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@@ -0,0 +1 @@ +/NudeNet-2.0.9.tar.gz diff --git a/python-nudenet.spec b/python-nudenet.spec new file mode 100644 index 0000000..6bd53bb --- /dev/null +++ b/python-nudenet.spec @@ -0,0 +1,539 @@ +%global _empty_manifest_terminate_build 0 +Name: python-NudeNet +Version: 2.0.9 +Release: 1 +Summary: An ensemble of Neural Nets for Nudity Detection and Censoring +License: GPLv3 +URL: https://github.com/bedapudi6788/NudeNet +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/d4/dc/efe7618feb71cc833ce3e208f0acde41c3b883815ebe382db5b9f0d7dee7/NudeNet-2.0.9.tar.gz +BuildArch: noarch + +Requires: python3-pillow +Requires: python3-opencv-python-headless +Requires: python3-pydload +Requires: python3-scikit-image +Requires: python3-onnxruntime + +%description + +# NudeNet: Neural Nets for Nudity Classification, Detection and selective censoring + +[](https://zenodo.org/badge/latestdoi/173154449) + +Uncensored version of the following image can be found at https://i.imgur.com/rga6845.jpg (NSFW) + + + +**Classifier classes:** +|class name | Description | +|--------|:--------------: +|safe | Image/Video is not sexually explicit | +|unsafe | Image/Video is sexually explicit| + +**Default Detector classes:** +|class name | Description | +|--------|:-------------------------------------: +|EXPOSED_ANUS | Exposed Anus; Any gender | +|EXPOSED_ARMPITS | Exposed Armpits; Any gender | +|COVERED_BELLY | Provocative, but covered Belly; Any gender | +|EXPOSED_BELLY | Exposed Belly; Any gender | +|COVERED_BUTTOCKS | Provocative, but covered Buttocks; Any gender | +|EXPOSED_BUTTOCKS | Exposed Buttocks; Any gender | +|FACE_F | Female Face| +|FACE_M | Male Face| +|COVERED_FEET |Covered Feet; Any gender | +|EXPOSED_FEET | Exposed Feet; Any gender| +|COVERED_BREAST_F | Provocative, but covered Breast; Female | +|EXPOSED_BREAST_F | Exposed Breast; Female | +|COVERED_GENITALIA_F |Provocative, but covered Genitalia; Female| +|EXPOSED_GENITALIA_F |Exposed Genitalia; Female | +|EXPOSED_BREAST_M |Exposed Breast; Male | +|EXPOSED_GENITALIA_M |Exposed Genitalia; Male | + +**Base Detector classes:** +|class name | Description | +|--------|:--------------: +|EXPOSED_BELLY | Exposed Belly; Any gender | +|EXPOSED_BUTTOCKS | Exposed Buttocks; Any gender | +|EXPOSED_BREAST_F | Exposed Breast; Female | +|EXPOSED_GENITALIA_F |Exposed Genitalia; Female | +|EXPOSED_GENITALIA_M |Exposed Genitalia; Male | +|EXPOSED_BREAST_M |Exposed Breast; Male | + +# As self-hostable API service +```bash +# Classifier +docker run -it -p8080:8080 notaitech/nudenet:classifier + +# Detector +docker run -it -p8080:8080 notaitech/nudenet:detector + +# See fastDeploy-file_client.py for running predictions via fastDeploy's REST endpoints +wget https://raw.githubusercontent.com/notAI-tech/fastDeploy/master/cli/fastDeploy-file_client.py +# Single input +python fastDeploy-file_client.py --file PATH_TO_YOUR_IMAGE + +# Client side batching +python fastDeploy-file_client.py --dir PATH_TO_FOLDER --ext jpg +``` + +**Note: golang example https://github.com/notAI-tech/NudeNet/issues/63#issuecomment-729555360**, thanks to [Preetham Kamidi](https://github.com/preetham) + + +# As Python module +**Installation**: +```bash +pip install --upgrade nudenet +``` + +**Classifier Usage**: +```python +# Import module +from nudenet import NudeClassifier + +# initialize classifier (downloads the checkpoint file automatically the first time) +classifier = NudeClassifier() + +# Classify single image +classifier.classify('path_to_image_1') +# Returns {'path_to_image_1': {'safe': PROBABILITY, 'unsafe': PROBABILITY}} +# Classify multiple images (batch prediction) +# batch_size is optional; defaults to 4 +classifier.classify(['path_to_image_1', 'path_to_image_2'], batch_size=BATCH_SIZE) +# Returns {'path_to_image_1': {'safe': PROBABILITY, 'unsafe': PROBABILITY}, +# 'path_to_image_2': {'safe': PROBABILITY, 'unsafe': PROBABILITY}} + +# Classify video +# batch_size is optional; defaults to 4 +classifier.classify_video('path_to_video', batch_size=BATCH_SIZE) +# Returns {"metadata": {"fps": FPS, "video_length": TOTAL_N_FRAMES, "video_path": 'path_to_video'}, +# "preds": {frame_i: {'safe': PROBABILITY, 'unsafe': PROBABILITY}, ....