automatic import of python-actspotteropeneuler20.03

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+/actspotter-0.1.3.tar.gz
diff --git a/python-actspotter.spec b/python-actspotter.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-actspotter
+Version:	0.1.3
+Release:	1
+Summary:	Actspotter library for detecting activities
+License:	GPL 3.0
+URL:		https://github.com/aaad/actspotter
+Source0:	https://mirrors.aliyun.com/pypi/web/packages/55/23/8a70229047d4c4d3b386656a97c54e9da03c1d43fe38ce708090fd36ec78/actspotter-0.1.3.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-tensorflow
+
+%description
+The `actspotter` is a library / tensorflow model for detecting activities. It allows to classify body activities in images or videos. The package is limited to videos and images with only one person by design.
+The following classes are available: 
+- none
+- pull_up_up
+- pull_up_down
+- pull_up_none
+- push_up_up
+- push_up_down
+- push_up_none
+- sit_up_up
+- sit_up_down
+- sit_up_none
+The package is currently in early development.
+Future plans
+~~~~~~~~~~~~~
+Tensorflow model deployment will be integrated soon. Currently this package allows to classify push-ups, sit-ups and pull-ups. In future version kicks and others body activities will follow. 
+It is also planned to provide a signal processing layer that allows to easily detect connected activites and count them. 
+Another application will be to integrate with keyboard drivers so that activities could be used for controlling video games (e.g. by kicks).
+Installation
+~~~~~~~~~~~~
+Install this library in a `virtualenv`_ using pip. `virtualenv`_ is a tool to
+create isolated Python environments. The basic problem it addresses is one of
+dependencies and versions, and indirectly permissions.
+With `virtualenv`_, it's possible to install this library without needing system
+install permissions, and without clashing with the installed system
+dependencies.
+Supported Python Versions
+^^^^^^^^^^^^^^^^^^^^^^^^^
+Python >= 3.6
+Mac/Linux
+^^^^^^^^^
+    pip install virtualenv
+    virtualenv <your-env>
+    source <your-env>/bin/activate
+    <your-env>/bin/pip install actspotter
+Windows
+^^^^^^^
+    pip install virtualenv
+    virtualenv <your-env>
+    <your-env>\Scripts\activate
+    <your-env>\Scripts\pip.exe install actspotter
+Example Usage
+~~~~~~~~~~~~~
+Requirement: cv2 (opencv) installed.
+Classification of images:
+    import cv2
+    import tensorflow as tf
+    from actspotter import ImageClassifier, classify_image_input_dimension, class_names
+    def _resize(frame, dim=classify_image_input_dimension):
+        frame = cv2.resize(frame, dim, interpolation=cv2.INTER_AREA)
+        return frame
+    def _to_tf_array(frame, dim=classify_image_input_dimension):
+        frame = _resize(frame, dim)
+        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        frame = tf.convert_to_tensor(frame, dtype=tf.float32)
+        return frame
+    images = [
+        to_tf_array(cv2.imread("test.jpg")),
+    ]
+    print(class_names)
+    print(image_classifier.classify_images(images))
+Classification of a video:
+    import cv2
+    import tensorflow as tf
+    from actspotter import VideoClassifier, classify_image_input_dimension
+    def _resize(frame, dim=classify_image_input_dimension):
+        return frame
+    def _to_tf_array(frame, dim=classify_image_input_dimension):
+        frame = _resize(frame, dim)
+        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        frame = tf.convert_to_tensor(frame, dtype=tf.float32)
+        return frame
+    cap = cv2.VideoCapture(0)
+    video_classifier = VideoClassifier(buffer_size=4)
+    video_classifier.start()
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if ret == True:
+            video_classifier.add_image(to_tf_array(frame))
+            state = video_classifier.get_last_classification()
+            print(state)
+            frame = resize(frame, dim=(600, 600))
+            cv2.putText(frame, f"{state}", (10, 40), 0, 2, 255)
+            cv2.imshow("Frame", frame)
+            waitkey = cv2.waitKey(25) & 0xFF
+            if waitkey == ord("q"):
+                break
+    video_classifier.exit()
+    cap.release()
+    cv2.destroyAllWindows()
+
+%package -n python3-actspotter
+Summary:	Actspotter library for detecting activities
+Provides:	python-actspotter
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-actspotter
+The `actspotter` is a library / tensorflow model for detecting activities. It allows to classify body activities in images or videos. The package is limited to videos and images with only one person by design.
