174 lines
6.2 KiB
Python
Executable File
174 lines
6.2 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.
|
|
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__, '../..')))
|
|
|
|
import tools.infer.utility as utility
|
|
from ppocr.utils.utility import initial_logger
|
|
logger = initial_logger()
|
|
import cv2
|
|
import tools.infer.predict_det as predict_det
|
|
import tools.infer.predict_rec as predict_rec
|
|
import copy
|
|
import numpy as np
|
|
import math
|
|
import time
|
|
from ppocr.utils.utility import get_image_file_list, check_and_read_gif
|
|
from PIL import Image
|
|
from tools.infer.utility import draw_ocr
|
|
from tools.infer.utility import draw_ocr_box_txt
|
|
|
|
|
|
class TextSystem(object):
|
|
def __init__(self, args):
|
|
self.text_detector = predict_det.TextDetector(args)
|
|
self.text_recognizer = predict_rec.TextRecognizer(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])
|
|
print(bno, rec_res[bno])
|
|
|
|
def __call__(self, img):
|
|
ori_im = img.copy()
|
|
dt_boxes, elapse = self.text_detector(img)
|
|
print("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)
|
|
rec_res, elapse = self.text_recognizer(img_crop_list)
|
|
print("rec_res num : {}, elapse : {}".format(len(rec_res), elapse))
|
|
# self.print_draw_crop_rec_res(img_crop_list, rec_res)
|
|
return dt_boxes, 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
|
|
tackle_img_num = 0
|
|
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()
|
|
tackle_img_num += 1
|
|
if not args.use_gpu and args.enable_mkldnn and tackle_img_num % 30 == 0:
|
|
text_sys = TextSystem(args)
|
|
dt_boxes, rec_res = text_sys(img)
|
|
elapse = time.time() - starttime
|
|
print("Predict time of %s: %.3fs" % (image_file, elapse))
|
|
|
|
drop_score = 0.5
|
|
dt_num = len(dt_boxes)
|
|
for dno in range(dt_num):
|
|
text, score = rec_res[dno]
|
|
if score >= drop_score:
|
|
text_str = "%s, %.3f" % (text, score)
|
|
print(text_str)
|
|
|
|
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(
|
|
image,
|
|
boxes,
|
|
txts,
|
|
scores,
|
|
draw_txt=True,
|
|
drop_score=drop_score)
|
|
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])
|
|
print("The visualized image saved in {}".format(
|
|
os.path.join(draw_img_save, os.path.basename(image_file))))
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main(utility.parse_args())
|