/Vision

Vision 👀

Primary LanguagePython

Vision

Computuer Vision and Image Processing with Python, OpenCV, and Keras.

🌁 1. Dehazing to Object Recognition 🚧

Human, and fire detection and recognition in indoor foggy environment.

i. Image Processing with OpenCV

A. Dehazing

a. Dark Channel Prior | GitHub | Paper

B. Object Recognition

a. Human detection with RPi | Github
b. HOG detectMultiScale | Blog (KR) | Blog (US)

ii. Image Processing with Deep Learning

📚 A. Dataset | Reference

a. RESIDE: V0 (REalistic Single Image DEhazing) | Homepage | Paper (IEEE) | Paper (arXiv)
b. I-HAZE | Homepage | Paper (arXiv)
c. D-HAZY | Homepage | Paper (IEEE)
d. O-HAZE | Paper (arXiv)

🏁 iii. Challenge

A. NTIRE2018 | Homepage

🎯 2. Vanishing Point to Control Posture ✔️

Using Image Processing with OpenCV

3. Depth to Obstacle Avoidance 🚧

i. Image Processing with Deep Learning

📚 A. Dataset

a. NYU Dataset V1 | Homepage | Paper
b. NYU Dataset V2 | Homepage | Paper
c. Sample Code for NYU
> pip install scikit-image
> python -m pip install -U matplotlib
import skimage.io as io

to

import matplotlib
matplotlib.use('TkAgg')
from skimage import io
io.use_plugin('matplotlib')
c. RGBD Dataset | Homepage

B. Model

a. FCRN | Paper (arXiv) | GitHub

4. Optical Character Recognition to Path Planning 🚧

i. Tesseract | GitHub | Demo

ii. PyTesseract | GitHub | Install with pip

> pip install pytesseract

A. Install Windows Version | GitHub | Download

B. Tesseract training | GitHub

C. Variable-size Graph Specification Language (VGSL) | GitHub

D. StreetView Tensorflow Recurrent End-to-End Transcription (STREET) | GitHub

E. Korean OCR | Blog (KR)

  • a. Remove spaces | Blog (KR)
  • b. Once you change the route, you need to turn off and restart Pycharm.
  • c. Remove special characters | Blog (KR)
  • d. Tesseract Optimal conditions | Blog (KR)
  • e. Color Reversal | Blog (KR)
image = cv2.bitwise_not(input_image)
image = cv2.resize(input_image, dsize=(0, 0), fx=0.3, fy=0.7, interpolation=cv2.INTER_LINEAR)

🏁 iii. Challenge

A. Robust Reading Competition ICDAR | Homepage

Overview - ICDAR 2019 Robust Reading Challenge on Multi-lingual scene text detection and recognition | Homepage

iv. Korean STR

Korean Scene Text Recognition by Character-level

📚 A. Dataset

a. Dataset Version 1 (.png) | MNIST.zip | PHD08.zip
Dataset Language # of fonts # of characters total # of images
PHD08 Korean 9 2,350 5,139,450
EMNIST (ByClass) Number & English Hand Writing 62 814,255
b. Dataset Version 2 (.png) | 7z
Language # of fonts # of characters total # of images
Korean 70 11,172 782,040
Number 70 10 700
English 70 52 (upper 26, lower 26) 3,640
c. Dataset Version 3 (.png) | 7z | zip
Language # of fonts # of characters total # of images
Korean 69 972 58,320
Number 69 10 690
English 69 52 (upper 26, lower 26) 3,588
d. Dataset Version 4 (.png) | 7z | zip | pkl
Language # of fonts # of characters total # of images
Korean 69 972 116,640
Number 69 10 1,240
English 69 26 3,588
  • Korean and numbers use two font sizes.
  • Incorporate uppercase letters into lowercase letters.
  • Font sizes of Korean and number are 44, and 54, font size of English is 44.
  • Image size is 64.
  • Hangul characters refer to this and calculated the frequency.

Install

i. Install OpenCV on Computer

ii. Install OpenCV on Raspberry Pi

iii. Install TensorFlow CPU on Computer

iv. Install TensorFlow GPU on Computer

v. Install TensorFlow on Raspberry Pi

vi. Install Keras & Reinforcement Learning

vii. Install Pytorch

viii. Install Theano

ix. Python Virtual Environment with Conda

x. Setup Ubuntu

xi. Setup CentOS

  • Image Processing vs. Computer Vision Reference
  • Dehazing - Image Processing/Object Recognition - Computer Vision
  • Vanishing Point - Computer Vision
  • Depth Prediction - Computer Vision
  • Opitcal Character Recognition - Computer Vision