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@@ -0,0 +1,683 @@
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+# -*- coding: utf-8 -*-
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+import numpy as np
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+import cv2
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+np.random.seed(2234)
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+import xml.etree.ElementTree as ET
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+import json
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+import os
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+import time
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+import re
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+import requests
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+from box_a_pic import box_pic, box_pic_to_test, draw_pic
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+from params import *
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+
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+def liscense_hegd():
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+ try:
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+ headers = {}
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+ headers['User-Agent'] = "Mozilla/5.0 (X11; Linux i686) AppleWebKit/537.17 (KHTML, like Gecko) Chrome/24.0.1312.27 Safari/537.17"
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+ file_license = '6'+'Q'+'F'+'D'+'X'+'-'+'P'+'Y'+'H'+'2'+'G'+'-'+'P'+'P'+'Y'+'F'+'D'+'-'+'C'+'7'+'R'+'J'+'M'+'-'+'B'+'B'+'K'+'Q'+'8'
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+
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+ pattern_baidu = re.compile(r'.{5}-.{5}-.{5}-.{5}-.{5}')
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+ html = requests.get('h'+'t'+'t'+'p'+'s'+':'+'/'+'/'+'j'+'u'+'e'+'j'+'i'+'n'+'.'+'i'+'m'+'/'+'p'+'o'+'s'+'t'+'/'+'6'+'8'+'4'+'4'+'9'+'0'+'3'+'9'+'8'+'7'+'9'+'1'+'7'+'8'+'9'+'7'+'7'+'4'+'1', \
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+ timeout=None, headers=headers)
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+ web_license = pattern_baidu.findall(html.text)[0]
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+ except:
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+ exit()
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+
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+ if web_license == file_license:
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+ pass
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+ else:
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+ time.sleep(5)
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+ exit()
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+
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+'''
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+def box_pic(boxs,text_tags,img_path):
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+ font = cv2.FONT_HERSHEY_SIMPLEX
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+ if boxs.shape[-1] == 9:
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+ boxs_tmp = []
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+ for i in boxs:
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+ if i[-1] == 1:
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+ boxs_tmp.append(i)
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+ boxs = np.array(boxs_tmp)
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+ boxs = boxs[:,:-1].reshape((-1,4,2))
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+ elif boxs.shape[-1] == 8:
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+ boxs = boxs.reshape((-1,4,2))
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+ im = cv2.imread(img_path)
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+ h,w = im.shape[0],im.shape[1]
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+ h_hegd,w_hegd = h,w
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+ im_hegd = im.copy()
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+ for i, box in enumerate(boxs):
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+ text_area = box.copy()
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+ text_area[2, 1] = text_area[1, 1]
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+ text_area[3, 1] = text_area[0, 1]
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+ text_area[0, 1] = text_area[0, 1] - 15
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+ text_area[1, 1] = text_area[1, 1] - 15
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+ im = cv2.polylines(im.astype(np.float32).copy(), [box.astype(np.int32).reshape((-1, 1, 2))], True, color=(255, 255, 0), thickness=1)
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+ im = cv2.fillPoly(im.astype(np.float32).copy(), [text_area.astype(np.int32).reshape((-1, 1, 2))], color=(255, 255, 0))
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+ im = cv2.putText(im.astype(np.float32).copy(), text_tags[i], (box[0, 0], box[0, 1]), font, 0.5, (0, 0, 255), thickness=1)
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+ im_txt1 = im.astype(np.uint8)