}} + +``` + +Thanks to [Johnny Urosevic](https://github.com/JohnnyUrosevic), NudeClassifier is also available in tflite. + +**TFLite Classifier Usage**: +```python +# Import module +from nudenet import NudeClassifierLite + +# initialize classifier (downloads the checkpoint file automatically the first time) +classifier_lite = NudeClassifierLite() + +# Classify single image +classifier_lite.classify('path_to_image_1') +# Returns {'path_to_image_1': {'safe': PROBABILITY, 'unsafe': PROBABILITY}} +# Classify multiple images (batch prediction) +# batch_size is optional; defaults to 4 +classifier_lite.classify(['path_to_image_1', 'path_to_image_2']) +# Returns {'path_to_image_1': {'safe': PROBABILITY, 'unsafe': PROBABILITY}, +# 'path_to_image_2': {'safe': PROBABILITY, 'unsafe': PROBABILITY}} + +``` + +Using the tflite classifier from flutter: **https://github.com/ndaysinaiK/nude-test** + +**Detector Usage**: +```python +# Import module +from nudenet import NudeDetector + +# initialize detector (downloads the checkpoint file automatically the first time) +detector = NudeDetector() # detector = NudeDetector('base') for the "base" version of detector. + +# Detect single image +detector.detect('path_to_image') +# fast mode is ~3x faster compared to default mode with slightly lower accuracy. +detector.detect('path_to_image', mode='fast') +# Returns [{'box': LIST_OF_COORDINATES, 'score': PROBABILITY, 'label': LABEL}, ...] + +# Detect video +# batch_size is optional; defaults to 2 +# show_progress is optional; defaults to True +detector.detect_video('path_to_video', batch_size=BATCH_SIZE, show_progress=BOOLEAN) +# fast mode is ~3x faster compared to default mode with slightly lower accuracy. +detector.detect_video('path_to_video', batch_size=BATCH_SIZE, show_progress=BOOLEAN, mode='fast') +# Returns {"metadata": {"fps": FPS, "video_length": TOTAL_N_FRAMES, "video_path": 'path_to_video'}, +# "preds": {frame_i: {'box': LIST_OF_COORDINATES, 'score': PROBABILITY, 'label': LABEL}, ...], ....}} + + + +``` + +# Notes: +- detect_video and classify_video first identify the "unique" frames in a video and run predictions on them for significant performance improvement. +- V1 of NudeDetector (available in master branch of this repo) was trained on 12000 images labelled by the good folks at cti-community. +- V2 (current version) of NudeDetector is trained on 160,000 entirely auto-labelled (using classification heat maps and various other hybrid techniques) images. +- The entire data for the classifier is available at https://archive.org/details/NudeNet_classifier_dataset_v1 +- A part of the auto-labelled data (Images are from the classifier dataset above) used to train the base Detector is available at https://github.com/notAI-tech/NudeNet/releases/download/v0/DETECTOR_AUTO_GENERATED_DATA.zip + + + + +%package -n python3-NudeNet +Summary: An ensemble of Neural Nets for Nudity Detection and Censoring +Provides: python-NudeNet +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-NudeNet + +# NudeNet: Neural Nets for Nudity Classification, Detection and selective censoring + +[](https://zenodo.org/badge/latestdoi/173154449) + +Uncensored version of the following image can be found at https://i.imgur.com/rga6845.jpg (NSFW) + + + +**Classifier classes:** +|class name | Description | +|--------|:--------------: +|safe | Image/Video is not sexually explicit | +|unsafe | Image/Video is sexually explicit| + +**Default Detector classes:** +|class name | Description | +|--------|:-------------------------------------: +|EXPOSED_ANUS | Exposed Anus; Any gender | +|EXPOSED_ARMPITS | Exposed Armpits; Any gender | +|COVERED_BELLY | Provocative, but covered Belly; Any gender | +|EXPOSED_BELLY | Exposed Belly; Any gender | +|COVERED_BUTTOCKS | Provocative, but covered Buttocks; Any gender | +|EXPOSED_BUTTOCKS | Exposed Buttocks; Any gender | +|FACE_F | Female Face| +|FACE_M | Male Face| +|COVERED_FEET |Covered Feet; Any gender | +|EXPOSED_FEET | Exposed Feet; Any gender| +|COVERED_BREAST_F | Provocative, but covered Breast; Female | +|EXPOSED_BREAST_F | Exposed Breast; Female | +|COVERED_GENITALIA_F |Provocative, but covered Genitalia; Female| +|EXPOSED_GENITALIA_F |Exposed Genitalia; Female | +|EXPOSED_BREAST_M |Exposed Breast; Male | +|EXPOSED_GENITALIA_M |Exposed Genitalia; Male | + +**Base Detector classes:** +|class name | Description | +|--------|:--------------: +|EXPOSED_BELLY | Exposed Belly; Any gender | +|EXPOSED_BUTTOCKS | Exposed Buttocks; Any gender | +|EXPOSED_BREAST_F | Exposed Breast; Female | +|EXPOSED_GENITALIA_F |Exposed Genitalia; Female | +|EXPOSED_GENITALIA_M |Exposed Genitalia; Male | +|EXPOSED_BREAST_M |Exposed Breast; Male | + +# As self-hostable API service +```bash +# Classifier +docker run -it -p8080:8080 notaitech/nudenet:classifier + +# Detector +docker run -it -p8080:8080 notaitech/nudenet:detector + +# See fastDeploy-file_client.py for running predictions via fastDeploy's REST endpoints +wget https://raw.githubusercontent.com/notAI-tech/fastDeploy/master/cli/fastDeploy-file_client.py +# Single input +python fastDeploy-file_client.py --file PATH_TO_YOUR_IMAGE + +# Client side batching +python fastDeploy-file_client.py --dir PATH_TO_FOLDER --ext jpg +``` + +**Note: golang example https://github.com/notAI-tech/NudeNet/issues/63#issuecomment-729555360**, thanks to [Preetham Kamidi](https://github.com/preetham) + + +# As Python module +**Installation**: +```bash +pip install --upgrade nudenet +``` + +**Classifier Usage**: +```python +# Import module +from nudenet import NudeClassifier + +# initialize classifier (downloads the checkpoint file automatically the first time) +classifier = NudeClassifier() + +# Classify single image +classifier.classify('path_to_image_1') +# Returns {'path_to_image_1': {'safe': PROBABILITY, 'unsafe': PROBABILITY}} +# Classify multiple images (batch prediction) +# batch_size is optional; defaults to 4 +classifier.classify(['path_to_image_1', 'path_to_image_2'], batch_size=BATCH_SIZE) +# Returns {'path_to_image_1': {'safe': PROBABILITY, 'unsafe': PROBABILITY}, +# 'path_to_image_2': {'safe': PROBABILITY, 'unsafe': PROBABILITY}} + +# Classify video +# batch_size is optional; defaults to 4 +classifier.classify_video('path_to_video', batch_size=BATCH_SIZE) +# Returns {"metadata": {"fps": FPS, "video_length": TOTAL_N_FRAMES, "video_path": 'path_to_video'}, +# "preds": {frame_i: {'safe': PROBABILITY, 'unsafe': PROBABILITY}, ....}} + +``` + +Thanks to [Johnny Urosevic](https://github.com/JohnnyUrosevic), NudeClassifier is also available in tflite. + +**TFLite Classifier Usage**: +```python +# Import module +from nudenet import NudeClassifierLite + +# initialize classifier (downloads the checkpoint file automatically the first time) +classifier_lite = NudeClassifierLite() + +# Classify single image +classifier_lite.classify('path_to_image_1') +# Returns {'path_to_image_1': {'safe': PROBABILITY, 'unsafe': PROBABILITY}} +# Classify multiple images (batch prediction) +# batch_size is optional; defaults to 4 +classifier_lite.classify(['path_to_image_1', 'path_to_image_2']) +# Returns {'path_to_image_1': {'safe': PROBABILITY, 'unsafe': PROBABILITY}, +# 'path_to_image_2': {'safe': PROBABILITY, 'unsafe': PROBABILITY}} + +``` + +Using the tflite classifier from flutter: **https://github.com/ndaysinaiK/nude-test** + +**Detector Usage**: +```python +# Import module +from nudenet import NudeDetector + +# initialize detector (downloads the checkpoint file automatically the first time) +detector = NudeDetector() # detector = NudeDetector('base') for the "base" version of detector. + +# Detect single image +detector.detect('path_to_image') +# fast mode is ~3x faster compared to default mode with slightly lower accuracy. +detector.detect('path_to_image', mode='fast') +# Returns [{'box': LIST_OF_COORDINATES, 'score': PROBABILITY, 'label': LABEL}, ...] + +# Detect video +# batch_size is optional; defaults to 2 +# show_progress is optional; defaults to True +detector.detect_video('path_to_video', batch_size=BATCH_SIZE, show_progress=BOOLEAN) +# fast mode is ~3x faster compared to default mode with slightly lower accuracy. +detector.detect_video('path_to_video', batch_size=BATCH_SIZE, show_progress=BOOLEAN, mode='fast') +# Returns {"metadata": {"fps": FPS, "video_length": TOTAL_N_FRAMES, "video_path": 'path_to_video'}, +# "preds": {frame_i: {'box': LIST_OF_COORDINATES, 'score': PROBABILITY, 'label': LABEL}, ...], ....