Computuer Vision and Image Processing with Python, OpenCV, and Keras.
Human, and fire detection and recognition in indoor foggy environment.
a. Human detection with RPi | Github
π 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)
A. NTIRE2018 | Homepage
Using Image Processing with OpenCV
- How to read NYU mat file with python | Blog (KR)
- Packages scikit-image matplotlib
> pip install scikit-image
> python -m pip install -U matplotlib
- Change Code for Windows or OS X | GitHub issue
import skimage.io as io
to
import matplotlib
matplotlib.use('TkAgg')
from skimage import io
io.use_plugin('matplotlib')
c. RGBD Dataset | Homepage
a. FCRN | Paper (arXiv) | GitHub
ii. PyTesseract | GitHub | Install with pip
> pip install pytesseract
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)
- f. Resize | Blog (KR)
image = cv2.resize(input_image, dsize=(0, 0), fx=0.3, fy=0.7, interpolation=cv2.INTER_LINEAR)
A. Robust Reading Competition ICDAR | Homepage
Overview - ICDAR 2019 Robust Reading Challenge on Multi-lingual scene text detection and recognition | Homepage
Korean Scene Text Recognition by Character-level
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 |
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 |
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.
vii. Install Pytorch
viii. Install Theano
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