Code for paper "SketchyGAN: Towards Diverse and Realistic Sketch to Image Synthesis"
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import imghdr
import itertools
import os
import sys
from time import time
import csv
import PIL.Image as im
import numpy as np
import scipy.io
import scipy.misc as spm
sys.path.append('..')
sys.path.append('../slim')
sys.path.append('../object_detection')
# Notice: you need to clone TF-slim and Tensorflow Object Detection API
# into data_processing:
# https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/slim
# https://github.com/tensorflow/models/tree/master/research/object_detection
import cv2
import coco_data_provider as coco
import tensorflow as tf
from slim.nets import nets_factory
from slim.preprocessing import preprocessing_factory
from object_detection.utils import label_map_util
inception_ckpt_path = '../../inception_resnet_v2/inception_resnet_v2_2016_08_30.ckpt'
slim = tf.contrib.slim
tf.logging.set_verbosity(tf.logging.INFO)
def load_image_into_numpy_array(image):
(im_width, im_height) = image.size
return np.array(image.getdata()).reshape(
(im_height, im_width, 3)).astype(np.uint8), im_width, im_height
def get_imagenet_class_labels():
synset_list = [s.strip() for s in open('./imagenet_lsvrc_2015_synsets.txt', 'r').readlines()]
num_synsets_in_ilsvrc = len(synset_list)
assert num_synsets_in_ilsvrc == 1000
synset_to_human_list = open('./imagenet_metadata.txt', 'r').readlines()
num_synsets_in_all_imagenet = len(synset_to_human_list)
assert num_synsets_in_all_imagenet == 21842
synset_to_human = {}
for s in synset_to_human_list:
parts = s.strip().split('\t')
assert len(parts) == 2
synset = parts[0]
human = parts[1]
synset_to_human[synset] = human
label_index = 1
labels_to_names = {0: 'background'}
for synset in synset_list:
name = synset_to_human[synset]
labels_to_names[label_index] = name
label_index += 1
return labels_to_names
def check_jpg_vadility_single(path):
if imghdr.what(path) == 'jpg':
return True
return False
def check_jpg_vadility(path):
file_list = [f for f in os.listdir(path) if os.path.isfile(os.path.join(path, f))]
invalid_file_list = []
# Time counter
prev_time = float("-inf")
curr_time = float("-inf")
for i in range(len(file_list)):
if i % 5000 == 0:
curr_time = time()
elapsed = curr_time - prev_time
print(
"Now at iteration %d. Elapsed time: %.5fs." % (i, elapsed))
prev_time = curr_time
print(len(invalid_file_list))
try:
img = im.open(os.path.join(path, file_list[i]))
format = img.format.lower()
if format != 'jpg' and format != 'jpeg':
raise ValueError
# img.load()
except:
invalid_file_list.append(file_list[i])
return invalid_file_list
def build_imagenet_graph(path):
tf.reset_default_graph()
print(path)
filename_queue = tf.train.string_input_producer(tf.train.match_filenames_once(path + "/*.jpg"),
num_epochs=1, shuffle=False, capacity=100)
image_reader = tf.WholeFileReader()
image_file_name, image_file = image_reader.read(filename_queue)
image = tf.image.decode_jpeg(image_file, channels=3, fancy_upscaling=True)
model_name = 'inception_resnet_v2'
network_fn = nets_factory.get_network_fn(model_name, is_training=False, num_classes=1001)
preprocessing_name = model_name
image_preprocessing_fn = preprocessing_factory.get_preprocessing(preprocessing_name, is_training=False)
eval_image_size = network_fn.default_image_size
image = image_preprocessing_fn(image, eval_image_size, eval_image_size)
filenames, images = tf.train.batch([image_file_name, image], batch_size=100, num_threads=2, capacity=500)
logits, _ = network_fn(images)
variables_to_restore = slim.get_variables_to_restore()
predictions = tf.argmax(logits, 1)
return filenames, logits, predictions, variables_to_restore
def filter_by_imagenet(path, cls_name):
labels_dict = get_imagenet_class_labels()
filenames, logits, predictions, variables_to_restore = build_imagenet_graph(path)
saver = tf.train.Saver(variables_to_restore)
output_filename_list = []
counter = 0
with tf.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
saver.restore(sess, inception_ckpt_path)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
while True:
try:
filename_list, logit_array, prediction_list = sess.run([filenames, logits, predictions])
except Exception as e:
break
if counter % 5000 == 0:
print("Evaluated %d files" % counter)
print(len(output_filename_list))
prediction_dict = {os.path.split(filename)[1]: labels_dict[prediction] for filename, prediction in
zip(filename_list, prediction_list)}
for i, j in prediction_dict.items():
j = [p.strip() for p in j.lower().split(',')]
if cls_name.lower() in j and len(j) == 1:
output_filename_list.append(i.decode('ascii'))
counter += 100
coord.request_stop()
coord.join(threads)
