def main(
img_filename=os.path.join('image', '2.jpg'),
nPixels_on_side=15,
i_std=20 # std dev for color Gaussian
):
if img_filename is None:
raise ValueError("img_filename cannot be None")
# img_filename = os.path.join(HOME,'Desktop','stairs2.jpg')
FilesDirs.raise_if_file_does_not_exist(img_filename)
# Part 1: Specify the parameters:
prior_count_const = 5 # the weight of Inverse-Wishart prior of space covariance(ex:1,5,10)
use_hex = True # toggle between hexagons and squares (for the init)
prior_weight = 0.5 # in the segmentation,
# we do argmax w * log_prior + (1-w) *log_likelihood.
# Keeping w (i.e., prior_weight) at 0.5 means we are trying
# to maximize the true posterior.
# We keep the paramter here in case the user will like
# to tweak it.
calc_s_cov = True # If this is False, then we avoid estimating the spatial cov.
num_EM_iters = nPixels_on_side
num_inner_iters = 10
# Part 2 : prepare for segmentation
sp_size = nPixels_on_side * nPixels_on_side
img = misc.imread(img_filename)
dimx = img.shape[1]
dimy = img.shape[0]
tic = time.clock()
sw = SuperpixelsWrapper(dimy=dimy,
dimx=dimx,
nPixels_in_square_side=nPixels_on_side,
i_std=i_std,
s_std=nPixels_on_side,
prior_count=prior_count_const * sp_size,
use_hex=use_hex)
toc = time.clock()
print('init time = ', toc - tic)
print('nSuperpixels =', sw.nSuperpixels)
sw.set_img(img)
# you can use the same SuperpixelsWrapper object with different imgs and/or,
# i_std, s_std, prior_count.
# Just call sw.set_img(new_img), sw.initialize_seg(), and/or
# sw.set_superpixels(i_std=..., s_std = ..., prior_count = ...)
# again and recompute the seg.
# Please see demo_for_direc for an example
# Part 3: Do the superpixel segmentation
print("Actual works starts now")
tic = time.clock()
# actual work
sw.calc_seg(nEMIters=num_EM_iters,
nItersInner=num_inner_iters,
calc_s_cov=calc_s_cov,
prior_weight=prior_weight)
# Copy the parameters from gpu to cpu
sw.gpu2cpu()
toc = time.clock()
print('superpixel calculation time = ', toc - tic)
# Part 4: Save the mean/boundary image and the resulting parameters
# Results will be save in the /result directory. You can change it.
root_slash_img_num = os.path.splitext(img_filename)[0]
image_direc = os.path.split(root_slash_img_num)[0]
img_num = os.path.split(root_slash_img_num)[1]
save_path_root = os.path.join(image_direc, 'result')
print("I am going to save results into", save_path_root)
FilesDirs.mkdirs_if_needed(save_path_root)
img_overlaid = sw.get_img_overlaid() # get the boundary image
img_cartoon = sw.get_cartoon() # get the cartoon image
grid = ['square', 'hex'][sw.use_hex]
fname_res_border = os.path.join(
save_path_root, '_'.join([
img_num, 'n', '{0:03}'.format(nPixels_on_side), 'std',
'{0:02}'.format(i_std), 'border', grid + '.png'
]))
fname_res_cartoon = os.path.join(
save_path_root, '_'.join([
img_num, 'n', '{0:03}'.format(nPixels_on_side), 'std',
'{0:02}'.format(i_std), 'mean', grid + '.png'
]))
print('saving', fname_res_border)
misc.imsave(fname_res_border, img_overlaid)
print('saving', fname_res_cartoon)
misc.imsave(fname_res_cartoon, img_cartoon)
# save the resulting parameters to MATLAB .mat file
mat_filename = os.path.join(
save_path_root # , img_num + '_std_'+str(i_std)+'.mat')
,
'_'.join([
img_num, 'n', '{0:03}'.format(nPixels_on_side), 'std',
'{0:02}'.format(i_std) + '.mat'
]))
print('Saving params to ', mat_filename)
pm = sw.superpixels.params
sio.savemat(
mat_filename, {
'pixel_to_super': sw.seg.cpu,
'count': pm.counts.cpu,
'mu_i': pm.mu_i.cpu,
'mu_s': pm.mu_s.cpu,
'sigma_s': pm.Sigma_s.cpu,
'sigma_i': pm.Sigma_i.cpu
})
def main(image_direc = 'image',
nPixels_on_side = 15,
i_std = 15, # std dev for color Gaussian
imgs_of_the_same_size=None
):
if image_direc is None:
raise ValueError("image_direc cannot be None")
image_direc = os.path.expanduser(image_direc)
image_direc = os.path.abspath(image_direc)
FilesDirs.raise_if_dir_does_not_exist(image_direc)
imgs = get_list_of_all_imgs_dir(image_direc)
if not imgs:
raise Exception("\n\nDirectory \n{}\ncontains no images.\n".format(image_direc))
