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roidbs.py
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# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved
#
# 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.
#
# Based on:
# --------------------------------------------------------
# Detectron
# Copyright (c) 2017-present, Facebook, Inc.
# Licensed under the Apache License, Version 2.0;
# Written by Ross Girshick
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import copy
import logging
import numpy as np
import os
import scipy.sparse
import random
import time
import matplotlib
import cv2
#import segm_utils
from config import cfg
from data_utils import DatasetPath
logger = logging.getLogger(__name__)
class ICDAR2015Dataset(object):
"""A class representing a ICDAR2015 dataset."""
def __init__(self, mode):
print('Creating: {}'.format(cfg.dataset))
self.name = cfg.data_dir
self.mode = mode
data_path = DatasetPath(mode, self.name)
data_dir = data_path.get_data_dir()
file_list = data_path.get_file_list()
self.image_dir = data_dir
self.gt_dir = file_list
def get_roidb(self):
"""Return an roidb corresponding to the txt dataset. Optionally:
- include ground truth boxes in the roidb
"""
image_list = os.listdir(self.image_dir)
image_list.sort()
im_infos = []
count = 0
for image in image_list:
prefix = image[:-4]
if image.split('.')[-1] != 'jpg':
continue
img_name = os.path.join(self.image_dir, image)
gt_name = os.path.join(self.gt_dir, 'gt_' + prefix + '.txt')
easy_boxes = []
hard_boxes = []
boxes = []
gt_obj = open(gt_name, 'r', encoding='UTF-8-sig')
gt_txt = gt_obj.read()
gt_split = gt_txt.split('\n')
img = cv2.imread(img_name)
f = False
for gt_line in gt_split:
gt_ind = gt_line.split(',')
# can get the text information
if len(gt_ind) > 3 and '###' not in gt_ind[8]:
pt1 = (int(gt_ind[0]), int(gt_ind[1]))
pt2 = (int(gt_ind[2]), int(gt_ind[3]))
pt3 = (int(gt_ind[4]), int(gt_ind[5]))
pt4 = (int(gt_ind[6]), int(gt_ind[7]))
edge1 = np.sqrt((pt1[0] - pt2[0]) * (pt1[0] - pt2[0]) + (
pt1[1] - pt2[1]) * (pt1[1] - pt2[1]))
edge2 = np.sqrt((pt2[0] - pt3[0]) * (pt2[0] - pt3[0]) + (
pt2[1] - pt3[1]) * (pt2[1] - pt3[1]))
angle = 0
if edge1 > edge2:
width = edge1
height = edge2
if pt1[0] - pt2[0] != 0:
angle = -np.arctan(
float(pt1[1] - pt2[1]) /
float(pt1[0] - pt2[0])) / np.pi * 180
else:
angle = 90.0
elif edge2 >= edge1:
width = edge2
height = edge1
# print pt2[0], pt3[0]
if pt2[0] - pt3[0] != 0:
angle = -np.arctan(
float(pt2[1] - pt3[1]) /
float(pt2[0] - pt3[0])) / np.pi * 180
else:
angle = 90.0
if angle < -45.0:
angle = angle + 180
x_ctr = float(pt1[0] + pt3[
0]) / 2 # pt1[0] + np.abs(float(pt1[0] - pt3[0])) / 2
y_ctr = float(pt1[1] + pt3[
1]) / 2 # pt1[1] + np.abs(float(pt1[1] - pt3[1])) / 2
if self.mode == 'val':
easy_boxes.append(
