def visualize_samples(session, experiment_path, patches_imgs_samples,
patches_gts_samples, patch_size):
patches_imgs_samples = (patches_imgs_samples[0:20] + 3) * 255. / 6.
patches_gts_samples = tf.cast(patches_gts_samples[0:20, 1] * 255.,
tf.float32)
patches_gts_samples = tf.reshape(patches_gts_samples,
(20, 1, patch_size[0], patch_size[1]))
imgs_samples = session.run(
tf.concat([patches_imgs_samples, patches_gts_samples], 0))
visualize(group_images(imgs_samples, 5),
experiment_path + '/' + "sample_input")
def get_data_training(train_imgs_original, train_groundTruth, patch_height,
patch_width, N_subimgs, inside_FOV, patches):
# Load train images from hdf5 files from pre-processing
train_imgs_original = load_hdf5(train_imgs_original)
train_groundTruth = load_hdf5(train_groundTruth) #masks always the same
# Normalize images
train_imgs = my_PreProc(train_imgs_original)
train_groundTruth = train_groundTruth / 255.
visualize(group_images(train_imgs[100:120, :, :, :], 5),
'imgs_train') #.show() #check original imgs train
# shuffle indices to shuffle data
idx = np.random.permutation(train_imgs.shape[0])
train_imgs = train_imgs[idx]
train_groundTruth = train_groundTruth[idx]
visualize(group_images(train_imgs[100:120, :, :, :], 5),
'imgs_train_random')
visualize(group_images(train_groundTruth[100:120, :, :, :], 5),
'gTruths_train_random')
#train_imgs = train_imgs[:,:,9:574,:] #cut bottom and top so now it is 565*565
#train_groundTruth = train_groundTruth[:,:,9:574,:] #cut bottom and top so now it is 565*565
# data_consistency_check(train_imgs,train_groundTruth)
# Check masks are within 0-1
assert (np.min(train_groundTruth) == 0 and np.max(train_groundTruth) == 1)
print "train images shape:" + str(train_imgs.shape)
print "train images range (min-max): " + str(
np.min(train_imgs)) + ' - ' + str(np.max(train_imgs))
print "train ground truths shape:" + str(train_groundTruth.shape)
print "train ground truths range (min-max): " + str(
np.min(train_groundTruth)) + ' - ' + str(np.max(train_groundTruth))
if patches == True:
# Extract the TRAINING patches from the full images
patches_imgs_train, patches_groundTruths_train = extract_random(
train_imgs, train_groundTruth, patch_height, patch_width,
N_subimgs, inside_FOV)
data_consistency_check(patches_imgs_train, patches_groundTruths_train)
print "train PATCHES images shape: " + str(patches_imgs_train.shape)
print "train PATCHES images range (min-max): " + str(
np.min(patches_imgs_train)) + ' - ' + str(
np.max(patches_imgs_train))
print "train PATCHES ground truths shape: " + str(
patches_groundTruths_train.shape)
print "train PATCHES ground truths range (min-max): " + str(
np.min(patches_groundTruths_train)) + ' - ' + str(
np.max(patches_groundTruths_train))
# visualize(group_images(patches_imgs_train[100:120,:,:,:],5),'imgs_train_patches')
# visualize(group_images(patches_groundTruths_train[100:120,:,:,:],5),'gTruth_train_patches')
return patches_imgs_train, patches_groundTruths_train
else:
return train_imgs, train_groundTruth
def get_data_training(train_imgs_original, train_groudTruth, patch_height,
patch_width, num_subimgs, label_mapping, inside_FOV,
save_path):
if os.path.isdir(save_path) == False:
os.mkdir(save_path)
else:
print('already exist the folder in this path : {}'.format(save_path))
print('number of subimages : ', num_subimgs)
train_imgs_original = load_hdf5(train_imgs_original) #Pillow (RGB format)
train_masks = load_hdf5(train_groudTruth)
print('[DEBUG] shape of train_imgs_original : ',
np.shape(train_imgs_original))
print('[DEBUG] shape of train_imgs_label : ', np.shape(train_masks))
visualize(group_images(train_imgs_original[:, :, :, :], 5),
'./' + save_path + '/imgs_train') #check original imgs train
visualize(group_images(train_masks[:, :, :, :], 5),
'./' + save_path + '/imgs_labels')
train_imgs = my_preprocessing(train_imgs_original)
visualize(group_images(train_imgs[:, :, :, :], 5),
'./' + save_path + '/preprocessed')
print('\n\n[get_data_training] preprocessed image shape : ',
train_imgs.shape)
print('\n[get_data_training] preprocessed mask shape : ',
train_masks.shape)
print('mask maximum val : ', np.max(train_masks))
#train_masks = train_masks/255.