return output_filename_list
# SSD filter for COCO classes in Tensorflow instead of Caffe.
# Not fully functional yet. It will not output filtered filenames.
def filter_by_coco(path, cls_name):
TEST_IMAGE_PATHS = [os.path.join(path, f) for f in os.listdir(path) if os.path.isfile(os.path.join(path, f))]
counter = 0
output_filename_list = []
tf.reset_default_graph()
print(path)
PATH_TO_CKPT = '../ssd_inception_v2/frozen_inference_graph.pb'
PATH_TO_LABELS = os.path.join('../../object_detection/data', 'mscoco_label_map.pbtxt')
NUM_CLASSES = 90
# Label map
label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES,
use_display_name=True)
category_index = label_map_util.create_category_index(categories)
for i in range(80):
if categories[i]['name'] == cls_name:
cls_index = categories[i]['id']
# Load graph
detection_graph = tf.Graph()
with detection_graph.as_default():
# Input queue
filename_queue = tf.train.string_input_producer(tf.train.match_filenames_once(path + "/*.jpg"),
num_epochs=1, shuffle=False, capacity=100)
image_reader = tf.WholeFileReader()
image_file_name, image_file = image_reader.read(filename_queue)
image = tf.image.decode_jpeg(image_file, channels=3, fancy_upscaling=True)
image0 = tf.image.resize_image_with_crop_or_pad(image, 500, 500)
image = tf.image.resize_images(image0, [250, 250], method=tf.image.ResizeMethod.BILINEAR)
image = tf.cast(image, tf.uint8)
filenames, images = tf.train.batch([image_file_name, image], batch_size=20, num_threads=2, capacity=500)
# Graph Def
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='', input_map={'image_tensor:0': images})
# Time counter
prev_time = float("-inf")
curr_time = float("-inf")
with detection_graph.as_default():
with tf.Session(graph=detection_graph, config=config) as sess:
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
# Definite input and output Tensors for detection_graph
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
# Each box represents a part of the image where a particular object was detected.
detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
# Each score represent how level of confidence for each of the objects.
# Score is shown on the result image, together with the class label.
detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
num_detections = detection_graph.get_tensor_by_name('num_detections:0')
for image_path in TEST_IMAGE_PATHS:
if counter % 5 == 0:
curr_time = time()
elapsed = curr_time - prev_time
print(
"Now at iteration %d. Elapsed time: %.5fs." % (counter, elapsed))
prev_time = curr_time
image = im.open(image_path)
# the array based representation of the image will be used later in order to prepare the
# result image with boxes and labels on it.
image_np, im_width, im_height = load_image_into_numpy_array(image)
image_np = scipy.misc.imresize(image_np, 0.5, 'bilinear')
# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
image_np_expanded = np.expand_dims(image_np, axis=0)
# Actual detection.
(boxes, scores, classes, num) = sess.run(
[detection_boxes, detection_scores, detection_classes, num_detections],
feed_dict={image_tensor: image_np_expanded})
# Filter results
boxes = np.squeeze(boxes)
classes = np.squeeze(classes).astype(np.int32),
scores = np.squeeze(scores)
idx = np.logical_and(scores > 0.9, classes == cls_index)
portion = np.prod(boxes[idx], axis=1) / (im_width * im_height)
if portion.size > 0:
print()
counter += 1
def filter_images(flickr_dir, cls_name):
imagenet_classes = [i[:-1] for i in open('./imagenet_share_classes.txt').readlines()]
coco_classes = [i[:-1] for i in open('./coco_share_classes.txt').readlines()]
this_dir = os.path.join(flickr_dir, cls_name)
invalid_file_list = []
print("Invalid file number: %d" % len(invalid_file_list))
for file_name in invalid_file_list:
os.remove(os.path.join(this_dir, file_name))
if cls_name in imagenet_classes:
output_filename_list = filter_by_imagenet(this_dir, cls_name)
elif cls_name in coco_classes:
output_filename_list = filter_by_coco(this_dir, cls_name)
else:
raise NotImplementedError
file_list = [f for f in os.listdir(this_dir) if os.path.isfile(os.path.join(this_dir, f))]
print(len(file_list) - len(output_filename_list))
for file_name in file_list:
if file_name not in output_filename_list:
os.remove(os.path.join(this_dir, file_name))
config = tf.ConfigProto(allow_soft_placement=True, log_device_placement=False,
intra_op_parallelism_threads=4)
config.gpu_options.allow_growth = True
config.gpu_options.per_process_gpu_memory_fraction = 0.9
with open('./imagenet_share_classes.txt', 'r') as f:
classes_list = [i[:-1] for i in f.readlines()]
if __name__ == '__main__':
# # Imagenet
# labels_dict = get_imagenet_class_labels()
# labels_list = [label + '\n' for i, label in labels_dict.items()]
# with open('./imagenet_classes.txt', 'w') as f:
# f.writelines(labels_list)
# COCO
labels_list = [cls['name'] for cls in coco.get_all_images_data_categories(split='train')[2]]
# with open('./all_classes', 'r') as f:
# sketchy_class_list = [i[:-1] for i in f.readlines()]
#
# filtered_classes = []
# for cls in sketchy_class_list:
# for large_cls in labels_list:
# large_cls_names = [i.strip() for i in large_cls.split(',')]
# for name in large_cls_names:
# if cls.lower() == name.lower() and cls.lower() not in filtered_classes:
# filtered_classes.append(cls + '\n')
#
# with open('./coco_share_classes.txt', 'w') as f:
# f.writelines(filtered_classes)
# Inference
filter_range = (8, 12)
class_list = ['airplane']
for class_name in class_list:
filter_images('../flickr_output', class_name)