def predict(self, options):
print('-' * 30)
print('Loading and preprocessing test data...')
print('-' * 30)
create_test_data(options)
imgs_test = load_test_data(options)
imgs_test = imgs_test.astype('float32')
imgs_test /= 255. # scale masks to [0, 1]
print('-' * 30)
print('Loading saved weights...')
print('-' * 30)
model = self.get_unet()
weight_dir = options.inputdir + '/weights'
if not os.path.exists(weight_dir):
os.mkdir(weight_dir)
model.load_weights(os.path.join(weight_dir, project_name + '.h5'))
print('-' * 30)
print('Predicting masks on test data...')
print('-' * 30)
imgs_mask_test = model.predict(imgs_test, batch_size=1, verbose=1)
npy_mask_dir = 'test_mask_npy'
if not os.path.exists(npy_mask_dir):
os.mkdir(npy_mask_dir)
np.save(os.path.join(options.outputdir, project_name + '_mask.npy'),
imgs_mask_test)
print('-' * 30)
print('Saving predicted masks to files...')
print('-' * 30)
imgs_mask_test = preprocess_squeeze(imgs_mask_test)
# imgs_mask_test /= 1.7
#imgs_mask_test = np.around(imgs_mask_test, decimals=0)
#info = np.iinfo(np.uint16) # Get the information of the incoming image type
#imgs_mask_test = imgs_mask_test.astype(np.uint16)
#imgs_mask_test=imgs_mask_test* info.max # convert back to original class/labels
imgs_mask_test = (imgs_mask_test * 255.).astype(np.uint8)
count_visualize = 1
count_processed = 0
pred_dir = 'preds'
if not os.path.exists(pred_dir):
os.mkdir(pred_dir)
pred_dir = os.path.join(options.outputdir, project_name)
if not os.path.exists(pred_dir):
os.mkdir(pred_dir)
for x in range(0, imgs_mask_test.shape[0]):
for y in range(0, imgs_mask_test.shape[1]):
if (count_visualize > 1) and (count_visualize < 16):
save_img = imgs_mask_test[x][y].astype(np.uint16)
imsave(
os.path.join(pred_dir,
'pred_' + str(count_processed) + '.png'),
save_img)
count_processed += 1
count_visualize += 1
if count_visualize == 17:
count_visualize = 1
if (count_processed % 100) == 0:
print('Done: {0}/{1} test images'.format(
count_processed, imgs_mask_test.shape[0] * 14))
print('-' * 30)
print('Prediction finished')
print('-' * 30)
def predict():
print('-' * 30)
print('Loading and preprocessing test data...')
print('-' * 30)
imgs_test = load_test_data()
imgs_test = imgs_test.astype('float32')
imgs_test /= 255. # scale masks to [0, 1]
print('-' * 30)
print('Loading saved weights...')
print('-' * 30)
model = get_unet()
weight_dir = 'weights'
if not os.path.exists(weight_dir):
os.mkdir(weight_dir)
model.load_weights(os.path.join(weight_dir, project_name + '.h5'))
print('-' * 30)
print('Predicting masks on test data...')
print('-' * 30)
imgs_mask_test = model.predict(imgs_test, batch_size=1, verbose=1)
npy_mask_dir = 'test_mask_npy'
if not os.path.exists(npy_mask_dir):
os.mkdir(npy_mask_dir)
np.save(os.path.join(npy_mask_dir, project_name + '_mask.npy'),
imgs_mask_test)
print('-' * 30)
print('Saving predicted masks to files...')
print('-' * 30)
imgs_mask_test = preprocess_squeeze(imgs_mask_test)
# imgs_mask_test /= 1.7
imgs_mask_test = np.around(imgs_mask_test, decimals=0)
imgs_mask_test = (imgs_mask_test * 255.).astype(np.uint8)
count_visualize = 1
count_processed = 0
pred_dir = 'preds'
if not os.path.exists(pred_dir):
os.mkdir(pred_dir)
pred_dir = os.path.join('preds/', project_name)
if not os.path.exists(pred_dir):
os.mkdir(pred_dir)
for x in range(0, imgs_mask_test.shape[0]):
for y in range(0, imgs_mask_test.shape[1]):
if (count_visualize > 1) and (count_visualize < 16):
imsave(
os.path.join(
pred_dir,
'pred_' + str(f"{count_processed:03}") + '.png'),
imgs_mask_test[x][y])
count_processed += 1
count_visualize += 1
if count_visualize == 17:
count_visualize = 1
if (count_processed % 100) == 0:
print('Done: {0}/{1} test images'.format(
count_processed, imgs_mask_test.shape[0] * 14))
print('-' * 30)
print('Prediction finished')
print('-' * 30)
def predict(self, options):
print('-' * 30)
print('Loading and preprocessing test data...')
print('-' * 30)
create_test_data(options)
imgs_test = load_test_data(options)
imgs_test = imgs_test.astype('float32')
print('-' * 30)
print('Test npy size:' + str(imgs_test.shape))
print('-' * 30)
imgs_test /= 255. # scale masks to [0, 1]
print('-' * 30)
print('Loading saved weights...')
print('-' * 30)
model = self.get_unet()
if not os.path.exists(options.inputdir + '/weights'):
print('Weight not found.')
return
weight_dir = os.listdir(options.inputdir + '/weights')
total = 0
for weight in weight_dir:
if not weight.endswith('.h5'):
print('Invalid file: ' + weight)
print('-' * 30)
continue
pred_name = weight.split('.')[0]
total += 1
print(weight)
model.load_weights(
os.path.join(options.inputdir + '/weights', weight))
print('Predicting masks using:', weight)
print('-' * 30)
imgs_mask_test = model.predict(imgs_test, batch_size=1, verbose=1)
if not os.path.exists(options.outputdir):
os.mkdir(options.outputdir)
np.save(os.path.join(options.outputdir, pred_name + '_mask.npy'),
imgs_mask_test)
print('Saving predicted masks to files...')
print('-' * 30)
imgs_mask_test = preprocess_squeeze(imgs_mask_test)
imgs_mask_test = (imgs_mask_test * 255.).astype(np.uint8)
count_visualize = 1
count_processed = 0
pred_dir = os.path.join(options.outputdir, pred_name)
if not os.path.exists(pred_dir):
os.mkdir(pred_dir)
for x in range(0, imgs_mask_test.shape[0]):
for y in range(0, imgs_mask_test.shape[1]):
if (count_visualize > 1) and (count_visualize < 16):
save_img = imgs_mask_test[x][y].astype(np.uint16)
imsave(
os.path.join(
pred_dir,
'pred_' + str(count_processed) + '.png'),
save_img)
count_processed += 1
count_visualize += 1
if count_visualize == 17:
count_visualize = 1
if (count_processed % 100) == 0:
print('Done: {0}/{1} test images'.format(
count_processed, imgs_mask_test.shape[0] * 14))
print('-' * 30)
print(pred_name, ' Done.')
print('-' * 30)
print('Prediction done: using ' + str(total) + ' models.')