+The following classes are available: 
+- none
+- pull_up_up
+- pull_up_down
+- pull_up_none
+- push_up_up
+- push_up_down
+- push_up_none
+- sit_up_up
+- sit_up_down
+- sit_up_none
+The package is currently in early development.
+Future plans
+~~~~~~~~~~~~~
+Tensorflow model deployment will be integrated soon. Currently this package allows to classify push-ups, sit-ups and pull-ups. In future version kicks and others body activities will follow. 
+It is also planned to provide a signal processing layer that allows to easily detect connected activites and count them. 
+Another application will be to integrate with keyboard drivers so that activities could be used for controlling video games (e.g. by kicks).
+Installation
+~~~~~~~~~~~~
+Install this library in a `virtualenv`_ using pip. `virtualenv`_ is a tool to
+create isolated Python environments. The basic problem it addresses is one of
+dependencies and versions, and indirectly permissions.
+With `virtualenv`_, it's possible to install this library without needing system
+install permissions, and without clashing with the installed system
+dependencies.
+Supported Python Versions
+^^^^^^^^^^^^^^^^^^^^^^^^^
+Python >= 3.6
+Mac/Linux
+^^^^^^^^^
+    pip install virtualenv
+    virtualenv <your-env>
+    source <your-env>/bin/activate
+    <your-env>/bin/pip install actspotter
+Windows
+^^^^^^^
+    pip install virtualenv
+    virtualenv <your-env>
+    <your-env>\Scripts\activate
+    <your-env>\Scripts\pip.exe install actspotter
+Example Usage
+~~~~~~~~~~~~~
+Requirement: cv2 (opencv) installed.
+Classification of images:
+    import cv2
+    import tensorflow as tf
+    from actspotter import ImageClassifier, classify_image_input_dimension, class_names
+    def _resize(frame, dim=classify_image_input_dimension):
+        frame = cv2.resize(frame, dim, interpolation=cv2.INTER_AREA)
+        return frame
+    def _to_tf_array(frame, dim=classify_image_input_dimension):
+        frame = _resize(frame, dim)
+        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        frame = tf.convert_to_tensor(frame, dtype=tf.float32)
+        return frame
+    images = [
+        to_tf_array(cv2.imread("test.jpg")),
+    ]
+    print(class_names)
+    print(image_classifier.classify_images(images))
+Classification of a video:
+    import cv2
+    import tensorflow as tf
+    from actspotter import VideoClassifier, classify_image_input_dimension
+    def _resize(frame, dim=classify_image_input_dimension):
+        return frame
+    def _to_tf_array(frame, dim=classify_image_input_dimension):
+        frame = _resize(frame, dim)
+        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        frame = tf.convert_to_tensor(frame, dtype=tf.float32)
+        return frame
+    cap = cv2.VideoCapture(0)
+    video_classifier = VideoClassifier(buffer_size=4)
+    video_classifier.start()
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if ret == True:
+            video_classifier.add_image(to_tf_array(frame))
+            state = video_classifier.get_last_classification()
+            print(state)
+            frame = resize(frame, dim=(600, 600))
+            cv2.putText(frame, f"{state}", (10, 40), 0, 2, 255)
+            cv2.imshow("Frame", frame)
+            waitkey = cv2.waitKey(25) & 0xFF
+            if waitkey == ord("q"):
+                break
+    video_classifier.exit()
+    cap.release()
+    cv2.destroyAllWindows()
+
+%package help
+Summary:	Development documents and examples for actspotter
+Provides:	python3-actspotter-doc
+%description help
+The `actspotter` is a library / tensorflow model for detecting activities. It allows to classify body activities in images or videos. The package is limited to videos and images with only one person by design.
