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@@ -0,0 +1 @@ +/mean_average_precision-2021.4.26.0.tar.gz diff --git a/python-mean-average-precision.spec b/python-mean-average-precision.spec new file mode 100644 index 0000000..589dcb1 --- /dev/null +++ b/python-mean-average-precision.spec @@ -0,0 +1,284 @@ +%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 @@ -0,0 +1 @@ +e9340c64fc11c38979a49da1441257d6 mean_average_precision-2021.4.26.0.tar.gz |