def feature_extraction_MSP(self,
img_path,
N1=0,
N2=10000,
template_path=None,
enhanced_img_path=None):
assert (N2 - N1 > 0)
assert (template_path is not None)
for i in range(N1, N2 + 1):
#if i<10000:
# continue
start = timeit.default_timer()
img_file = os.path.join(img_path, str(i) + '.bmp')
img_name = os.path.basename(img_file)
fname = template_path + os.path.splitext(img_name)[0] + '.dat'
if os.path.exists(fname):
continue
rolled_template = self.feature_extraction_single_rolled(img_file)
stop = timeit.default_timer()
if rolled_template is not None:
template.Template2Bin_Byte_TF(fname,
rolled_template,
isLatent=True)
print stop - start
def feature_extraction_longitudinal_batch(self,
img_path,
img_type='bmp',
N=100,
texture_path=None,
enhanced_path=None,
template_path=None):
subjects = glob.glob(img_path + 'MI*')
n = 0
for subject in subjects:
img_list = glob.glob(os.path.join(subject, '*.bmp'))
if len(img_list) < N:
continue
print n
# else:
# n = n + 1
# continue
subjectID = os.path.basename(subject)
texture_folder = os.path.join(texture_path, subjectID)
if not os.path.exists(texture_folder):
os.makedirs(texture_folder)
enhanced_folder = os.path.join(enhanced_path, subjectID)
if not os.path.exists(enhanced_folder):
os.makedirs(enhanced_folder)
template_folder = os.path.join(template_path, subjectID)
if not os.path.exists(template_folder):
os.makedirs(template_folder)
for img_file in img_list:
img_name = os.path.splitext(os.path.basename(img_file))[0]
template_name = os.path.join(template_folder,
img_name + '.dat')
#if os.path.exists(template_name):
# continue
#fname = os.path.basename(img_file)
rolled_template, texture_img, enh_constrast_img = self.feature_extraction_longitudinal(
img_file)
texture_img = np.asarray(texture_img, dtype=np.uint8)
enh_constrast_img = np.asarray(enh_constrast_img,
dtype=np.uint8)
io.imsave(os.path.join(texture_folder, img_name + '.jpeg'),
texture_img)
io.imsave(os.path.join(enhanced_folder, img_name + '.jpeg'),
enh_constrast_img)
if template_path is not None:
#img_name = os.path.basename(img_file)
#fname = os.path.join(template_folder, img_name+'.dat')
template.Template2Bin_Byte_TF(template_name,
rolled_template,
isLatent=True)
n = n + 1
print n
def feature_extraction_MSP(self, img_path, N=10000, template_path=None):
assert (N > 0)
for i in range(1, N + 1):
#if i<=1113:
# continue
start = timeit.default_timer()
img_file = os.path.join(img_path, str(i) + '.bmp')
rolled_template = self.feature_extraction_single_rolled(img_file)
stop = timeit.default_timer()
print stop - start
if template_path is not None:
img_name = os.path.basename(img_file)
fname = template_path + os.path.splitext(img_name)[0] + '.dat'
template.Template2Bin_Byte_TF(fname,
rolled_template,
isLatent=True)
def feature_extraction(self, img_path, img_type='bmp', template_path=None):
img_files = glob.glob(img_path + '*.' + img_type)
assert (len(img_files) > 0)
img_files.sort()
for i, img_file in enumerate(img_files):
if i > 20:
break
start = timeit.default_timer()
rolled_template = self.feature_extraction_single_rolled(img_file)
stop = timeit.default_timer()
print stop - start
if template_path is not None:
img_name = os.path.basename(img_file)
fname = template_path + os.path.splitext(img_name)[0] + '.dat'
template.Template2Bin_Byte_TF(fname,
rolled_template,
isLatent=True)
def feature_extraction(self,
img_path,
img_type='bmp',
template_path=None,
enhancement=False):
img_files = glob.glob(img_path + 'S*.' + img_type)
assert (len(img_files) > 0)
img_files.sort()
for i, img_file in enumerate(img_files):
print img_file
# if i<5:
# continue
if i > 7:
break
continue
#break
start = timeit.default_timer()
img_name = os.path.basename(img_file)
img_name = os.path.splitext(img_name)[0]
if enhancement:
rolled_template, enhanced_img = self.feature_extraction_single_rolled_enhancement(
img_file)
if template_path is not None:
enhanced_img = np.asarray(enhanced_img, dtype=np.uint8)
io.imsave(os.path.join(template_path, img_name + '.jpeg'),
enhanced_img)
else:
rolled_template = self.feature_extraction_single_rolled(
img_file)
stop = timeit.default_timer()
print stop - start
if template_path is not None:
fname = template_path + img_name + '.dat'
template.Template2Bin_Byte_TF(fname,
rolled_template,
isLatent=False)
def feature_extraction(self,img_path,template_path=None):
img_files = glob.glob(img_path+'*.bmp')
assert(len(img_files)>0)
img_files.sort()
for i, img_file in enumerate(img_files):
if i<11:
continue
start = timeit.default_timer()
#latent_template = self.feature_extraction_single_latent(img_file,output_path=template_path)
latent_template = self.feature_extraction_single_latent_demo(img_file, output_path=template_path)
stop = timeit.default_timer()
print (stop - start)
if template_path is not None and rolled_template is not None:
img_name = os.path.basename(img_file)
fname = template_path + os.path.splitext(img_name)[0]+'.dat'
template.Template2Bin_Byte_TF(fname, latent_template, isLatent=True)