list(np.array([pt1, pt2, pt3, pt4]).reshape(8)))
else:
easy_boxes.append([x_ctr, y_ctr, width, height, angle])
# can‘t get the text information
if len(gt_ind) > 3 and '###' in gt_ind[8]:
pt1 = (int(gt_ind[0]), int(gt_ind[1]))
pt2 = (int(gt_ind[2]), int(gt_ind[3]))
pt3 = (int(gt_ind[4]), int(gt_ind[5]))
pt4 = (int(gt_ind[6]), int(gt_ind[7]))
edge1 = np.sqrt((pt1[0] - pt2[0]) * (pt1[0] - pt2[0]) + (
pt1[1] - pt2[1]) * (pt1[1] - pt2[1]))
edge2 = np.sqrt((pt2[0] - pt3[0]) * (pt2[0] - pt3[0]) + (
pt2[1] - pt3[1]) * (pt2[1] - pt3[1]))
angle = 0
if edge1 > edge2:
width = edge1
height = edge2
if pt1[0] - pt2[0] != 0:
angle = -np.arctan(
float(pt1[1] - pt2[1]) /
float(pt1[0] - pt2[0])) / np.pi * 180
else:
angle = 90.0
elif edge2 >= edge1:
width = edge2
height = edge1
if pt2[0] - pt3[0] != 0:
angle = -np.arctan(
float(pt2[1] - pt3[1]) /
float(pt2[0] - pt3[0])) / np.pi * 180
else:
angle = 90.0
if angle < -45.0:
angle = angle + 180
x_ctr = float(pt1[0] + pt3[
0]) / 2 # pt1[0] + np.abs(float(pt1[0] - pt3[0])) / 2
y_ctr = float(pt1[1] + pt3[
1]) / 2 # pt1[1] + np.abs(float(pt1[1] - pt3[1])) / 2
if self.mode == 'val':
hard_boxes.append(
list(np.array([pt1, pt2, pt3, pt4]).reshape(8)))
else:
hard_boxes.append([x_ctr, y_ctr, width, height, angle])
#print(easy_boxes)
if self.mode == 'train':
boxes.extend(easy_boxes)
# hard box only get 1/3 for train
boxes.extend(hard_boxes[0:int(len(hard_boxes) / 3)])
is_difficult = [0] * len(easy_boxes)
is_difficult.extend([1] * int(len(hard_boxes) / 3))
else:
boxes.extend(easy_boxes)
boxes.extend(hard_boxes)
is_difficult = [0] * len(easy_boxes)
is_difficult.extend([1] * int(len(hard_boxes)))
len_of_bboxes = len(boxes)
#is_difficult = [0] * len(easy_boxes)
#is_difficult.extend([1] * int(len(hard_boxes)))
is_difficult = np.array(is_difficult).reshape(
1, len_of_bboxes).astype(np.int32)
if self.mode == 'train':
gt_boxes = np.zeros((len_of_bboxes, 5), dtype=np.int32)
else:
gt_boxes = np.zeros((len_of_bboxes, 8), dtype=np.int32)
gt_classes = np.zeros((len_of_bboxes), dtype=np.int32)
is_crowd = np.zeros((len_of_bboxes), dtype=np.int32)
for idx in range(len(boxes)):
if self.mode == 'train':
gt_boxes[idx, :] = [
boxes[idx][0], boxes[idx][1], boxes[idx][2],
boxes[idx][3], boxes[idx][4]
]
else:
gt_boxes[idx, :] = [
boxes[idx][0], boxes[idx][1], boxes[idx][2],
boxes[idx][3], boxes[idx][4], boxes[idx][5],
boxes[idx][6], boxes[idx][7]
]
gt_classes[idx] = 1
if gt_boxes.shape[0] <= 0:
continue
gt_boxes = gt_boxes.astype(np.float64)
im_info = {
'im_id': count,
'gt_classes': gt_classes,
'image': img_name,
'boxes': gt_boxes,
'height': img.shape[0],
'width': img.shape[1],
'is_crowd': is_crowd,
'is_difficult': is_difficult
}
im_infos.append(im_info)
count += 1
return im_infos
class ICDAR2017Dataset(object):
"""A class representing a ICDAR2017 dataset."""
