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https://github.com/whiteblackgoose/hi19
Official repository of HI-19
https://github.com/whiteblackgoose/hi19
Last synced: 18 days ago
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Official repository of HI-19
- Host: GitHub
- URL: https://github.com/whiteblackgoose/hi19
- Owner: WhiteBlackGoose
- License: mit
- Created: 2019-05-01T16:28:31.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2020-03-05T16:38:22.000Z (over 4 years ago)
- Last Synced: 2024-10-19T18:46:35.236Z (30 days ago)
- Language: Jupyter Notebook
- Homepage: http://asc-community.org/
- Size: 7.86 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
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README
# HI19
HI-19 is a system developed for identifying and distinguishing peoples' handrwritings. This is the first such product.
## Generally
### How does HI-19 work?
1. First, the received photo is segmented into words
2. Afterwards, we need to fit the model. HIModel из model
3. To work with HI-19 API we recommend to use RunEnv for run-time and FitEnv for fitting, both from hypo2.api
### Structure
- config.py - project configuration file
- model.py - implementation and functions of the HIModel
- api.py
## Model
### First of all
First, we configure the project. Create file myconfig.py and insert the following code:
```python
from hypo2.config import RunConfig
class MyConfig(RunConfig):
BACKUP_DIRECTORY = ???
MODEL_PATH = ???
```
Substitute ??? with either the appropriate path or None. BACKUP_DIRECTORY is a path to the directory, where backup models will be saved. MODEL_PATH - a path to the final model.
Then, let us create the config variable. It is simple:
```python
from myconfig import MyConfig
config = MyConfig()
```
So, you can change some settings right in the run-time. To have a look at the configs just type
```
print(config)
```
### Fitting
The dataset we have should be structured in the following way:
```
[
[
"0/0.jpg",
"0/1.jpg",
"0/2.jpg",
],
[
"1/0.jpg",
"1/1.jpg"
],
[
"2/0.jpg"
]
]
```
In other words, every item of the array is an array of links to photos of one class. You have to change the config variable, at least, CLASS_COUNT.
Let us say we already have dataset.
Imporint api
```python
from hypo2.api import FitEnv
```
FitTime enables to fit the model easily. Creating an instance of FitEnv:
```python
fitenv = FitEnv(config)
```
The environment is ready to fit. FitEnv will create a model, process the input, and set the system. Let us fit the model:
```python
model = fitenv.fit(dataset, verbose=True, plot=False)
```
verbose is whether to output epoch results.
plot is whether to plot loss diagram.
model is the result of fitting.
#### Model methods
You can save your model via
```python
model.save()
```
The path the model will be saved to is config.MODEL_PATH
If you want to do it manually, specify the path (deprecated method)
```python
model.saveto("D:/HI.19")
```
To open the model, specify it in config.MODEL_PATH and then create an instance.
```python
config.MODEL_PATH = "D:/HI.19"
model = HIModel(config)
```
If you want to reopen the model
```python
model.open()
```
Or use a deprecated method:
```python
model.openfrom("HI.19")
```
### Usage
Importing and creating RunEnv
```python
from hypo2.api import RunEnv
runenv = RunEnv(config)
```
Let us try to get a Center by photo:
```python
image = runenv.open_image("johns_text.jpg")
cw = runenv.get_center(image)
assert cw is not None, "0 words found"
center, weight = cw
```
Where center is a vector of length config.FEATURES_COUNT, and weight is an integer, that is equal to the total amound of
found words.
To compare two images and get the difference between them, use differ
```python
distance = runenv.differ(image1, image2)
```
If you already have centers, then you can find out the distance between them using function
```python
distance = runenv.dist(john_center, mary_center)
```