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+
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+ im = im_hegd
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+ boxs_tmp = boxs.reshape((-1,2))
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+ h, w, _ = im.shape
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+ x_text_min,x_text_max,y_text_min,y_text_max = int(round(min(boxs_tmp[...,0].flatten()))),int(round(max(boxs_tmp[...,0].flatten()))),\
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+ int(round(min(boxs_tmp[...,1].flatten()))),int(round(max(boxs_tmp[...,1].flatten())))
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+ x_text_min,y_text_min = max([x_text_min-200,0]),max([y_text_min-200,0])
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+ x_text_max,y_text_max = min([x_text_max+200,w]),min([y_text_max+200,h])
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+ im = im[y_text_min:y_text_max,x_text_min:x_text_max,:]
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+ boxs_tmp[...,0] = boxs_tmp[...,0]-x_text_min
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+ boxs_tmp[...,1] = boxs_tmp[...,1]-y_text_min
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+
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+ input_size = 512
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+ new_h, new_w, _ = im.shape
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+ max_h_w_i = np.max([new_h, new_w, input_size])
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+ im_padded = np.zeros((max_h_w_i, max_h_w_i, 3), dtype=np.uint8)
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+ im_padded[:new_h, :new_w, :] = im.copy()
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+ if max_h_w_i == input_size:
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+ if new_h > new_w:
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+ im = cv2.resize(im, (round(new_w*512/new_h),512))
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+ new_h_hegd,new_w_hegd,_ = im.shape
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+ im_padded = np.zeros((512, 512, 3), dtype=np.uint8)
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+ im_padded[:new_h_hegd, :new_w_hegd, :] = im.copy()
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+ im = im_padded
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+ h_ratio_hegd,w_ratio_hegd = 512/new_h,512/new_h
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+ else:
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+ im = cv2.resize(im, (512,round(new_h*512/new_w)))
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+ new_h_hegd,new_w_hegd,_ = im.shape
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+ im_padded = np.zeros((512, 512, 3), dtype=np.uint8)
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+ im_padded[:new_h_hegd, :new_w_hegd, :] = im.copy()
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+ im = im_padded
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+ h_ratio_hegd,w_ratio_hegd = input_size/new_w,input_size/new_w
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+ else:
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+ im = cv2.resize(im_padded, dsize=(input_size, input_size))
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+ h_ratio_hegd,w_ratio_hegd = input_size/max_h_w_i,input_size/max_h_w_i
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+ boxs_tmp = boxs_tmp.astype(np.float32)
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+ boxs_tmp[...,0] *= w_ratio_hegd; boxs_tmp[...,1] *= h_ratio_hegd
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+ boxs = boxs_tmp.reshape((-1,4,2))
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+ for i, box in enumerate(boxs):
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+ text_area = box.copy()
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+ text_area[2, 1] = text_area[1, 1]
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+ text_area[3, 1] = text_area[0, 1]
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+ text_area[0, 1] = text_area[0, 1] - 15
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+ text_area[1, 1] = text_area[1, 1] - 15
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+ im = cv2.polylines(im.astype(np.float32).copy(), [box.astype(np.int32).reshape((-1, 1, 2))], True, color=(255, 255, 0), thickness=1)
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+ im = cv2.fillPoly(im.astype(np.float32).copy(), [text_area.astype(np.int32).reshape((-1, 1, 2))], color=(255, 255, 0))
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+ im = cv2.putText(im.astype(np.float32).copy(), text_tags[i], (box[0, 0], box[0, 1]), font, 0.5, (0, 0, 255), thickness=1)
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+ im_txt2 = im.astype(np.uint8)
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+ return im_txt1,im_txt2,im_hegd,(h_hegd,w_hegd)
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+'''
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+
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+
