mirror of
https://github.com/PaddlePaddle/PaddleOCR.git
synced 2025-11-04 03:39:22 +00:00
135 lines
4.7 KiB
Python
Executable File
135 lines
4.7 KiB
Python
Executable File
# 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.
|
|
|
|
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
|
|
import numpy as np
|
|
|
|
import os
|
|
import sys
|
|
|
|
__dir__ = os.path.dirname(os.path.abspath(__file__))
|
|
sys.path.append(__dir__)
|
|
sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
|
|
|
|
os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
|
|
|
|
import cv2
|
|
import json
|
|
import paddle
|
|
|
|
from ppocr.data import create_operators, transform
|
|
from ppocr.modeling.architectures import build_model
|
|
from ppocr.postprocess import build_post_process
|
|
from ppocr.utils.save_load import load_model
|
|
from ppocr.utils.utility import get_image_file_list
|
|
import tools.program as program
|
|
|
|
|
|
def draw_det_res(dt_boxes, config, img, img_name, save_path):
|
|
if len(dt_boxes) > 0:
|
|
import cv2
|
|
src_im = img
|
|
for box in dt_boxes:
|
|
box = box.astype(np.int32).reshape((-1, 1, 2))
|
|
cv2.polylines(src_im, [box], True, color=(255, 255, 0), thickness=2)
|
|
if not os.path.exists(save_path):
|
|
os.makedirs(save_path)
|
|
save_path = os.path.join(save_path, os.path.basename(img_name))
|
|
cv2.imwrite(save_path, src_im)
|
|
logger.info("The detected Image saved in {}".format(save_path))
|
|
|
|
|
|
@paddle.no_grad()
|
|
def main():
|
|
global_config = config['Global']
|
|
|
|
# build model
|
|
model = build_model(config['Architecture'])
|
|
|
|
load_model(config, model)
|
|
# build post process
|
|
post_process_class = build_post_process(config['PostProcess'])
|
|
|
|
# create data ops
|
|
transforms = []
|
|
for op in config['Eval']['dataset']['transforms']:
|
|
op_name = list(op)[0]
|
|
if 'Label' in op_name:
|
|
continue
|
|
elif op_name == 'KeepKeys':
|
|
op[op_name]['keep_keys'] = ['image', 'shape']
|
|
transforms.append(op)
|
|
|
|
ops = create_operators(transforms, global_config)
|
|
|
|
save_res_path = config['Global']['save_res_path']
|
|
if not os.path.exists(os.path.dirname(save_res_path)):
|
|
os.makedirs(os.path.dirname(save_res_path))
|
|
|
|
model.eval()
|
|
with open(save_res_path, "wb") as fout:
|
|
for file in get_image_file_list(config['Global']['infer_img']):
|
|
logger.info("infer_img: {}".format(file))
|
|
with open(file, 'rb') as f:
|
|
img = f.read()
|
|
data = {'image': img}
|
|
batch = transform(data, ops)
|
|
|
|
images = np.expand_dims(batch[0], axis=0)
|
|
shape_list = np.expand_dims(batch[1], axis=0)
|
|
images = paddle.to_tensor(images)
|
|
preds = model(images)
|
|
post_result = post_process_class(preds, shape_list)
|
|
|
|
src_img = cv2.imread(file)
|
|
|
|
dt_boxes_json = []
|
|
# parser boxes if post_result is dict
|
|
if isinstance(post_result, dict):
|
|
det_box_json = {}
|
|
for k in post_result.keys():
|
|
boxes = post_result[k][0]['points']
|
|
dt_boxes_list = []
|
|
for box in boxes:
|
|
tmp_json = {"transcription": ""}
|
|
tmp_json['points'] = box.tolist()
|
|
dt_boxes_list.append(tmp_json)
|
|
det_box_json[k] = dt_boxes_list
|
|
save_det_path = os.path.dirname(config['Global'][
|
|
'save_res_path']) + "/det_results_{}/".format(k)
|
|
draw_det_res(boxes, config, src_img, file, save_det_path)
|
|
else:
|
|
boxes = post_result[0]['points']
|
|
dt_boxes_json = []
|
|
# write result
|
|
for box in boxes:
|
|
tmp_json = {"transcription": ""}
|
|
tmp_json['points'] = box.tolist()
|
|
dt_boxes_json.append(tmp_json)
|
|
save_det_path = os.path.dirname(config['Global'][
|
|
'save_res_path']) + "/det_results/"
|
|
draw_det_res(boxes, config, src_img, file, save_det_path)
|
|
otstr = file + "\t" + json.dumps(dt_boxes_json) + "\n"
|
|
fout.write(otstr.encode())
|
|
|
|
logger.info("success!")
|
|
|
|
|
|
if __name__ == '__main__':
|
|
config, device, logger, vdl_writer = program.preprocess()
|
|
main()
|