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preprocess_img in tf #7

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22 changes: 18 additions & 4 deletions osvos.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,7 +17,7 @@
from PIL import Image

slim = tf.contrib.slim

mean = np.array((104.00699, 116.66877, 122.67892), dtype=np.float32)

def osvos_arg_scope(weight_decay=0.0002):
"""Defines the OSVOS arg scope.
Expand Down Expand Up @@ -160,6 +160,20 @@ def interp_surgery(variables):
return interp_tensors


def preprocess_img_tf(image):
"""Preprocess the image to adapt it to network requirements
Args:
Image we want to input the network (W,H,3) numpy array
Returns:
Image ready to input the network (1,W,H,3)
"""
img = tf.cast(image, tf.float32)
img = tf.reverse(img, axis=[-1])
img = tf.subtract(img, mean)
img = tf.expand_dims(img, 0)
return img


# TO DO: Move preprocessing into Tensorflow
def preprocess_img(image):
"""Preprocess the image to adapt it to network requirements
Expand Down Expand Up @@ -544,7 +558,7 @@ def _train(dataset, initial_ckpt, supervison, learning_rate, logs_path, max_trai
# Average the gradient
for _ in range(0, iter_mean_grad):
batch_image, batch_label = dataset.next_batch(batch_size, 'train')
image = preprocess_img(batch_image[0])
image = preprocess_img_tf(batch_image[0]).eval()
label = preprocess_labels(batch_label[0])
run_res = sess.run([total_loss, merged_summary_op] + grad_accumulator_ops,
feed_dict={input_image: image, input_label: label})
Expand All @@ -564,7 +578,7 @@ def _train(dataset, initial_ckpt, supervison, learning_rate, logs_path, max_trai
# Save a checkpoint
if step % save_step == 0:
if test_image_path is not None:
curr_output = sess.run(img_summary, feed_dict={input_image: preprocess_img(test_image_path)})
curr_output = sess.run(img_summary, feed_dict={input_image: preprocess_img_tf(test_image_path)})
summary_writer.add_summary(curr_output, step)
save_path = saver.save(sess, model_name, global_step=global_step)
print "Model saved in file: %s" % save_path
Expand Down Expand Up @@ -644,7 +658,7 @@ def test(dataset, checkpoint_file, result_path, config=None):
for frame in range(0, dataset.get_test_size()):
img, curr_img = dataset.next_batch(batch_size, 'test')
curr_frame = curr_img[0].split('/')[-1].split('.')[0] + '.png'
image = preprocess_img(img[0])
image = preprocess_img_tf(img[0]).eval()
res = sess.run(probabilities, feed_dict={input_image: image})
res_np = res.astype(np.float32)[0, :, :, 0] > 162.0/255.0
scipy.misc.imsave(os.path.join(result_path, curr_frame), res_np.astype(np.float32))
Expand Down