Code for paper "SketchyGAN: Towards Diverse and Realistic Sketch to Image Synthesis"
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import csv
import numpy as np
import tensorflow as tf
import cv2
def check_repeat(seq):
seen = set()
seen_add = seen.add
seen_twice = set(x for x in seq if x in seen or seen_add(x))
return list(seen_twice)
def binarize(sketch, threshold=245):
sketch[sketch < threshold] = 0
sketch[sketch >= threshold] = 255
return sketch
def showImg(img):
cv2.imshow("test", img)
cv2.waitKey(-1)
def dense_to_one_hot(labels_dense, num_classes):
"""Convert class labels from scalars to one-hot vectors."""
num_labels = labels_dense.shape[0]
index_offset = np.arange(num_labels) * num_classes
labels_one_hot = np.zeros((num_labels, num_classes), dtype=np.int32)
labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1
return labels_one_hot
def bytes_feature(value):
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
def int64_feature(value):
return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))
def read_csv(filename):
with open(filename) as csvfile:
reader = csv.DictReader(csvfile)
l = list(reader)
return l
def read_txt(filename):
with open(filename) as txtfile:
lines = txtfile.readlines()
return [l[:-1] for l in lines]
def split_csvlist(stat_info):
cat = list(set([item['Category'] for item in stat_info]))
l = []
for c in cat:
li = [item for item in stat_info if item['Category'] == c]
l.append(li)
return cat, l