436 lines
13 KiB
Python
436 lines
13 KiB
Python
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
import math
|
|
import cv2
|
|
import numpy as np
|
|
import random
|
|
|
|
from .text_image_aug import tia_perspective, tia_stretch, tia_distort
|
|
|
|
|
|
class RecAug(object):
|
|
def __init__(self, use_tia=True, aug_prob=0.4, **kwargs):
|
|
self.use_tia = use_tia
|
|
self.aug_prob = aug_prob
|
|
|
|
def __call__(self, data):
|
|
img = data['image']
|
|
img = warp(img, 10, self.use_tia, self.aug_prob)
|
|
data['image'] = img
|
|
return data
|
|
|
|
|
|
class ClsResizeImg(object):
|
|
def __init__(self, image_shape, **kwargs):
|
|
self.image_shape = image_shape
|
|
|
|
def __call__(self, data):
|
|
img = data['image']
|
|
norm_img = resize_norm_img(img, self.image_shape)
|
|
data['image'] = norm_img
|
|
return data
|
|
|
|
|
|
class RecResizeImg(object):
|
|
def __init__(self,
|
|
image_shape,
|
|
infer_mode=False,
|
|
character_type='ch',
|
|
**kwargs):
|
|
self.image_shape = image_shape
|
|
self.infer_mode = infer_mode
|
|
self.character_type = character_type
|
|
|
|
def __call__(self, data):
|
|
img = data['image']
|
|
if self.infer_mode and self.character_type == "ch":
|
|
norm_img = resize_norm_img_chinese(img, self.image_shape)
|
|
else:
|
|
norm_img = resize_norm_img(img, self.image_shape)
|
|
data['image'] = norm_img
|
|
return data
|
|
|
|
|
|
class SRNRecResizeImg(object):
|
|
def __init__(self, image_shape, num_heads, max_text_length, **kwargs):
|
|
self.image_shape = image_shape
|
|
self.num_heads = num_heads
|
|
self.max_text_length = max_text_length
|
|
|
|
def __call__(self, data):
|
|
img = data['image']
|
|
norm_img = resize_norm_img_srn(img, self.image_shape)
|
|
data['image'] = norm_img
|
|
[encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, gsrm_slf_attn_bias2] = \
|
|
srn_other_inputs(self.image_shape, self.num_heads, self.max_text_length)
|
|
|
|
data['encoder_word_pos'] = encoder_word_pos
|
|
data['gsrm_word_pos'] = gsrm_word_pos
|
|
data['gsrm_slf_attn_bias1'] = gsrm_slf_attn_bias1
|
|
data['gsrm_slf_attn_bias2'] = gsrm_slf_attn_bias2
|
|
return data
|
|
|
|
|
|
def resize_norm_img(img, image_shape):
|
|
imgC, imgH, imgW = image_shape
|
|
h = img.shape[0]
|
|
w = img.shape[1]
|
|
ratio = w / float(h)
|
|
if math.ceil(imgH * ratio) > imgW:
|
|
resized_w = imgW
|
|
else:
|
|
resized_w = int(math.ceil(imgH * ratio))
|
|
resized_image = cv2.resize(img, (resized_w, imgH))
|
|
resized_image = resized_image.astype('float32')
|
|
if image_shape[0] == 1:
|
|
resized_image = resized_image / 255
|
|
resized_image = resized_image[np.newaxis, :]
|
|
else:
|
|
resized_image = resized_image.transpose((2, 0, 1)) / 255
|
|
resized_image -= 0.5
|
|
resized_image /= 0.5
|
|
padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32)
|
|
padding_im[:, :, 0:resized_w] = resized_image
|
|
return padding_im
|
|
|
|
|
|
def resize_norm_img_chinese(img, image_shape):
|
|
imgC, imgH, imgW = image_shape
|
|
