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https://github.com/vfdev-5/imagedatasetviz
Observe the dataset of images and targets in few shots
https://github.com/vfdev-5/imagedatasetviz
datasets deeplearning images python visualization
Last synced: 16 days ago
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Observe the dataset of images and targets in few shots
- Host: GitHub
- URL: https://github.com/vfdev-5/imagedatasetviz
- Owner: vfdev-5
- License: mit
- Created: 2018-03-18T22:25:47.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2022-09-27T16:33:44.000Z (about 2 years ago)
- Last Synced: 2024-10-10T17:30:50.977Z (29 days ago)
- Topics: datasets, deeplearning, images, python, visualization
- Language: Python
- Homepage:
- Size: 7.23 MB
- Stars: 10
- Watchers: 3
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
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README
# ImageDatasetViz
[](https://github.com/vfdev-5/ImageDatasetViz/actions/workflows/tests.yml)
Observe dataset of images and targets in few shots
## Descriptions
Idea is to create tools to store images, targets from a dataset as a few large images to observe the dataset
in few shots.
## Installation
#### with pip
```bash
pip install image-dataset-viz
```
#### from sources
```bash
python setup.py install
```
or
```bash
pip install git+https://github.com/vfdev-5/ImageDatasetViz.git
```
## Usage
### Render a single datapoint
First, we can just take a look on a single data point rendering. Let's assume that we
have `img` as, for example, `PIL.Image` and `target` as acceptable target type (`str` or list of points or
`PIL.Image` mask, etc), thus we can generate a single image with target.
```python
from image_dataset_viz import render_datapoint
# if target is a simple label
res = render_datapoint(img, "test label", text_color=(0, 255, 0), text_size=10)
plt.imshow(res)
# if target is a mask image (PIL.Image)
res = render_datapoint(img, target, blend_alpha=0.5)
plt.imshow(res)
# if target is a bounding box, e.g. np.array([[10, 10], [55, 10], [55, 77], [10, 77]])
res = render_datapoint(img, target, geom_color=(255, 0, 0))
plt.imshow(res)
```
#### Example output on Leaf Segmentation dataset from CVPPP2017
  
### Export complete dataset
For example, we have a dataset of image files and annotations files (polygons with labels):
```python
img_files = [
'/path/to/image_1.ext',
'/path/to/image_2.ext',
...
'/path/to/image_1000.ext',
]
target_files = [
'/path/to/target_1.ext2',
'/path/to/target_2.ext2',
...
'/path/to/target_1000.ext2',
]
```
We can produce a single image composed of 20x50 small samples with targets to better visualize the whole dataset.
Let's assume that we do need a particular processing to open the images in RGB 8bits format:
```python
from PIL import Image
def read_img_fn(img_filepath):
return Image.open(img_filepath).convert('RGB')
```
and let's say the annotations are just lines with points and a label, e.g. `12 23 34 45 56 67 car`
```python
from pathlib import Path
import numpy as np
def read_target_fn(target_filepath):
with Path(target_filepath).open('r') as handle:
points_labels = []
while True:
line = handle.readline()
if len(line) == 0:
break
splt = line[:-1].split(' ') # Split into points and labels
label = splt[-1]
points = np.array(splt[:-1]).reshape(-1, 2)
points_labels.append((points, label))
return points_labels
```
Now we can export the dataset
```python
de = DatasetExporter(read_img_fn=read_img_fn, read_target_fn=read_target_fn,
img_id_fn=lambda fp: Path(fp).stem, n_cols=20)
de.export(img_files, target_files, output_folder="dataset_viz")
```
and thus we should obtain a single png image with composed of 20x50 small samples.
## Examples
- [CIFAR10](examples/example_CIFAR10.ipynb)
- [VEDAI](examples/example_VEDAI.ipynb)
### Other basic examples
#### Image and Mask/BBox/Label
```python
import numpy as np
from image_dataset_viz import render_datapoint, bbox_to_points
img = ((0, 0, 255) * np.ones((256, 256, 3))).astype(np.uint8)
bbox = (
(bbox_to_points((10, 12, 145, 156)), "A"),
(bbox_to_points((109, 120, 215, 236)), "B"),
)
mask = 0 * np.ones((256, 256, 3), dtype=np.uint8)
mask[34:145, 56:123, :] = 255
res = render_datapoint(img, (mask, "mask", bbox), blend_alpha=0.5)
```
#### Image and Multi-Colored BBoxes
```python
import numpy as np
from image_dataset_viz import render_datapoint, bbox_to_points
img = ((0, 0, 255) * np.ones((256, 256, 3))).astype(np.uint8)
mask = 0 * np.ones((256, 256, 3), dtype=np.uint8)
mask[34:145, 56:123, :] = 255
targets = (
(mask, {"blend_alpha": 0.6}),
(
(bbox_to_points((10, 12, 145, 156)), "A"),
(bbox_to_points((109, 120, 215, 236)), "B"),
{"geom_color": (255, 255, 0)}
),
(bbox_to_points((129, 140, 175, 186)), "C"),
)
res = render_datapoint(img, targets, blend_alpha=0.5)
```
