Textboxes : Image Text Detection Model : python package (tensorflow)

Jayne Shin (신재인)

Last update: Dec 15, 2022

Overview

shinTB

Abstract

A python package for use Textboxes : Image Text Detection Model

implemented by tensorflow, cv2

Textboxes Paper Review in Korean (My Blog) : shinjayne.github.io/textboxes

shintb : useable textboxes python package (Source codes are in here)

svt1 : Street view Text dataset. can use with shintb.svt_data_loader.SVTDataLoader when training Textboxes model

config.py : (NECESSARY) configuration of model building and training with shinTB

main.py : simple example useage of shinTB package

Dependancies

python Version: 3.5.3
numpy Version: 1.13.0
tensorflow Version: 1.2.1
cv2

How to use

Clone this repository to your local.
You will use shintb python package and config.py for building and training your own Textboxes model.
svt1 gives us training / test data.
Open new python file.
Import config.config and shintb.

from config import config
from shintb import graph_drawer, default_box_control, svt_data_loader, runner

Initialize GraphDrawer,DefaultBoxControl,SVTDataLoader instance.

graphdrawer = graph_drawer.GraphDrawer(config)

dataloader = svt_data_loader.SVTDataLoader('./svt1/train.xml', './svt1/test.xml')

dbcontrol = default_box_control.DefaultBoxControl(config, graphdrawer)

GraphDrawer instance contains a tensorflow graph of Textboxes.
DefaultboxControl instance contains methods and attributes which is related to default box.
SVTDataLoader instance loads data from svt1.
Initialize Runner instance.

runner = runner.Runner(config, graphdrawer, dataloader, dbcontrol)

Runner uses GraphDrawer,DefaultBoxControl,SVTDataLoader instance.
If you want to train your Textboxes model, use Runner.train(). Every 1000 step, shintb will save ckpt file in the directory you set in config.py.

runner.train()

If you want to validate/test your model, use Runner.test()

runner.test()

After training, if you want to detect texts from one image use Runner.image().

runner.image(<your_image_directory>)

Comments

Module output_drawer has no attribute 'formal_output'

Has anyone encountered this error when tryint to "test" or "image"?

Traceback (most recent call last):
  File "testShinTB.py", line 15, in <module>
    runner.test(5)
  File "/home/great/Documentos/spi/shinTB/shintb/runner.py", line 194, in test
    output_boxes, output_confidence = self.outputdrawer.format_output(pred_conf[0], pred_loc[0])
AttributeError: module 'shintb.output_drawer' has no attribute 'format_output'

I am just tryint to run an example based on the README.

opened by romulofff 1

Error happens while saving checkpoint. How to fix it? Thanks a lot

GLOBAL STEP : 1000 / LEARNING RATE : 0.0008 / LOSS : 1.18235 ( 0.27370095253 secs) Traceback (most recent call last): File "main.py", line 30, in runner.train(FLAGS.jobname, FLAGS.iter) File "/home/wanghz/shinTB/shintb/runner.py", line 101, in train ckpt_path = self.saver.save(self.sess, "%s.ckpt" % (c["saved_dir"]+"/"+jobname),global_step) File "/opt/DL/tensorflow/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 1369, in save self.last_checkpoints, latest_filename) File "/opt/DL/tensorflow/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 796, in update_checkpoint_state text_format.MessageToString(ckpt)) File "/opt/DL/tensorflow/lib/python2.7/site-packages/tensorflow/python/lib/io/file_io.py", line 353, in atomic_write_string_to_file rename(temp_pathname, filename, overwrite=True) File "/opt/DL/tensorflow/lib/python2.7/site-packages/tensorflow/python/lib/io/file_io.py", line 335, in rename compat.as_bytes(oldname), compat.as_bytes(newname), overwrite, status) File "/usr/lib/python2.7/contextlib.py", line 24, in exit self.gen.next() File "/opt/DL/tensorflow/lib/python2.7/site-packages/tensorflow/python/framework/errors_impl.py", line 466, in raise_exception_on_not_ok_status pywrap_tensorflow.TF_GetCode(status)) tensorflow.python.framework.errors_impl.FailedPreconditionError: saved/checkpoint.tmp205d246fabab4cb488968fcc6989f6d7

opened by white2018 0
Model not correct

I tried to train and test the model but I think that there are many and many errors within it. The most important is that confidence and delta are predicted as really big numbers (i.e. 1.8e+8). This make the project not usable until a real deep analysis of it

opened by DavidSeas 1
how to runner.image?

1.when I run main.py --mode = image occur this problem,how to solve this? If you know, please call me. Thank you @shinjayne .

OpenCV(3.4.1) Error: Assertion failed (depth == 0 || depth == 2 || depth == 5) in cvtColor, file /io/opencv/modules/imgproc/src/color.cpp, line 11109 Traceback (most recent call last): File "main.py", line 34, in runner.image() File "/home/dell/gen/shinTB/shintb/runner.py", line 260, in image self.outputdrawer.draw_outputs(test_img, output_boxes , output_confidence , wait=1) File "/home/dell/gen/shinTB/shintb/output_drawer.py", line 100, in draw_outputs I = cv2.cvtColor(I, cv2.COLOR_RGB2BGR) cv2.error: OpenCV(3.4.1) /io/opencv/modules/imgproc/src/color.cpp:11109: error: (-215) depth == 0 || depth == 2 || depth == 5 in function cvtColor

2.when I run "runner.image" occur this problem,how to solve this? If you know, please call me. Thank you @shinjayne .

Textboxes information! rect_start : (0, 0) // rect_end : (0, 0) confidence: 0.499774 Textboxes information! rect_start : (0, 0) // rect_end : (0, 0) confidence: 0.499774 Textboxes information! rect_start : (0, 0) // rect_end : (0, 0) confidence: 0.499774 Textboxes information! rect_start : (0, 0) // rect_end : (0, 0) confidence: 0.499774 Textboxes information! rect_start : (0, 0) // rect_end : (0, 0) confidence: 0.499774 Textboxes information! rect_start : (0, 0) // rect_end : (0, 0) confidence: 0.499774 Textboxes information! rect_start : (0, 0) // rect_end : (0, 0) confidence: 0.499774 Textboxes information! rect_start : (0, 0) // rect_end : (0, 0) confidence: 0.499774

opened by porridgeshoes 2

It seemed that "h" should be like this "h = float(rectangle.get('height')) / imageHeight"???

https://github.com/shinjayne/shinTB/blob/c6c46817a13b8f26c7d6a6809370a15d44538365/shintb/svt_data_loader.py#L35

		imageWidth = float(image.find('Resolution').get('x'))
		imageHeight = float(image.find('Resolution').get('y'))
		taggedRectangles = image.find('taggedRectangles')
		for rectangle in taggedRectangles.findall('taggedRectangle'):
			h = float(rectangle.get('height')) / imageHeight
			w = float(rectangle.get('width'))  / imageWidth
			x = float(rectangle.get('x'))      / imageWidth
			y = float(rectangle.get('y'))      / imageHeight

opened by matrix246 0

Textboxes : Image Text Detection Model : python package (tensorflow)

Related tags

Overview

shinTB

Abstract

Dependancies

How to use

Comments

Module output_drawer has no attribute 'formal_output'

Error happens while saving checkpoint. How to fix it? Thanks a lot

Model not correct

how to runner.image?

It seemed that "h" should be like this "h = float(rectangle.get('height')) / imageHeight"???

Owner

Jayne Shin (신재인)

Textboxes implementation with Tensorflow (python)

TextBoxes re-implement using tensorflow

TextBoxes++: A Single-Shot Oriented Scene Text Detector

Motion detector, Full body detection, Upper body detection, Cat face detection, Smile detection, Face detection (haar cascade), Silverware detection, Face detection (lbp), and Sending email notifications

This is a c++ project deploying a deep scene text reading pipeline with tensorflow. It reads text from natural scene images. It uses frozen tensorflow graphs. The detector detect scene text locations. The recognizer reads word from each detected bounding box.

text detection mainly based on ctpn model in tensorflow, id card detect, connectionist text proposal network

A Tensorflow model for text recognition (CNN + seq2seq with visual attention) available as a Python package and compatible with Google Cloud ML Engine.

This project modify tensorflow object detection api code to predict oriented bounding boxes. It can be used for scene text detection.

Handwritten Text Recognition (HTR) system implemented with TensorFlow (TF) and trained on the IAM off-line HTR dataset. This Neural Network (NN) model recognizes the text contained in the images of segmented words.

Deskew is a command line tool for deskewing scanned text documents. It uses Hough transform to detect "text lines" in the image. As an output, you get an image rotated so that the lines are horizontal.

Repository for Scene Text Detection with Supervised Pyramid Context Network with tensorflow.

This is a tensorflow re-implementation of PSENet: Shape Robust Text Detection with Progressive Scale Expansion Network.My blog:

🔎 Like Chardet. 🚀 Package for encoding & language detection. Charset detection.

A novel region proposal network for more general object detection ( including scene text detection ).

An Implementation of the alogrithm in paper IncepText: A New Inception-Text Module with Deformable PSROI Pooling for Multi-Oriented Scene Text Detection

Code for the paper STN-OCR: A single Neural Network for Text Detection and Text Recognition

deployment of a hybrid model for automatic weapon detection/ anomaly detection for surveillance applications

keras复现场景文本检测网络CPTN: 《Detecting Text in Natural Image with Connectionist Text Proposal Network》；欢迎试用，关注，并反馈问题...

Detecting Text in Natural Image with Connectionist Text Proposal Network (ECCV'16)