}} + + + +``` + +# Notes: +- detect_video and classify_video first identify the "unique" frames in a video and run predictions on them for significant performance improvement. +- V1 of NudeDetector (available in master branch of this repo) was trained on 12000 images labelled by the good folks at cti-community. +- V2 (current version) of NudeDetector is trained on 160,000 entirely auto-labelled (using classification heat maps and various other hybrid techniques) images. +- The entire data for the classifier is available at https://archive.org/details/NudeNet_classifier_dataset_v1 +- A part of the auto-labelled data (Images are from the classifier dataset above) used to train the base Detector is available at https://github.com/notAI-tech/NudeNet/releases/download/v0/DETECTOR_AUTO_GENERATED_DATA.zip + + + + +%package help +Summary: Development documents and examples for NudeNet +Provides: python3-NudeNet-doc +%description help + +# NudeNet: Neural Nets for Nudity Classification, Detection and selective censoring + +[](https://zenodo.org/badge/latestdoi/173154449) + +Uncensored version of the following image can be found at https://i.imgur.com/rga6845.jpg (NSFW) + + + +**Classifier classes:** +|class name | Description | +|--------|:--------------: +|safe | Image/Video is not sexually explicit | +|unsafe | Image/Video is sexually explicit| + +**Default Detector classes:** +|class name | Description | +|--------|:-------------------------------------: +|EXPOSED_ANUS | Exposed Anus; Any gender | +|EXPOSED_ARMPITS | Exposed Armpits; Any gender | +|COVERED_BELLY | Provocative, but covered Belly; Any gender | +|EXPOSED_BELLY | Exposed Belly; Any gender | +|COVERED_BUTTOCKS | Provocative, but covered Buttocks; Any gender | +|EXPOSED_BUTTOCKS | Exposed Buttocks; Any gender | +|FACE_F | Female Face| +|FACE_M | Male Face| +|COVERED_FEET |Covered Feet; Any gender | +|EXPOSED_FEET | Exposed Feet; Any gender| +|COVERED_BREAST_F | Provocative, but covered Breast; Female | +|EXPOSED_BREAST_F | Exposed Breast; Female | +|COVERED_GENITALIA_F |Provocative, but covered Genitalia; Female| +|EXPOSED_GENITALIA_F |Exposed Genitalia; Female | +|EXPOSED_BREAST_M |Exposed Breast; Male | +|EXPOSED_GENITALIA_M |Exposed Genitalia; Male | + +**Base Detector classes:** +|class name | Description | +|--------|:--------------: +|EXPOSED_BELLY | Exposed Belly; Any gender | +|EXPOSED_BUTTOCKS | Exposed Buttocks; Any gender | +|EXPOSED_BREAST_F | Exposed Breast; Female | +|EXPOSED_GENITALIA_F |Exposed Genitalia; Female | +|EXPOSED_GENITALIA_M |Exposed Genitalia; Male | +|EXPOSED_BREAST_M |Exposed Breast; Male | + +# As self-hostable API service +```bash +# Classifier +docker run -it -p8080:8080 notaitech/nudenet:classifier + +# Detector +docker run -it -p8080:8080 notaitech/nudenet:detector + +# See fastDeploy-file_client.py for running predictions via fastDeploy's REST endpoints +wget https://raw.githubusercontent.com/notAI-tech/fastDeploy/master/cli/fastDeploy-file_client.py +# Single input +python fastDeploy-file_client.py --file PATH_TO_YOUR_IMAGE + +# Client side batching +python fastDeploy-file_client.py --dir PATH_TO_FOLDER --ext jpg +``` + +**Note: golang example https://github.com/notAI-tech/NudeNet/issues/63#issuecomment-729555360**, thanks to [Preetham Kamidi](https://github.com/preetham) + + +# As Python module +**Installation**: +```bash +pip install --upgrade nudenet +``` + +**Classifier Usage**: +```python +# Import module +from nudenet