image_train_size = [384, 384]
number_of_classes = 2
tfrecord_filename = 'custom_augmented_train.tfrecords'
pascal_voc_lut = pascal_segmentation_lut()
class_labels = [0, 1, 255] #pascal_voc_lut.keys()
samples = 79
filename_queue = tf.train.string_input_producer(
[tfrecord_filename], num_epochs=10)
image, annotation = read_tfrecord_and_decode_into_image_annotation_pair_tensors(filename_queue)
# Various data augmentation stages
image, annotation = flip_randomly_left_right_image_with_annotation(image, annotation)
# image = distort_randomly_image_color(image)
resized_image, resized_annotation = scale_randomly_image_with_annotation_with_fixed_size_output(image, annotation, image_train_size)
resized_annotation = tf.squeeze(resized_annotation)
image_batch, annotation_batch = tf.train.shuffle_batch( [resized_image, resized_annotation],
batch_size=1,
capacity=samples,
num_threads=2,
min_after_dequeue=70)
upsampled_logits_batch, vgg_16_variables_mapping = FCN_32s(image_batch_tensor=image_batch,
def read_photo_annotation_depth(batch_size=1):
def convert2float(image):
""" Transfrom from int image ([0,255]) to float tensor ([-1.,1.])
"""
image = tf.image.convert_image_dtype(image, dtype=tf.float32)
return (image / 127.5) - 1.0
image_train_size = [256, 256]
number_of_classes = 21
# tfrecord_filename = '/home/yaxing/DATA/scene_net_augmented_train_200.tfrecords'
tfrecord_filename = '/home/yaxing/data/scene5M/scene_net_augmented_train_16k.tfrecords'
pascal_voc_lut = {
0: 'Unknown',
1: 'Bed',
2: 'Books',
3: 'Ceiling',
4: 'Chair',
5: 'Floor',
6: 'Furniture',
7: 'Objects',
8: 'Picture',
9: 'Sofa',
10: 'Table',
11: 'TV',
12: 'Wall',
13: 'Window'
}
class_labels = pascal_voc_lut.keys()
filename_queue = tf.train.string_input_producer([tfrecord_filename],
num_epochs=50000)
image1, image2, annotation1, depth2, annotation2, depth1 = read_tfrecord_and_decode_into_image_annotation_pair_tensors(
filename_queue)
# Various data augmentation stages
image1, annotation1, depth2, image2, annotation2, depth1 = flip_randomly_left_right_image_with_annotation(
image1, annotation1, depth2, image2, annotation2, depth1)
# image = distort_randomly_image_color(image)
resized_image1, resized_annotation1, resized_depth2, resized_image2, resized_annotation2, resized_depth1 = scale_randomly_image_with_annotation_with_fixed_size_output(
image1,
annotation1,
depth2,
image_train_size,
img_tensor2=image2,
annotation_tensor2=annotation2,
depth_tensor1=depth1)
resized_annotation1 = tf.squeeze(resized_annotation1)
resized_annotation2 = tf.squeeze(resized_annotation2)
image_batch1, annotation_batch1, depth_batch2, image_batch2, annotation_batch2, depth_batch1 = tf.train.shuffle_batch(
[
resized_image1, resized_annotation1, resized_depth2,
resized_image2, resized_annotation2, resized_depth1
],
batch_size=batch_size,
capacity=3000,
num_threads=2,
min_after_dequeue=1000)
return tf.to_float(image_batch1) / 127.5 - 1., annotation_batch1, (
tf.exp(depth_batch1) - 1.
) / 1000., tf.to_float(image_batch2) / 127.5 - 1., annotation_batch2, (
tf.exp(depth_batch2) - 1.) / 1000.
