i+Lw,j+Lw] = outImage[i:i+Lw,j+Lw] + np.mean(inImage[i:i+Lw,j+Lw])
#outImage[i:i+Lw,j+Lw] = (outImage[i:i+Lw,j+Lw] + inImage[i:i+Lw,j+Lw])/2.0
#outImage[i:i+Lw,j:j+Lw] = np.reshape(flatwindow+np.mean(flatwindow),(Lw, Lw))
eprint(i)
#outImage = (outImage + inImage)/2.0
#outImage = (255*preprocessing.normalize(outImage)).astype(np.int);
print(outImage)
outImage = 255 * (outImage - np.min(outImage)) / (np.max(outImage) -
np.min(outImage))
return outImage
#inImage = pgmread("mri_NOISE.pgm")
#inImage = pgmread("glassware_NOISE.pgm")
inImage = pgmread("ardilla_NOISE.pgm")
inImagef = inImage[0].astype(np.float)
#inImagef = inImagef[100:200,100:200]
Nw = 5 #Dictionary size..
Lw = 2 #width size of each window
#D = buildDictionary(inImagef, Nw, Lw)
outImage = processImage(inImagef, Nw, Lw)
pgmwrite(outImage, "out.pgm")
def plot_hist(img, plotTitle):
imgarray = img.calc_hist()
plt.figure()
plt.plot(imgarray)
plt.title(plotTitle)
def plot_fig(imgarr, plotTitle):
plt.figure()
plt.imshow(imgarr, cmap=cm.gray)
plt.title(plotTitle)
if __name__ == '__main__':
import pgm
(imgarray, w, h) = pgm.pgmread('academy.pgm')
img = Img.Img(imgarray)
# plot_fig(np.int32(img.pix), 'Original mage')
# plot_hist(img, 'Original image')
imgarray = add_noise(imgarray)
imgs = Img.Img(imgarray)
plot_fig(imgs.pix, 'Image with additive Gaussian with sigma=5')
pgm.pgmwrite(np.int32(imgs.pix), 'academy_with_noise.pgm')
# plot_hist(img, 'Noise image')
##
# f1(imgs, 1, 1)
# f1(imgs, 1, 10)
# f1(imgs, 1, 100)
# f1(imgs, 1, 300)
##
# f1(imgs, 3, 1)