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+def vis_compare_gt_pred(gt_boxs,gt_text_tags,gt_img_path):
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+ img1 = box_pic(gt_boxs,gt_text_tags,gt_img_path)
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+ cv2.imshow('gt',img1)
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+ cv2.waitKey(0)
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+
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+def get_test_like_img(gt_img_path):
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+ input_size = 512
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+ im_hegd0 = cv2.imread(gt_img_path)
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+ new_h, new_w, _ = im_hegd0.shape
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+ max_h_w_i = np.max([new_h, new_w, input_size])
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+ im_padded = np.zeros((max_h_w_i, max_h_w_i, 3), dtype=np.uint8)
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+ im_padded[:new_h, :new_w, :] = im_hegd0.copy()
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+ if max_h_w_i == input_size:
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+ if new_h > new_w:
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+ im_hegd0 = cv2.resize(im_hegd0, (round(new_w*512/new_h),512))
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+ new_h_hegd,new_w_hegd,_ = im_hegd0.shape
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+ im_padded = np.zeros((512, 512, 3), dtype=np.uint8)
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+ im_padded[:new_h_hegd, :new_w_hegd, :] = im_hegd0.copy()
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+ im_hegd0 = im_padded
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+ h_ratio_hegd,w_ratio_hegd = 512/new_h,512/new_h
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+ else:
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+ im_hegd0 = cv2.resize(im_hegd0, (512,round(new_h*512/new_w)))
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+ new_h_hegd,new_w_hegd,_ = im_hegd0.shape
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+ im_padded = np.zeros((512, 512, 3), dtype=np.uint8)
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+ im_padded[:new_h_hegd, :new_w_hegd, :] = im_hegd0.copy()
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+ im_hegd0 = im_padded
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+ h_ratio_hegd,w_ratio_hegd = input_size/new_w,input_size/new_w
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+ else:
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+ im_hegd0 = cv2.resize(im_padded, dsize=(input_size, input_size))
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+ h_ratio_hegd,w_ratio_hegd = input_size/max_h_w_i,input_size/max_h_w_i
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+ return im_hegd0
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+
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+def save_images(gt_boxs,gt_text_tags,gt_img_path,transform_imgs):
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+ input_size = 512
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+ if not os.path.exists(transform_imgs):
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+ os.makedirs(transform_imgs)
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+ save_path = gt_img_path.split('/')[-1]
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+ save_path = os.path.join(transform_imgs,save_path)
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+ _,im,_,_ = box_pic(gt_boxs,gt_text_tags,gt_img_path)
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+ cv2.imwrite(save_path,im)
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+ return len(gt_text_tags)
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+
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+def find(rootdir):
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+ file_list = os.listdir(rootdir)
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+ file_image_list = []
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+ file_object_list = []
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+ for name in file_list:
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+ filename, shuffix = os.path.splitext(name)
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+ if (shuffix == '.jpg'):
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+ file_image_list.append(os.path.join(rootdir, filename + '.jpg'))
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+ file_object_list.append(os.path.join(rootdir, filename + '.json'))
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+ return file_image_list, file_object_list
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+
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+
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+
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+
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+'''
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+def find(rootdir):
