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run.py
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run.py
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# Copyright (c) 2020 Complicateddd Authors. All Rights Reserved.
import os
import sys
import warnings
warnings.filterwarnings('ignore')
import pandas as pd
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
from help_filter import RotateAntiClockWise90,fine_colone_text,find_box_range
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])
def __call__(self, img):
ori_im = img.copy()
dt_boxes, elapse = self.text_detector(img)
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)
rec_res, elapse = self.text_recognizer(img_crop_list)
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(path_img_test, path_submit):
from hyper_config import hyper_params
args=hyper_params()
image_file_list,sigle_lists = get_image_file_list(path_img_test)
text_sys = TextSystem(args)
# is_visualize = True
font_path = args.vis_font_path
drop_score = args.drop_score
down_result_pred=['' for _ in range(len(sigle_lists))]
for index,image_file in enumerate(image_file_list):
img = cv2.imread(image_file)
if img is None:
continue
height,width=img.shape[0],img.shape[1]
if height>width:
img=RotateAntiClockWise90(img)
height,width=img.shape[0],img.shape[1]
limit_range_val_width=width*0.045
limit_range_val_height=height*0.013
starttime = time.time()
dt_boxes, rec_res = text_sys(img)
if not rec_res:
continue
elapse = time.time() - starttime
xiangmu_name,jieguo_name,danwei_name=find_box_range(dt_boxes,rec_res)
for xiangmu_name_id in range(len(xiangmu_name)):
xiangmumingcheng_colomn_list,xiangmumingcheng_box_colomn_list=fine_colone_text(dt_boxes,xiangmu_name[xiangmu_name_id],rec_res,limit_range=limit_range_val_width)
jieguo_colomn_list,jieguo_box_colomn_list=fine_colone_text(dt_boxes,jieguo_name[xiangmu_name_id],rec_res,limit_range=limit_range_val_width/2)
danwei_colomn_list,danwei_box_colomn_list=fine_colone_text(dt_boxes,danwei_name[xiangmu_name_id],rec_res,limit_range=limit_range_val_width/1.5)
ans=[]
###### if match correctly :
if len(xiangmumingcheng_colomn_list)==len(jieguo_colomn_list)==len(danwei_colomn_list):
for i in range(len(jieguo_colomn_list)):
ele_set=['','','']
ele_set[0]=xiangmumingcheng_colomn_list[i]
ele_set[1]=jieguo_colomn_list[i]
ele_set[2]=danwei_colomn_list[i]
ans.append(ele_set)
else:
for i in range(len(jieguo_colomn_list)):
ele_set=['','','']
ele_set[1]=jieguo_colomn_list[i]
for name_id in range(len(xiangmumingcheng_colomn_list)):
if abs(xiangmumingcheng_box_colomn_list[name_id][3][1]-jieguo_box_colomn_list[i][3][1])<=limit_range_val_height:
ele_set[0]=xiangmumingcheng_colomn_list[name_id].replace('*', '')
xiangmumingcheng_colomn_list.pop(name_id)
xiangmumingcheng_box_colomn_list.pop(name_id)
break
for name_id in range(len(danwei_colomn_list)):
if abs(danwei_box_colomn_list[name_id][3][1]-jieguo_box_colomn_list[i][3][1])<=limit_range_val_height:
ele_set[2]=danwei_colomn_list[name_id]
danwei_colomn_list.pop(name_id)
danwei_box_colomn_list.pop(name_id)
break
ans.append(ele_set)
ans=[''.join(ele) for ele in ans]
down_result=''
for ele in ans:
down_result+=ele
down_result_pred[index]+=down_result
submit = pd.DataFrame({'id': sigle_lists, 'content': down_result_pred})
submit.to_csv(path_submit, index=None, encoding='utf-8')
if __name__ == "__main__":
path_img_test = sys.argv[1]
path_submit = sys.argv[2]
main(path_img_test, path_submit)