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import cv2
import numpy as np
from PIL import Image, ImageOps
import torchvision.transforms as transforms
from wand.image import Image as WandImage
from scipy.ndimage import zoom as scizoom
from skimage.filters import gaussian
from wand.api import library as wandlibrary
from io import BytesIO
#from skimage import color
from .ops import MotionImage, clipped_zoom, disk, plasma_fractal
'''
PIL resize (W,H)
'''
class GaussianBlur:
def __init__(self):
pass
def __call__(self, img, mag=-1, prob=1.):
if np.random.uniform(0,1) > prob:
return img
W, H = img.size
#kernel = [(31,31)] prev 1 level only
kernel = (31, 31)
sigmas = [.5, 1, 2]
if mag<0 or mag>=len(kernel):
index = np.random.randint(0, len(sigmas))
else:
index = mag
sigma = sigmas[index]
return transforms.GaussianBlur(kernel_size=kernel, sigma=sigma)(img)
class DefocusBlur:
def __init__(self):
pass
def __call__(self, img, mag=-1, prob=1.):
if np.random.uniform(0,1) > prob:
return img
n_channels = len(img.getbands())
isgray = n_channels == 1
#c = [(3, 0.1), (4, 0.5), (6, 0.5), (8, 0.5), (10, 0.5)]
c = [(2, 0.1), (3, 0.1), (4, 0.1)] #, (6, 0.5)] #prev 2 levels only
if mag<0 or mag>=len(c):
index = np.random.randint(0, len(c))
else:
index = mag
c = c[index]
img = np.array(img) / 255.
if isgray:
img = np.expand_dims(img, axis=2)
img = np.repeat(img, 3, axis=2)
n_channels = 3
kernel = disk(radius=c[0], alias_blur=c[1])
channels = []
for d in range(n_channels):
channels.append(cv2.filter2D(img[:, :, d], -1, kernel))
channels = np.array(channels).transpose((1, 2, 0)) # 3x224x224 -> 224x224x3
#if isgray:
# img = img[:,:,0]
# img = np.squeeze(img)
img = np.clip(channels, 0, 1) * 255
img = Image.fromarray(img.astype(np.uint8))
if isgray:
img = ImageOps.grayscale(img)
return img
class MotionBlur:
def __init__(self):
pass
def __call__(self, img, mag=-1, prob=1.):
if np.random.uniform(0,1) > prob:
return img
n_channels = len(img.getbands())
isgray = n_channels == 1
#c = [(10, 3), (15, 5), (15, 8), (15, 12), (20, 15)]
c = [(10, 3), (12, 4), (14, 5)]
if mag<0 or mag>=len(c):
index = np.random.randint(0, len(c))
else:
index = mag
c = c[index]
output = BytesIO()
img.save(output, format='PNG')
img = MotionImage(blob=output.getvalue())
img.motion_blur(radius=c[0], sigma=c[1], angle=np.random.uniform(-45, 45))
img = cv2.imdecode(np.fromstring(img.make_blob(), np.uint8), cv2.IMREAD_UNCHANGED)
if len(img.shape) > 2:
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = Image.fromarray(img.astype(np.uint8))
if isgray:
img = ImageOps.grayscale(img)
return img
class GlassBlur:
def __init__(self):
pass
def __call__(self, img, mag=-1, prob=1.):
if np.random.uniform(0,1) > prob:
return img
W, H = img.size
#c = [(0.7, 1, 2), (0.9, 2, 1), (1, 2, 3), (1.1, 3, 2), (1.5, 4, 2)][severity - 1]
c = [(0.7, 1, 2), (0.75, 1, 2), (0.8, 1, 2)] #, (1, 2, 3)] #prev 2 levels only
if mag<0 or mag>=len(c):
index = np.random.randint(0, len(c))
else:
index = mag
c = c[index]
img = np.uint8(gaussian(np.array(img) / 255., sigma=c[0], multichannel=True) * 255)
# locally shuffle pixels
for i in range(c[2]):
for h in range(H - c[1], c[1], -1):
for w in range(W - c[1], c[1], -1):
dx, dy = np.random.randint(-c[1], c[1], size=(2,))
h_prime, w_prime = h + dy, w + dx
# swap
img[h, w], img[h_prime, w_prime] = img[h_prime, w_prime], img[h, w]
img = np.clip(gaussian(img / 255., sigma=c[0], multichannel=True), 0, 1) * 255
return Image.fromarray(img.astype(np.uint8))
class ZoomBlur:
def __init__(self):
pass
def __call__(self, img, mag=-1, prob=1.):
if np.random.uniform(0,1) > prob:
return img
W, H = img.size
c = [np.arange(1, 1.11, .01),
np.arange(1, 1.16, .01),
np.arange(1, 1.21, .02)]
if mag<0 or mag>=len(c):
index = np.random.randint(0, len(c))
else:
index = mag
c = c[index]
n_channels = len(img.getbands())
isgray = n_channels == 1
uint8_img = img
img = (np.array(img) / 255.).astype(np.float32)
out = np.zeros_like(img)
for zoom_factor in c:
ZW = int(W*zoom_factor)
ZH = int(H*zoom_factor)
zoom_img = uint8_img.resize((ZW, ZH), Image.BICUBIC)
x1 = (ZW - W) // 2
y1 = (ZH - H) // 2
x2 = x1 + W
y2 = y1 + H
zoom_img = zoom_img.crop((x1,y1,x2,y2))
out += (np.array(zoom_img) / 255.).astype(np.float32)
img = (img + out) / (len(c) + 1)
img = np.clip(img, 0, 1) * 255
img = Image.fromarray(img.astype(np.uint8))
return img
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