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db_process.py
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db_process.py
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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
#
# https://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 json
import sys
from ppocr.utils.utility import initial_logger
logger = initial_logger()
from .data_augment import AugmentData
from .random_crop_data import RandomCropData
from .make_shrink_map import MakeShrinkMap
from .make_border_map import MakeBorderMap
class DBProcessTrain(object):
"""
DB pre-process for Train mode
"""
def __init__(self, params):
self.img_set_dir = params['img_set_dir']
self.image_shape = params['image_shape']
def order_points_clockwise(self, pts):
rect = np.zeros((4, 2), dtype="float32")
s = pts.sum(axis=1)
rect[0] = pts[np.argmin(s)]
rect[2] = pts[np.argmax(s)]
diff = np.diff(pts, axis=1)
rect[1] = pts[np.argmin(diff)]
rect[3] = pts[np.argmax(diff)]
return rect
def make_data_dict(self, imgvalue, entry):
boxes = []
texts = []
ignores = []
for rect in entry:
points = rect['points']
transcription = rect['transcription']
try:
box = self.order_points_clockwise(
np.array(points).reshape(-1, 2))
if cv2.contourArea(box) > 0:
boxes.append(box)
texts.append(transcription)
ignores.append(transcription in ['*', '###'])
except:
print('load label failed!')
data = {
'image': imgvalue,
'shape': [imgvalue.shape[0], imgvalue.shape[1]],
'polys': np.array(boxes),
'texts': texts,
'ignore_tags': ignores,
}
return data
def NormalizeImage(self, data):
im = data['image']
img_mean = [0.485, 0.456, 0.406]
img_std = [0.229, 0.224, 0.225]
im = im.astype(np.float32, copy=False)
im = im / 255
im -= img_mean
im /= img_std
channel_swap = (2, 0, 1)
im = im.transpose(channel_swap)
data['image'] = im
return data
def FilterKeys(self, data):
filter_keys = ['polys', 'texts', 'ignore_tags', 'shape']
for key in filter_keys:
if key in data:
del data[key]
return data
def convert_label_infor(self, label_infor):
label_infor = label_infor.decode()
label_infor = label_infor.encode('utf-8').decode('utf-8-sig')
substr = label_infor.strip("\n").split("\t")
img_path = self.img_set_dir + substr[0]
label = json.loads(substr[1])
return img_path, label
def __call__(self, label_infor):
img_path, gt_label = self.convert_label_infor(label_infor)
imgvalue = cv2.imread(img_path)
if imgvalue is None:
logger.info("{} does not exist!".format(img_path))
return None
if len(list(imgvalue.shape)) == 2 or imgvalue.shape[2] == 1:
imgvalue = cv2.cvtColor(imgvalue, cv2.COLOR_GRAY2BGR)
data = self.make_data_dict(imgvalue, gt_label)
data = AugmentData(data)
data = RandomCropData(data, self.image_shape[1:])
data = MakeShrinkMap(data)
data = MakeBorderMap(data)
data = self.NormalizeImage(data)
data = self.FilterKeys(data)
return data['image'], data['shrink_map'], data['shrink_mask'], data[
'threshold_map'], data['threshold_mask']
class DBProcessTest(object):
"""
DB pre-process for Test mode
"""
def __init__(self, params):
super(DBProcessTest, self).__init__()
self.resize_type = 0
if 'test_image_shape' in params:
self.image_shape = params['test_image_shape']
# print(self.image_shape)
self.resize_type = 1
if 'max_side_len' in params:
self.max_side_len = params['max_side_len']
else:
self.max_side_len = 2400
def resize_image_type0(self, im):
"""
resize image to a size multiple of 32 which is required by the network
args:
img(array): array with shape [h, w, c]
return(tuple):
img, (ratio_h, ratio_w)
"""
max_side_len = self.max_side_len
h, w, _ = im.shape
resize_w = w
resize_h = h
# limit the max side
if max(resize_h, resize_w) > max_side_len:
if resize_h > resize_w:
ratio = float(max_side_len) / resize_h
else:
ratio = float(max_side_len) / resize_w
else:
ratio = 1.
resize_h = int(resize_h * ratio)
resize_w = int(resize_w * ratio)
if resize_h % 32 == 0:
resize_h = resize_h
elif resize_h // 32 <= 1:
resize_h = 32
else:
resize_h = (resize_h // 32 - 1) * 32
if resize_w % 32 == 0:
resize_w = resize_w
elif resize_w // 32 <= 1:
resize_w = 32
else:
resize_w = (resize_w // 32 - 1) * 32
try:
if int(resize_w) <= 0 or int(resize_h) <= 0:
return None, (None, None)
im = cv2.resize(im, (int(resize_w), int(resize_h)))
except:
print(im.shape, resize_w, resize_h)
sys.exit(0)
ratio_h = resize_h / float(h)
ratio_w = resize_w / float(w)
return im, (ratio_h, ratio_w)
def resize_image_type1(self, im):
resize_h, resize_w = self.image_shape
ori_h, ori_w = im.shape[:2] # (h, w, c)
im = cv2.resize(im, (int(resize_w), int(resize_h)))
ratio_h = float(resize_h) / ori_h
ratio_w = float(resize_w) / ori_w
return im, (ratio_h, ratio_w)
def normalize(self, im):
img_mean = [0.485, 0.456, 0.406]
img_std = [0.229, 0.224, 0.225]
im = im.astype(np.float32, copy=False)
im = im / 255
im -= img_mean
im /= img_std
channel_swap = (2, 0, 1)
im = im.transpose(channel_swap)
return im
def __call__(self, im):
if self.resize_type == 0:
im, (ratio_h, ratio_w) = self.resize_image_type0(im)
else:
im, (ratio_h, ratio_w) = self.resize_image_type1(im)
im = self.normalize(im)
im = im[np.newaxis, :]
return [im, (ratio_h, ratio_w)]