def view_encoded_patches(img_ids,
files,
patchDir,
model_name,
img_dir,
cust_name=''):
if model_name == 'unet':
input_size = 572
else:
input_size = 321
fig = plt.figure(figsize=(11, 2))
grid = Grid(fig,
rect=111,
nrows_ncols=(1, 6),
axes_pad=0.1,
label_mode='L')
for plt_cnt, iid in enumerate(img_ids):
img = np.zeros((input_size, input_size, 3), dtype=np.uint8)
for cnt in range(3):
img[:, :, cnt] = imageio.imread(
os.path.join(patchDir,
files[iid][:-1] + '_RGB{}.jpg'.format(cnt)))
grid[plt_cnt].imshow(crop_center(img, 224, 224))
grid[plt_cnt].set_axis_off()
plt.tight_layout()
if not os.path.exists(os.path.join(img_dir, cust_name)):
os.makedirs(os.path.join(img_dir, cust_name))
plt.savefig(
os.path.join(
img_dir, cust_name,
'{}_{}.png'.format(model_name,
'_'.join([str(a) for a in img_ids]))))
plt.show()
def make_res50_features(model_name, task_dir, GPU=0, force_run=False):
tf.reset_default_graph()
feature_file_name = os.path.join(task_dir, 'res50_atlanta_{}.csv'.format(model_name))
patch_file_name = os.path.join(task_dir, 'res50_atlanta_{}.txt'.format(model_name))
if model_name == 'deeplab':
input_size = (321, 321)
overlap = 0
else:
input_size = (572, 572)
overlap = 184
blCol = uab_collectionFunctions.uabCollection('atlanta')
img_mean = blCol.getChannelMeans([0, 1, 2])
extrObj = uab_DataHandlerFunctions.uabPatchExtr([0, 1, 2, 3],
cSize=input_size,
numPixOverlap=overlap,
extSave=['jpg', 'jpg', 'jpg', 'png'],
isTrain=True,
gtInd=3,
pad=overlap // 2)
patchDir = extrObj.run(blCol)
if not os.path.exists(feature_file_name) or not os.path.exists(patch_file_name) or force_run:
os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
os.environ['CUDA_VISIBLE_DEVICES'] = '{}'.format(GPU)
import keras
input_size_fit = (224, 224)
file_name = os.path.join(patchDir, 'fileList.txt')
with open(file_name, 'r') as f:
files = f.readlines()
res50 = keras.applications.resnet50.ResNet50(include_top=True, weights='imagenet')
fc2048 = keras.models.Model(inputs=res50.input, outputs=res50.get_layer('flatten_1').output)
with open(feature_file_name, 'w+') as f:
with open(patch_file_name, 'w+') as f2:
for file_line in tqdm(files):
patch_name = file_line.split('.')[0][:-5]
img = np.zeros((input_size[0], input_size[1], 3), dtype=np.uint8)
for cnt, file in enumerate(file_line.strip().split(' ')[:3]):
img[:, :, cnt] = imageio.imread(os.path.join(patchDir, file)) - img_mean[cnt]
img = np.expand_dims(crop_center(img, input_size_fit[0], input_size_fit[1]), axis=0)
fc1000 = fc2048.predict(img).reshape((-1,)).tolist()
writer = csv.writer(f, lineterminator='\n')
writer.writerow(['{}'.format(x) for x in fc1000])
f2.write('{}\n'.format(patch_name))
return feature_file_name, patch_file_name, input_size[0], patchDir
input_size_fit = (224, 224)
img_dir, task_dir = sis_utils.get_task_img_folder()
feature_file_name = os.path.join(task_dir, 'ucmerced_res50_inria.csv')
patch_file_name = os.path.join(task_dir, 'ucmerced_res50_inria.txt')
res50 = keras.applications.resnet50.ResNet50(include_top=True,
weights='imagenet')
fc2048 = keras.models.Model(inputs=res50.input,
outputs=res50.get_layer('flatten_1').output)
if not os.path.exists(feature_file_name) and not os.path.exists(
os.path.join(patch_file_name)):
with open(feature_file_name, 'w+') as f, open(patch_file_name,
'w+') as f2:
for file_line in tqdm(file_list):
img = imageio.imread(file_line)
img = np.expand_dims(crop_center(img, input_size_fit[0],
input_size_fit[1]),
axis=0)
fc1000 = fc2048.predict(img).reshape((-1, )).tolist()
writer = csv.writer(f, lineterminator='\n')
writer.writerow(['{}'.format(x) for x in fc1000])
f2.write('{}\n'.format(file_line.split('/')[-1]))
feature = pd.read_csv(feature_file_name, sep=',', header=None).values
with open(patch_file_name, 'r') as f:
patch_names = f.readlines()
perplex = 40
file_name = os.path.join(task_dir, 'land_inria_p{}.npy'.format(perplex))
feature_encode = run_tsne(feature,
file_name,