# Results will be saved in the /results folder under input directory. You can change it.
save_path_root = os.path.join(image_direc , 'result')
print "I am going to save results into",save_path_root
FilesDirs.mkdirs_if_needed(save_path_root)
# Part 1: Specify the parameters:
prior_count_const = 5 # the weight of Inverse-Wishart prior of space covariance(ex:1,5,10)
use_hex = True # toggle between hexagons and squares (for the init)
prior_weight = 0.5 # in the segmentation,
# we do argmax w * log_prior + (1-w) *log_likelihood.
# Keeping w (i.e., prior_weight) at 0.5 means we are trying
# to maximize the true posterior.
# We keep the paramter here in case the user will like
# to tweak it.
calc_s_cov = True # If this is False, then we avoid estimating the spatial cov.
num_EM_iters = nPixels_on_side
num_inner_iters = 10
sp_size = nPixels_on_side*nPixels_on_side
for i,img_filename in enumerate(imgs):
# Part 2 : prepare for segmentation
print img_filename
fullfilename = os.path.join(image_direc,img_filename)
FilesDirs.raise_if_file_does_not_exist(fullfilename)
img = misc.imread(fullfilename)
dimx=img.shape[1]
dimy=img.shape[0]
if not((img.ndim in [1,3]) and (img.shape[2] in [1,3])):
raise ValueError("\n\nProblem with {0}\n\nI was expecting 3 channels, but img.shape = {1}\n\n".format(fullfilename,img.shape))
tic = time.clock()
if (imgs_of_the_same_size and i !=0):
sw.initialize_seg()