'''
train_masks mapper
5classes (RGB format)
example) class01 : 0, class02 : 1, class03 : 2
'''
# convert images shape to square, input = 565 * 565
print('[get_data_training] preprocessed2 image shape : ', train_imgs.shape)
#zero padding
train_imgs, train_masks = zero_padding(train_imgs, train_masks,
patch_height)
'''
print(np.shape(train_masks))
temp_path = './data/inha_oct_20_01_09/pad_label/'
temp = train_masks
temp = np.transpose(temp,(0,2,3,1))
for i in range(40):
temp2 = Image.fromarray(temp[i].astype('uint8'))
temp2.save(temp_path+str(i)+'.png')
'''
print('\n\n[get_data_training] train images/masks shape : {}'.format(
train_imgs.shape))
print('[get_data_training] train images range (min-max) [{} , {}] '.format(
str(np.min(train_imgs)), str(np.max(train_imgs))))
print('[get_data_training] train masks are within 0-1\n')
patches_imgs_train, patches_masks_train, class_freq_tabel = extract_random(
train_imgs, train_masks, patch_height, patch_width, num_subimgs,
label_mapping, inside_FOV)
print('[After patch] mask shape : ', patches_masks_train.shape)
patches_imgs_train, patches_masks_train = augmentations(
patches_imgs_train, patches_masks_train, 20, 0.3)
#visualize(group_images(patches_masks_train[:,:,:,:],5),'./'+save_path+'/imgs_labels_aug')
mode = None
if mode == 'dynamic':
#extract the TRAINING patches from the full images
#extraact the random patches for data augmentation
patches_imgs_train, patches_masks_train = extract_dynamic_random(
train_imgs, train_masks, patch_height, patch_width, num_subimgs,
inside_FOV)
temp3_totimg = visualize(
group_images(patches_imgs_train[0:50, :, :, :], 5),
'./' + save_path + '/train_patch_img')
patches_imgs_train = augmentations(patches_imgs_train, 0.2)
print('[augmentation] patches_imgs_train : {}'.format(
patches_imgs_train.shape))
temp3_totimg = visualize(
group_images(patches_imgs_train[0:50, :, :, :], 5),
'./' + save_path + '/augmented_patch_img')
data_consistency_check(patches_imgs_train, patches_masks_train)
return patches_imgs_train, patches_masks_train
print(
'\n\n[get_data_training] train PATCHES images/masks shape : {}'.format(
patches_imgs_train.shape))
print('[get_data_training] train PATCHES images range (min-max): ' +
str(np.min(patches_imgs_train)) + ' - ' +
str(np.max(patches_imgs_train)))
print('[get_data_training] patches_imgs_train : {}'.format(
patches_imgs_train.shape))
temp3_totimg = visualize(
group_images(patches_imgs_train[0:50, :, :, :], 5),
'./' + save_path + '/train_patch_img')
#data_consistency_check(patches_imgs_train, patches_masks_train)
return patches_imgs_train, patches_masks_train, class_freq_tabel
def predict_and_save(list_input_images, model):
print("-------------------------------------")
print("Found " + str(len(list_input_images)) + " images in directory " +
PATH_INPUT + "\n")
print(
"It should take minutes per image, depending on your CPU capacity. You can go out and come back later ;)\n"
)
for input_image in list_input_images:
start_time_image = time.time()
print("Working on " + input_image)
img_test = get_image(PATH_INPUT + input_image)
### get image sizes
# test_imgs_orig = img_test
full_img_height = img_test.shape[2]
full_img_width = img_test.shape[3]
print("Size: " + str(full_img_height) + "x" + str(full_img_width))
### Images to patches:
patches_imgs_test = None
new_height = None
new_width = None
patches_imgs_test, new_height, new_width = get_data(
imgs_test=img_test,
patch_height=patch_height,
patch_width=patch_width,
stride_height=stride_height,
stride_width=stride_width)
### Calculate the predictions
print("Computing output...")
