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label_image.py
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label_image.py
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#!/usr/bin/env python
# Copyright 2022 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
from google.api_core.client_options import ClientOptions
# [START datalabeling_label_image_beta]
def label_image(
dataset_resource_name, instruction_resource_name, annotation_spec_set_resource_name
):
"""Labels an image dataset."""
from google.cloud import datalabeling_v1beta1 as datalabeling
client = datalabeling.DataLabelingServiceClient()
# [END datalabeling_label_image_beta]
# If provided, use a provided test endpoint - this will prevent tests on
# this snippet from triggering any action by a real human
if "DATALABELING_ENDPOINT" in os.environ:
opts = ClientOptions(api_endpoint=os.getenv("DATALABELING_ENDPOINT"))
client = datalabeling.DataLabelingServiceClient(client_options=opts)
# [START datalabeling_label_image_beta]
basic_config = datalabeling.HumanAnnotationConfig(
instruction=instruction_resource_name,
annotated_dataset_display_name="YOUR_ANNOTATED_DATASET_DISPLAY_NAME",
label_group="YOUR_LABEL_GROUP",
replica_count=1,
)
feature = datalabeling.LabelImageRequest.Feature.CLASSIFICATION
# annotation_spec_set_resource_name needs to be created beforehand.
# See the examples in the following:
# https://cloud.google.com/ai-platform/data-labeling/docs/label-sets
config = datalabeling.ImageClassificationConfig(
annotation_spec_set=annotation_spec_set_resource_name,
allow_multi_label=False,
answer_aggregation_type=datalabeling.StringAggregationType.MAJORITY_VOTE,
)
response = client.label_image(
request={
"parent": dataset_resource_name,
"basic_config": basic_config,
"feature": feature,
"image_classification_config": config,
}
)
print("Label_image operation name: {}".format(response.operation.name))
return response
# [END datalabeling_label_image_beta]
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter
)
parser.add_argument(
"--dataset-resource-name",
help="Dataset resource name. Required.",
required=True,
)
parser.add_argument(
"--instruction-resource-name",
help="Instruction resource name. Required.",
required=True,
)
parser.add_argument(
"--annotation-spec-set-resource-name",
help="Annotation spec set resource name. Required.",
required=True,
)
args = parser.parse_args()
label_image(
args.dataset_resource_name,
args.instruction_resource_name,
args.annotation_spec_set_resource_name,
)