import NudeClassifier + +# initialize classifier (downloads the checkpoint file automatically the first time) +classifier = NudeClassifier() + +# Classify single image +classifier.classify('path_to_image_1') +# Returns {'path_to_image_1': {'safe': PROBABILITY, 'unsafe': PROBABILITY}} +# Classify multiple images (batch prediction) +# batch_size is optional; defaults to 4 +classifier.classify(['path_to_image_1', 'path_to_image_2'], batch_size=BATCH_SIZE) +# Returns {'path_to_image_1': {'safe': PROBABILITY, 'unsafe': PROBABILITY}, +# 'path_to_image_2': {'safe': PROBABILITY, 'unsafe': PROBABILITY}} + +# Classify video +# batch_size is optional; defaults to 4 +classifier.classify_video('path_to_video', batch_size=BATCH_SIZE) +# Returns {"metadata": {"fps": FPS, "video_length": TOTAL_N_FRAMES, "video_path": 'path_to_video'}, +# "preds": {frame_i: {'safe': PROBABILITY, 'unsafe': PROBABILITY}, ....}} + +``` + +Thanks to [Johnny Urosevic](https://github.com/JohnnyUrosevic), NudeClassifier is also available in tflite. + +**TFLite Classifier Usage**: +```python +# Import module +from nudenet import NudeClassifierLite + +# initialize classifier (downloads the checkpoint file automatically the first time) +classifier_lite = NudeClassifierLite() + +# Classify single image +classifier_lite.classify('path_to_image_1') +# Returns {'path_to_image_1': {'safe': PROBABILITY, 'unsafe': PROBABILITY}} +# Classify multiple images (batch prediction) +# batch_size is optional; defaults to 4 +classifier_lite.classify(['path_to_image_1', 'path_to_image_2']) +# Returns {'path_to_image_1': {'safe': PROBABILITY, 'unsafe': PROBABILITY}, +# 'path_to_image_2': {'safe': PROBABILITY, 'unsafe': PROBABILITY}} + +``` + +Using the tflite classifier from flutter: **https://github.com/ndaysinaiK/nude-test** + +**Detector Usage**: +```python +# Import module +from nudenet import NudeDetector + +# initialize detector (downloads the checkpoint file automatically the first time) +detector = NudeDetector() # detector = NudeDetector('base') for the "base" version of detector. + +# Detect single image +detector.detect('path_to_image') +# fast mode is ~3x faster compared to default mode with slightly lower accuracy. +detector.detect('path_to_image', mode='fast') +# Returns [{'box': LIST_OF_COORDINATES, 'score': PROBABILITY, 'label': LABEL}, ...] + +# Detect video +# batch_size is optional; defaults to 2 +# show_progress is optional; defaults to True +detector.detect_video('path_to_video', batch_size=BATCH_SIZE, show_progress=BOOLEAN) +# fast mode is ~3x faster compared to default mode with slightly lower accuracy. +detector.detect_video('path_to_video', batch_size=BATCH_SIZE, show_progress=BOOLEAN, mode='fast') +# Returns {"metadata": {"fps": FPS, "video_length": TOTAL_N_FRAMES, "video_path": 'path_to_video'}, +# "preds": {frame_i: {'box': LIST_OF_COORDINATES, 'score': PROBABILITY, 'label': LABEL}, ...], ....}} + + + +``` + +# Notes: +- detect_video and classify_video first identify the "unique" frames in a video and run predictions on them for significant performance improvement. +- V1 of NudeDetector (available in master branch of this repo) was trained on 12000 images labelled by the good folks at cti-community. +- V2 (current version) of NudeDetector is trained on 160,000 entirely auto-labelled (using classification heat maps and various other hybrid techniques) images. +- The entire data for the classifier is available at https://archive.org/details/NudeNet_classifier_dataset_v1 +- A part of the auto-labelled data (Images are from the classifier dataset above) used to train the base Detector is available at https://github.com/notAI-tech/NudeNet/releases/download/v0/DETECTOR_AUTO_GENERATED_DATA.zip + + + + +%prep +%autosetup -n NudeNet-2.0.9 + +%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-NudeNet -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Wed May 10 2023 Python_Bot <Python_Bot@openeuler.org> - 2.0.9-1 +- Package Spec generated @@ -0,0 +1 @@ +d0030939975000c6bbac5cb4c6788daf NudeNet-2.0.9.tar.gz |