tools.py

import scipy.io as sio
import numpy as np
import tensorflow as tf
import os

#IMG_MEAN = np.array((0.000, 0.000, 0.000), dtype=np.float32)
IMG_MEAN = np.array((103.939, 116.779, 123.68), dtype=np.float32)
label_colours = [(128, 64, 128), (244, 35, 231), (69, 69, 69)
                # 0 = road, 1 = sidewalk, 2 = building
                ,(102, 102, 156)]
                #, (190, 153, 153), (153, 153, 153)
                # 3 = wall, 4 = fence, 5 = pole
                #,(250, 170, 29), (219, 219, 0), (106, 142, 35)
                # 6 = traffic light, 7 = traffic sign, 8 = vegetation
                #,(152, 250, 152), (69, 129, 180), (219, 19, 60)
                # 9 = terrain, 10 = sky, 11 = person
                #,(255, 0, 0), (0, 0, 142), (0, 0, 69)
                # 12 = rider, 13 = car, 14 = truck
                #,(0, 60, 100), (0, 79, 100), (0, 0, 230)
                # 15 = bus, 16 = train, 17 = motocycle
                #,(119, 10, 32)]
                # 18 = bicycle

matfn = './utils/color150.mat'

def read_labelcolours(matfn):
    mat = sio.loadmat(matfn)
    color_table = mat['colors']
    shape = color_table.shape
    color_list = [tuple(color_table[i]) for i in range(shape[0])]

    return color_list

def decode_labels(mask, img_shape, num_classes):
    if num_classes == 150:
        color_table = read_labelcolours(matfn)
    else:
        color_table = label_colours

    color_mat = tf.constant(color_table, dtype=tf.float32)
    onehot_output = tf.one_hot(mask, depth=num_classes)
    onehot_output = tf.reshape(onehot_output, (-1, num_classes))
    pred = tf.matmul(onehot_output, color_mat)
    pred = tf.reshape(pred, (1, img_shape[0], img_shape[1], 3))
    
    return pred

def prepare_label(input_batch, new_size, num_classes, one_hot=True):
    with tf.name_scope('label_encode'):
        input_batch = tf.image.resize_nearest_neighbor(input_batch, new_size) # as labels are integer numbers, need to use NN interp.
        input_batch = tf.squeeze(input_batch, squeeze_dims=[3]) # reducing the channel dimension.
        if one_hot:
            input_batch = tf.one_hot(input_batch, depth=num_classes)
            
    return input_batch


def load_img(img_path):
    if os.path.isfile(img_path):
        print('successful load img: {0}'.format(img_path))
    else:
        print('not found file: {0}'.format(img_path))
        sys.exit(0)

    filename = img_path.split('/')[-1]
    ext = filename.split('.')[-1]

    if ext.lower() == 'png':
        img = tf.image.decode_png(tf.read_file(img_path), channels=3)
    elif ext.lower() == 'jpg':
        img = tf.image.decode_jpeg(tf.read_file(img_path), channels=3)
    else:
        print('cannot process {0} file.'.format(file_type))

    return img, filename

def preprocess(img, h, w):
    # Convert RGB to BGR
    img_r, img_g, img_b = tf.split(axis=2, num_or_size_splits=3, value=img)
    img = tf.cast(tf.concat(axis=2, values=[img_b, img_g, img_r]), dtype=tf.float32)
    # Extract mean.
    img -= IMG_MEAN

    pad_img = tf.image.pad_to_bounding_box(img, 0, 0, h, w)
    pad_img = tf.expand_dims(pad_img, dim=0)

    return pad_img