# 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.
import os
import sys
import subprocess
__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(__dir__)
sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '../..')))
os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
import cv2
import copy
import numpy as np
import json
import time
import logging
from PIL import Image
import tools.infer.utility as utility
import tools.infer.predict_rec as predict_rec
import tools.infer.predict_det as predict_det
import tools.infer.predict_cls as predict_cls
from ppocr.utils.utility import get_image_file_list, check_and_read
from ppocr.utils.logging import get_logger
from tools.infer.utility import draw_ocr_box_txt, get_rotate_crop_image, get_minarea_rect_crop
logger = get_logger()
class TextSystem(object):
def __init__(self, args):
if not args.show_log:
logger.setLevel(logging.INFO)
self.text_detector = predict_det.TextDetector(args)
self.text_recognizer = predict_rec.TextRecognizer(args)
self.use_angle_cls = args.use_angle_cls
self.drop_score = args.drop_score
if self.use_angle_cls:
self.text_classifier = predict_cls.TextClassifier(args)
self.args = args
self.crop_image_res_index = 0
def draw_crop_rec_res(self, output_dir, img_crop_list, rec_res):
os.makedirs(output_dir, exist_ok=True)
bbox_num = len(img_crop_list)
for bno in range(bbox_num):
cv2.imwrite(
os.path.join(output_dir,
f"mg_crop_{bno+self.crop_image_res_index}.jpg"),
img_crop_list[bno])
logger.debug(f"{bno}, {rec_res[bno]}")
self.crop_image_res_index += bbox_num
def __call__(self, img, cls=True):
time_dict = {'det': 0, 'rec': 0, 'csl': 0, 'all': 0}
start = time.time()
ori_im = img.copy()
dt_boxes, elapse = self.text_detector(img)
time_dict['det'] = elapse
logger.debug("dt_boxes num : {}, elapse : {}".format(
len(dt_boxes), elapse))
if dt_boxes is None:
return None, None
img_crop_list = []
dt_boxes = sorted_boxes(dt_boxes)
for bno in range(len(dt_boxes)):
tmp_box = copy.deepcopy(dt_boxes[bno])
if self.args.det_box_type == "quad":
img_crop = get_rotate_crop_image(ori_im, tmp_box)
else:
img_crop = get_minarea_rect_crop(ori_im, tmp_box)
img_crop_list.append(img_crop)
if self.use_angle_cls and cls:
img_crop_list, angle_list, elapse = self.text_classifier(
img_crop_list)
time_dict['cls'] = elapse
logger.debug("cls num : {}, elapse : {}".format(
len(img_crop_list), elapse))
rec_res, elapse = self.text_recognizer(img_crop_list)
time_dict['rec'] = elapse
logger.debug("rec_res num : {}, elapse : {}".format(
len(rec_res), elapse))
if self.args.save_crop_res:
self.draw_crop_rec_res(self.args.crop_res_save_dir, img_crop_list,
rec_res)
filter_boxes, filter_rec_res = [], []
for box, rec_result in zip(dt_boxes, rec_res):
text, score = rec_result
if score >= self.drop_score:
filter_boxes.append(box)
filter_rec_res.append(rec_result)
end = time.time()
time_dict['all'] = end - start
return filter_boxes, filter_rec_res, time_dict
def sorted_boxes(dt_boxes):
"""
Sort text boxes in order from top to bottom, left to right
args:
dt_boxes(array):detected text boxes with shape [4, 2]
return:
sorted boxes(array) with shape [4, 2]
"""
num_boxes = dt_boxes.shape[0]
sorted_boxes = sorted(dt_boxes, key=lambda x: (x[0][1], x[0][0]))
_boxes = list(sorted_boxes)
for i in range(num_boxes - 1):
for j in range(i, 0, -1):
if abs(_boxes[j + 1][0][1] - _boxes[j][0][1]) < 10 and \
(_boxes[j + 1][0][0] < _boxes[j][0][0]):
tmp = _boxes[j]
_boxes[j] = _boxes[j + 1]
