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')
def f1(imgs, sigmaD, sigmaR, filtType='bilateral'):
import pgm
imgt = imgs.bilateral_filter(sigmaD, sigmaR, filtType)
plotTitle = caps(filtType) + ' filter with sigmaD=' + str(sigmaD) +\
' and sigmaR=' + str(sigmaR)
plot_fig(imgt.pix, plotTitle)
# plot_hist(imgt, plotTitle)
# print np.sum((imgt.pix - imgs.pix)**2)
filename = 'academy_' + filtType + '_' + str(sigmaD) + '_' +\
str(sigmaR) + '.pgm'
pgm.pgmwrite(np.int32(imgt.pix), filename)
print '.'
def f1(imgs, sigmaD, sigmaR, filtType='bilateral'):
import pgm
imgt = imgs.bilateral_filter(sigmaD, sigmaR, filtType)
plotTitle = caps(filtType) + ' filter with sigmaD=' + str(sigmaD) +\
' and sigmaR=' + str(sigmaR)
plot_fig(imgt.pix, plotTitle)
# plot_hist(imgt, plotTitle)
# print np.sum((imgt.pix - imgs.pix)**2)
filename = 'academy_' + filtType + '_' + str(sigmaD) + '_' +\
str(sigmaR) + '.pgm'
pgm.pgmwrite(np.int32(imgt.pix), filename)
print '.'
def alargamento_contraste(imagem):
print("Executando a thread: {}".format(threading.current_thread().name))
nova_imagem = np.zeros((480, 640))
imax = __extrair_imax(imagem)
imin = __extrair_imin(imagem)
for x in range(len(imagem[0])):
for y in range(len(imagem[0][x])):
processamento = (255 /
(imax - imin)) * (imagem[0][x][y].astype(np.int) -
imin)
nova_imagem[x][y] = round(processamento)
pgmwrite(nova_imagem, 'alargamento_final.pgm')
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')
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_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)
# f1(imgs, 3, 10)
f1(imgs, 3, 50)
# f1(imgs, 3, 100)
# f1(imgs, 3, 300)
##
# f1(imgs, 5, 1)
# f1(imgs, 5, 10)
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)
# f1(imgs, 3, 10)
f1(imgs, 3, 50)
# f1(imgs, 3, 100)
# f1(imgs, 3, 300)
##
# f1(imgs, 5, 1)
# f1(imgs, 5, 10)