https://github.com/zfturbo/mean-average-precision-for-boxes
Function to calculate mAP for set of detected boxes and annotated boxes.
https://github.com/zfturbo/mean-average-precision-for-boxes
average-precision metrics object-detection
Last synced: about 2 months ago
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Function to calculate mAP for set of detected boxes and annotated boxes.
- Host: GitHub
- URL: https://github.com/zfturbo/mean-average-precision-for-boxes
- Owner: ZFTurbo
- Created: 2019-09-22T18:26:02.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2024-05-28T18:47:22.000Z (12 months ago)
- Last Synced: 2025-03-30T16:12:45.164Z (about 2 months ago)
- Topics: average-precision, metrics, object-detection
- Language: Python
- Size: 1.73 MB
- Stars: 133
- Watchers: 3
- Forks: 21
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
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README
Function to calculate mean average precision (mAP) for set of boxes. Useful for object detection pipelines.
# Requirements
python 3.*, numpy, pandas
# Installation
```
pip install map-boxes
```
## Usage example:
You can provide paths to CSV-files:
```python
from map_boxes import mean_average_precision_for_boxes
annotations_file = 'example/annotations.csv'
detections_file = 'example/detections.csv'
mean_ap, average_precisions = mean_average_precision_for_boxes(annotations_file, detections_file)
```
or you can pass directly numpy arrays of shapes **(N, 6)** and **(M, 7)**. **Be careful about order of variables in arrays!**:
```python
from map_boxes import mean_average_precision_for_boxes
import pandas as pd
ann = pd.read_csv('example/annotations.csv')
det = pd.read_csv('example/detections.csv')
ann = ann[['ImageID', 'LabelName', 'XMin', 'XMax', 'YMin', 'YMax']].values
det = det[['ImageID', 'LabelName', 'Conf', 'XMin', 'XMax', 'YMin', 'YMax']].values
mean_ap, average_precisions = mean_average_precision_for_boxes(ann, det)
```
## Input files format
Boxes must be in normalized form e.g. coordinates must be in range: `[0, 1]`. To normalize pixel values you need to recalculate them as: `x_norm = x / width`, `y_norm = y / height`
* Annotation CSV-file:
```csv
ImageID,LabelName,XMin,XMax,YMin,YMax
i0.jpg,Shellfish,0.0875,0.8171875,0.35625,0.8958333
i0.jpg,Seafood,0.0875,0.8171875,0.35625,0.8958333
i1.jpg,Tin can,0.1296875,0.3375,0.31875,0.68958336
i1.jpg,Drink,0.4234375,0.546875,0.58958334,0.92083335
i1.jpg,Drink,0.5375,0.7375,0.16666667,0.575
...
```
* Detection CSV-file:
```csv
ImageID,LabelName,Conf,XMin,XMax,YMin,YMax
i0.jpg,Turtle,0.41471,0.1382,0.7440,0.3585,0.8951
i0.jpg,Reptile,0.32093,0.1391,0.7439,0.3582,0.8944
i0.jpg,Seahorse,0.11860,0.1393,0.7434,0.3589,0.8943
i0.jpg,Caterpillar,0.11275,0.1390,0.7438,0.3588,0.8948
i1.jpg,Personal care,0.42326,0.2624,0.5473,0.1112,0.7274
i1.jpg,Personal care,0.31120,0.1318,0.3381,0.3149,0.6863
i1.jpg,Personal care,0.34866,0.4277,0.5446,0.5861,0.9211
i1.jpg,Blender,0.10578,0.7678,0.9476,0.2674,0.5847
...
```