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import cv2
import numpy as np
from skimage import transform as stf
from numpy import random, floor
from PIL import Image, ImageOps
from cv2 import erode, dilate, normalize
from torchvision.transforms import RandomCrop
import math
class Dilation:
"""
OCR: stroke width increasing
"""
def __init__(self, kernel, iterations):
self.kernel = np.ones(kernel, np.uint8)
self.iterations = iterations
def __call__(self, x):
return Image.fromarray(dilate(np.array(x), self.kernel, iterations=self.iterations))
class Erosion:
"""
OCR: stroke width decreasing
"""
def __init__(self, kernel, iterations):
self.kernel = np.ones(kernel, np.uint8)
self.iterations = iterations
def __call__(self, x):
return Image.fromarray(erode(np.array(x), self.kernel, iterations=self.iterations))
class ElasticDistortion:
"""
Elastic Distortion adapted from https://github.com/IntuitionMachines/OrigamiNet
Used in "OrigamiNet: Weakly-Supervised, Segmentation-Free, One-Step, Full Page TextRecognition by learning to unfold",
Yousef, Mohamed and Bishop, Tom E., The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020
"""
def __init__(self, grid, magnitude, min_sep):
self.grid_width, self.grid_height = grid
self.xmagnitude, self.ymagnitude = magnitude
self.min_h_sep, self.min_v_sep = min_sep
def __call__(self, x):
w, h = x.size
horizontal_tiles = self.grid_width
vertical_tiles = self.grid_height
width_of_square = int(floor(w / float(horizontal_tiles)))
height_of_square = int(floor(h / float(vertical_tiles)))
width_of_last_square = w - (width_of_square * (horizontal_tiles - 1))
height_of_last_square = h - (height_of_square * (vertical_tiles - 1))
dimensions = []
shift = [[(0, 0) for x in range(horizontal_tiles)] for y in range(vertical_tiles)]
for vertical_tile in range(vertical_tiles):
for horizontal_tile in range(horizontal_tiles):
if vertical_tile == (vertical_tiles - 1) and horizontal_tile == (horizontal_tiles - 1):
dimensions.append([horizontal_tile * width_of_square,
vertical_tile * height_of_square,
width_of_last_square + (horizontal_tile * width_of_square),
height_of_last_square + (height_of_square * vertical_tile)])
elif vertical_tile == (vertical_tiles - 1):
dimensions.append([horizontal_tile * width_of_square,
vertical_tile * height_of_square,
width_of_square + (horizontal_tile * width_of_square),
height_of_last_square + (height_of_square * vertical_tile)])
elif horizontal_tile == (horizontal_tiles - 1):
dimensions.append([horizontal_tile * width_of_square,
vertical_tile * height_of_square,
width_of_last_square + (horizontal_tile * width_of_square),
height_of_square + (height_of_square * vertical_tile)])
else:
dimensions.append([horizontal_tile * width_of_square,
vertical_tile * height_of_square,
width_of_square + (horizontal_tile * width_of_square),
height_of_square + (height_of_square * vertical_tile)])
sm_h = min(self.xmagnitude,
width_of_square - (self.min_h_sep + shift[vertical_tile][horizontal_tile - 1][
0])) if horizontal_tile > 0 else self.xmagnitude
sm_v = min(self.ymagnitude,
height_of_square - (self.min_v_sep + shift[vertical_tile - 1][horizontal_tile][
1])) if vertical_tile > 0 else self.ymagnitude
dx = random.randint(-sm_h, self.xmagnitude)
dy = random.randint(-sm_v, self.ymagnitude)
shift[vertical_tile][horizontal_tile] = (dx, dy)
shift = list(itertools.chain.from_iterable(shift))
last_column = []
for i in range(vertical_tiles):
last_column.append((horizontal_tiles - 1) + horizontal_tiles * i)
last_row = range((horizontal_tiles * vertical_tiles) - horizontal_tiles, horizontal_tiles * vertical_tiles)
polygons = []
for x1, y1, x2, y2 in dimensions:
polygons.append([x1, y1, x1, y2, x2, y2, x2, y1])
polygon_indices = []
for i in range((vertical_tiles * horizontal_tiles) - 1):
if i not in last_row and i not in last_column:
polygon_indices.append([i, i + 1, i + horizontal_tiles, i + 1 + horizontal_tiles])
for id, (a, b, c, d) in enumerate(polygon_indices):
dx = shift[id][0]
dy = shift[id][1]
x1, y1, x2, y2, x3, y3, x4, y4 = polygons[a]
polygons[a] = [x1, y1,
x2, y2,
x3 + dx, y3 + dy,
x4, y4]
x1, y1, x2, y2, x3, y3, x4, y4 = polygons[b]
polygons[b] = [x1, y1,
x2 + dx, y2 + dy,
x3, y3,
x4, y4]
x1, y1, x2, y2, x3, y3, x4, y4 = polygons[c]
polygons[c] = [x1, y1,
x2, y2,
x3, y3,
x4 + dx, y4 + dy]
x1, y1, x2, y2, x3, y3, x4, y4 = polygons[d]
polygons[d] = [x1 + dx, y1 + dy,
x2, y2,
x3, y3,
x4, y4]
generated_mesh = []
for i in range(len(dimensions)):
generated_mesh.append([dimensions[i], polygons[i]])