# you can use the same SuperpixelsWrapper object with different imgs and/or,
# i_std, s_std, prior_count.
# Just call sw.set_img(new_img), sw.initialize_seg(), and/or
# sw.set_superpixels(i_std=..., s_std = ..., prior_count = ...)
# again and recompute the seg.
else:
sw = SuperpixelsWrapper(dimy=dimy,dimx=dimx, nPixels_in_square_side=nPixels_on_side,
i_std = i_std , s_std = nPixels_on_side,
prior_count = prior_count_const*sp_size,
use_hex = use_hex
)
toc = time.clock()
print 'init time = ', toc-tic
print 'nSuperpixels =', sw.nSuperpixels
sw.set_img(img)
# Part 3: Do the superpixel segmentation
tic = time.clock()
#actual work
sw.calc_seg(nEMIters=num_EM_iters, nItersInner=num_inner_iters, calc_s_cov=calc_s_cov, prior_weight=prior_weight)
# Copy the parameters from gpu to cpu
sw.gpu2cpu()
toc = time.clock()
print 'superpixel calculation time = ',toc-tic
# Part 4: Save the mean/boundary image and the resulting parameters
img_overlaid = sw.get_img_overlaid() # get the boundary image
img_cartoon = sw.get_cartoon() # get the cartoon image
grid = ['square','hex'][sw.use_hex]
root_slash_img_num = os.path.splitext(img_filename)[0]
img_num = os.path.split(root_slash_img_num)[1]
#fname_res_border = '_'.join([img_num , 'std', str(i_std), 'border', grid+'.png'])
#fname_res_cartoon = '_'.join([img_num , 'std', str(i_std), 'mean', grid+'.png'])
fname_res_border = '_'.join([img_num,'n','{0:03}'.format(nPixels_on_side), 'std', '{0:02}'.format(i_std), 'border', grid+'.png'])
fname_res_cartoon = '_'.join([img_num, 'n','{0:03}'.format(nPixels_on_side), 'std', '{0:02}'.format(i_std), 'mean', grid+'.png'])
fname_res_border = os.path.join(save_path_root,fname_res_border)
fname_res_cartoon = os.path.join(save_path_root,fname_res_cartoon)
print 'saving',fname_res_border
misc.imsave(fname_res_border, img_overlaid)
print 'saving',fname_res_cartoon
misc.imsave(fname_res_cartoon, img_cartoon)
#save the resulting parameters to MATLAB .mat file
#mat_filename = os.path.join(save_path_root , img_num + '_std_'+str(i_std)+'.mat')
mat_filename = os.path.join(save_path_root#, img_num + '_std_'+str(i_std)+'.mat')
,'_'.join([img_num,'n','{0:03}'.format(nPixels_on_side), 'std', '{0:02}'.format(i_std)+'.mat']))
print 'Saving params to ',mat_filename
pm = sw.superpixels.params
sio.savemat(mat_filename,{'pixel_to_super':sw.seg.cpu, 'count':pm.counts.cpu,
'mu_i': pm.mu_i.cpu, 'mu_s':pm.mu_s.cpu,
'sigma_s':pm.Sigma_s.cpu, 'sigma_i':pm.Sigma_i.cpu})
if (not imgs_of_the_same_size):
del sw
def main(img_filename = os.path.join('image','2.jpg'),
nPixels_on_side = 15,
i_std = 20 # std dev for color Gaussian
):
if img_filename is None:
raise ValueError("img_filename cannot be None")
#img_filename = os.path.join(HOME,'Desktop','stairs2.jpg')
FilesDirs.raise_if_file_does_not_exist(img_filename)
# Part 1: Specify the parameters:
prior_count_const = 5 # the weight of Inverse-Wishart prior of space covariance(ex:1,5,10)
use_hex = True # toggle between hexagons and squares (for the init)
prior_weight = 0.5 # in the segmentation,
# we do argmax w * log_prior + (1-w) *log_likelihood.
# Keeping w (i.e., prior_weight) at 0.5 means we are trying
# to maximize the true posterior.
# We keep the paramter here in case the user will like
# to tweak it.
calc_s_cov = True # If this is False, then we avoid estimating the spatial cov.
num_EM_iters = nPixels_on_side
num_inner_iters = 10
# Part 2 : prepare for segmentation
sp_size = nPixels_on_side*nPixels_on_side
img = misc.imread(img_filename)
dimx=img.shape[1]
dimy=img.shape[0]
tic = time.clock()
sw = SuperpixelsWrapper(dimy=dimy,dimx=dimx, nPixels_in_square_side=nPixels_on_side,
i_std = i_std , s_std = nPixels_on_side,
prior_count = prior_count_const*sp_size,
use_hex = use_hex
)
toc = time.clock()
print 'init time = ', toc-tic
print 'nSuperpixels =', sw.nSuperpixels
sw.set_img(img)
