def __getitem__(self, idx):
batchx = self.train_files[idx * self.batch_size:(idx + 1) *
self.batch_size]
batchy = self.train_outputs[idx * self.batch_size:(idx + 1) *
self.batch_size]
X_RGB = np.zeros((self.batch_size, 256, 256, 3))
X_Per = np.zeros((self.batch_size, 32, 32, 1))
Y = np.zeros((self.batch_size, 2))
for i in range(self.batch_size):
image_id = int(float(re.findall("\d+\.\d+", batchx[i])[0]))
fnameRGB = batchx[i] + '.jpg'
fnamePer = batchx[i] + '.pkl'
if batchy[i] == self.label['malignant']:
pathRGB = os.path.join(self.path_mal_train, fnameRGB)
pathPer = os.path.join(self.path_mal_train_per, fnamePer)
elif batchy[i] == self.label['benign']:
pathRGB = os.path.join(self.path_ben_train, fnameRGB)
pathPer = os.path.join(self.path_ben_train_per, fnamePer)
img = skimage.io.imread(pathRGB)
if img.shape == (1024, 1024, 3):
img = img[::4, ::4, :]
image_id = int(float(re.findall("\d+\.\d+", pathRGB)[0]))
if image_id in self.config['stats'].keys():
[src_mu, src_sigma] = self.stats[image_id]
img_nmzd = htk_cnorm.reinhard(
img,
self.ref_mu_lab,
self.ref_std_lab,
src_mu=src_mu,
src_sigma=src_sigma).astype('float')
else:
print '#### stats for %d not present' % (image_id)
img_nmzd = htk_cnorm.reinhard(img, self.ref_mu_lab,
self.ref_std_lab).astype('float')
imgRGB = preprocess_resnet(img_nmzd)
with open(pathPer, 'rb') as f:
img = pickle.load(f)
imgPer = self.preprocess_persistence(img)
X_RGB[i] = imgRGB
X_Per[i] = imgPer
Y[i] = to_categorical(batchy[i], num_classes=2)
return ([X_RGB, X_Per], Y)
def preprocess(self, img_path):
img = skimage.io.imread(img_path)
if img.shape == (1024, 1024, 3):
img = img[::4, ::4, :]
image_id = int(float(re.findall("\d+\.\d+", img_path)[0]))
if image_id in self.stats.keys():
[src_mu, src_sigma] = self.stats[image_id]
img_nmzd = htk_cnorm.reinhard(img,
self.ref_mu_lab,
self.ref_std_lab,
src_mu=src_mu,
src_sigma=src_sigma).astype('float')
else:
print '#### stats for %d not present' % (image_id)
img_nmzd = htk_cnorm.reinhard(img, self.ref_mu_lab,
self.ref_std_lab).astype('float')
img = preprocess_resnet(img_nmzd)
return img
def CombinedTestData(config):
print 'Loading combined data'
path_mal_test, _, files_malignant_test = next(
os.walk(os.path.join(config.test_dir, 'malignant', 'rgb')))
path_ben_test, _, files_benign_test = next(
os.walk(os.path.join(config.test_dir, 'benign', 'rgb')))
path_mal_test_per, _, _ = next(
os.walk(
os.path.join(config.test_dir, 'malignant', 'persistence_images')))
path_ben_test_per, _, _ = next(
os.walk(os.path.join(config.test_dir, 'benign', 'persistence_images')))
batch_size = config.trainer.batch_size
label = config.label
mal_paths_test = glob.glob(os.path.join(path_mal_test_per, '*'))
ben_paths_test = glob.glob(os.path.join(path_ben_test_per, '*'))
mal_outputs_test = [label['malignant']] * len(mal_paths_test)
ben_outputs_test = [label['benign']] * len(ben_paths_test)
test_paths = mal_paths_test + ben_paths_test
test_outputs = mal_outputs_test + ben_outputs_test
test_files = [os.path.basename(elem) for elem in test_paths]
test_files = [elem.replace('.pkl', '') for elem in test_files]
ref_std_lab = (0.57506023, 0.10403329, 0.01364062)
ref_mu_lab = (8.63234435, -0.11501964, 0.03868433)
if os.path.isfile('configs/stats.pkl'):
with open('configs/stats.pkl', 'rb') as f:
stats = pickle.load(f)
print 'Stats loaded'
config['stats'] = stats
else:
print 'No stats file found (To obtain Mu and Sigma from original whole image).'
