reader.py

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import cv2
import numpy as np
import os
import six

default_config = {
    "shuffle": True,
    "min_resize": 0.5,
    "max_resize": 2,
    "crop_size": 769,
}


def slice_with_pad(a, s, value=0):
    pads = []
    slices = []
    for i in range(len(a.shape)):
        if i >= len(s):
            pads.append([0, 0])
            slices.append([0, a.shape[i]])
        else:
            l, r = s[i]
            if l < 0:
                pl = -l
                l = 0
            else:
                pl = 0
            if r > a.shape[i]:
                pr = r - a.shape[i]
                r = a.shape[i]
            else:
                pr = 0
            pads.append([pl, pr])
            slices.append([l, r])
    slices = list(map(lambda x: slice(x[0], x[1], 1), slices))
    a = a[slices]
    a = np.pad(a, pad_width=pads, mode='constant', constant_values=value)
    return a


class CityscapeDataset:
    def __init__(self, dataset_dir, subset='train', config=default_config):
        label_dirname = os.path.join(dataset_dir, 'gtFine/' + subset)
        if six.PY2:
            import commands
            label_files = commands.getoutput(
                "find %s -type f | grep labelTrainIds | sort" %
                label_dirname).splitlines()
        else:
            import subprocess
            label_files = subprocess.getstatusoutput(
                "find %s -type f | grep labelTrainIds | sort" %
                label_dirname)[-1].splitlines()
        self.label_files = label_files
        self.label_dirname = label_dirname
        self.index = 0
        self.subset = subset
        self.dataset_dir = dataset_dir
        self.config = config
        self.reset()
        print("total number", len(label_files))

    def reset(self, shuffle=False):
        self.index = 0
        if self.config["shuffle"]:
            np.random.shuffle(self.label_files)

    def next_img(self):
        self.index += 1
        if self.index >= len(self.label_files):
            self.reset()

    def get_img(self):
        shape = self.config["crop_size"]
        while True:
            ln = self.label_files[self.index]
            img_name = os.path.join(
                self.dataset_dir,
                'leftImg8bit/' + self.subset + ln[len(self.label_dirname):])
            img_name = img_name.replace('gtFine_labelTrainIds', 'leftImg8bit')
            label = cv2.imread(ln)
            img = cv2.imread(img_name)
            if img is None:
                print("load img failed:", img_name)
                self.next_img()
            else:
                break
        if shape == -1:
            return img, label, ln
        random_scale = np.random.rand(1) * (self.config['max_resize'] -
                                            self.config['min_resize']
                                            ) + self.config['min_resize']
        crop_size = int(shape / random_scale)
        bb = crop_size // 2

        def _randint(low, high):
            return int(np.random.rand(1) * (high - low) + low)

        offset_x = np.random.randint(bb, max(bb + 1, img.shape[0] -
                                             bb)) - crop_size // 2
        offset_y = np.random.randint(bb, max(bb + 1, img.shape[1] -
                                             bb)) - crop_size // 2
        img_crop = slice_with_pad(img, [[offset_x, offset_x + crop_size],
                                        [offset_y, offset_y + crop_size]], 128)
        img = cv2.resize(img_crop, (shape, shape))
        label_crop = slice_with_pad(label, [[offset_x, offset_x + crop_size],
                                            [offset_y, offset_y + crop_size]],
                                    255)
        label = cv2.resize(
            label_crop, (shape, shape), interpolation=cv2.INTER_NEAREST)
        return img, label, ln + str(
            (offset_x, offset_y, crop_size, random_scale))

    def get_batch(self, batch_size=1):
        imgs = []
        labels = []
        names = []
        while len(imgs) < batch_size:
            img, label, ln = self.get_img()
            imgs.append(img)
            labels.append(label)
            names.append(ln)
            self.next_img()
        return np.array(imgs), np.array(labels), names

    def get_batch_generator(self, batch_size, total_step):
        def do_get_batch():
            for i in range(total_step):
                imgs, labels, names = self.get_batch(batch_size)
                labels = labels.astype(np.int32)[:, :, :, 0]
                imgs = imgs[:, :, :, ::-1].transpose(
                    0, 3, 1, 2).astype(np.float32) / (255.0 / 2) - 1
                yield i, imgs, labels, names

        batches = do_get_batch()
        try:
            from prefetch_generator import BackgroundGenerator
            batches = BackgroundGenerator(batches, 100)
        except:
            print(
                "You can install 'prefetch_generator' for acceleration of data reading."
            )
        return batches