import cv2
import time
import math
import os
import numpy as np
import tensorflow as tf
import json
import detect_text_east.lib_east.locality_aware_nms as nms_locality
import detect_text_east.lib_east.lanms as lanms
import detect_text_east.lib_east.model as model
from detect_text_east.lib_east.icdar import restore_rectangle
from config.CONFIG import Config
cfg = Config()
def resize_label(bboxes, gt_height, d_height, bias=0):
bboxes_new = []
scale = gt_height / d_height
for bbox in bboxes:
bbox = [int(b * scale + bias) for b in bbox]
bboxes_new.append(bbox)
return bboxes_new
def save_corners_json(file_path, corners, new=True):
if not new:
f_in = open(file_path, 'r')
components = json.load(f_in)
else:
import json
import cv2
import numpy as np
from os.path import join as pjoin
import os
import time
from random import randint as rint
from utils.util_merge import *
from config.CONFIG import Config
from utils.Element import Element
C = Config()
def reclassify_text_by_ocr(org, compos, texts):
compos_new = []
for i, compo in enumerate(compos):
# broad = draw_bounding_box(org, [compo], show=True)
new_compo = None
text_area = 0
for j, text in enumerate(texts):
# get the intersected area
inter = compo.calc_intersection_area(text)
if inter == 0:
continue
# calculate IoU
ioa = inter / compo.area
iob = inter / text.area
iou = inter / (compo.area + text.area - inter)
# print('ioa:%.3f, iob:%.3f, iou:%.3f' %(ioa, iob, iou))
import cv2
import numpy as np
import tensorflow as tf
from tensorflow.python.platform import gfile
sys.path.append(os.getcwd())
from lib_ctpn.fast_rcnn.config import cfg, cfg_from_file
from lib_ctpn.fast_rcnn.test import _get_blobs
from lib_ctpn.text_connector.detectors import TextDetector
from lib_ctpn.text_connector.text_connect_cfg import Config as TextLineCfg
from lib_ctpn.rpn_msr.proposal_layer_tf import proposal_layer
import lib_ip.ip_preprocessing as pre
from config.CONFIG import Config
UI2CODECfg = Config()
def resize_im(im, scale, max_scale=None):
f = float(scale) / min(im.shape[0], im.shape[1])
if max_scale != None and f * max(im.shape[0], im.shape[1]) > max_scale:
f = float(max_scale) / max(im.shape[0], im.shape[1])
return cv2.resize(im,
None,
None,
fx=f,
fy=f,
interpolation=cv2.INTER_LINEAR), f
def draw_boxes(img, boxes, scale, output_path_label, output_path_img):