len_test = len(test_outputs)
X_RGB = np.zeros((len_test, 256, 256, 3))
X_Per = np.zeros((len_test, 32, 32, 1))
Y = [-1] * len_test
for i in range(len_test):
image_id = int(float(re.findall("\d+\.\d+", test_files[i])[0]))
fnameRGB = test_files[i] + '.jpg'
fnamePer = test_files[i] + '.pkl'
if test_outputs[i] == config.label['malignant']:
pathRGB = os.path.join(path_mal_test, fnameRGB)
pathPer = os.path.join(path_mal_test_per, fnamePer)
elif test_outputs[i] == config.label['benign']:
pathRGB = os.path.join(path_ben_test, fnameRGB)
pathPer = os.path.join(path_ben_test_per, fnamePer)
img = skimage.io.imread(pathRGB)
if img.shape == (1024, 1024, 3):
img = img[::4, ::4, :]
image_id = int(float(re.findall("\d+\.\d+", pathRGB)[0]))
if image_id in stats.keys():
[src_mu, src_sigma] = stats[image_id]
img_nmzd = htk_cnorm.reinhard(img,
ref_mu_lab,
ref_std_lab,
src_mu=src_mu,
src_sigma=src_sigma).astype('float')
else:
print '#### stats for %d not present' % (image_id)
img_nmzd = htk_cnorm.reinhard(img, ref_mu_lab,
ref_std_lab).astype('float')
imgRGB = preprocess_resnet(img_nmzd)
with open(pathPer, 'rb') as f:
img = pickle.load(f)
img = img / config.trainer.percentile_factor
img = np.array([img])
imgPer = np.moveaxis(img, 0, 2)
X_RGB[i] = imgRGB
X_Per[i] = imgPer
Y[i] = test_outputs[i]
print 'RGB : ', X_RGB.shape
print 'Per : ', X_Per.shape
print 'len(Y) : ', len(Y)
return [X_RGB, X_Per, Y]
def predict(self, x):
if self.data_format == "channels_first":
x = x.transpose(0, 3, 1, 2)
x = preprocess_resnet(x.astype(K.floatx()))
return self.model.predict(x, batch_size=self.batch_size)
def RGBTestData(config):
print 'loading RGB data'
path_mal_test, _, files_malignant_test = next(
os.walk(os.path.join(config.test_dir, 'malignant', 'rgb')))
path_ben_test, _, files_benign_test = next(
os.walk(os.path.join(config.test_dir, 'benign', 'rgb')))
mal_paths_test = glob.glob(os.path.join(path_mal_test, '*'))
ben_paths_test = glob.glob(os.path.join(path_ben_test, '*'))
mal_outputs_test = [config.label.malignant] * len(mal_paths_test)
ben_outputs_test = [config.label.benign] * len(ben_paths_test)
test_paths = mal_paths_test + ben_paths_test
test_outputs = mal_outputs_test + ben_outputs_test
z = zip(test_paths, test_outputs)
random.shuffle(z)
test_paths, test_outputs = zip(*z)
ref_std_lab = (0.57506023, 0.10403329, 0.01364062)
ref_mu_lab = (8.63234435, -0.11501964, 0.03868433)
if os.path.isfile('configs/stats.pkl'):
with open('configs/stats.pkl', 'rb') as f:
stats = pickle.load(f)
print '################### Stats loaded Test ####################'
config['stats'] = stats
else:
print 'No stats file found (To obtain Mu and Sigma from original whole image).'
len_test = len(test_outputs)
X = np.zeros((len_test, 256, 256, 3))
Y = [-1] * len_test
for i in range(len_test):
img = skimage.io.imread(test_paths[i])
if img.shape == (1024, 1024, 3):
img = img[::4, ::4, :]
image_id = int(float(re.findall("\d+\.\d+", test_paths[i])[0]))
if image_id in stats.keys():
[src_mu, src_sigma] = stats[image_id]
img_nmzd = htk_cnorm.reinhard(img,
ref_mu_lab,
ref_std_lab,
src_mu=src_mu,
src_sigma=src_sigma).astype('float')
else:
print '#### stats for %d not present' % (image_id)
img_nmzd = htk_cnorm.reinhard(img, ref_mu_lab,
ref_std_lab).astype('float')
img = preprocess_resnet(img_nmzd)
X[i] = img
Y[i] = test_outputs[i]
return (X, Y)