526 lines
12 KiB
ReStructuredText
526 lines
12 KiB
ReStructuredText
Notebook Utils
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==============
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This notebook contains helper functions and classes for use with
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OpenVINO™ Notebooks. The code is synchronized with the
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``notebook_utils.py`` file in the same directory as this notebook.
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There are four categories:
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- `Files <#files>`__
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- `Images <#images>`__
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- `Videos <#videos>`__
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- `Visualization <#visualization>`__
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- `Checks and Alerts <#checks-and-alerts>`__
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Each category contains a test cell that also shows how to use the
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functions in the section.
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.. code:: ipython3
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# Install requirements
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!pip install -q "openvino>=2023.0.0" opencv-python
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!pip install -q pillow tqdm requests matplotlib
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.. parsed-literal::
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DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.0 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
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DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.0 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
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Files
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-----
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Load an image, download a file, download an OpenVINO IR model, and
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create a progress bar to show download progress.
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.. code:: ipython3
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import os
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import shutil
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from PIL import Image
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from notebook_utils import load_image, download_file, download_ir_model
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.. code:: ipython3
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??load_image
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.. code:: ipython3
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??download_file
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.. code:: ipython3
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??download_ir_model
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Test File Functions
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~~~~~~~~~~~~~~~~~~~
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.. code:: ipython3
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model_url = "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/models/002-example-models/segmentation.xml"
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download_ir_model(model_url, "model")
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assert os.path.exists("model/segmentation.xml")
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assert os.path.exists("model/segmentation.bin")
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.. parsed-literal::
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model/segmentation.bin: 0%| | 0.00/1.09M [00:00<?, ?B/s]
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.. code:: ipython3
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url = "https://github.com/intel-iot-devkit/safety-gear-detector-python/raw/master/resources/Safety_Full_Hat_and_Vest.mp4"
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if os.path.exists(os.path.basename(url)):
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os.remove(os.path.basename(url))
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video_file = download_file(url)
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print(video_file)
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assert os.path.exists(video_file)
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.. parsed-literal::
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Safety_Full_Hat_and_Vest.mp4: 0%| | 0.00/26.3M [00:00<?, ?B/s]
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.. parsed-literal::
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/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-561/.workspace/scm/ov-notebook/notebooks/utils/Safety_Full_Hat_and_Vest.mp4
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.. code:: ipython3
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url = "https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/README.md"
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filename = "openvino_notebooks_readme.md"
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if os.path.exists(filename):
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os.remove(filename)
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readme_file = download_file(url, filename=filename)
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print(readme_file)
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assert os.path.exists(readme_file)
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.. parsed-literal::
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openvino_notebooks_readme.md: 0%| | 0.00/14.9k [00:00<?, ?B/s]
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.. parsed-literal::
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/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-561/.workspace/scm/ov-notebook/notebooks/utils/openvino_notebooks_readme.md
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.. code:: ipython3
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url = "https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/README.md"
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filename = "openvino_notebooks_readme.md"
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directory = "temp"
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video_file = download_file(
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url, filename=filename, directory=directory, show_progress=False, silent=True
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)
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print(readme_file)
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assert os.path.exists(readme_file)
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shutil.rmtree("temp")
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.. parsed-literal::
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/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-561/.workspace/scm/ov-notebook/notebooks/utils/openvino_notebooks_readme.md
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.. code:: ipython3
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url = "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/coco.jpg"
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image = load_image(url)
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Image.fromarray(image[:, :, ::-1])
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.. image:: notebook_utils-with-output_files/notebook_utils-with-output_12_0.png
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Images
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------
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Convert Pixel Data
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~~~~~~~~~~~~~~~~~~
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Normalize image pixel values between 0 and 1, and convert images to
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``RGB`` and ``BGR``.