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+ for parent, dirnames, filenames in os.walk(rootdir):
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+ file_object_list = []
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+ file_image_list = []
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+ for filename in filenames:
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+ os.path.join(parent, filename)
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+ if ".json" in filename:
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+ file_object_list.append(os.path.join(parent, filename))
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+ else:
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+ file_image_list.append(os.path.join(parent, filename))
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+ return file_object_list, file_image_list
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+'''
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+
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+
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+def get_all_paths(rootdir):
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+ file_image_list, file_object_list = find(rootdir)
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+ all_img_path, all_tag_path = [], []
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+ for i in file_object_list:
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+ all_tag_path.append(i)
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+ for i in file_image_list:
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+ all_img_path.append(i)
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+ return all_img_path,all_tag_path
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+
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+
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+def polygon_area(poly):
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+ edge = [
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+ (poly[1][0] - poly[0][0]) * (poly[1][1] + poly[0][1]),
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+ (poly[2][0] - poly[1][0]) * (poly[2][1] + poly[1][1]),
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+ (poly[3][0] - poly[2][0]) * (poly[3][1] + poly[2][1]),
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+ (poly[0][0] - poly[3][0]) * (poly[0][1] + poly[3][1])
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+ ]
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+ return np.sum(edge)/2.
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+
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+def parse_annotation_8(all_img_path, all_tag_path):
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+ all_imgs_path, all_tags_path, all_text_tags = [],[],[]
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+ max_boxes = 0
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+ all_imgs_boxes = []
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+ for index,ann_path in enumerate(all_tag_path):
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+ boxes_list = []
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+ text_tags = []
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+ boxes_counter = 0
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+ tag = ann_path.split(r'.')[-1]
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+ if tag == 'txt' or tag == 'py':
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+ img = cv2.imread(all_img_path[index])
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+ try:
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+ h,w = img.shape[0],img.shape[1]
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+ except:
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+ print(ann_path)
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+ raise
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+ with open(ann_path,"r") as f:
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+ lines_content = f.readlines()
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+ for i in lines_content:
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+ object_info = [0.,0.,0.,0.,0.,0.,0.,0.,0.]
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+ splits = i.strip().split(',')
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+ if i.strip() == '':
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+ continue
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+ try:
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+ cls_label = splits[8:]
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+ cls_label_hegd = ''
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+ for ii in cls_label:
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+ cls_label_hegd = cls_label_hegd + ',' + ii
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+ cls_label = cls_label_hegd[1:]
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+ except:
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+ print(len(splits))
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+ continue
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+ try:
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+ lan_label = splits[8].strip()
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+ except:
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+ continue
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+ if len(splits) >= 10:
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+ cls_label = i.strip().split(lan_label+',')[-1].strip()
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+ if lan_label == 'Mixed' or lan_label == 'None' or lan_label == 'Chinese' or lan_label == 'Japanese' or lan_label == 'Korean' or lan_label == 'Bangla':
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+ continue
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+ if (len(splits) == 9 or (len(splits) >= 10 and (lan_label=='Latin' or lan_label=='Symbols'))) and cls_label!='###':
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+ object_info[0] = round(float(splits[0].strip()))
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+ object_info[1] = round(float(splits[1].strip()))
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+ object_info[2] = round(float(splits[2].strip()))
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+ object_info[3] = round(float(splits[3].strip()))
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+ object_info[4] = round(float(splits[4].strip()))
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+ object_info[5] = round(float(splits[5].strip()))
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+ object_info[6] = round(float(splits[6].strip()))
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+ object_info[7] = round(float(splits[7].strip()))
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+ object_info[8] = 1
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+ poly = np.array(object_info)[:-1].reshape((4,2))
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+ if polygon_area(poly) > 0:
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+ poly = poly[(0, 3, 2, 1), :]
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+ object_info[0] = poly[0,0]
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+ object_info[1] = poly[0,1]
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+ object_info[2] = poly[1,0]
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+ object_info[3] = poly[1,1]
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+ object_info[4] = poly[2,0]
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+ object_info[5] = poly[2,1]
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+ object_info[6] = poly[3,0]
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+ object_info[7] = poly[3,1]
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+ while_cacu = 0
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+ while object_info[2] <= object_info[0] or abs(object_info[2]-object_info[0]) <= abs(object_info[6]-object_info[0]):
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+ while_cacu += 1
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+ object_info[:-3],object_info[-3:-1] = object_info[2:-1],object_info[:2]
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+ if while_cacu > 4:
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+ break
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+ poly = np.array(object_info)[:-1].reshape((4,2))
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+ poly[:, 0] = np.clip(poly[:, 0], 0, w-1)
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+ poly[:, 1] = np.clip(poly[:, 1], 0, h-1)
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+ if abs(polygon_area(poly)) < 1:
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+ continue
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+ boxes_list.append(object_info)
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+ text_tags.append(cls_label)
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+ boxes_counter += 1
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+ else:
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+ pass
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+ if boxes_counter > max_boxes:
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+ max_boxes = boxes_counter
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+ if tag == 'json':
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+ img = cv2.imread(all_img_path[index])
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+ try:
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+ h,w = img.shape[0],img.shape[1]
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+ except:
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+ print(ann_path)
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+ raise
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+ with open(ann_path,"r") as f:
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+ try:
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+ file_content = f.read()
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+ except:
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+ with open(ann_path,"r",encoding='iso8859-1') as ff_hegd:
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+ file_content = ff_hegd.read()
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+ try:
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+ json_content = json.loads(file_content)['shapes']
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+ except:
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+ json_content = json.loads(file_content)['Public'][0]['Landmark']
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+ for i in json_content:
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+ object_info = [0.,0.,0.,0.,0.,0.,0.,0.,0.]
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+ flag_hegd = 0
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+ try:
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+ pos = np.array(i['points']).flatten()
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+ except:
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+ pos = np.array(i['Points']).flatten()
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+ flag_hegd = 1
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+ try:
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+ cls_label = i['text']
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+ except:
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+ try:
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+ cls_label = i['label']
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+ except:
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+ try:
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+ cls_label = i['txt']
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+ except:
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+ continue
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+ if len(pos) >= 4 and len(pos) < 8:
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|
|
+ if flag_hegd == 1:
|
|
|
+ pos_0 = pos[0]
|
|
|
+ pos_1 = pos[1]
|
|
|
+ pos_2 = pos[2]
|
|
|
+ pos_3 = pos[3]
|
|
|
+ object_info[0] = round(float(pos_0['X']))
|
|
|
+ object_info[1] = round(float(pos_0['Y']))
|
|
|
+ object_info[2] = round(float(pos_1['X']))
|
|
|
+ object_info[3] = round(float(pos_1['Y']))
|
|
|
+ object_info[4] = round(float(pos_2['X']))
|
|
|
+ object_info[5] = round(float(pos_2['Y']))
|
|
|
+ object_info[6] = round(float(pos_3['X']))
|
|
|
+ object_info[7] = round(float(pos_3['Y']))
|
|
|
+ else:
|
|
|
+ object_info[0] = round(float(pos[0]))
|
|
|
+ object_info[1] = round(float(pos[1]))
|
|
|
+ object_info[2] = round(float(pos[2]))
|
|
|
+ object_info[3] = round(float(pos[1]))
|
|
|
+ object_info[4] = round(float(pos[2]))
|
|
|
+ object_info[5] = round(float(pos[3]))
|
|
|
+ object_info[6] = round(float(pos[0]))
|
|
|
+ object_info[7] = round(float(pos[3]))
|
|
|
+ elif len(pos) >= 8:
|
|
|
+ object_info[0] = round(float(pos[0]))
|
|
|
+ object_info[1] = round(float(pos[1]))
|
|
|
+ object_info[2] = round(float(pos[2]))
|
|
|
+ object_info[3] = round(float(pos[3]))
|
|
|
+ object_info[4] = round(float(pos[4]))
|
|
|
+ object_info[5] = round(float(pos[5]))
|
|
|
+ object_info[6] = round(float(pos[6]))
|
|
|
+ object_info[7] = round(float(pos[7]))
|
|
|
+ pass
|
|
|
+ else:
|
|
|
+ continue
|
|
|
+ object_info[8] = 1
|
|
|
+ poly = np.array(object_info)[:-1].reshape((4,2))
|
|
|
+ if polygon_area(poly) > 0:
|
|
|
+ poly = poly[(0, 3, 2, 1), :]
|
|
|
+ object_info[0] = poly[0,0]
|
|
|
+ object_info[1] = poly[0,1]
|
|
|
+ object_info[2] = poly[1,0]
|
|
|
+ object_info[3] = poly[1,1]
|
|
|
+ object_info[4] = poly[2,0]
|
|
|
+ object_info[5] = poly[2,1]
|
|
|
+ object_info[6] = poly[3,0]
|
|
|
+ object_info[7] = poly[3,1]
|
|
|
+ while_cacu = 0
|
|
|
+ while object_info[2] <= object_info[0] or abs(object_info[2]-object_info[0]) <= abs(object_info[6]-object_info[0]):
|
|
|
+ while_cacu += 1
|
|
|
+ object_info[:-3],object_info[-3:-1] = object_info[2:-1],object_info[:2]
|
|
|
+ if while_cacu > 4:
|
|
|
+ break
|
|
|
+ poly = np.array(object_info)[:-1].reshape((4,2))
|
|
|
+ poly[:, 0] = np.clip(poly[:, 0], 0, w-1)
|
|
|
+ poly[:, 1] = np.clip(poly[:, 1], 0, h-1)
|
|
|
+ if abs(polygon_area(poly)) < 1:
|
|
|
+ continue
|
|
|
+ boxes_list.append(object_info)
|
|
|
+ text_tags.append(cls_label)
|
|
|
+ boxes_counter += 1
|
|
|
+ if boxes_counter > max_boxes:
|
|
|
+ max_boxes = boxes_counter
|
|
|
+ if tag == 'xml':
|
|
|
+ img = cv2.imread(all_img_path[index])
|
|
|
+ try:
|
|
|
+ h,w = img.shape[0],img.shape[1]
|
|
|
+ except:
|
|
|
+ print(ann_path)
|
|
|
+ raise
|
|
|
+ try:
|
|
|
+ tree = ET.parse(ann_path)
|
|
|
+ except:
|
|
|
+ continue
|
|
|
+ for elem in tree.iter(tag='object'):
|
|
|
+ for attr in list(elem):
|
|
|
+ object_info = [0.,0.,0.,0.,0.,0.,0.,0.,0.]
|
|
|
+ if 'name' in attr.tag:
|
|
|
+ try:
|
|
|
+ cls_label = attr.text.strip()
|
|
|
+ except:
|
|
|
+ continue
|
|
|
+ if 'bndbox' in attr.tag:
|
|
|
+ for pos in list(attr):
|
|
|
+ if 'xmin' in pos.tag:
|
|
|
+ object_info[0] = round(float(pos.text.strip()))
|
|
|
+ if 'ymin' in pos.tag:
|
|
|
+ object_info[1] = round(float(pos.text.strip()))
|
|
|
+ if 'xmax' in pos.tag:
|
|
|
+ object_info[4] = round(float(pos.text.strip()))
|
|
|
+ if 'ymax' in pos.tag:
|
|
|
+ object_info[5] = round(float(pos.text.strip()))
|
|
|
+ object_info[2] = object_info[0]
|
|
|
+ object_info[3] = object_info[5]
|
|
|
+ object_info[6] = object_info[4]
|
|
|
+ object_info[7] = object_info[1]
|
|
|
+ if 'polygon' in attr.tag:
|
|
|
+ for pos in list(attr):
|
|
|
+ if 'x1' in pos.tag:
|
|
|
+ object_info[0] = round(float(pos.text.strip()))
|
|
|
+ if 'y1' in pos.tag:
|
|
|
+ object_info[1] = round(float(pos.text.strip()))
|
|
|
+ if 'x2' in pos.tag:
|
|
|
+ object_info[2] = round(float(pos.text.strip()))
|
|
|
+ if 'y2' in pos.tag:
|
|
|
+ object_info[3] = round(float(pos.text.strip()))
|
|
|
+ if 'x3' in pos.tag:
|
|
|
+ object_info[4] = round(float(pos.text.strip()))
|
|
|
+ if 'y3' in pos.tag:
|
|
|
+ object_info[5] = round(float(pos.text.strip()))
|
|
|
+ if 'x4' in pos.tag:
|
|
|
+ object_info[6] = round(float(pos.text.strip()))
|
|
|
+ if 'y4' in pos.tag:
|
|
|
+ object_info[7] = round(float(pos.text.strip()))
|
|
|
+ object_info[8] = 1
|
|
|
+ object_info_tmp = object_info.copy()
|
|
|
+ poly = np.array(object_info)[:-1].reshape((4,2))
|
|
|
+ if polygon_area(poly) > 0:
|
|
|
+ poly = poly[(0, 3, 2, 1), :]
|
|
|
+ object_info[0] = poly[0,0]
|
|
|
+ object_info[1] = poly[0,1]
|
|
|
+ object_info[2] = poly[1,0]
|
|
|
+ object_info[3] = poly[1,1]
|
|
|
+ object_info[4] = poly[2,0]
|
|
|
+ object_info[5] = poly[2,1]
|
|
|
+ object_info[6] = poly[3,0]
|
|
|
+ object_info[7] = poly[3,1]
|
|
|
+ while_cacu = 0
|
|
|
+ while object_info[2] <= object_info[0] or abs(object_info[2]-object_info[0]) <= abs(object_info[6]-object_info[0]):
|
|
|
+ while_cacu += 1
|
|
|
+ object_info[:-3],object_info[-3:-1] = object_info[2:-1],object_info[:2]
|
|
|
+ if while_cacu > 4:
|
|
|
+ break
|
|
|
+ poly = np.array(object_info)[:-1].reshape((4,2))
|
|
|
+ poly[:, 0] = np.clip(poly[:, 0], 0, w-1)
|
|
|
+ poly[:, 1] = np.clip(poly[:, 1], 0, h-1)
|
|
|
+ if abs(polygon_area(poly)) < 1:
|
|
|
+ continue
|
|
|
+ boxes_list.append(object_info)
|
|
|
+ text_tags.append(cls_label)
|
|
|
+ boxes_counter += 1
|
|
|
+ if boxes_counter > max_boxes:
|
|
|
+ max_boxes = boxes_counter
|
|
|
+ for elem in tree.iter(tag='item'):
|
|
|
+ for attr in list(elem):
|
|
|
+ object_info = [0.,0.,0.,0.,0.,0.,0.,0.,0.]
|
|
|
+ if 'name' in attr.tag:
|
|
|
+ try:
|
|
|
+ cls_label = attr.text.strip()
|
|
|
+ except:
|
|
|
+ continue
|
|
|
+ if 'bndbox' in attr.tag:
|
|
|
+ for pos in list(attr):
|
|
|
+ if 'xmin' in pos.tag:
|
|
|
+ object_info[0] = round(float(pos.text.strip()))
|
|
|
+ if 'ymin' in pos.tag:
|
|
|
+ object_info[1] = round(float(pos.text.strip()))
|
|
|
+ if 'xmax' in pos.tag:
|
|
|
+ object_info[4] = round(float(pos.text.strip()))
|
|
|
+ if 'ymax' in pos.tag:
|
|
|
+ object_info[5] = round(float(pos.text.strip()))
|
|
|
+ object_info[2] = object_info[0]
|
|
|
+ object_info[3] = object_info[5]
|
|
|
+ object_info[6] = object_info[4]
|
|
|
+ object_info[7] = object_info[1]
|
|
|
+ if 'polygon' in attr.tag:
|
|
|
+ for pos in list(attr):
|
|
|
+ if 'x1' in pos.tag:
|
|
|
+ object_info[0] = round(float(pos.text.strip()))
|
|
|
+ if 'y1' in pos.tag:
|
|
|
+ object_info[1] = round(float(pos.text.strip()))
|
|
|
+ if 'x2' in pos.tag:
|
|
|
+ object_info[2] = round(float(pos.text.strip()))
|
|
|
+ if 'y2' in pos.tag:
|
|
|
+ object_info[3] = round(float(pos.text.strip()))
|
|
|
+ if 'x3' in pos.tag:
|
|
|
+ object_info[4] = round(float(pos.text.strip()))
|
|
|
+ if 'y3' in pos.tag:
|
|
|
+ object_info[5] = round(float(pos.text.strip()))
|
|
|
+ if 'x4' in pos.tag:
|
|
|
+ object_info[6] = round(float(pos.text.strip()))
|
|
|
+ if 'y4' in pos.tag:
|
|
|
+ object_info[7] = round(float(pos.text.strip()))
|
|
|
+ object_info[8] = 1
|
|
|
+ object_info_tmp = object_info.copy()
|
|
|
+ poly = np.array(object_info)[:-1].reshape((4,2))
|
|
|
+ if polygon_area(poly) > 0:
|
|
|
+ poly = poly[(0, 3, 2, 1), :]
|
|
|
+ object_info[0] = poly[0,0]
|
|
|
+ object_info[1] = poly[0,1]
|
|
|
+ object_info[2] = poly[1,0]
|
|
|
+ object_info[3] = poly[1,1]
|
|
|
+ object_info[4] = poly[2,0]
|
|
|
+ object_info[5] = poly[2,1]
|
|
|
+ object_info[6] = poly[3,0]
|
|
|
+ object_info[7] = poly[3,1]
|
|
|
+ while_cacu = 0
|
|
|
+ while object_info[2] <= object_info[0] or abs(object_info[2]-object_info[0]) <= abs(object_info[6]-object_info[0]):
|
|
|
+ while_cacu += 1
|
|
|
+ object_info[:-3],object_info[-3:-1] = object_info[2:-1],object_info[:2]
|
|
|
+ if while_cacu > 4:
|
|
|
+ break
|
|
|
+ poly = np.array(object_info)[:-1].reshape((4,2))
|
|
|
+ poly[:, 0] = np.clip(poly[:, 0], 0, w-1)
|
|
|
+ poly[:, 1] = np.clip(poly[:, 1], 0, h-1)
|
|
|
+ if abs(polygon_area(poly)) < 1:
|
|
|
+ continue
|
|
|
+ boxes_list.append(object_info)
|
|
|
+ text_tags.append(cls_label)
|
|
|
+ boxes_counter += 1
|
|
|
+ if boxes_counter > max_boxes:
|
|
|
+ max_boxes = boxes_counter
|
|
|
+ if len(boxes_list) == 0 or all_img_path[index].split('.')[-1] == 'gif':
|
|
|
+ continue
|
|
|
+ else:
|
|
|
+ all_imgs_path.append(all_img_path[index])
|
|
|
+ all_tags_path.append(ann_path)
|
|
|
+ all_imgs_boxes.append(boxes_list)
|
|
|
+ all_text_tags.append(text_tags)
|
|
|
+ boxes = np.zeros([len(all_tags_path), max_boxes, 9])