_boxes[j + 1] = tmp
else:
break
return _boxes
def main(args):
image_file_list = get_image_file_list(args.image_dir)
image_file_list = image_file_list[args.process_id::args.total_process_num]
text_sys = TextSystem(args)
is_visualize = True
font_path = args.vis_font_path
drop_score = args.drop_score
draw_img_save_dir = args.draw_img_save_dir
os.makedirs(draw_img_save_dir, exist_ok=True)
save_results = []
logger.info(
"In PP-OCRv3, rec_image_shape parameter defaults to '3, 48, 320', "
"if you are using recognition model with PP-OCRv2 or an older version, please set --rec_image_shape='3,32,320"
)
# warm up 10 times
if args.warmup:
img = np.random.uniform(0, 255, [640, 640, 3]).astype(np.uint8)
for i in range(10):
res = text_sys(img)
total_time = 0
cpu_mem, gpu_mem, gpu_util = 0, 0, 0
_st = time.time()
count = 0
for idx, image_file in enumerate(image_file_list):
img, flag_gif, flag_pdf = check_and_read(image_file)
if not flag_gif and not flag_pdf:
img = cv2.imread(image_file)
if not flag_pdf:
if img is None:
logger.debug("error in loading image:{}".format(image_file))
continue
imgs = [img]
else:
page_num = args.page_num
if page_num > len(img) or page_num == 0:
page_num = len(img)
imgs = img[:page_num]
for index, img in enumerate(imgs):
starttime = time.time()
dt_boxes, rec_res, time_dict = text_sys(img)
elapse = time.time() - starttime
total_time += elapse
if len(imgs) > 1:
logger.debug(
str(idx) + '_' + str(index) + " Predict time of %s: %.3fs"
% (image_file, elapse))
else:
logger.debug(
str(idx) + " Predict time of %s: %.3fs" % (image_file,
elapse))
for text, score in rec_res:
logger.debug("{}, {:.3f}".format(text, score))
res = [{
"transcription": rec_res[i][0],
"points": np.array(dt_boxes[i]).astype(np.int32).tolist(),
} for i in range(len(dt_boxes))]
if len(imgs) > 1:
save_pred = os.path.basename(image_file) + '_' + str(
index) + "\t" + json.dumps(
res, ensure_ascii=False) + "\n"
else:
save_pred = os.path.basename(image_file) + "\t" + json.dumps(
res, ensure_ascii=False) + "\n"
save_results.append(save_pred)
if is_visualize:
image = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
boxes = dt_boxes
txts = [rec_res[i][0] for i in range(len(rec_res))]
scores = [rec_res[i][1] for i in range(len(rec_res))]
draw_img = draw_ocr_box_txt(
image,
boxes,
txts,
scores,
drop_score=drop_score,
font_path=font_path)
if flag_gif:
save_file = image_file[:-3] + "png"
elif flag_pdf:
save_file = image_file.replace('.pdf',
'_' + str(index) + '.png')
else:
save_file = image_file
cv2.imwrite(
os.path.join(draw_img_save_dir,
os.path.basename(save_file)),
draw_img[:, :, ::-1])
logger.debug("The visualized image saved in {}".format(
os.path.join(draw_img_save_dir, os.path.basename(
save_file))))
logger.info("The predict total time is {}".format(time.time() - _st))
if args.benchmark:
text_sys.text_detector.autolog.report()
text_sys.text_recognizer.autolog.report()
with open(
os.path.join(draw_img_save_dir, "system_results.txt"),
'w',
encoding='utf-8') as f:
f.writelines(save_results)
if __name__ == "__main__":
args = utility.parse_args()
if args.use_mp:
p_list = []
total_process_num = args.total_process_num
for process_id in range(total_process_num):
cmd = [sys.executable, "-u"] + sys.argv + [
"--process_id={}".format(process_id),
"--use_mp={}".format(False)
]
p = subprocess.Popen(cmd, stdout=sys.stdout, stderr=sys.stdout)
p_list.append(p)
for p in p_list:
p.wait()
else:
main(args)