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utils.py
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utils.py
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import cv2
import tqdm
import torch
import ctypes
import numpy as np
import matplotlib.pyplot as plt
from scipy.ndimage import zoom as scizoom
from wand.image import Image as WandImage
from wand.api import library as wandlibrary
# Tell Python about the C method
wandlibrary.MagickMotionBlurImage.argtypes = (ctypes.c_void_p, # wand
ctypes.c_double, # radius
ctypes.c_double, # sigma
ctypes.c_double) # angle
class MotionImage(WandImage):
def motion_blur(self, radius=0.0, sigma=0.0, angle=0.0):
wandlibrary.MagickMotionBlurImage(self.wand, radius, sigma, angle)
def clipped_zoom(img, zoom_factor):
h = img.shape[0]
# ceil crop height(= crop width)
ch = int(np.ceil(h / float(zoom_factor)))
top = (h - ch) // 2
img = scizoom(img[top:top + ch, top:top + ch], (zoom_factor, zoom_factor, 1), order=1)
# trim off any extra pixels
trim_top = (img.shape[0] - h) // 2
return img[trim_top:trim_top + h, trim_top:trim_top + h]
def disk(radius, alias_blur=0.1, dtype=np.float32):
if radius <= 8:
L = np.arange(-8, 8 + 1)
ksize = (3, 3)
else:
L = np.arange(-radius, radius + 1)
ksize = (5, 5)
X, Y = np.meshgrid(L, L)
aliased_disk = np.array((X ** 2 + Y ** 2) <= radius ** 2, dtype=dtype)
aliased_disk /= np.sum(aliased_disk)
# supersample disk to antialias
return cv2.GaussianBlur(aliased_disk, ksize=ksize, sigmaX=alias_blur)
def plasma_fractal(mapsize=256, wibbledecay=3):
"""
Generate a heightmap using diamond-square algorithm.
Return square 2d array, side length 'mapsize', of floats in range 0-255.
'mapsize' must be a power of two.
"""
assert (mapsize & (mapsize - 1) == 0)
maparray = np.empty((mapsize, mapsize), dtype=np.float_)
maparray[0, 0] = 0
stepsize = mapsize
wibble = 100
def wibbledmean(array):
return array / 4 + wibble * np.random.uniform(-wibble, wibble, array.shape)
def fillsquares():
"""For each square of points stepsize apart,
calculate middle value as mean of points + wibble"""
cornerref = maparray[0:mapsize:stepsize, 0:mapsize:stepsize]
squareaccum = cornerref + np.roll(cornerref, shift=-1, axis=0)
squareaccum += np.roll(squareaccum, shift=-1, axis=1)
maparray[stepsize // 2:mapsize:stepsize,
stepsize // 2:mapsize:stepsize] = wibbledmean(squareaccum)
def filldiamonds():
"""For each diamond of points stepsize apart,
calculate middle value as mean of points + wibble"""
mapsize = maparray.shape[0]
drgrid = maparray[stepsize // 2:mapsize:stepsize, stepsize // 2:mapsize:stepsize]
ulgrid = maparray[0:mapsize:stepsize, 0:mapsize:stepsize]
ldrsum = drgrid + np.roll(drgrid, 1, axis=0)
lulsum = ulgrid + np.roll(ulgrid, -1, axis=1)
ltsum = ldrsum + lulsum
maparray[0:mapsize:stepsize, stepsize // 2:mapsize:stepsize] = wibbledmean(ltsum)
tdrsum = drgrid + np.roll(drgrid, 1, axis=1)
tulsum = ulgrid + np.roll(ulgrid, -1, axis=0)
ttsum = tdrsum + tulsum
maparray[stepsize // 2:mapsize:stepsize, 0:mapsize:stepsize] = wibbledmean(ttsum)
while stepsize >= 2:
fillsquares()
filldiamonds()
stepsize //= 2
wibble /= wibbledecay
maparray -= maparray.min()
return maparray / maparray.max()
def local_shuffle(image, patch_size=(80, 80)):
imhigh, imwidth, imch = image.shape
range_y = np.arange(0, imhigh - patch_size[0], patch_size[0])
range_x = np.arange(0, imwidth - patch_size[1], patch_size[1])
if range_y[-1] != imhigh - patch_size[0]:
range_y = np.append(range_y, imhigh - patch_size[0])
if range_x[-1] != imwidth - patch_size[1]:
range_x = np.append(range_x, imwidth - patch_size[1])
sz = len(range_y) * len(range_x)
res = np.zeros((sz, patch_size[0], patch_size[1], imch))
index = 0
for y in range_y:
for x in range_x:
patch = image[y:y + patch_size[0], x:x + patch_size[1]]
res[index] = patch
index = index + 1
np.random.shuffle(res)
idx = 0
new_img = np.zeros(image.shape)
for y in range_y:
for x in range_x:
new_img[y:y + patch_size[0], x:x + patch_size[1]] = res[idx]
idx += 1
return new_img
def mixup_data(x, y, alpha=1.0):
'''Returns mixed inputs, pairs of targets, and lambda'''
if alpha > 0:
lam = np.random.beta(alpha, alpha)
else:
lam = 1
