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# Copyright (c) 2020 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.
# -*- encoding: utf-8 -*-
# @Author: SWHL
# @Contact: liekkaskono@163.com
import argparse
import time
import cv2
from pathlib import Path
import numpy as np
try:
from .utils import (DBPostProcess, create_operators,
transform, read_yaml, OrtInferSession)
except:
from utils import (DBPostProcess, create_operators,
transform, read_yaml, OrtInferSession)
root_dir = Path(__file__).resolve().parent
class TextDetector():
def __init__(self, config=str(root_dir / 'config.yaml')):
if isinstance(config, str):
config = read_yaml(config)
config['model_path'] = str(root_dir / config['model_path'])
self.preprocess_op = create_operators(config['pre_process'])
self.postprocess_op = DBPostProcess(**config['post_process'])
session_instance = OrtInferSession(config)
self.session = session_instance.session
self.input_name = session_instance.get_input_name()
def __call__(self, img):
if img is None:
raise ValueError('img is None')
ori_im_shape = img.shape[:2]
data = {'image': img}
data = transform(data, self.preprocess_op)
img, shape_list = data
if img is None:
return None, 0
img = np.expand_dims(img, axis=0).astype(np.float32)
shape_list = np.expand_dims(shape_list, axis=0)
starttime = time.time()
preds = self.session.run(None, {self.input_name: img})
post_result = self.postprocess_op(preds[0], shape_list)
dt_boxes = post_result[0]['points']
dt_boxes = self.filter_tag_det_res(dt_boxes, ori_im_shape)
elapse = time.time() - starttime
return dt_boxes, elapse
def order_points_clockwise(self, pts):
"""
reference from:
https://github.com/jrosebr1/imutils/blob/master/imutils/perspective.py
sort the points based on their x-coordinates
"""
xSorted = pts[np.argsort(pts[:, 0]), :]
# grab the left-most and right-most points from the sorted
# x-roodinate points
leftMost = xSorted[:2, :]
rightMost = xSorted[2:, :]
# now, sort the left-most coordinates according to their
# y-coordinates so we can grab the top-left and bottom-left
# points, respectively
leftMost = leftMost[np.argsort(leftMost[:, 1]), :]
(tl, bl) = leftMost
rightMost = rightMost[np.argsort(rightMost[:, 1]), :]
(tr, br) = rightMost
rect = np.array([tl, tr, br, bl], dtype="float32")
return rect
def clip_det_res(self, points, img_height, img_width):
for pno in range(points.shape[0]):
points[pno, 0] = int(min(max(points[pno, 0], 0), img_width - 1))
points[pno, 1] = int(min(max(points[pno, 1], 0), img_height - 1))
return points
def filter_tag_det_res(self, dt_boxes, image_shape):
img_height, img_width = image_shape[:2]
dt_boxes_new = []
for box in dt_boxes:
box = self.order_points_clockwise(box)
box = self.clip_det_res(box, img_height, img_width)
rect_width = int(np.linalg.norm(box[0] - box[1]))
rect_height = int(np.linalg.norm(box[0] - box[3]))
if rect_width <= 3 or rect_height <= 3:
continue
dt_boxes_new.append(box)
dt_boxes = np.array(dt_boxes_new)
return dt_boxes
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--config_path', type=str, default='config.yaml')
parser.add_argument('--image_path', type=str, default=None)
args = parser.parse_args()
config = read_yaml(args.config_path)
text_detector = TextDetector(config)
img = cv2.imread(args.image_path)
dt_boxes, elapse = text_detector(img)
from utils import draw_text_det_res
src_im = draw_text_det_res(dt_boxes, args.image_path)
cv2.imwrite('det_results.jpg', src_im)
print('The det_results.jpg has been saved in the current directory.')
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