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utils.py
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utils.py
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from __future__ import division
import math
import pprint
import scipy.misc
import scipy.ndimage
import numpy as np
import copy
pp = pprint.PrettyPrinter()
get_stddev = lambda x, k_h, k_w: 1/math.sqrt(k_w*k_h*x.get_shape()[-1])
class ImagePool(object):
def __init__(self, maxsize=50):
self.maxsize = maxsize
self.num_img = 0
self.images = []
def __call__(self, image):
if self.maxsize <= 0:
return image
if self.num_img < self.maxsize:
self.images.append(image)
self.num_img += 1
return image
if np.random.rand() > 0.5:
idx = int(np.random.rand()*self.maxsize)
tmp1 = copy.copy(self.images[idx])[0]
tmp3 = copy.copy(self.images[idx])[2]
self.images[idx][0] = image[0]
self.images[idx][2] = image[2]
idx = int(np.random.rand()*self.maxsize)
tmp2 = copy.copy(self.images[idx])[1]
tmp4 = copy.copy(self.images[idx])[3]
self.images[idx][1] = image[1]
self.images[idx][3] = image[3]
return [tmp1, tmp2, tmp3, tmp4]
else:
return image
def load_test_data(image_path, image_width=512, image_height=256):
img = imread(image_path)
img = scipy.misc.imresize(img, [image_height, image_width])
img = img/127.5 - 1
return img
def one_hot(image_in, num_classes=8):
hot = np.zeros((image_in.shape[0], image_in.shape[1], num_classes))
layer_idx = np.arange(image_in.shape[0]).reshape(image_in.shape[0], 1)
component_idx = np.tile(np.arange(image_in.shape[1]), (image_in.shape[0], 1))
hot[layer_idx, component_idx, image_in] = 1
return hot.astype(np.int)
def load_train_data(image_path, image_width=512, image_height=256, num_seg_masks=8, is_testing=False):
img_A = imread(image_path[0])
img_B = imread(image_path[1])
seg_A = imread(image_path[0].replace("trainA","trainA_seg"))
seg_class_A = scipy.misc.imread(image_path[0].replace("trainA","trainA_seg_class")) if not is_testing else None
seg_B = imread(image_path[1].replace("trainB","trainB_seg"))
seg_class_B = scipy.misc.imread(image_path[1].replace("trainB","trainB_seg_class")) if not is_testing else None
# preprocess seg masks
if not is_testing:
seg_mask_A = one_hot(seg_class_A.astype(np.int), num_seg_masks)
seg_mask_B = one_hot(seg_class_B.astype(np.int), num_seg_masks)
else:
seg_mask_A = None
seg_mask_B = None
if not is_testing:
img_A = scipy.misc.imresize(img_A, [image_height, image_width])
seg_A = scipy.misc.imresize(seg_A, [image_height, image_width])
seg_mask_A = scipy.ndimage.interpolation.zoom(seg_mask_A, (image_height/8.0/seg_mask_A.shape[0], image_width/8.0/seg_mask_A.shape[1],1), mode="nearest")
img_B = scipy.misc.imresize(img_B, [image_height, image_width])
seg_B = scipy.misc.imresize(seg_B, [image_height, image_width])
seg_mask_B = scipy.ndimage.interpolation.zoom(seg_mask_B, (image_height/8.0/seg_mask_B.shape[0], image_width/8.0/seg_mask_B.shape[1],1), mode="nearest")
if np.random.random() > 0.5:
img_A = np.fliplr(img_A)
img_B = np.fliplr(img_B)
seg_A = np.fliplr(seg_A)
seg_B = np.fliplr(seg_B)
seg_mask_A = np.fliplr(seg_mask_A)
seg_mask_B = np.fliplr(seg_mask_B)
else:
img_A = scipy.misc.imresize(img_A, [image_height, image_width])
img_B = scipy.misc.imresize(img_B, [image_height, image_width])
seg_A = scipy.misc.imresize(seg_A, [image_height, image_width])
seg_B = scipy.misc.imresize(seg_B, [image_height, image_width])
img_A = img_A/127.5 - 1.
img_B = img_B/127.5 - 1.
seg_A = seg_A/127.5 - 1.
seg_B = seg_B/127.5 - 1.
img_AB = np.concatenate((img_A, img_B), axis=2)
seg_AB = np.concatenate((seg_A, seg_B), axis=2)
# img_AB shape: (image_height, image_width, input_c_dim + output_c_dim)
return img_AB, seg_AB, seg_mask_A, seg_mask_B
# -----------------------------
#def get_image(image_path, image_size, is_crop=True, resize_w=64, is_grayscale = False):
# return transform(imread(image_path, is_grayscale), image_size, is_crop, resize_w)
def save_images(images, size, image_path):
return imsave(inverse_transform(images), size, image_path)
def imread(path, is_grayscale = False):
if (is_grayscale):
return scipy.misc.imread(path, flatten = True).astype(np.float)
else:
return scipy.misc.imread(path, mode='RGB').astype(np.float)
def merge_images(images, size):
return inverse_transform(images)
def merge(images, size):
h, w = images.shape[1], images.shape[2]
img = np.zeros((h * size[0], w * size[1], 3))
for idx, image in enumerate(images):
i = idx % size[1]
j = idx // size[1]
img[j*h:j*h+h, i*w:i*w+w, :] = image
return img
def imsave(images, size, path):
return scipy.misc.imsave(path, merge(images, size))
def center_crop(x, crop_h, crop_w,
resize_h=64, resize_w=64):
if crop_w is None:
crop_w = crop_h
h, w = x.shape[:2]
j = int(round((h - crop_h)/2.))
i = int(round((w - crop_w)/2.))
return scipy.misc.imresize(
x[j:j+crop_h, i:i+crop_w], [resize_h, resize_w])
def transform(image, npx=64, is_crop=True, resize_w=64):
# npx : # of pixels width/height of image
if is_crop:
cropped_image = center_crop(image, npx, resize_w=resize_w)
else:
cropped_image = image
return np.array(cropped_image)/127.5 - 1.
def inverse_transform(images):
return (images+1.)/2.