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.. code:: ipython3
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import numpy as np
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from notebook_utils import normalize_minmax, to_rgb, to_bgr
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.. code:: ipython3
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??normalize_minmax
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.. code:: ipython3
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??to_bgr
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.. code:: ipython3
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??to_rgb
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Test Data Conversion Functions
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. code:: ipython3
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test_array = np.random.randint(0, 255, (100, 100, 3))
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normalized_array = normalize_minmax(test_array)
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assert normalized_array.min() == 0
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assert normalized_array.max() == 1
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.. code:: ipython3
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bgr_array = np.ones((100, 100, 3), dtype=np.uint8)
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bgr_array[:, :, 0] = 0
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bgr_array[:, :, 1] = 1
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bgr_array[:, :, 2] = 2
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rgb_array = to_rgb(bgr_array)
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assert np.all(bgr_array[:, :, 0] == rgb_array[:, :, 2])
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bgr_array_converted = to_bgr(rgb_array)
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assert np.all(bgr_array_converted == bgr_array)
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Videos
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------
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Video Player
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~~~~~~~~~~~~
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A custom video player to fulfill FPS requirements. You can set target
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FPS and output size, flip the video horizontally or skip first N frames.
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.. code:: ipython3
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import cv2
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from IPython.display import Image, clear_output, display
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from notebook_utils import VideoPlayer
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??VideoPlayer
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Test Video Player
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~~~~~~~~~~~~~~~~~
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.. code:: ipython3
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video = "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/video/Coco%20Walking%20in%20Berkeley.mp4"
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player = VideoPlayer(video, fps=15, skip_first_frames=10)
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player.start()
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for i in range(50):
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frame = player.next()
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_, encoded_img = cv2.imencode(".jpg", frame, params=[cv2.IMWRITE_JPEG_QUALITY, 90])
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img = Image(data=encoded_img)
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clear_output(wait=True)
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display(img)
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player.stop()
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print("Finished")
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.. image:: notebook_utils-with-output_files/notebook_utils-with-output_26_0.png
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.. parsed-literal::
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Finished
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Visualization
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-------------
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Segmentation
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~~~~~~~~~~~~
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Define a ``SegmentationMap NamedTuple`` that keeps the labels and
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colormap for a segmentation project/dataset. Create
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``CityScapesSegmentation`` and ``BinarySegmentation SegmentationMaps``.
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Create a function to convert a segmentation map to an ``RGB`` image with
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a ``colormap``, and to show the segmentation result as an overlay over
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the original image.
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.. code:: ipython3
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from notebook_utils import CityScapesSegmentation, BinarySegmentation, segmentation_map_to_image, segmentation_map_to_overlay
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.. code:: ipython3
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??Label
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.. parsed-literal::
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Object `Label` not found.
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.. code:: ipython3
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??SegmentationMap
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.. parsed-literal::
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Object `SegmentationMap` not found.
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.. code:: ipython3
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??CityScapesSegmentation
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.. code:: ipython3
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print(f"cityscapes segmentation lables: \n{CityScapesSegmentation.get_labels()}")
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print(f"cityscales segmentation colors: \n{CityScapesSegmentation.get_colormap()}")
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.. parsed-literal::
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cityscapes segmentation lables:
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['road', 'sidewalk', 'building', 'wall', 'fence', 'pole', 'traffic light', 'traffic sign', 'vegetation', 'terrain', 'sky', 'person', 'rider', 'car', 'truck', 'bus', 'train', 'motorcycle', 'bicycle', 'background']
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cityscales segmentation colors:
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[[128 64 128]
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[244 35 232]
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[ 70 70 70]
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[102 102 156]
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[190 153 153]
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[153 153 153]
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[250 170 30]
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[220 220 0]
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[107 142 35]
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[152 251 152]
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[ 70 130 180]
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[220 20 60]
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[255 0 0]
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[ 0 0 142]
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[ 0 0 70]
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[ 0 60 100]
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[ 0 80 100]
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[ 0 0 230]
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[119 11 32]
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[255 255 255]]
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.. code:: ipython3
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??BinarySegmentation
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.. code:: ipython3
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print(f"binary segmentation lables: \n{BinarySegmentation.get_labels()}")
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print(f"binary segmentation colors: \n{BinarySegmentation.get_colormap()}")
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.. parsed-literal::
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binary segmentation lables:
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['background', 'foreground']
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binary segmentation colors:
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[[255 255 255]
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[ 0 0 0]]
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.. code:: ipython3
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??segmentation_map_to_image
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.. code:: ipython3
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??segmentation_map_to_overlay
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Network Results
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~~~~~~~~~~~~~~~
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Show network result image, optionally together with the source image and
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a legend with labels.