predictions = model.predict(patches_imgs_test,
batch_size=32,
verbose=2)
# print ( "predicted images size :")
# print ( predictions.shape)
### Patches back to image
pred_patches = pred_to_imgs(predictions, patch_height, patch_width,
"original")
pred_imgs = None
pred_imgs = recompone_overlap(pred_patches, new_height, new_width,
stride_height, stride_width)
pred_imgs = pred_imgs[:, :, 0:full_img_height, 0:full_img_width]
assert (pred_imgs.shape[0] == 1)
# N_predicted = pred_imgs.shape[0]
# group = 1
# Save predictions to files
# for i in range(int(N_predicted)):
pred_stripe = group_images(pred_imgs[:, :, :, :], 1)
# file_name = input_image
visualize(pred_stripe,
"output/" + input_image[0:len(input_image) - 4] + "_pred",
mode="jpg")
elapsed_time_image = time.time() - start_time_image
print("Time consumed: " + str(int(elapsed_time_image)) + "s\n")
def temp_get_data_training(train_imgs_original, train_groudTruth, patch_height,
patch_width, num_subimgs, label_mapping, inside_FOV,
save_path):
if os.path.isdir(save_path) == False:
os.mkdir(save_path)
else:
print('already exist the folder in this path : {}'.format(save_path))
print('number of subimages : ', num_subimgs)
train_imgs_original = load_hdf5(train_imgs_original) #Pillow (RGB format)
train_masks = load_hdf5(train_groudTruth)
#print('[DEBUG] shape of train_imgs_original : ',np.shape(train_imgs_original))
#print('[DEBUG] shape of train_imgs_label : ',np.shape(train_masks))
'''
ori_num, ori_ch, ori_h, ori_w = temp_train_imgs_original.shape
label_num, label_ch, label_h, label_w = temp_train_imgs_original.shape
train_imgs_original = np.zeros((ori_num -5, ori_ch, ori_h, ori_w))
train_masks = np.zeros((label_num-5, label_ch, label_h, label_w))
for i in range(np.shape(train_imgs_original)[0]):
if i != 6:
train_imgs_original[i] = temp_train_imgs_original[i]
train_masks[i] = temp_train_masks[i]
elif i > 40:
pass
elif i == 6:
pass'''
print('[DEBUG] shape of train_imgs_original : ',
np.shape(train_imgs_original))
print('[DEBUG] shape of train_imgs_label : ', np.shape(train_masks))
visualize(group_images(train_imgs_original[:, :, :, :], 5),
'./' + save_path + '/imgs_train') #check original imgs train
visualize(group_images(train_masks[:, :, :, :], 5),
'./' + save_path + '/imgs_labels')
train_imgs = my_preprocessing(train_imgs_original)
train_imgs = train_imgs.astype(np.uint8)
visualize(group_images(train_imgs[:, :, :, :], 5),
'./' + save_path + '/preprocessed')
print('\n\n[get_data_training] preprocessed image shape : ',
train_imgs.shape)
print('\n[get_data_training] preprocessed mask shape : ',
train_masks.shape)
print('mask maximum val : ', np.max(train_masks))
#train_masks = train_masks/255.
'''
train_masks mapper
5classes (RGB format)
example) class01 : 0, class02 : 1, class03 : 2
'''
# convert images shape to square, input = 565 * 565
print('[get_data_training] preprocessed2 image shape : ', train_imgs.shape)
#zero padding
train_imgs, train_masks = zero_padding(train_imgs, train_masks,
patch_height)
print('\n\n[get_data_training] train images/masks shape : {}'.format(
train_imgs.shape))
print('[get_data_training] train images range (min-max) [{} , {}] '.format(
str(np.min(train_imgs)), str(np.max(train_imgs))))
print('[get_data_training] train masks are within 0-1\n')
#patches_imgs_train, patches_masks_train,class_freq_tabel = extract_random(train_imgs,train_masks,patch_height,patch_width,num_subimgs,label_mapping,inside_FOV)
patches_imgs_train, patches_masks_train, class_freq_tabel = extract_oversampling(
train_imgs, train_masks, patch_height, patch_width, num_subimgs,
label_mapping, inside_FOV)
print('[After patch] mask shape : ', patches_masks_train.shape)
#patches_imgs_train,patches_masks_train = augmentations(patches_imgs_train,patches_masks_train,20,0.5)
#visualize(group_images(patches_masks_train[:,:,:,:],5),'./'+save_path+'/imgs_labels_aug')
print(
'\n\n[get_data_training] train PATCHES images/masks shape : {}'.format(
patches_imgs_train.shape))
print('[get_data_training] train PATCHES images range (min-max): ' +
str(np.min(patches_imgs_train)) + ' - ' +
str(np.max(patches_imgs_train)))
print('[get_data_training] patches_imgs_train : {}'.format(
patches_imgs_train.shape))
visualize(group_images(patches_imgs_train[0:50, :, :, :], 5),
'./' + save_path + '/train_patch_img')
visualize(group_images(patches_masks_train[::50, :, :, :] * 255, 5),
'./' + save_path + '/imgs_after_labels')
#data_consistency_check(patches_imgs_train, patches_masks_train)
return patches_imgs_train, patches_masks_train, class_freq_tabel