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https://github.com/bnbsking/cosmos
Classification, Object detection, Segmentation MOduleS. Such as automatic format conversion, visualization, metrics with plotting and exporting suspected data.
https://github.com/bnbsking/cosmos
classification conversion-tool metrics-exporter object-detection segmentation visualization-tools
Last synced: 20 days ago
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Classification, Object detection, Segmentation MOduleS. Such as automatic format conversion, visualization, metrics with plotting and exporting suspected data.
- Host: GitHub
- URL: https://github.com/bnbsking/cosmos
- Owner: bnbsking
- Created: 2022-02-11T07:44:09.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2024-11-27T16:13:33.000Z (28 days ago)
- Last Synced: 2024-11-27T17:24:35.274Z (28 days ago)
- Topics: classification, conversion-tool, metrics-exporter, object-detection, segmentation, visualization-tools
- Language: Jupyter Notebook
- Homepage:
- Size: 69.2 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: Readme.md
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README
# COSMOs: Classification, Object detection, Segmentation MOduleS
## **Introduction**
This repo provides tools for common computer vision tasks.
+ Classification
+ `Output Analysis`
+ support tasks:
+ binary classification
+ binary classification treat class-0 as background
+ multi-class classification
+ multi-class classification treat class-0 as background
+ multi-label classification (multibinary, treat class-0 as background)
+ Comprehensive `metrics` e.g. AP, PR-Curves, Confusion matrices, threshold optimization, etc.
+ `Plotting` of the above metrics
+ `Export` data for label correctness
+ Object detection
+ `Format Conversion` between coco, voc, yolo and self-defined format [general](./example/detection/data/general.json).
+ `Visualization` of all the above formats for **labels** (and **predictions** if specified). The visualization tool automatically converts the data into general format in the cache directory in advance.
+ `Output Analysis`
+ Comprehensive `metrics` e.g. AP, PR-Curves, Confusion matrices, threshold optimization, etc.
+ `Plotting` of the above metrics
+ `Export` data for label correctness
+ Segmentation
+ `Visualization`
+ Comprehensive `Metrics`
## **Installation**
```bash
git clone https://github.com/bnbsking/COSMOs
pip install numpy matplotlib opencv-python
```
## **Quick Start - Classification**
+ Output Analysis:
+ Please conform your data format as either of one
+ [single_label](./example/classification/data/single_label.json)
+ [multilabel](./example/classification/data/multi_label.json)
+ [single_label_background](./example/classification/data/single_label_background.json)
+ The analysis pipeline is at [here](./cosmos/classification/output_analysis.yaml)
+ See more in the [example](./example/classification/output_analysis.ipynb)
```python
from cosmos.classification import ClassificationAnalysis
ClassificationAnalysis(
ant_path = "example/classification/data/single_label.json",
save_folder = "example/classification/output/single_label",
)
```
## **Quick Start - Object detection**
+ Format Conversion (see more in the [example](./example/detection/s2_format_conversion.ipynb))
```python
from cosmos.detection import coco2any
coco2any(
tgt_foramt = "voc",
img_folder = "example/detection/data/coco",
ant_path = "example/detection/data/coco/coco.json",
save_folder = "example/detection/output/visualization_gt_conversion/coco2voc"
)
```
or
```python
from cosmos.detection import coco2general
coco2general(
img_folder = "example/detection/data/coco",
ant_path = "example/detection/data/coco/coco.json",
save_path = "example/detection/output/visualization_gt_conversion/coco2general/general.json"
)
```
+ Visualization (see more in the [example](./example/detection/s1_visualization_gt_and_pd.ipynb))
```python
from cosmos.detection import show_coco
show_coco(
img_name = f"pic0.jpg",
img_folder = "example/detection/data/coco",
ant_path = "example/detection/data/coco/coco.json"
)
```
or
```python
from cosmos.detection import show_general
show_general(
img_name = f"pic0.jpg",
ant_path = "example/detection/data/general.json",
) # when the anntotation includes predictions it will be shown!
```
+ Output Analysis
+ Please use the above `Format conversion` to change data format as [general](./example/detection/data/general.json)
+ The analysis pipeline is at [here](./cosmos/detection/output_analysis.yaml)
```python
from cosmos.detection import DetectionAnalysis
DetectionAnalysis(
ant_path = f"example/detection/data/general.json",
save_folder = f"example/detection/output/metrics"
)
```
## **Concepts**
+ **[Classification]** Complicated tasks
| task | label idx min | compute class-0 metrics | threshold optimization | data format |
| - | - | - | - | - |
| binary classification | 0 | V | V | [single_label](./example/classification/data/single_label.json) |
| binary classification (cls-0 background) | 1 | | V | [single_label_background](./example/classification/data/single_label_background.json) |
| multi-class classification | 0 | V | | single_label |
| multi-class classification (cls-0 background) | 1 | | V | single_label_background |
| multi-label classification (cls-0 background) | 0 | | V | [multi_label](./example/classification/data/multi_label.json) |
+ **[Classification]** threshold optimization
+ `multi-class classification (cls-0 background)` checks whether prob-cls-0 < threshold, if yes, the pd-cls is pd[1:].argmax()
+ `multi-class classification (cls-0 background)` and `multi-label classification (cls-0 background)` take the mean of all optimized thresghold for each foreground class
+ **[Object Detection]** A general format, the most convenient
The formats can be summarized as following:
| format | extension | files | type | box | disadvantage |
| - | - | - | - | - | - |
| coco | .json | 1 | int | (xmin, ymin, w, h) | get label of an image |
| yolo | .txt | len(imgs) | float | (cx, cy, w/2, h/2) | visualization, compute metrics, etc. |
| voc | .xml | len(imgs) | int | (xmin, ymin, xmax, ymax) | get class list |
| [general](./example/detection/data/general.json)| .json | 1 | int | (xmin, ymin, w, h) | **NO** |
## **Examples**
+ detection format conversion workflow
+ detection visualization
+ confusion
+ prf curves
