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predict_system.py
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predict_system.py
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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
#
# 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 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 copy
import numpy as np
import time
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_gif
from ppocr.utils.logging import get_logger
from tools.infer.utility import draw_ocr_box_txt
logger = get_logger()
class TextSystem(object):
def __init__(self, args):
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)
def get_rotate_crop_image(self, img, points):
'''
img_height, img_width = img.shape[0:2]
left = int(np.min(points[:, 0]))
right = int(np.max(points[:, 0]))
top = int(np.min(points[:, 1]))
bottom = int(np.max(points[:, 1]))
img_crop = img[top:bottom, left:right, :].copy()
points[:, 0] = points[:, 0] - left
points[:, 1] = points[:, 1] - top
'''
img_crop_width = int(
max(
np.linalg.norm(points[0] - points[1]),
np.linalg.norm(points[2] - points[3])))
img_crop_height = int(
max(
np.linalg.norm(points[0] - points[3]),
np.linalg.norm(points[1] - points[2])))
pts_std = np.float32([[0, 0], [img_crop_width, 0],
[img_crop_width, img_crop_height],
[0, img_crop_height]])
M = cv2.getPerspectiveTransform(points, pts_std)
dst_img = cv2.warpPerspective(
img,
M, (img_crop_width, img_crop_height),
borderMode=cv2.BORDER_REPLICATE,
flags=cv2.INTER_CUBIC)
dst_img_height, dst_img_width = dst_img.shape[0:2]
if dst_img_height * 1.0 / dst_img_width >= 1.5:
dst_img = np.rot90(dst_img)
return dst_img
def print_draw_crop_rec_res(self, img_crop_list, rec_res):
bbox_num = len(img_crop_list)
for bno in range(bbox_num):
cv2.imwrite("./output/img_crop_%d.jpg" % bno, img_crop_list[bno])
logger.info(bno, rec_res[bno])
def __call__(self, img):
ori_im = img.copy()
dt_boxes, elapse = self.text_detector(img)
logger.info("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])
img_crop = self.get_rotate_crop_image(ori_im, tmp_box)
img_crop_list.append(img_crop)
if self.use_angle_cls:
img_crop_list, angle_list, elapse = self.text_classifier(
img_crop_list)
logger.info("cls num : {}, elapse : {}".format(
len(img_crop_list), elapse))
rec_res, elapse = self.text_recognizer(img_crop_list)
logger.info("rec_res num : {}, elapse : {}".format(
len(rec_res), elapse))
# self.print_draw_crop_rec_res(img_crop_list, rec_res)
filter_boxes, filter_rec_res = [], []
for box, rec_reuslt in zip(dt_boxes, rec_res):
text, score = rec_reuslt
if score >= self.drop_score:
filter_boxes.append(box)
filter_rec_res.append(rec_reuslt)
return filter_boxes, filter_rec_res
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):
if abs(_boxes[i + 1][0][1] - _boxes[i][0][1]) < 10 and \
(_boxes[i + 1][0][0] < _boxes[i][0][0]):
tmp = _boxes[i]
_boxes[i] = _boxes[i + 1]
_boxes[i + 1] = tmp
return _boxes
def main(args):
image_file_list = get_image_file_list(args.image_dir)
text_sys = TextSystem(args)
is_visualize = True
font_path = args.vis_font_path
drop_score = args.drop_score
for image_file in image_file_list:
img, flag = check_and_read_gif(image_file)
if not flag:
img = cv2.imread(image_file)
if img is None:
logger.info("error in loading image:{}".format(image_file))
continue
starttime = time.time()
dt_boxes, rec_res = text_sys(img)
elapse = time.time() - starttime
logger.info("Predict time of %s: %.3fs" % (image_file, elapse))
for text, score in rec_res:
logger.info("{}, {:.3f}".format(text, score))
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)
draw_img_save = "./inference_results/"
if not os.path.exists(draw_img_save):
os.makedirs(draw_img_save)
cv2.imwrite(
os.path.join(draw_img_save, os.path.basename(image_file)),
draw_img[:, :, ::-1])
logger.info("The visualized image saved in {}".format(
os.path.join(draw_img_save, os.path.basename(image_file))))
if __name__ == "__main__":
main(utility.parse_args())