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# 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.
"""
This code is refer from:
https://github.com/WenmuZhou/DBNet.pytorch/blob/master/data_loader/modules/random_crop_data.py
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import numpy as np
import cv2
import random
def is_poly_in_rect(poly, x, y, w, h):
poly = np.array(poly)
if poly[:, 0].min() < x or poly[:, 0].max() > x + w:
return False
if poly[:, 1].min() < y or poly[:, 1].max() > y + h:
return False
return True
def is_poly_outside_rect(poly, x, y, w, h):
poly = np.array(poly)
if poly[:, 0].max() < x or poly[:, 0].min() > x + w:
return True
if poly[:, 1].max() < y or poly[:, 1].min() > y + h:
return True
return False
def split_regions(axis):
regions = []
min_axis = 0
for i in range(1, axis.shape[0]):
if axis[i] != axis[i - 1] + 1:
region = axis[min_axis:i]
min_axis = i
regions.append(region)
return regions
def random_select(axis, max_size):
xx = np.random.choice(axis, size=2)
xmin = np.min(xx)
xmax = np.max(xx)
xmin = np.clip(xmin, 0, max_size - 1)
xmax = np.clip(xmax, 0, max_size - 1)
return xmin, xmax
def region_wise_random_select(regions, max_size):
selected_index = list(np.random.choice(len(regions), 2))
selected_values = []
for index in selected_index:
axis = regions[index]
xx = int(np.random.choice(axis, size=1))
selected_values.append(xx)
xmin = min(selected_values)
xmax = max(selected_values)
return xmin, xmax
def crop_area(im, text_polys, min_crop_side_ratio, max_tries):
h, w, _ = im.shape
h_array = np.zeros(h, dtype=np.int32)
w_array = np.zeros(w, dtype=np.int32)
for points in text_polys:
points = np.round(points, decimals=0).astype(np.int32)
minx = np.min(points[:, 0])
maxx = np.max(points[:, 0])
w_array[minx:maxx] = 1
miny = np.min(points[:, 1])
maxy = np.max(points[:, 1])
h_array[miny:maxy] = 1
# ensure the cropped area not across a text
h_axis = np.where(h_array == 0)[0]
w_axis = np.where(w_array == 0)[0]
if len(h_axis) == 0 or len(w_axis) == 0:
return 0, 0, w, h
h_regions = split_regions(h_axis)
w_regions = split_regions(w_axis)
for i in range(max_tries):
if len(w_regions) > 1:
xmin, xmax = region_wise_random_select(w_regions, w)
else:
xmin, xmax = random_select(w_axis, w)
if len(h_regions) > 1:
ymin, ymax = region_wise_random_select(h_regions, h)
else:
ymin, ymax = random_select(h_axis, h)
if xmax - xmin < min_crop_side_ratio * w or ymax - ymin < min_crop_side_ratio * h:
# area too small
continue
num_poly_in_rect = 0
for poly in text_polys:
if not is_poly_outside_rect(poly, xmin, ymin, xmax - xmin,
ymax - ymin):
num_poly_in_rect += 1
break
if num_poly_in_rect > 0:
return xmin, ymin, xmax - xmin, ymax - ymin
return 0, 0, w, h
class EastRandomCropData(object):
def __init__(self,
size=(640, 640),
max_tries=10,
min_crop_side_ratio=0.1,
keep_ratio=True,
**kwargs):
self.size = size
self.max_tries = max_tries
self.min_crop_side_ratio = min_crop_side_ratio
self.keep_ratio = keep_ratio
def __call__(self, data):
img = data['image']
text_polys = data['polys']
ignore_tags = data['ignore_tags']
texts = data['texts']
all_care_polys = [
text_polys[i] for i, tag in enumerate(ignore_tags) if not tag
]
# 计算crop区域
crop_x, crop_y, crop_w, crop_h = crop_area(
img, all_care_polys, self.min_crop_side_ratio, self.max_tries)
# crop 图片 保持比例填充
scale_w = self.size[0] / crop_w
scale_h = self.size[1] / crop_h
scale = min(scale_w, scale_h)
h = int(crop_h * scale)
w = int(crop_w * scale)
if self.keep_ratio:
padimg = np.zeros((self.size[1], self.size[0], img.shape[2]),
img.dtype)
padimg[:h, :w] = cv2.resize(
img[crop_y:crop_y + crop_h, crop_x:crop_x + crop_w], (w, h))
img = padimg
else:
img = cv2.resize(
img[crop_y:crop_y + crop_h, crop_x:crop_x + crop_w],
tuple(self.size))
# crop 文本框
text_polys_crop = []
ignore_tags_crop = []
texts_crop = []
for poly, text, tag in zip(text_polys, texts, ignore_tags):
poly = ((poly - (crop_x, crop_y)) * scale).tolist()
if not is_poly_outside_rect(poly, 0, 0, w, h):
text_polys_crop.append(poly)
ignore_tags_crop.append(tag)
texts_crop.append(text)
data['image'] = img
data['polys'] = np.array(text_polys_crop)
data['ignore_tags'] = ignore_tags_crop
data['texts'] = texts_crop
return data
class RandomCropImgMask(object):
def __init__(self, size, main_key, crop_keys, p=3 / 8, **kwargs):
self.size = size
self.main_key = main_key
self.crop_keys = crop_keys
self.p = p
def __call__(self, data):
image = data['image']
h, w = image.shape[0:2]
th, tw = self.size
if w == tw and h == th:
return data
mask = data[self.main_key]
if np.max(mask) > 0 and random.random() > self.p:
# make sure to crop the text region
tl = np.min(np.where(mask > 0), axis=1) - (th, tw)
tl[tl < 0] = 0
br = np.max(np.where(mask > 0), axis=1) - (th, tw)
br[br < 0] = 0
br[0] = min(br[0], h - th)
br[1] = min(br[1], w - tw)
i = random.randint(tl[0], br[0]) if tl[0] < br[0] else 0
j = random.randint(tl[1], br[1]) if tl[1] < br[1] else 0
else:
i = random.randint(0, h - th) if h - th > 0 else 0
j = random.randint(0, w - tw) if w - tw > 0 else 0
# return i, j, th, tw
for k in data:
if k in self.crop_keys:
if len(data[k].shape) == 3:
if np.argmin(data[k].shape) == 0:
img = data[k][:, i:i + th, j:j + tw]
if img.shape[1] != img.shape[2]:
a = 1
elif np.argmin(data[k].shape) == 2:
img = data[k][i:i + th, j:j + tw, :]
if img.shape[1] != img.shape[0]:
a = 1
else:
img = data[k]
else:
img = data[k][i:i + th, j:j + tw]
if img.shape[0] != img.shape[1]:
a = 1
data[k] = img
return data
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