# todo: change to 0 and modified image shape
|
|
max_wh_ratio = imgW * 1.0 / imgH
|
|
h, w = img.shape[0], img.shape[1]
|
|
ratio = w * 1.0 / h
|
|
max_wh_ratio = max(max_wh_ratio, ratio)
|
|
imgW = int(32 * max_wh_ratio)
|
|
if math.ceil(imgH * ratio) > imgW:
|
|
resized_w = imgW
|
|
else:
|
|
resized_w = int(math.ceil(imgH * ratio))
|
|
resized_image = cv2.resize(img, (resized_w, imgH))
|
|
resized_image = resized_image.astype('float32')
|
|
if image_shape[0] == 1:
|
|
resized_image = resized_image / 255
|
|
resized_image = resized_image[np.newaxis, :]
|
|
else:
|
|
resized_image = resized_image.transpose((2, 0, 1)) / 255
|
|
resized_image -= 0.5
|
|
resized_image /= 0.5
|
|
padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32)
|
|
padding_im[:, :, 0:resized_w] = resized_image
|
|
return padding_im
|
|
|
|
|
|
def resize_norm_img_srn(img, image_shape):
|
|
imgC, imgH, imgW = image_shape
|
|
|
|
img_black = np.zeros((imgH, imgW))
|
|
im_hei = img.shape[0]
|
|
im_wid = img.shape[1]
|
|
|
|
if im_wid <= im_hei * 1:
|
|
img_new = cv2.resize(img, (imgH * 1, imgH))
|
|
elif im_wid <= im_hei * 2:
|
|
img_new = cv2.resize(img, (imgH * 2, imgH))
|
|
elif im_wid <= im_hei * 3:
|
|
img_new = cv2.resize(img, (imgH * 3, imgH))
|
|
else:
|
|
img_new = cv2.resize(img, (imgW, imgH))
|
|
|
|
img_np = np.asarray(img_new)
|
|
img_np = cv2.cvtColor(img_np, cv2.COLOR_BGR2GRAY)
|
|
img_black[:, 0:img_np.shape[1]] = img_np
|
|
img_black = img_black[:, :, np.newaxis]
|
|
|
|
row, col, c = img_black.shape
|
|
c = 1
|
|
|
|
return np.reshape(img_black, (c, row, col)).astype(np.float32)
|
|
|
|
|
|
def srn_other_inputs(image_shape, num_heads, max_text_length):
|
|
|
|
imgC, imgH, imgW = image_shape
|
|
feature_dim = int((imgH / 8) * (imgW / 8))
|
|
|
|
encoder_word_pos = np.array(range(0, feature_dim)).reshape(
|
|
(feature_dim, 1)).astype('int64')
|
|
gsrm_word_pos = np.array(range(0, max_text_length)).reshape(
|
|
(max_text_length, 1)).astype('int64')
|
|
|
|
gsrm_attn_bias_data = np.ones((1, max_text_length, max_text_length))
|
|
gsrm_slf_attn_bias1 = np.triu(gsrm_attn_bias_data, 1).reshape(
|
|
[1, max_text_length, max_text_length])
|
|
gsrm_slf_attn_bias1 = np.tile(gsrm_slf_attn_bias1,
|
|
[num_heads, 1, 1]) * [-1e9]
|
|
|
|
gsrm_slf_attn_bias2 = np.tril(gsrm_attn_bias_data, -1).reshape(
|
|
[1, max_text_length, max_text_length])
|
|
gsrm_slf_attn_bias2 = np.tile(gsrm_slf_attn_bias2,
|
|
[num_heads, 1, 1]) * [-1e9]
|
|
|
|
return [
|
|
encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1,
|
|
gsrm_slf_attn_bias2
|
|
]
|
|
|
|
|
|
def flag():
|
|
"""
|
|
flag
|
|
"""
|
|
return 1 if random.random() > 0.5000001 else -1
|
|
|
|
|
|
def cvtColor(img):
|
|
"""
|
|
cvtColor
|
|
"""
|
|
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
|
|
delta = 0.001 * random.random() * flag()
|
|
hsv[:, :, 2] = hsv[:, :, 2] * (1 + delta)
|
|
new_img = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
|
|
return new_img
|
|
|
|
|
|
def blur(img):
