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utils.py
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utils.py
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import numpy as np
from scipy import misc
from scipy.ndimage import imread
from random import shuffle
import random
import matplotlib.pyplot as plt
def normalize_images(images):
"""
Normalizing a batch of images so that they have mean (0,0) and std = 1
:param images: batch_size x 64 x 64 x 3
:return:
"""
batch_size, s, s, c = images.shape
channel_mean = np.einsum('nijk -> k', images) / (batch_size * s * s)
ims = images - channel_mean
channel_sd = np.sqrt(np.einsum('nijk -> k', ims**2) / (batch_size * s * s))
images = ims / channel_sd
return images
def sample_image_crop_flip(image, output_size=64, scale_size=70, deterministic=False, return_multiple=False):
im = resize_image_with_smallest_side(image, scale_size)
h, w, c = im.shape
os = output_size
def crop_middle(im): return im[(h - os) // 2:(h + os) // 2, (w - os) // 2:(w + os) // 2, :]
def crop_topleft(im): return im[:os, :os, :]
def crop_topright(im): return im[:os, w-os:, :]
def crop_bottomright(im): return im[h-os:, w-os:, :]
def crop_bottomleft(im): return im[h-os:, :os, :]
def flip(im): return np.fliplr(im)
def donothing(im): return im
crop = random.choice([crop_middle,crop_topleft,crop_topright,crop_bottomright,crop_bottomleft])
flip_maybe = random.choice([flip,donothing])
if return_multiple:
images = []
for crop_op in [crop_middle,crop_topleft,crop_topright,crop_bottomright,crop_bottomleft]:
cropped = crop_op(im)
images.append(cropped)
images.append(flip(cropped))
return np.array(images)
if deterministic: return crop_middle(im)
return flip_maybe(crop(im))
def crop_and_flip(image,os=224, scales = [256],crop_just_one=False):
"""
:param image: An image on tensor form, h x w x 3
:param size: output
:return:
"""
h, w, c = image.shape
#scales = [80]
images = []
for l in scales:
im = resize_image_with_smallest_side(image, l)
h, w, c = im.shape
# crop middle
im_middle = im[(h - os) // 2:(h + os) // 2, (w - os) // 2:(w + os) // 2, :]
if crop_just_one:
return im_middle
else:
images.append(im_middle)
images.append(np.fliplr(im_middle))
im_upperleft = im[:os, :os, :]
images.append(im_upperleft)
images.append(np.fliplr(im_upperleft))
im_upperright = im[:os, w-os:, :]
images.append(im_upperright)
images.append(np.fliplr(im_upperright))
im_lowerleft = im[h-os:, :os, :]
images.append(im_lowerleft)
images.append(np.fliplr(im_lowerleft))
im_lowerright = im[h-os:, w-os:, :]
images.append(im_lowerright)
images.append(np.fliplr(im_lowerright))
#shuffle(images)
return images
def resize_image_with_smallest_side(image, small_size=224):
"""
Resize single image array with smallest side = small_size and
keep the original aspect ratio.
Author: Qian Ge <[email protected]>
Args:
image (np.array): 2-D image of shape
[height, width] or 3-D image of shape
[height, width, channels] or 4-D of shape
[1, height, width, channels].
small_size (int): A 1-D int. The smallest side of resize image.
"""
im_shape = image.shape
shape_dim = len(im_shape)
assert shape_dim <= 4 and shape_dim >= 2,\
'Wrong format of image!Shape is {}'.format(im_shape)
if shape_dim == 4:
image = np.squeeze(image, axis=0)
height = float(im_shape[1])
width = float(im_shape[2])
else:
height = float(im_shape[0])
width = float(im_shape[1])
if height <= width:
new_height = int(small_size)
new_width = int(new_height/height * width)
else:
new_width = int(small_size)
new_height = int(new_width/width * height)
if shape_dim == 2:
im = misc.imresize(image, (new_height, new_width))
elif shape_dim == 3:
im = misc.imresize(image, (new_height, new_width, image.shape[2]))
else:
im = misc.imresize(image, (new_height, new_width, im_shape[3]))
im = np.expand_dims(im, axis=0)
return im
'''output_size=224
#image = imread('implementation/result.png', mode='RGB')
image = imread('assets/image_00001.jpg', mode='RGB')
plt.imshow(image)
plt.show()
images=crop_and_flip(image,output_size)
for image in images:
plt.imshow(image)
plt.show()'''