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.. code:: ipython3
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from notebook_utils import viz_result_image
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??viz_result_image
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Test Visualization Functions
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. code:: ipython3
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testimage = np.zeros((100, 100, 3), dtype=np.uint8)
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testimage[30:80, 30:80, :] = [0, 255, 0]
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testimage[0:10, 0:10, :] = 100
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testimage[40:60, 40:60, :] = 128
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testimage[testimage == 0] = 128
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testmask1 = np.zeros((testimage.shape[:2]))
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testmask1[30:80, 30:80] = 1
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testmask1[40:50, 40:50] = 0
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testmask1[0:15, 0:10] = 2
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result_image_overlay = segmentation_map_to_overlay(
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image=testimage,
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result=testmask1,
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alpha=0.6,
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colormap=np.array([[0, 0, 0], [255, 0, 0], [255, 255, 0]]),
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)
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result_image = segmentation_map_to_image(testmask1, CityScapesSegmentation.get_colormap())
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result_image_no_holes = segmentation_map_to_image(
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testmask1, CityScapesSegmentation.get_colormap(), remove_holes=True
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)
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resized_result_image = cv2.resize(result_image, (50, 50))
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overlay_result_image = segmentation_map_to_overlay(
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testimage, testmask1, 0.6, CityScapesSegmentation.get_colormap(), remove_holes=False
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)
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fig1 = viz_result_image(result_image, testimage)
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fig2 = viz_result_image(result_image_no_holes, testimage, labels=CityScapesSegmentation)
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fig3 = viz_result_image(
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resized_result_image,
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testimage,
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source_title="Source Image",
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result_title="Resized Result Image",
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resize=True,
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)
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fig4 = viz_result_image(
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overlay_result_image,
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labels=CityScapesSegmentation,
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result_title="Image with Result Overlay",
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)
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display(fig1, fig2, fig3, fig4)
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.. image:: notebook_utils-with-output_files/notebook_utils-with-output_41_0.png
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.. image:: notebook_utils-with-output_files/notebook_utils-with-output_41_1.png
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.. image:: notebook_utils-with-output_files/notebook_utils-with-output_41_2.png
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.. image:: notebook_utils-with-output_files/notebook_utils-with-output_41_3.png
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Checks and Alerts
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-----------------
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Create an alert class to show stylized info/error/warning messages and a
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``check_device`` function that checks whether a given device is
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available.
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.. code:: ipython3
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from notebook_utils import NotebookAlert, DeviceNotFoundAlert, check_device, check_openvino_version
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.. code:: ipython3
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??NotebookAlert
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.. code:: ipython3
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??DeviceNotFoundAlert
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.. code:: ipython3
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??check_device
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.. code:: ipython3
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??check_openvino_version
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Test Alerts
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~~~~~~~~~~~
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.. code:: ipython3
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NotebookAlert(message="Hello, world!", alert_class="info")
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DeviceNotFoundAlert("GPU");
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.. raw:: html
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<div class="alert alert-info">Hello, world!
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.. raw:: html
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<div class="alert alert-warning">Running this cell requires a GPU device, which is not available on this system. The following device is available: CPU
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.. code:: ipython3
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assert check_device("CPU")
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.. code:: ipython3
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if check_device("HELLOWORLD"):
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print("Hello World device found.")
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.. raw:: html
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<div class="alert alert-warning">Running this cell requires a HELLOWORLD device, which is not available on this system. The following device is available: CPU
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.. code:: ipython3
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check_openvino_version("2022.1");
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.. raw:: html
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<div class="alert alert-danger">This notebook requires OpenVINO 2022.1. The version on your system is: <i>2023.1.0-12185-9e6b00e51cd-releases/2023/1</i>.<br>Please run <span style='font-family:monospace'>pip install --upgrade -r requirements.txt</span> in the openvino_env environment to install this version. See the <a href='https://github.com/openvinotoolkit/openvino_notebooks'>OpenVINO Notebooks README</a> for detailed instructions
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