|
|
"""
|
|
blur
|
|
"""
|
|
h, w, _ = img.shape
|
|
if h > 10 and w > 10:
|
|
return cv2.GaussianBlur(img, (5, 5), 1)
|
|
else:
|
|
return img
|
|
|
|
|
|
def jitter(img):
|
|
"""
|
|
jitter
|
|
"""
|
|
w, h, _ = img.shape
|
|
if h > 10 and w > 10:
|
|
thres = min(w, h)
|
|
s = int(random.random() * thres * 0.01)
|
|
src_img = img.copy()
|
|
for i in range(s):
|
|
img[i:, i:, :] = src_img[:w - i, :h - i, :]
|
|
return img
|
|
else:
|
|
return img
|
|
|
|
|
|
def add_gasuss_noise(image, mean=0, var=0.1):
|
|
"""
|
|
Gasuss noise
|
|
"""
|
|
|
|
noise = np.random.normal(mean, var**0.5, image.shape)
|
|
out = image + 0.5 * noise
|
|
out = np.clip(out, 0, 255)
|
|
out = np.uint8(out)
|
|
return out
|
|
|
|
|
|
def get_crop(image):
|
|
"""
|
|
random crop
|
|
"""
|
|
h, w, _ = image.shape
|
|
top_min = 1
|
|
top_max = 8
|
|
top_crop = int(random.randint(top_min, top_max))
|
|
top_crop = min(top_crop, h - 1)
|
|
crop_img = image.copy()
|
|
ratio = random.randint(0, 1)
|
|
if ratio:
|
|
crop_img = crop_img[top_crop:h, :, :]
|
|
else:
|
|
crop_img = crop_img[0:h - top_crop, :, :]
|
|
return crop_img
|
|
|
|
|
|
class Config:
|
|
"""
|
|
Config
|
|
"""
|
|
|
|
def __init__(self, use_tia):
|
|
self.anglex = random.random() * 30
|
|
self.angley = random.random() * 15
|
|
self.anglez = random.random() * 10
|
|
self.fov = 42
|
|
self.r = 0
|
|
self.shearx = random.random() * 0.3
|
|
self.sheary = random.random() * 0.05
|
|
self.borderMode = cv2.BORDER_REPLICATE
|
|
self.use_tia = use_tia
|
|
|
|
def make(self, w, h, ang):
|
|
"""
|
|
make
|
|
"""
|
|
self.anglex = random.random() * 5 * flag()
|
|
self.angley = random.random() * 5 * flag()
|
|
self.anglez = -1 * random.random() * int(ang) * flag()
|
|
self.fov = 42
|
|
self.r = 0
|
|
self.shearx = 0
|
|
self.sheary = 0
|
|
self.borderMode = cv2.BORDER_REPLICATE
|
|
self.w = w
|
|
self.h = h
|
|
|
|
self.perspective = self.use_tia
|
|
self.stretch = self.use_tia
|
|
self.distort = self.use_tia
|
|
|
|
self.crop = True
|
|
self.affine = False
|
|
self.reverse = True
|
|
self.noise = True
|
|
self.jitter = True
|
|
self.blur = True
|
|
self.color = True
|
|
|
|
|
|
def rad(x):
|
|
"""
|
|
rad
|
|
"""
|
|
return x * np.pi / 180
|
|
|
|
|
|
def get_warpR(config):
|
|
"""
|
|
get_warpR
|
|
"""
|
|
anglex, angley, anglez, fov, w, h, r = \
|
|
config.anglex, config.angley, config.anglez, config.fov, config.w, config.h, config.r
|
|
if w > 69 and w < 112:
|
|
anglex = anglex * 1.5
|
|
|
|
z = np.sqrt(w**2 + h**2) / 2 / np.tan(rad(fov / 2))
|
|
# Homogeneous coordinate transformation matrix
|
|
rx = np.array([[1, 0, 0, 0],
|
|
[0, np.cos(rad(anglex)), -np.sin(rad(anglex)), 0], [
|
|
0,
|
|
-np.sin(rad(anglex)),
|
|
np.cos(rad(anglex)),
|
|
0,
|
|
], [0, 0, 0, 1]], np.float32)
|
|
ry = np.array([[np.cos(rad(angley)), 0, np.sin(rad(angley)), 0],
|
|
[0, 1, 0, 0], [
|
|
-np.sin(rad(angley)),
|
|
0,
|
|
np.cos(rad(angley)),
|
|
0,
|
|
], [0, 0, 0, 1]], np.float32)
|
|
rz = np.array([[np.cos(rad(anglez)), np.sin(rad(anglez)), 0, 0],
|
|
[-np.sin(rad(anglez)), np.cos(rad(anglez)), 0, 0],
|
|
[0, 0, 1, 0], [0, 0, 0, 1]], np.float32)
|
|
r = rx.dot(ry).dot(rz)
|
|
# generate 4 points
|
|
pcenter = np.array([h / 2, w / 2, 0, 0], np.float32)
|
|
p1 = np.array([0, 0, 0, 0], np.float32) - pcenter
|
|
p2 = np.array([w, 0, 0, 0], np.float32) - pcenter
|
|
p3 = np.array([0, h, 0, 0], np.float32) - pcenter
|
|
p4 = np.array([w, h, 0, 0], np.float32) - pcenter
|
|
dst1 = r.dot(p1)
|
|
dst2 = r.dot(p2)
|
|
dst3 = r.dot(p3)
|
|
dst4 = r.dot(p4)
|
|
list_dst = np.array([dst1, dst2, dst3, dst4])
|
|
org = np.array([[0, 0], [w, 0], [0, h], [w, h]], np.float32)
|
|
dst = np.zeros((4, 2), np.float32)
|
|
# Project onto the image plane
|
|
dst[:, 0] = list_dst[:, 0] * z / (z - list_dst[:, 2]) + pcenter[0]
|
|
dst[:, 1] = list_dst[:, 1] * z / (z - list_dst[:, 2]) + pcenter[1]
|
|
|
|
warpR = cv2.getPerspectiveTransform(org, dst)
|
|
|
|
dst1, dst2, dst3, dst4 = dst
|
|
r1 = int(min(dst1[1], dst2[1]))
|
|
r2 = int(max(dst3[1], dst4[1]))
|
|
c1 = int(min(dst1[0], dst3[0]))
|
|
c2 = int(max(dst2[0], dst4[0]))
|
|
|
|
try:
|
|
ratio = min(1.0 * h / (r2 - r1), 1.0 * w / (c2 - c1))
|
|
|
|
dx = -c1
|
|
dy = -r1
|
|
T1 = np.float32([[1., 0, dx], [0, 1., dy], [0, 0, 1.0 / ratio]])
|
|
ret = T1.dot(warpR)
|
|
except:
|
|
ratio = 1.0
|
|
T1 = np.float32([[1., 0, 0], [0, 1., 0], [0, 0, 1.]])
|
|
ret = T1
|
|
return ret, (-r1, -c1), ratio, dst
|
|
|
|
|
|
def get_warpAffine(config):
|
|
"""
|
|
get_warpAffine
|
|
"""
|
|
anglez = config.anglez
|
|
rz = np.array([[np.cos(rad(anglez)), np.sin(rad(anglez)), 0],
|
|
[-np.sin(rad(anglez)), np.cos(rad(anglez)), 0]], np.float32)
|
|
return rz
|
|
|
|
|
|
def warp(img, ang, use_tia=True, prob=0.4):
|
|
"""
|
|
warp
|
|
"""
|
|
h, w, _ = img.shape
|
|
config = Config(use_tia=use_tia)
|
|
config.make(w, h, ang)
|
|
new_img = img
|
|
|
|
if config.distort:
|
|
img_height, img_width = img.shape[0:2]
|
|
if random.random() <= prob and img_height >= 20 and img_width >= 20:
|
|
new_img = tia_distort(new_img, random.randint(3, 6))
|
|
|
|
if config.stretch:
|
|
img_height, img_width = img.shape[0:2]
|
|
if random.random() <= prob and img_height >= 20 and img_width >= 20:
|
|
new_img = tia_stretch(new_img, random.randint(3, 6))
|
|
|
|
if config.perspective:
|
|
if random.random() <= prob:
|
|
new_img = tia_perspective(new_img)
|
|
|
|
if config.crop:
|
|
img_height, img_width = img.shape[0:2]
|
|
if random.random() <= prob and img_height >= 20 and img_width >= 20:
|
|
new_img = get_crop(new_img)
|
|
|
|
if config.blur:
|
|
if random.random() <= prob:
|
|
new_img = blur(new_img)
|
|
if config.color:
|
|
if random.random() <= prob:
|
|
new_img = cvtColor(new_img)
|
|
if config.jitter:
|
|
new_img = jitter(new_img)
|
|
if config.noise:
|
|
if random.random() <= prob:
|
|
new_img = add_gasuss_noise(new_img)
|
|
if config.reverse:
|
|
if random.random() <= prob:
|
|
new_img = 255 - new_img
|
|
return new_img
|