# -*- 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 imshow
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'):
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=np.str)
self.data_num = self.data_list.shape[0]
self.fake_flow_occ_dir = fake_flow_occ_dir
# 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):
img1_name = tf.string_join([self.img_dir, '/', filename_queue[0]])
img2_name = tf.string_join([self.img_dir, '/', filename_queue[1]])
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 img1, img2
# For Flying Chairs, the image type is ppm, please use "read_and_decode_ppm" instead of "read_and_decode".
# Similarily, for other image types, please write their decode functions by yourself.
def read_and_decode_ppm(self, filename_queue):
def read_ppm(self, filename):
img = misc.imread(filename).astype('float32')
return img
flying_h = 384
flying_w = 512
img1_name = tf.string_join([self.img_dir, '/', filename_queue[0]])
img2_name = tf.string_join([self.img_dir, '/', filename_queue[1]])
img1 = tf.py_func(read_ppm, [img1_name], tf.float32)
img2 = tf.py_func(read_ppm, [img2_name], tf.float32)
img1 = tf.reshape(img1, [flying_h, flying_w, 3])
img2 = tf.reshape(img2, [flying_h, flying_w, 3])
return img1, img2
def read_and_decode_distillation(self, filename_queue):
img1_name = tf.string_join([self.img_dir, '/', filename_queue[0]])
img2_name = tf.string_join([self.img_dir, '/', filename_queue[1]])
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)
flow_occ_fw_name = tf.string_join([self.fake_flow_occ_dir, '/flow_occ_fw_', filename_queue[2], '.png'])
flow_occ_bw_name = tf.string_join([self.fake_flow_occ_dir, '/flow_occ_bw_', filename_queue[2], '.png'])
flow_occ_fw = tf.image.decode_png(tf.read_file(flow_occ_fw_name), dtype=tf.uint16, channels=3)
flow_occ_fw = tf.cast(flow_occ_fw, tf.float32)
flow_occ_bw = tf.image.decode_png(tf.read_file(flow_occ_bw_name), dtype=tf.uint16, channels=3)
flow_occ_bw = tf.cast(flow_occ_bw, tf.float32)
flow_fw, occ_fw = self.extract_flow_and_mask(flow_occ_fw)
flow_bw, occ_bw = self.extract_flow_and_mask(flow_occ_bw)
return img1, img2, flow_fw, flow_bw, occ_fw, occ_bw
def augmentation(self, img1, img2):
img1, img2 = random_crop([img1, img2], self.crop_h, self.crop_w)
img1, img2 = random_flip([img1, img2])
img1, img2 = random_channel_swap([img1, img2])
return img1, img2
def augmentation_distillation(self, img1, img2, flow_fw, flow_bw, occ_fw, occ_bw):
[img1, img2, flow_fw, flow_bw, occ_fw, occ_bw] = random_crop([img1, img2, flow_fw, flow_bw, occ_fw, occ_bw], self.crop_h, self.crop_w)
[img1, img2, occ_fw, occ_bw], [flow_fw, flow_bw] = random_flip_with_flow([img1, img2, occ_fw, occ_bw], [flow_fw, flow_bw])
img1, img2 = random_channel_swap([img1, img2])
return img1, img2, flow_fw, flow_bw, occ_fw, occ_bw
def preprocess_augmentation(self, filename_queue):
img1, img2 = self.read_and_decode(filename_queue)
img1 = img1 / 255.
img2 = img2 / 255.
img1, img2 = self.augmentation(img1, img2)
return img1, img2
def preprocess_augmentation_distillation(self, filename_queue):
img1, img2, flow_fw, flow_bw, occ_fw, occ_bw = self.read_and_decode_distillation(filename_queue)
img1 = img1 / 255.
img2 = img2 / 255.
img1, img2, flow_fw, flow_bw, occ_fw, occ_bw = self.augmentation_distillation(img1, img2, flow_fw, flow_bw, occ_fw, occ_bw)
return img1, img2, flow_fw, flow_bw, occ_fw, occ_bw
def preprocess_one_shot(self, filename_queue):
img1, img2 = self.read_and_decode(filename_queue)
img1 = img1 / 255.
img2 = img2 / 255.
return img1, img2
def create_batch_iterator(self, data_list, batch_size, shuffle=True, buffer_size=5000, num_parallel_calls=4):
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_augmentation, num_parallel_calls=num_parallel_calls)
if shuffle:
dataset = dataset.shuffle(buffer_size=buffer_size)
dataset = dataset.batch(batch_size)
dataset = dataset.repeat()
iterator = dataset.make_initializable_iterator()
return iterator
def create_batch_distillation_iterator(self, data_list, batch_size, shuffle=True, buffer_size=5000, num_parallel_calls=4):
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_augmentation_distillation, num_parallel_calls=num_parallel_calls)
if shuffle:
dataset = dataset.shuffle(buffer_size=buffer_size)
dataset = dataset.batch(batch_size)
dataset = dataset.repeat()
iterator = dataset.make_initializable_iterator()
return iterator
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
