%global _empty_manifest_terminate_build 0
Name:		python-mean-average-precision
Version:	2021.4.26.0
Release:	1
Summary:	Mean Average Precision evaluator for object detection.
License:	MIT
URL:		https://github.com/bes-dev/mean_average_precision
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/d0/c8/e0fa7f81b32e5e698d13ff19f2899a854728a60d8eae40b73b6d0dde7568/mean_average_precision-2021.4.26.0.tar.gz
BuildArch:	noarch

Requires:	python3-numpy
Requires:	python3-pandas

%description
# mAP: Mean Average Precision for Object Detection

A simple library for the evaluation of object detectors.

<p align="center">
  <img src="resources/img0.jpeg"/>
</p>

In practice, a **higher mAP** value indicates a **better performance** of your detector, given your ground-truth and set of classes.

## Install package

```bash
pip install mean_average_precision
```

## Install the latest version

```bash
pip install --upgrade git+https://github.com/bes-dev/mean_average_precision.git
```

## Example
```python
import numpy as np
from mean_average_precision import MetricBuilder

# [xmin, ymin, xmax, ymax, class_id, difficult, crowd]
gt = np.array([
    [439, 157, 556, 241, 0, 0, 0],
    [437, 246, 518, 351, 0, 0, 0],
    [515, 306, 595, 375, 0, 0, 0],
    [407, 386, 531, 476, 0, 0, 0],
    [544, 419, 621, 476, 0, 0, 0],
    [609, 297, 636, 392, 0, 0, 0]
])

# [xmin, ymin, xmax, ymax, class_id, confidence]
preds = np.array([
    [429, 219, 528, 247, 0, 0.460851],
    [433, 260, 506, 336, 0, 0.269833],
    [518, 314, 603, 369, 0, 0.462608],
    [592, 310, 634, 388, 0, 0.298196],
    [403, 384, 517, 461, 0, 0.382881],
    [405, 429, 519, 470, 0, 0.369369],
    [433, 272, 499, 341, 0, 0.272826],
    [413, 390, 515, 459, 0, 0.619459]
])

# print list of available metrics
print(MetricBuilder.get_metrics_list())

# create metric_fn
metric_fn = MetricBuilder.build_evaluation_metric("map_2d", async_mode=True, num_classes=1)

# add some samples to evaluation
for i in range(10):
    metric_fn.add(preds, gt)

# compute PASCAL VOC metric
print(f"VOC PASCAL mAP: {metric_fn.value(iou_thresholds=0.5, recall_thresholds=np.arange(0., 1.1, 0.1))['mAP']}")

# compute PASCAL VOC metric at the all points
print(f"VOC PASCAL mAP in all points: {metric_fn.value(iou_thresholds=0.5)['mAP']}")

# compute metric COCO metric
print(f"COCO mAP: {metric_fn.value(iou_thresholds=np.arange(0.5, 1.0, 0.05), recall_thresholds=np.arange(0., 1.01, 0.01), mpolicy='soft')['mAP']}")
```




%package -n python3-mean-average-precision
Summary:	Mean Average Precision evaluator for object detection.
Provides:	python-mean-average-precision
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-mean-average-precision
# mAP: Mean Average Precision for Object Detection

A simple library for the evaluation of object detectors.

<p align="center">
  <img src="resources/img0.jpeg"/>
</p>

In practice, a **higher mAP** value indicates a **better performance** of your detector, given your ground-truth and set of classes.

## Install package

```bash
pip install mean_average_precision
```

## Install the latest version

```bash
pip install --upgrade git+https://github.com/bes-dev/mean_average_precision.git
```

