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https://github.com/rentainhe/visualization
a collection of visualization function
https://github.com/rentainhe/visualization
attention attention-map attention-mechanism data-visualization deep-learning transformer vision vision-mlp vision-transformer visualization
Last synced: 5 days ago
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a collection of visualization function
- Host: GitHub
- URL: https://github.com/rentainhe/visualization
- Owner: rentainhe
- License: mit
- Created: 2021-01-25T06:24:58.000Z (almost 4 years ago)
- Default Branch: master
- Last Pushed: 2022-01-15T18:22:25.000Z (almost 3 years ago)
- Last Synced: 2024-10-06T06:44:58.141Z (about 1 month ago)
- Topics: attention, attention-map, attention-mechanism, data-visualization, deep-learning, transformer, vision, vision-mlp, vision-transformer, visualization
- Language: Python
- Homepage:
- Size: 2.82 MB
- Stars: 383
- Watchers: 2
- Forks: 41
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# visualization
a collection of visualization operation for easier usage, check [usage](#usage) for a quick start.
## New Features
**2021/10/4**
- Add `draw_line_chart` function, please check [drawer.py](/visualize/line_chart/drawer.py)
**2021/09/29**
- Add pip installation
- Build a cleaner repo
## Contents
### Visualization Function
- [__Grid Attention Visualization__](/visualize/grid_attention_visualization/)
- [__Region Attention Visualization__](/visualize/region_attention_visualization/)
- [__Draw Line Chart__](/visualize/line_chart/drawer.py)
### Learning Notes Sharing
- [__Image Reading Difference__](/notes)
### Relative Blogs
- [Region Attention Map Visualize](https://zhuanlan.zhihu.com/p/364486740)
- [Grid Attention Map Visualize](https://zhuanlan.zhihu.com/p/356798637)
## Installation
```bash
pip install visualize==0.5.1
```
## Usage
Run Example
You can try [example.py](/example.py) by cloning this repo for a quick start.
```bash
git clone https://github.com/rentainhe/visualization.git
python example.py
```
results will be saved to `./test_grid_attention` and `./test_region_attention`
Region Attention Visualization
**download the [example.jpg](/visualize/test_data/example.jpg) to any folder you like**
```bash
$ wget https://github.com/rentainhe/visualization/blob/master/visualize/test_data/example.jpg
```
**build the following python script for a quick start**
```python
import numpy as np
from visualize import visualize_region_attention
img_path="path/to/example.jpg"
save_path="example"
attention_retio=1.0
boxes = np.array([[14, 25, 100, 200], [56, 75, 245, 300]], dtype='int')
boxes_attention = [0.36, 0.64]
visualize_region_attention(img_path,
save_path=save_path,
boxes=boxes,
box_attentions=boxes_attention,
attention_ratio=attention_retio,
save_image=True,
save_origin_image=True,
quality=100)
```
- `img_path`: where to load the original image
- `boxes`: a list of coordinates for the bounding boxes
- `box_attentions`: a list of attention scores for each bounding box
- `attention_ratio`: a special param, if you set the attention_ratio larger, it will make the attention map look more shallow. Just try!
- `save_image=True`: save the image with attention map or not, e.g., default: True.
- `save_original_image=True`: save the original image at the same time, e.g., default: True
**Note that you can check [Region Attention Visualization](/visualize/region_attention_visualization/) here for more details**
Grid Attention Visualization
**download the [example.jpg](/visualize/test_data/example.jpg) to any folder you like**
```bash
$ wget https://github.com/rentainhe/visualization/blob/master/visualize/test_data/example.jpg
```
**build the following python script for a quick start**
```python
from visualize import visualize_grid_attention_v2
import numpy as np
img_path="./example.jpg"
save_path="test"
attention_mask = np.random.randn(14, 14)
visualize_grid_attention_v2(img_path,
save_path=save_path,
attention_mask=attention_mask,
save_image=True,
save_original_image=True,
quality=100)
```
- `img_path`: where the image you want to put an attention mask on.
- `save_path`: where to save the image.
- `attention_mask`: the attention mask with format `numpy.ndarray`, its shape is (H, W)
- `save_image=True`: save the image with attention map or not, e.g., default: True.
- `save_original_image=True`: save the original image at the same time, e.g., default: True
**Note that you can check [Grid Attention Visualization](https://github.com/rentainhe/visualization/tree/master/visualize/grid_attention_visualization) here for more details**
Draw Line Chart
**build the following python script for a quick start**
```python
from visualize import draw_line_chart
# test data
data1 = {"data": [13.15, 14.64, 15.83, 17.99], "name": "data 1"}
data2 = {"data": [14.16, 14.81, 16.11, 18.62], "name": "data 2"}
data_list = []
data_list.append(data1["data"])
data_list.append(data2["data"])
name_list = []
name_list.append(data1["name"])
name_list.append(data2["name"])
draw_line_chart(data_list=data_list,
labels=name_list,
xlabel="test_x",
ylabel="test_y",
save_path="./test.jpg",
legend={"loc": "upper left", "frameon": True, "fontsize": 12},
title="example")
```
- `data_list`: a list of data to draw.
- `labels`: the label corresponds to each data in data_list.
- `xlabel`: label of x-axis.
- `ylabel`: label of y-axis.
- `save_path`: the path to save image.
- `legend`: the params of legend.
- `title`: the title of the saved image.
You will get the result like this:
