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object detection
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‎vision/automl/automl_vision_od_dataset.py

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#!/usr/bin/env python
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# Copyright 2019 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""This application demonstrates how to perform basic operations on
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object detection datasets with the Google AutoML API. For more information, the
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documentation at
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https://cloud.google.com/vision/automl/docs.
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"""
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from __future__ import print_function
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import argparse
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import os
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def encode_image_example(image):
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"""Demonstrate how to base64 an image (or any file)."""
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# Import the base64 encoding library.
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import base64
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# Pass the image data to an encoding function.
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def encode_image(image):
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image_content = image.read()
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return base64.b64encode(image_content)
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return encode_image(image)
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def create_dataset(project_id, compute_region, dataset_name):
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"""Creates a dataset."""
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# [START automl_vision_iod_create_dataset]
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## To do: Uncomment and set the following variables
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# project_id = '[PROJECT_ID]'
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# compute_region = '[COMPUTE_REGION]'
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# dataset_name = '[DATASET_NAME]'
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from google.cloud import automl_v1beta1 as automl
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client = automl.AutoMlClient()
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# A resource that represents Google Cloud Platform location.
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project_location = client.location_path(project_id, compute_region)
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# Set dataset name and metadata of the dataset.
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my_dataset = {
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'display_name': dataset_name,
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'image_object_detection_dataset_metadata': {},
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}
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# Create a dataset with the dataset metadata in the region.
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dataset = client.create_dataset(project_location, my_dataset)
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# Display the dataset information.
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print('Dataset name: {}'.format(dataset.name))
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print('Dataset id: {}'.format(dataset.name.split('/')[-1]))
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print('Dataset display name: {}'.format(dataset.display_name))
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print('Dataset example count: {}'.format(dataset.example_count))
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print('Dataset create time:')
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print('\tseconds: {}'.format(dataset.create_time.seconds))
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print('\tnanos: {}'.format(dataset.create_time.nanos))
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dataset_spec = {}
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my_dataset = {
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'display_name': dataset_name,
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'image_object_detection_dataset_metadata': dataset_spec
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}
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response = client.create_dataset(project_location, my_dataset)
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print('\nDataset creation: {}'.format(response))
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dataset_full_id = response.name
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print('Dataset full id: {}'.format(dataset_full_id))
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# [END automl_vision_iod_import_data]
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def get_dataset(project_id, compute_region, dataset_id):
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"""Describes a dataset."""
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# [START automl_vision_iod_get_dataset]
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## To do: Uncomment and set the following variables
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# project_id = '[PROJECT_ID]' 6D40
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# compute_region = '[COMPUTE_REGION]'
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# dataset_id = '[DATASET_ID]'
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from google.cloud import automl_v1beta1 as automl
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client = automl.AutoMlClient()
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# A resource that represents Google Cloud Platform location.
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project_location = client.location_path(project_id, compute_region)
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# Get the full path of the dataset.
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dataset_full_id = client.dataset_path(project_id, compute_region, dataset_id)
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# Get the dataset
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response = client.get_dataset(dataset_full_id)
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print('\nDataset description: {}'.format(response))
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# [END automl_vision_iod_get_dataset]
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def list_datasets(project_id, compute_region, filter_):
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"""Lists all datasets."""
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# [START automl_vision_iod_lists_dataset]
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## To do: Uncomment and set the following variables
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# project_id = '[PROJECT_ID]'
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# compute_region = '[COMPUTE_REGION]'
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# filter_ = ''
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from google.cloud import automl_v1beta1 as automl
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client = automl.AutoMlClient()
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# A resource that represents Google Cloud Platform location.
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project_location = client.location_path(project_id, compute_region)
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# List all the datasets available in the region by applying filter.
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response = client.list_datasets(project_location, filter_=filter_)
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print('List of datasets:')
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for dataset in response:
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# Display the dataset information.
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print('Dataset name: {}'.format(dataset.name))
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print('Dataset id: {}'.format(dataset.name.split('/')[-1]))
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print('Dataset display name: {}'.format(dataset.display_name))
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print('Dataset example count: {}'.format(dataset.example_count))
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print('Dataset create time:&# F438 39;)
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print('\tseconds: {}'.format(dataset.create_time.seconds))
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print('\tnanos: {}'.format(dataset.create_time.nanos))
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# [END automl_vision_iod_lists_dataset]
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def import_data(project_id, compute_region, dataset_id, paths):
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"""Imports images and bounding boxes."""
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# [START automl_vision_iod_import_data]
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## To do: Uncomment and set the following variables
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# project_id = '[PROJECT_ID]'
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# compute_region = '[COMPUTE_REGION]'
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# dataset_id = '[DATASET_ID]'
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# paths = '[Storage path. For example: gs://path/to/file.csv]'
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from google.cloud import automl_v1beta1 as automl
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client = automl.AutoMlClient()
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# Get the full path of the dataset.
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dataset_full_id = client.dataset_path(project_id, compute_region, dataset_id)
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# Get the multiple Google Cloud Storage URIs.
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input_config = {'gcs_source': {'input_uris': paths}}
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# Import data from the input URI.
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response = client.import_data(dataset_full_id, input_config)
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print('Processing import...')
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# synchronous check of operation status.
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print('Data imported. {}'.format(response.result()))
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# [END automl_vision_iod_import_data]
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def delete_dataset(project_id, compute_region, dataset_id):
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"""Deletes a dataset."""
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# [START automl_vision_iod_delete_dataset]
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## To do: Uncomment and set the following variables
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# project_id = '[PROJECT_ID]'
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# compute_region = '[COMPUTE_REGION]'
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# dataset_id = '[DATASET_ID]'
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from google.cloud import automl_v1beta1 as automl
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client = automl.AutoMlClient()
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# Get the full path of the dataset.
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dataset_full_id = client.dataset_path(project_id, compute_region, dataset_id)
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# Delete a dataset.
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response = client.delete_dataset(dataset_full_id)
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# synchronous check of operation status.
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print('Dataset deleted. {}'.format(response.result()))
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# [END automl_vision_iod_delete_dataset]
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(
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description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
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subparsers = parser.add_subparsers(dest='command')
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create_dataset_parser = subparsers.add_parser(
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'create_dataset', help=create_dataset.__doc__)
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create_dataset_parser.add_argument('dataset_name')
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get_dataset_parser = subparsers.add_parser(
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'get_dataset', help=get_dataset.__doc__)
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get_dataset_parser.add_argument('dataset_id')
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import_data_parser = subparsers.add_parser(
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'import_data', help=import_data.__doc__)
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import_data_parser.add_argument('path')
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import_data_parser.add_argument('dataset_id')
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list_datasets_parser = subparsers.add_parser(
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'list_datasets', help=list_datasets.__doc__)
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delete_dataset_parser = subparsers.add_parser(
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'delete_dataset', help=delete_dataset.__doc__)
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delete_dataset_parser.add_argument('dataset_id')
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args = parser.parse_args()
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# Setup.
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project_id = os.environ['PROJECT_ID']
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compute_region = 'us-central1'
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if args.command == 'create_dataset':
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create_dataset(project_id, compute_region, args.dataset_name)
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if args.command == 'get_dataset':
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get_dataset(project_id, compute_region, args.dataset_id)
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if args.command == 'delete_dataset':
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delete_dataset(project_id, compute_region, args.dataset_id)
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if args.command == 'list_datasets':
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list_datasets(project_id, compute_region, '')
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if args.command == 'import_data':
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import_data(project_id, compute_region, args.dataset_id, [args.path])

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