# you can use the same SuperpixelsWrapper object with different imgs and/or,
# i_std, s_std, prior_count.
# Just call sw.set_img(new_img), sw.initialize_seg(), and/or
# sw.set_superpixels(i_std=..., s_std = ..., prior_count = ...)
# again and recompute the seg.
# Please see demo_for_direc for an example
# Part 3: Do the superpixel segmentation
print "Actual works starts now"
tic = time.clock()
#actual work
sw.calc_seg(nEMIters=num_EM_iters, nItersInner=num_inner_iters, calc_s_cov=calc_s_cov, prior_weight=prior_weight)
# Copy the parameters from gpu to cpu
sw.gpu2cpu()
toc = time.clock()
print 'superpixel calculation time = ',toc-tic
# Part 4: Save the mean/boundary image and the resulting parameters
# Results will be save in the /result directory. You can change it.
root_slash_img_num = os.path.splitext(img_filename)[0]
image_direc = os.path.split(root_slash_img_num)[0]
img_num = os.path.split(root_slash_img_num)[1]
save_path_root = os.path.join(image_direc , 'result')
print "I am going to save results into",save_path_root
FilesDirs.mkdirs_if_needed(save_path_root)
img_overlaid = sw.get_img_overlaid() # get the boundary image
img_cartoon = sw.get_cartoon() # get the cartoon image
grid = ['square','hex'][sw.use_hex]
fname_res_border = os.path.join(save_path_root, '_'.join([img_num,'n','{0:03}'.format(nPixels_on_side), 'std', '{0:02}'.format(i_std), 'border', grid+'.png']))
fname_res_cartoon = os.path.join(save_path_root, '_'.join([img_num, 'n','{0:03}'.format(nPixels_on_side), 'std', '{0:02}'.format(i_std), 'mean', grid+'.png']))
print 'saving',fname_res_border
misc.imsave(fname_res_border, img_overlaid)
print 'saving',fname_res_cartoon
misc.imsave(fname_res_cartoon, img_cartoon)
#save the resulting parameters to MATLAB .mat file
mat_filename = os.path.join(save_path_root#, img_num + '_std_'+str(i_std)+'.mat')
,'_'.join([img_num,'n','{0:03}'.format(nPixels_on_side), 'std', '{0:02}'.format(i_std)+'.mat']))
print 'Saving params to ',mat_filename
pm = sw.superpixels.params
sio.savemat(mat_filename,{'pixel_to_super':sw.seg.cpu, 'count':pm.counts.cpu,
'mu_i': pm.mu_i.cpu, 'mu_s':pm.mu_s.cpu,
'sigma_s':pm.Sigma_s.cpu, 'sigma_i':pm.Sigma_i.cpu})
def main(
image_direc='image',
nPixels_on_side=15,
i_std=15, # std dev for color Gaussian
imgs_of_the_same_size=None):
if image_direc is None:
raise ValueError("image_direc cannot be None")
image_direc = os.path.expanduser(image_direc)
image_direc = os.path.abspath(image_direc)
FilesDirs.raise_if_dir_does_not_exist(image_direc)
imgs = get_list_of_all_imgs_dir(image_direc)
if not imgs:
raise Exception(
"\n\nDirectory \n{}\ncontains no images.\n".format(image_direc))
# Results will be saved in the /results folder under input directory. You can change it.
save_path_root = os.path.join(image_direc, 'result')
print("I am going to save results into", save_path_root)
FilesDirs.mkdirs_if_needed(save_path_root)
# Part 1: Specify the parameters:
prior_count_const = 5 # the weight of Inverse-Wishart prior of space covariance(ex:1,5,10)
use_hex = True # toggle between hexagons and squares (for the init)
prior_weight = 0.5 # in the segmentation,
# we do argmax w * log_prior + (1-w) *log_likelihood.
# Keeping w (i.e., prior_weight) at 0.5 means we are trying
# to maximize the true posterior.
# We keep the paramter here in case the user will like
# to tweak it.
calc_s_cov = True # If this is False, then we avoid estimating the spatial cov.