## Example
```python
import numpy as np
from mean_average_precision import MetricBuilder

# [xmin, ymin, xmax, ymax, class_id, difficult, crowd]
gt = np.array([
    [439, 157, 556, 241, 0, 0, 0],
    [437, 246, 518, 351, 0, 0, 0],
    [515, 306, 595, 375, 0, 0, 0],
    [407, 386, 531, 476, 0, 0, 0],
    [544, 419, 621, 476, 0, 0, 0],
    [609, 297, 636, 392, 0, 0, 0]
])

# [xmin, ymin, xmax, ymax, class_id, confidence]
preds = np.array([
    [429, 219, 528, 247, 0, 0.460851],
    [433, 260, 506, 336, 0, 0.269833],
    [518, 314, 603, 369, 0, 0.462608],
    [592, 310, 634, 388, 0, 0.298196],
    [403, 384, 517, 461, 0, 0.382881],
    [405, 429, 519, 470, 0, 0.369369],
    [433, 272, 499, 341, 0, 0.272826],
    [413, 390, 515, 459, 0, 0.619459]
])

# print list of available metrics
print(MetricBuilder.get_metrics_list())

# create metric_fn
metric_fn = MetricBuilder.build_evaluation_metric("map_2d", async_mode=True, num_classes=1)

# add some samples to evaluation
for i in range(10):
    metric_fn.add(preds, gt)

# compute PASCAL VOC metric
print(f"VOC PASCAL mAP: {metric_fn.value(iou_thresholds=0.5, recall_thresholds=np.arange(0., 1.1, 0.1))['mAP']}")

# compute PASCAL VOC metric at the all points
print(f"VOC PASCAL mAP in all points: {metric_fn.value(iou_thresholds=0.5)['mAP']}")

# compute metric COCO metric
print(f"COCO mAP: {metric_fn.value(iou_thresholds=np.arange(0.5, 1.0, 0.05), recall_thresholds=np.arange(0., 1.01, 0.01), mpolicy='soft')['mAP']}")
```




%package help
Summary:	Development documents and examples for mean-average-precision
Provides:	python3-mean-average-precision-doc
%description help
# mAP: Mean Average Precision for Object Detection

A simple library for the evaluation of object detectors.

<p align="center">
  <img src="resources/img0.jpeg"/>
</p>

In practice, a **higher mAP** value indicates a **better performance** of your detector, given your ground-truth and set of classes.

## Install package

```bash
pip install mean_average_precision
```

## Install the latest version

```bash
pip install --upgrade git+https://github.com/bes-dev/mean_average_precision.git
```

## Example
```python
import numpy as np
from mean_average_precision import MetricBuilder

# [xmin, ymin, xmax, ymax, class_id, difficult, crowd]
gt = np.array([
    [439, 157, 556, 241, 0, 0, 0],
    [437, 246, 518, 351, 0, 0, 0],
    [515, 306, 595, 375, 0, 0, 0],
    [407, 386, 531, 476, 0, 0, 0],
    [544, 419, 621, 476, 0, 0, 0],
    [609, 297, 636, 392, 0, 0, 0]
])

# [xmin, ymin, xmax, ymax, class_id, confidence]
preds = np.array([
    [429, 219, 528, 247, 0, 0.460851],
    [433, 260, 506, 336, 0, 0.269833],
    [518, 314, 603, 369, 0, 0.462608],
    [592, 310, 634, 388, 0, 0.298196],
    [403, 384, 517, 461, 0, 0.382881],
    [405, 429, 519, 470, 0, 0.369369],
    [433, 272, 499, 341, 0, 0.272826],
    [413, 390, 515, 459, 0, 0.619459]
])

# print list of available metrics
print(MetricBuilder.get_metrics_list())

# create metric_fn
metric_fn = MetricBuilder.build_evaluation_metric("map_2d", async_mode=True, num_classes=1)

# add some samples to evaluation
for i in range(10):
    metric_fn.add(preds, gt)

# compute PASCAL VOC metric
print(f"VOC PASCAL mAP: {metric_fn.value(iou_thresholds=0.5, recall_thresholds=np.arange(0., 1.1, 0.1))['mAP']}")

# compute PASCAL VOC metric at the all points
print(f"VOC PASCAL mAP in all points: {metric_fn.value(iou_thresholds=0.5)['mAP']}")

# compute metric COCO metric
print(f"COCO mAP: {metric_fn.value(iou_thresholds=np.arange(0.5, 1.0, 0.05), recall_thresholds=np.arange(0., 1.01, 0.01), mpolicy='soft')['mAP']}")
```




%prep
%autosetup -n mean-average-precision-2021.4.26.0

%build
%py3_build

%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .

%files -n python3-mean-average-precision -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 2021.4.26.0-1
- Package Spec generated