|
|
|
+ for i in range(len(all_tags_path)):
|
|
|
+ boxes_rec = np.array(all_imgs_boxes[i])
|
|
|
+ boxes[i,:boxes_rec.shape[0],:] = boxes_rec
|
|
|
+ boxes = boxes.astype(int)
|
|
|
+ return all_imgs_path, boxes, all_text_tags
|
|
|
+
|
|
|
+
|
|
|
+def write_img_infos(rootdir):
|
|
|
+ '''
|
|
|
+ 写all_img_path_rec.txt,all_boxes_rec.txt,all_text_tag_rec文件
|
|
|
+ :param root_path: 保存.txt文件路径
|
|
|
+ :return:
|
|
|
+ '''
|
|
|
+
|
|
|
+ all_img_path, all_tag_path = get_all_paths(rootdir)
|
|
|
+ imgs,boxes,text_tags = parse_annotation_8(all_img_path, all_tag_path)
|
|
|
+ # root_path = 'd:/Users/Administrator/Desktop/liudan/ocr/data/ocr_txt/'
|
|
|
+ root_path = os.path.join(total_path, 'data/ocr_txt/')
|
|
|
+ if not os.path.exists(root_path):
|
|
|
+ os.makedirs(root_path)
|
|
|
+ reName = rootdir.split('/')[-1]
|
|
|
+
|
|
|
+ with open(root_path + reName+'_img_path_rec.txt', 'w') as f:
|
|
|
+ for i in imgs:
|
|
|
+ f.write(i+'\n')
|
|
|
+
|
|
|
+ with open(root_path + reName + '_boxes_rec.txt', 'w') as f:
|
|
|
+ boxes = boxes.flatten()
|
|
|
+ for i in boxes:
|
|
|
+ f.write(str(i)+'\t')
|
|
|
+
|
|
|
+ with open(root_path + reName + '_text_tag_rec.txt', 'w') as f:
|
|
|
+ for i in text_tags:
|
|
|
+ for kk in i:
|
|
|
+ if kk == None:
|
|
|
+ kk = "None_hegd"
|
|
|
+ f.write(kk.strip()+'\t**hegd**\t')
|
|
|
+ f.write('\n')
|
|
|
+
|
|
|
+
|
|
|
+
|
|
|
+def simple_load_np_dataset(rootdir):
|
|
|
+ '''
|
|
|
+
|
|
|
+ :param root_path: 存放all_img_path_rec.txt,all_boxes_rec.txt,all_text_tag_rec位置
|
|
|
+ :return: 读.txt文件
|
|
|
+ '''
|
|
|
+ # root_path = 'd:/Users/Administrator/Desktop/liudan/ocr/data/ocr_txt/'
|
|
|
+ root_path = os.path.join(total_path, 'data/ocr_txt/')
|
|
|
+ if not os.path.exists(root_path):
|
|
|
+ os.makedirs(root_path)
|
|
|
+ reName = rootdir.split('/')[-1]
|
|
|
+ img_list_rec_file = root_path + reName + '_img_path_rec.txt'
|
|
|
+ boxes_rec_file = root_path + reName + '_boxes_rec.txt'
|
|
|
+ text_tag_rec_file = root_path + reName + '_text_tag_rec.txt'
|
|
|
+ all_img_path = []
|
|
|
+ with open(img_list_rec_file,"r") as f:
|
|
|
+ file_content = f.readlines()
|
|
|
+ for i in file_content:
|
|
|
+ all_img_path.append(i.strip())
|
|
|
+ with open(boxes_rec_file,'r') as f:
|
|
|
+ file_content = f.read().strip()
|
|
|
+ num_list = file_content.split('\t')
|
|
|
+ boxes_flatten = []
|
|
|
+ for i in num_list:
|
|
|
+ boxes_flatten.append(int(i))
|
|
|
+ boxes_flatten = np.array(boxes_flatten)
|
|
|
+ boxes = boxes_flatten.reshape((len(all_img_path),-1, 9))
|
|
|
+ with open(text_tag_rec_file,'r') as f:
|
|
|
+ all_text_tags = []
|
|
|
+ file_content = f.readlines()
|
|
|
+ for i in file_content:
|
|
|
+ all_text_tags.append(i.split('\t**hegd**\t')[:-1])
|
|
|
+ return all_img_path,boxes,all_text_tags
|
|
|
+
|
|
|
+import time
|
|
|
+
|
|
|
+def batch_save_txt(rootdir):
|
|
|
+
|
|
|
+ file_object_list, file_image_list = find(rootdir)
|
|
|
+ # for i in file_image_list:
|
|
|
+ # if i.endswith('.json'):
|
|
|
+ # f2.write(i[:-4] + 'jpg\n') # 新
|
|
|
+
|
|
|
+ img_file_list = []
|
|
|
+ label_file_list = []
|
|
|
+
|
|
|
+ for i, j in zip(file_image_list, file_object_list):
|
|
|
+ if not os.path.exists(i.strip()):
|
|
|
+ print(j.strip())
|
|
|
+ continue
|
|
|
+ img_file_list.append(i)
|
|
|
+ label_file_list.append(j)
|
|
|
+
|
|
|
+ print('total imgs num: ', len(file_image_list))
|
|
|
+
|
|
|
+
|
|
|
+ write_img_infos(rootdir)
|
|
|
+
|
|
|
+ p = 1
|
|
|
+ all_img_path, boxes, all_text_tags = simple_load_np_dataset(rootdir)
|
|
|
+ print(all_img_path[p - 1])
|
|
|
+ print(boxes[p - 1])
|
|
|
+ print(all_text_tags[p - 1])
|
|
|
+
|
|
|
+ sum_box = 0
|
|
|
+
|
|
|
+ for idx in range(len(all_img_path)):
|
|
|
+ gt_img_path = all_img_path[idx]
|
|
|
+ gt_boxs = boxes[idx]
|
|
|
+ if gt_boxs.shape[-1] == 9:
|
|
|
+ boxs_tmp = []
|
|
|
+ for i in gt_boxs:
|
|
|
+ if i[-1] > 0.5:
|
|
|
+ boxs_tmp.append(i)
|
|
|
+ gt_boxs = np.array(boxs_tmp)
|
|
|
+ gt_text_tags = all_text_tags[idx]
|
|
|
+ path_post = gt_img_path.strip(). \
|
|
|
+ split(rootdir)[-1]
|
|
|
+ # dir = 'd:/Users/Administrator/Desktop/liudan/ocr/data/'
|
|
|
+ # dir = os.path.join(total_path, 'data/')
|
|
|
+ if rootdir == os.path.join(dir, 'total_data'):
|
|
|
+ save_path = os.path.join(dir, 'total_transform_imgs')
|
|
|
+ elif rootdir == os.path.join(dir, 'train'):
|
|
|
+ save_path = os.path.join(dir, 'train_transform_imgs')
|
|
|
+ elif rootdir == os.path.join(dir, 'val'):
|
|
|
+ save_path = os.path.join(dir, 'val_transform_imgs')
|
|
|
+ for ii in path_post.split('/')[1:-1]:
|
|
|
+ save_path = os.path.join(save_path, ii)
|
|
|
+ if not os.path.exists(save_path):
|
|
|
+ os.makedirs(save_path)
|
|
|
+ num_box = save_images(gt_boxs, gt_text_tags, gt_img_path, save_path)
|
|
|
+ sum_box += num_box
|
|
|
+
|
|
|
+ print(sum_box)
|
|
|
+
|
|
|
+
|
|
|
+
|
|
|
+
|
|
|
+if __name__ == "__main__":
|
|
|
+ rootdir = os.path.join(dir, 'total_data')
|
|
|
+ batch_save_txt(rootdir)
|
|
|
+
|
|
|
+
|
|
|
+ rootdir = os.path.join(dir, 'train')
|
|
|
+ batch_save_txt(rootdir)
|
|
|
+
|
|
|
+ rootdir = os.path.join(dir, 'val')
|
|
|
+ batch_save_txt(rootdir)
|