+The following classes are available: 
+- none
+- pull_up_up
+- pull_up_down
+- pull_up_none
+- push_up_up
+- push_up_down
+- push_up_none
+- sit_up_up
+- sit_up_down
+- sit_up_none
+The package is currently in early development.
+Future plans
+~~~~~~~~~~~~~
+Tensorflow model deployment will be integrated soon. Currently this package allows to classify push-ups, sit-ups and pull-ups. In future version kicks and others body activities will follow. 
+It is also planned to provide a signal processing layer that allows to easily detect connected activites and count them. 
+Another application will be to integrate with keyboard drivers so that activities could be used for controlling video games (e.g. by kicks).
+Installation
+~~~~~~~~~~~~
+Install this library in a `virtualenv`_ using pip. `virtualenv`_ is a tool to
+create isolated Python environments. The basic problem it addresses is one of
+dependencies and versions, and indirectly permissions.
+With `virtualenv`_, it's possible to install this library without needing system
+install permissions, and without clashing with the installed system
+dependencies.
+Supported Python Versions
+^^^^^^^^^^^^^^^^^^^^^^^^^
+Python >= 3.6
+Mac/Linux
+^^^^^^^^^
+    pip install virtualenv
+    virtualenv <your-env>
+    source <your-env>/bin/activate
+    <your-env>/bin/pip install actspotter
+Windows
+^^^^^^^
+    pip install virtualenv
+    virtualenv <your-env>
+    <your-env>\Scripts\activate
+    <your-env>\Scripts\pip.exe install actspotter
+Example Usage
+~~~~~~~~~~~~~
+Requirement: cv2 (opencv) installed.
+Classification of images:
+    import cv2
+    import tensorflow as tf
+    from actspotter import ImageClassifier, classify_image_input_dimension, class_names
+    def _resize(frame, dim=classify_image_input_dimension):
+        frame = cv2.resize(frame, dim, interpolation=cv2.INTER_AREA)
+        return frame
+    def _to_tf_array(frame, dim=classify_image_input_dimension):
+        frame = _resize(frame, dim)
+        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        frame = tf.convert_to_tensor(frame, dtype=tf.float32)
+        return frame
+    images = [
+        to_tf_array(cv2.imread("test.jpg")),
+    ]
+    print(class_names)
+    print(image_classifier.classify_images(images))
+Classification of a video:
+    import cv2
+    import tensorflow as tf
+    from actspotter import VideoClassifier, classify_image_input_dimension
+    def _resize(frame, dim=classify_image_input_dimension):
+        return frame
+    def _to_tf_array(frame, dim=classify_image_input_dimension):
+        frame = _resize(frame, dim)
+        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        frame = tf.convert_to_tensor(frame, dtype=tf.float32)
+        return frame
+    cap = cv2.VideoCapture(0)
+    video_classifier = VideoClassifier(buffer_size=4)
+    video_classifier.start()
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if ret == True:
+            video_classifier.add_image(to_tf_array(frame))
+            state = video_classifier.get_last_classification()
+            print(state)
+            frame = resize(frame, dim=(600, 600))
+            cv2.putText(frame, f"{state}", (10, 40), 0, 2, 255)
+            cv2.imshow("Frame", frame)
+            waitkey = cv2.waitKey(25) & 0xFF
+            if waitkey == ord("q"):
+                break
+    video_classifier.exit()
+    cap.release()
+    cv2.destroyAllWindows()
+
+%prep
+%autosetup -n actspotter-0.1.3
+
+%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-actspotter -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Tue Jun 20 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.3-1
+- Package Spec generated
diff --git a/sources b/sources
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+6da89d8e99c622c0e6338cf8a64e133d  actspotter-0.1.3.tar.gz