batch_size = x.size()[0]
index = torch.randperm(batch_size).to(x.device)
mixed_x = lam * x + (1 - lam) * x[index, :]
y_a, y_b = y, y[index]
return mixed_x, y_a, y_b, lam
def mixup_criterion(criterion, pred, y_a, y_b, lam):
return lam * criterion(pred, y_a) + (1 - lam) * criterion(pred, y_b)
def extend(src, offsets=5):
dst = src.copy()
rows, cols, _ = src.shape
x = np.array(list(range(rows)))
y = np.array(list(range(cols)))
yv, xv = np.meshgrid(y, x)
random1 = np.random.randint(0, offsets, size=yv.shape)
random2 = np.random.randint(0, offsets, size=xv.shape)
yv_random = yv + random1
xv_random = xv + random2
xv_random[xv_random >= rows] = rows - 1
yv_random[yv_random >= cols] = cols - 1
dst[xv, yv] = src[xv_random, yv_random]
return dst
def rand_bbox(size, lam):
W, H = size[2], size[3]
cut_rat = np.sqrt(1. - lam)
cut_w = np.int(W * cut_rat)
cut_h = np.int(H * cut_rat)
# uniform
cx = np.random.randint(W)
cy = np.random.randint(H)
bbx1 = np.clip(cx - cut_w // 2, 0, W)
bby1 = np.clip(cy - cut_h // 2, 0, H)
bbx2 = np.clip(cx + cut_w // 2, 0, W)
bby2 = np.clip(cy + cut_h // 2, 0, H)
return bbx1, bby1, bbx2, bby2
def cutmix(x, y):
lam = np.random.beta(1.0, 1.0)
rand_index = torch.randperm(x.size()[0])
target_a = y
target_b = y[rand_index]
bbx1, bby1, bbx2, bby2 = rand_bbox(x.size(), lam)
x[:, :, bbx1:bbx2, bby1:bby2] = x[rand_index, :, bbx1:bbx2, bby1:bby2]
lam = 1 - ((bbx2 - bbx1) * (bby2 - bby1) / (x.size()[-1] * x.size()[-2]))
return x, target_a, target_b, lam
def rain_noise(img, value, angle, beta):
noise = get_noise(img, value=value)
rain = rain_blur(noise, length=10, angle=angle, w=3)
result_img = alpha_rain(rain, img, beta=beta)
return result_img
def get_noise(img, value=10):
noise = np.random.uniform(0, 256, img.shape[0:2])
v = value * 0.01
noise[np.where(noise < (256 - v))] = 0
k = np.array([[0, 0.1, 0],
[0.1, 8, 0.1],
[0, 0.1, 0]])
noise = cv2.filter2D(noise, -1, k)
return noise
def rain_blur(noise, length=10, angle=0, w=1):
trans = cv2.getRotationMatrix2D((length / 2, length / 2), angle - 45, 1 - length / 100.0)
dig = np.diag(np.ones(length))
k = cv2.warpAffine(dig, trans, (length, length))
k = cv2.GaussianBlur(k, (w, w), 0)
blurred = cv2.filter2D(noise, -1, k)
cv2.normalize(blurred, blurred, 0, 255, cv2.NORM_MINMAX)
blurred = np.array(blurred, dtype=np.uint8)
return blurred
def alpha_rain(rain, img, beta=0.8):
rain = np.expand_dims(rain, 2)
rain_effect = np.concatenate((img, rain), axis=2) # add alpha channel
rain_result = img.copy()
rain = np.array(rain, dtype=np.float32)
rain_result[:, :, 0] = rain_result[:, :, 0] * (255 - rain[:, :, 0]) / 255.0 + beta * rain[:, :, 0]
rain_result[:, :, 1] = rain_result[:, :, 1] * (255 - rain[:, :, 0]) / 255 + beta * rain[:, :, 0]
rain_result[:, :, 2] = rain_result[:, :, 2] * (255 - rain[:, :, 0]) / 255 + beta * rain[:, :, 0]
return rain_result
def motion_blur(image, degree=12, angle=45):
# image : numpy array
M = cv2.getRotationMatrix2D((degree / 2, degree / 2), angle, 1)
motion_blur_kernel = np.diag(np.ones(degree))
motion_blur_kernel = cv2.warpAffine(motion_blur_kernel, M, (degree, degree))
motion_blur_kernel = motion_blur_kernel / degree
blurred = cv2.filter2D(image, -1, motion_blur_kernel)
# convert to uint8
cv2.normalize(blurred, blurred, 0, 255, cv2.NORM_MINMAX)
blurred = np.array(blurred, dtype=np.uint8)
return blurred
def draw_hist(img_dict):
x = [str(i) for i in range(20)]
y = [img_dict[name] for name in x]
plt.bar(x, y, align='center', color='b', tick_label=x, alpha=0.6)
plt.xlabel('image category')
plt.ylabel('image counts')
plt.title('Distribution within Training Images')
plt.grid(True, axis='y', ls=':', color='r', alpha=0.3)
plt.show()
def parse_txt(txt_name):
with open(txt_name, 'r') as f:
lines = f.readlines()
f.close()
training_pairs = list()
img_counts = dict()
for line in tqdm.tqdm(lines):
img_name, label = line.strip().split(' ')
training_pairs.append((img_name, int(label)))
if label not in img_counts.keys():
img_counts[label] = 0
img_counts[label] += 1
return training_pairs, img_counts
if __name__ == '__main__':
txt_name = 'data/train_phase1/label.txt'
training_pairs, img_counts = parse_txt(txt_name)
draw_hist(img_counts)