def __init__(self, mode):
print('Creating: {}'.format(cfg.dataset))
self.name = cfg.data_dir
#print('**************', self.name)
self.mode = mode
data_path = DatasetPath(mode, self.name)
data_dir = data_path.get_data_dir()
#print("&**************", data_dir)
file_list = data_path.get_file_list()
self.image_dir = data_dir
self.gt_dir = file_list
def get_roidb(self):
"""Return an roidb corresponding to the json dataset. Optionally:
- include ground truth boxes in the roidb
"""
image_list = os.listdir(self.image_dir)
image_list.sort()
im_infos = []
count = 0
class_idx = 1
class_name = {}
post_fix = ['jpg', 'bmp', 'png']
if self.mode == 'val':
labels_map = get_labels_maps()
for image in image_list:
prefix = image[:-4]
#print(image)
if image.split('.')[-1] not in post_fix:
continue
img_name = os.path.join(self.image_dir, image)
gt_name = os.path.join(self.gt_dir, 'gt_' + prefix + '.txt')
gt_classes = []
#boxes = []
#hard_boxes = []
boxes = []
gt_obj = open(gt_name, 'r', encoding='UTF-8-sig')
gt_txt = gt_obj.read()
gt_split = gt_txt.split('\n')
img = cv2.imread(img_name)
f = False
for gt_line in gt_split:
gt_ind = gt_line.split(',')
# can get the text information
if len(gt_ind) > 3:
if self.mode == 'val':
gt_classes.append(labels_map[gt_ind[-1]])
else:
if gt_ind[-1] not in class_name:
class_name[gt_ind[-1]] = class_idx
#gt_classes.append(class_idx)
class_idx += 1
gt_classes.append(class_name[gt_ind[-1]])
pt1 = (int(gt_ind[0]), int(gt_ind[1]))
pt2 = (int(gt_ind[2]), int(gt_ind[3]))
pt3 = (int(gt_ind[4]), int(gt_ind[5]))
pt4 = (int(gt_ind[6]), int(gt_ind[7]))
edge1 = np.sqrt((pt1[0] - pt2[0]) * (pt1[0] - pt2[0]) + (
pt1[1] - pt2[1]) * (pt1[1] - pt2[1]))
edge2 = np.sqrt((pt2[0] - pt3[0]) * (pt2[0] - pt3[0]) + (
pt2[1] - pt3[1]) * (pt2[1] - pt3[1]))
angle = 0
if edge1 > edge2:
width = edge1
height = edge2
if pt1[0] - pt2[0] != 0:
angle = -np.arctan(
float(pt1[1] - pt2[1]) /
float(pt1[0] - pt2[0])) / np.pi * 180
else:
angle = 90.0
elif edge2 >= edge1:
width = edge2
height = edge1
# print pt2[0], pt3[0]
if pt2[0] - pt3[0] != 0:
angle = -np.arctan(
float(pt2[1] - pt3[1]) /
float(pt2[0] - pt3[0])) / np.pi * 180
else:
angle = 90.0
if angle < -45.0:
angle = angle + 180
x_ctr = float(pt1[0] + pt3[
0]) / 2 # pt1[0] + np.abs(float(pt1[0] - pt3[0])) / 2
y_ctr = float(pt1[1] + pt3[
1]) / 2 # pt1[1] + np.abs(float(pt1[1] - pt3[1])) / 2
if self.mode == 'val':
boxes.append(
list(np.array([pt1, pt2, pt3, pt4]).reshape(8)))
else:
boxes.append([x_ctr, y_ctr, width, height, angle])
len_of_bboxes = len(boxes)
#print(len_of_bboxes)
is_difficult = np.zeros((len_of_bboxes, 1), dtype=np.int32)
if self.mode == 'train':
gt_boxes = np.zeros((len_of_bboxes, 5), dtype=np.int32)
else:
gt_boxes = np.zeros((len_of_bboxes, 8), dtype=np.int32)
gt_classes = np.array(gt_classes).reshape(len_of_bboxes, 1)
is_crowd = np.zeros((len_of_bboxes), dtype=np.int32)
for idx in range(len(boxes)):
if self.mode == 'train':
gt_boxes[idx, :] = [
boxes[idx][0], boxes[idx][1], boxes[idx][2],
boxes[idx][3], boxes[idx][4]
]
else:
gt_boxes[idx, :] = [
boxes[idx][0], boxes[idx][1], boxes[idx][2],
boxes[idx][3], boxes[idx][4], boxes[idx][5],
boxes[idx][6], boxes[idx][7]
]
#gt_classes[idx] = 1
if gt_boxes.shape[0] <= 0:
continue
gt_boxes = gt_boxes.astype(np.float64)
im_info = {
'im_id': count,
'gt_classes': gt_classes,
'image': img_name,
'boxes': gt_boxes,
'height': img.shape[0],
'width': img.shape[1],
'is_crowd': is_crowd,
'is_difficult': is_difficult
}
im_infos.append(im_info)
count += 1
if self.mode == 'train':
with open(os.path.join(cfg.data_dir, 'label_list'), 'w') as g:
for k in class_name:
g.write(k + "\n")
return im_infos
def get_labels_maps():
labels_map = {}
with open(os.path.join(cfg.data_dir, 'label_list')) as f:
lines = f.readlines()
for idx, line in enumerate(lines):
labels_map[line.strip()] = idx + 1
return labels_map