num_EM_iters = nPixels_on_side
num_inner_iters = 10
sp_size = nPixels_on_side * nPixels_on_side
for i, img_filename in enumerate(imgs):
# Part 2 : prepare for segmentation
print(img_filename)
fullfilename = os.path.join(image_direc, img_filename)
FilesDirs.raise_if_file_does_not_exist(fullfilename)
img = misc.imread(fullfilename)
dimx = img.shape[1]
dimy = img.shape[0]
if not ((img.ndim in [1, 3]) and (img.shape[2] in [1, 3])):
raise ValueError(
"\n\nProblem with {0}\n\nI was expecting 3 channels, but img.shape = {1}\n\n"
.format(fullfilename, img.shape))
tic = time.clock()
if (imgs_of_the_same_size and i != 0):
sw.initialize_seg()
# you can use the same SuperpixelsWrapper object with different imgs and/or,
# i_std, s_std, prior_count.
# Just call sw.set_img(new_img), sw.initialize_seg(), and/or
# sw.set_superpixels(i_std=..., s_std = ..., prior_count = ...)
# again and recompute the seg.
else:
sw = SuperpixelsWrapper(dimy=dimy,
dimx=dimx,
nPixels_in_square_side=nPixels_on_side,
i_std=i_std,
s_std=nPixels_on_side,
prior_count=prior_count_const * sp_size,
use_hex=use_hex)
toc = time.clock()
print('init time = ', toc - tic)
print('nSuperpixels =', sw.nSuperpixels)
sw.set_img(img)
# Part 3: Do the superpixel segmentation
tic = time.clock()
# actual work
sw.calc_seg(nEMIters=num_EM_iters,
nItersInner=num_inner_iters,
calc_s_cov=calc_s_cov,
prior_weight=prior_weight)
# Copy the parameters from gpu to cpu
sw.gpu2cpu()
toc = time.clock()
print('superpixel calculation time = ', toc - tic)
# Part 4: Save the mean/boundary image and the resulting parameters
img_overlaid = sw.get_img_overlaid() # get the boundary image
img_cartoon = sw.get_cartoon() # get the cartoon image
grid = ['square', 'hex'][sw.use_hex]
root_slash_img_num = os.path.splitext(img_filename)[0]
img_num = os.path.split(root_slash_img_num)[1]
# fname_res_border = '_'.join([img_num , 'std', str(i_std), 'border', grid+'.png'])
# fname_res_cartoon = '_'.join([img_num , 'std', str(i_std), 'mean', grid+'.png'])
fname_res_border = '_'.join([
img_num, 'n', '{0:03}'.format(nPixels_on_side), 'std',
'{0:02}'.format(i_std), 'border', grid + '.png'
])
fname_res_cartoon = '_'.join([
img_num, 'n', '{0:03}'.format(nPixels_on_side), 'std',
'{0:02}'.format(i_std), 'mean', grid + '.png'
])
fname_res_border = os.path.join(save_path_root, fname_res_border)
fname_res_cartoon = os.path.join(save_path_root, fname_res_cartoon)
print('saving', fname_res_border)
misc.imsave(fname_res_border, img_overlaid)
print('saving', fname_res_cartoon)
misc.imsave(fname_res_cartoon, img_cartoon)
# save the resulting parameters to MATLAB .mat file
# mat_filename = os.path.join(save_path_root , img_num + '_std_'+str(i_std)+'.mat')
mat_filename = os.path.join(
save_path_root # , img_num + '_std_'+str(i_std)+'.mat')
,
'_'.join([
img_num, 'n', '{0:03}'.format(nPixels_on_side), 'std',
'{0:02}'.format(i_std) + '.mat'
]))
print('Saving params to ', mat_filename)
pm = sw.superpixels.params
sio.savemat(
mat_filename, {
'pixel_to_super': sw.seg.cpu,
'count': pm.counts.cpu,
'mu_i': pm.mu_i.cpu,
'mu_s': pm.mu_s.cpu,
'sigma_s': pm.Sigma_s.cpu,
'sigma_i': pm.Sigma_i.cpu
})
if (not imgs_of_the_same_size):
del sw
