# -*- coding: utf-8 -*-
import tensorflow as tf
import numpy as np
import scipy.misc as misc
import cv2
import matplotlib.pyplot as plt
from flowlib import read_flo, read_pfm
from data_augmentation import *
from utils import mvn
class BasicDataset(object):
def __init__(self, crop_h=320, crop_w=896, batch_size=4, data_list_file='path_to_your_data_list_file',
img_dir='path_to_your_image_directory', fake_flow_occ_dir='path_to_your_fake_flow_occlusion_directory', is_normalize_img=True):
self.crop_h = crop_h
self.crop_w = crop_w
self.batch_size = batch_size
self.img_dir = img_dir
self.data_list = np.loadtxt(data_list_file, dtype=bytes).astype(np.str)
self.data_num = self.data_list.shape[0]
self.fake_flow_occ_dir = fake_flow_occ_dir
self.is_normalize_img = is_normalize_img
# KITTI's data format for storing flow and mask
# The first two channels are flow, the third channel is mask
def extract_flow_and_mask(self, flow):
optical_flow = flow[:, :, :2]
optical_flow = (optical_flow - 32768) / 64.0
mask = tf.cast(tf.greater(flow[:, :, 2], 0), tf.float32)
#mask = tf.cast(flow[:, :, 2], tf.float32)
mask = tf.expand_dims(mask, -1)
return optical_flow, mask
# The default image type is PNG.
def read_and_decode(self, filename_queue):
img0_name = tf.string_join([self.img_dir, '/', filename_queue[0]])
img1_name = tf.string_join([self.img_dir, '/', filename_queue[1]])
img2_name = tf.string_join([self.img_dir, '/', filename_queue[2]])
img0 = tf.image.decode_png(tf.read_file(img0_name), channels=3)
img0 = tf.cast(img0, tf.float32)
img1 = tf.image.decode_png(tf.read_file(img1_name), channels=3)
img1 = tf.cast(img1, tf.float32)
img2 = tf.image.decode_png(tf.read_file(img2_name), channels=3)
img2 = tf.cast(img2, tf.float32)
return img0, img1, img2
# For Validation or Testing
def preprocess_one_shot(self, filename_queue):
img0, img1, img2 = self.read_and_decode(filename_queue)
img0 = img0 / 255.
img1 = img1 / 255.
img2 = img2 / 255.
if self.is_normalize_img:
img0 = mvn(img0)
img1 = mvn(img1)
img2 = mvn(img2)
return img0, img1, img2
def create_one_shot_iterator(self, data_list, num_parallel_calls=4):
""" For Validation or Testing
Generate image and flow one_by_one without cropping, image and flow size may change every iteration
"""
data_list = tf.convert_to_tensor(data_list, dtype=tf.string)
dataset = tf.data.Dataset.from_tensor_slices(data_list)
dataset = dataset.map(self.preprocess_one_shot, num_parallel_calls=num_parallel_calls)
dataset = dataset.batch(1)
dataset = dataset.repeat()
iterator = dataset.make_initializable_iterator()
return iterator
