#!/usr/bin/env python
# -*- coding: utf-8 -*-
# File: symbolic_imagenet.py
import os
import tensorflow as tf
import numpy as np
from tensorpack.dataflow import dataset
from tensorpack.utils import logger
__all__ = ['get_imglist', 'build_pipeline', 'lighting']
def get_imglist(dir, name):
"""
Args:
dir(str): directory which contains name
name(str): 'train' or 'val'
Returns:
[(full filename, label)]
"""
dir = os.path.join(dir, name)
meta = dataset.ILSVRCMeta()
imglist = meta.get_image_list(
name,
dataset.ILSVRCMeta.guess_dir_structure(dir))
def _filter(fname):
# png
return 'n02105855_2933.JPEG' in fname
ret = []
for fname, label in imglist:
if _filter(fname):
logger.info("Image {} was filtered out.".format(fname))
continue
fname = os.path.join(dir, fname)
ret.append((fname, label))
return ret
def uint8_resize_bicubic(image, shape):
ret = tf.image.resize_bicubic([image], shape)
return tf.cast(tf.clip_by_value(ret, 0, 255), tf.uint8)[0]
def resize_shortest_edge(image, image_shape, size):
shape = tf.cast(image_shape, tf.float32)
w_greater = tf.greater(image_shape[0], image_shape[1])
shape = tf.cond(w_greater,
lambda: tf.cast([shape[0] / shape[1] * size, size], tf.int32),
lambda: tf.cast([size, shape[1] / shape[0] * size], tf.int32))
return uint8_resize_bicubic(image, shape)
def center_crop(image, size):
image_height = tf.shape(image)[0]
image_width = tf.shape(image)[1]
offset_height = (image_height - size) // 2
offset_width = (image_width - size) // 2
image = tf.slice(image, [offset_height, offset_width, 0], [size, size, -1])
return image
def lighting(image, std, eigval, eigvec):
v = tf.random_uniform(shape=[3]) * std * eigval
inc = tf.matmul(eigvec, tf.reshape(v, [3, 1]))
image = tf.cast(tf.cast(image, tf.float32) + tf.reshape(inc, [3]), image.dtype)
return image
def training_mapper(filename, label):
byte = tf.read_file(filename)
jpeg_opt = {'fancy_upscaling': True, 'dct_method': 'INTEGER_ACCURATE'}
jpeg_shape = tf.image.extract_jpeg_shape(byte) # hwc
bbox_begin, bbox_size, distort_bbox = tf.image.sample_distorted_bounding_box(
jpeg_shape,
bounding_boxes=tf.zeros(shape=[0, 0, 4]),
min_object_covered=0,
aspect_ratio_range=[0.75, 1.33],
area_range=[0.08, 1.0],
max_attempts=10,
use_image_if_no_bounding_boxes=True)
is_bad = tf.reduce_sum(tf.cast(tf.equal(bbox_size, jpeg_shape), tf.int32)) >= 2
def good():
offset_y, offset_x, _ = tf.unstack(bbox_begin)
target_height, target_width, _ = tf.unstack(bbox_size)
crop_window = tf.stack([offset_y, offset_x, target_height, target_width])
image = tf.image.decode_and_crop_jpeg(
byte, crop_window, channels=3, **jpeg_opt)
image = uint8_resize_bicubic(image, [224, 224])
return image
def bad():
image = tf.image.decode_jpeg(
tf.reshape(byte, shape=[]), 3, **jpeg_opt)
image = resize_shortest_edge(image, jpeg_shape, 224)
image = center_crop(image, 224)
return image
image = tf.cond(is_bad, bad, good)
# TODO other imgproc
# image = lighting(image, 0.1,
# eigval=np.array([0.2175, 0.0188, 0.0045], dtype='float32') * 255.0,
# eigvec=np.array([[-0.5675, 0.7192, 0.4009],
# [-0.5808, -0.0045, -0.8140],
# [-0.5836, -0.6948, 0.4203]], dtype='float32'))
image = tf.image.random_flip_left_right(image)
return image, label
def validation_mapper(filename, label):
byte = tf.read_file(filename)
jpeg_opt = {'fancy_upscaling': True, 'dct_method': 'INTEGER_ACCURATE'}
image = tf.image.decode_jpeg(
tf.reshape(byte, shape=[]), 3, **jpeg_opt)
image = resize_shortest_edge(image, tf.shape(image), 256)
image = center_crop(image, 224)
return image, label
def build_pipeline(imglist, training, batch, parallel):
"""
Args:
imglist (list): [(full filename, label)]
training (bool):
batch (int):
parallel (int):
If training, returns an infinite dataset.
Note that it produces RGB images, not BGR.
"""
N = len(imglist)
filenames = tf.constant([k[0] for k in imglist], name='filenames')
labels = tf.constant([k[1] for k in imglist], dtype=tf.int32, name='labels')
ds = tf.data.Dataset.from_tensor_slices((filenames, labels))
if training:
ds = ds.shuffle(N, reshuffle_each_iteration=True).repeat()
mapper = training_mapper if training else validation_mapper
ds = ds.apply(
tf.contrib.data.map_and_batch(
mapper,
batch_size=batch,
num_parallel_batches=parallel))
ds = ds.prefetch(100)
return ds