self.generated_mesh = generated_mesh
return x.transform(x.size, Image.MESH, self.generated_mesh, resample=Image.BICUBIC)
class RandomTransform:
"""
Random Transform adapted from https://github.com/IntuitionMachines/OrigamiNet
Used in "OrigamiNet: Weakly-Supervised, Segmentation-Free, One-Step, Full Page TextRecognition by learning to unfold",
Yousef, Mohamed and Bishop, Tom E., The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020
"""
def __init__(self, val):
self.val = val
def __call__(self, x):
w, h = x.size
dw, dh = (self.val, 0) if random.randint(0, 2) == 0 else (0, self.val)
def rd(d):
return random.uniform(-d, d)
def fd(d):
return random.uniform(-dw, d)
# generate a random projective transform
# adapted from https://navoshta.com/traffic-signs-classification/
tl_top = rd(dh)
tl_left = fd(dw)
bl_bottom = rd(dh)
bl_left = fd(dw)
tr_top = rd(dh)
tr_right = fd(min(w * 3 / 4 - tl_left, dw))
br_bottom = rd(dh)
br_right = fd(min(w * 3 / 4 - bl_left, dw))
tform = stf.ProjectiveTransform()
tform.estimate(np.array(( #从对应点估计变换矩阵
(tl_left, tl_top),
(bl_left, h - bl_bottom),
(w - br_right, h - br_bottom),
(w - tr_right, tr_top)
)), np.array((
[0, 0],
[0, h - 1],
[w - 1, h - 1],
[w - 1, 0]
)))
# determine shape of output image, to preserve size
# trick take from the implementation of skimage.transform.rotate
corners = np.array([
[0, 0],
[0, h - 1],
[w - 1, h - 1],
[w - 1, 0]
])
corners = tform.inverse(corners)
minc = corners[:, 0].min()
minr = corners[:, 1].min()
maxc = corners[:, 0].max()
maxr = corners[:, 1].max()
out_rows = maxr - minr + 1
out_cols = maxc - minc + 1
output_shape = np.around((out_rows, out_cols))
# fit output image in new shape
translation = (minc, minr)
tform4 = stf.SimilarityTransform(translation=translation)
tform = tform4 + tform
# normalize
tform.params /= tform.params[2, 2]
x = stf.warp(np.array(x), tform, output_shape=output_shape, cval=255, preserve_range=True)
x = stf.resize(x, (h, w), preserve_range=True).astype(np.uint8)
return Image.fromarray(x)
class SignFlipping:
"""
Color inversion
"""
def __init__(self):
pass
def __call__(self, x):
return ImageOps.invert(x)
class DPIAdjusting:
"""
Resolution modification
"""
def __init__(self, factor, preserve_ratio):
self.factor = factor
def __call__(self, x):
w, h = x.size
return x.resize((int(np.ceil(w * self.factor)), int(np.ceil(h * self.factor))), Image.BILINEAR)
class GaussianNoise:
"""
Add Gaussian Noise
"""
def __init__(self, std):
self.std = std
def __call__(self, x):
x_np = np.array(x)
mean, std = np.mean(x_np), np.std(x_np)
std = math.copysign(max(abs(std), 0.000001), std)
min_, max_ = np.min(x_np,), np.max(x_np)
normal_noise = np.random.randn(*x_np.shape)
if len(x_np.shape) == 3 and x_np.shape[2] == 3 and np.all(x_np[:, :, 0] == x_np[:, :, 1]) and np.all(x_np[:, :, 0] == x_np[:, :, 2]):
normal_noise[:, :, 1] = normal_noise[:, :, 2] = normal_noise[:, :, 0]
x_np = ((x_np-mean)/std + normal_noise*self.std) * std + mean
x_np = normalize(x_np, x_np, max_, min_, cv2.NORM_MINMAX)
return Image.fromarray(x_np.astype(np.uint8))
class Sharpen:
"""
Add Gaussian Noise
"""
def __init__(self, alpha, strength):
self.alpha = alpha
self.strength = strength
def __call__(self, x):
x_np = np.array(x)
id_matrix = np.array([[0, 0, 0],
[0, 1, 0],
[0, 0, 0]]
)
effect_matrix = np.array([[1, 1, 1],
[1, -(8+self.strength), 1],
[1, 1, 1]]
)
kernel = (1 - self.alpha) * id_matrix - self.alpha * effect_matrix
kernel = np.expand_dims(kernel, axis=2)
kernel = np.concatenate([kernel, kernel, kernel], axis=2)
sharpened = cv2.filter2D(x_np, -1, kernel=kernel[:, :, 0])
return Image.fromarray(sharpened.astype(np.uint8))
class ZoomRatio:
"""
Crop by ratio
Preserve dimensions if keep_dim = True (= zoom)
"""
def __init__(self, ratio_h, ratio_w, keep_dim=True):
self.ratio_w = ratio_w
self.ratio_h = ratio_h
self.keep_dim = keep_dim
def __call__(self, x):
w, h = x.size
x = RandomCrop((int(h * self.ratio_h), int(w * self.ratio_w)))(x)
if self.keep_dim:
x = x.resize((w, h), Image.BILINEAR)
return x
class Tightening:
"""
Reduce interline spacing
"""
def __init__(self, color=255, remove_proba=0.75):
self.color = color
self.remove_proba = remove_proba
def __call__(self, x):
x_np = np.array(x)
interline_indices = [np.all(line == 255) for line in x_np]
indices_to_removed = np.logical_and(np.random.choice([True, False], size=len(x_np), replace=True, p=[self.remove_proba, 1-self.remove_proba]), interline_indices)
new_x = x_np[np.logical_not(indices_to_removed)]
return Image.fromarray(new_x.astype(np.uint8)) |