[formrecognizer] Add samples for converting to and from dictionary

sdk/formrecognizer/azure-ai-formrecognizer/samples/v3.1/async_samples/sample_convert_to_and_from_dict_async.py

@@ -0,0 +1,89 @@
+# coding: utf-8
+
+# -------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for
+# license information.
+# --------------------------------------------------------------------------
+
+"""
+FILE: sample_convert_to_and_from_dict_async.py
+
+DESCRIPTION:
+    This sample demonstrates how to convert models returned from a recognize operation
+    to and from a dictionary. The dictionary in this sample is then converted to a
+    JSON file, then the same dictionary is converted back to its original model.
+
+USAGE:
+    python sample_convert_to_and_from_dict_async.py
+
+    Set the environment variables with your own values before running the sample:
+    1) AZURE_FORM_RECOGNIZER_ENDPOINT - the endpoint to your Cognitive Services resource.
+    2) AZURE_FORM_RECOGNIZER_KEY - your Form Recognizer API key
+"""
+
+import os
+import json
+import asyncio
+
+async def convert_to_and_from_dict_async():
+    path_to_sample_forms = os.path.abspath(
+        os.path.join(
+            os.path.abspath(__file__),
+            "..",
+            "..",
+            "..",
+            "./sample_forms/id_documents/license.jpg",
+        )
+    )
+
+    from azure.core.serialization import AzureJSONEncoder
+    from azure.core.credentials import AzureKeyCredential
+    from azure.ai.formrecognizer.aio import FormRecognizerClient
+    from azure.ai.formrecognizer import RecognizedForm
+
+    endpoint = os.environ["AZURE_FORM_RECOGNIZER_ENDPOINT"]
+    key = os.environ["AZURE_FORM_RECOGNIZER_KEY"]
+
+    form_recognizer_client = FormRecognizerClient(
+        endpoint=endpoint, credential=AzureKeyCredential(key)
+    )
+    async with form_recognizer_client:
+        with open(path_to_sample_forms, "rb") as f:
+            poller = await form_recognizer_client.begin_recognize_identity_documents(identity_document=f)
+
+        id_documents = await poller.result()
+
+    # convert the received model to a dictionary
+    recognized_form_dict = [doc.to_dict() for doc in id_documents]
+
+    # save the dictionary as a JSON content in a JSON file
+    with open('data.json', 'w') as f:
+        json.dump(recognized_form_dict, f, cls=AzureJSONEncoder)
+
+    # convert the dictionary back to the original model
+    model = [RecognizedForm.from_dict(doc) for doc in recognized_form_dict]
+
+    # use the model as normal
+    for idx, id_document in enumerate(model):
+        print("--------Recognizing converted ID document #{}--------".format(idx+1))
+        first_name = id_document.fields.get("FirstName")
+        if first_name:
+            print("First Name: {} has confidence: {}".format(first_name.value, first_name.confidence))
+        last_name = id_document.fields.get("LastName")
+        if last_name:
+            print("Last Name: {} has confidence: {}".format(last_name.value, last_name.confidence))
+        document_number = id_document.fields.get("DocumentNumber")
+        if document_number:
+            print("Document Number: {} has confidence: {}".format(document_number.value, document_number.confidence))
+
+    print("----------------------------------------")
+
+
+async def main():
+    await convert_to_and_from_dict_async()
+
+
+if __name__ == '__main__':
+    loop = asyncio.get_event_loop()
+    loop.run_until_complete(main())

sdk/formrecognizer/azure-ai-formrecognizer/samples/v3.1/sample_convert_to_and_from_dict.py

@@ -0,0 +1,81 @@
+# coding: utf-8
+
+# -------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for
+# license information.
+# --------------------------------------------------------------------------
+
+"""
+FILE: sample_convert_to_and_from_dict.py
+
+DESCRIPTION:
+    This sample demonstrates how to convert models returned from an analyze operation
+    to and from a dictionary. The dictionary in this sample is then converted to a
+    JSON file, then the same dictionary is converted back to its original model.
+
+USAGE:
+    python sample_convert_to_and_from_dict.py
+
+    Set the environment variables with your own values before running the sample:
+    1) AZURE_FORM_RECOGNIZER_ENDPOINT - the endpoint to your Cognitive Services resource.
+    2) AZURE_FORM_RECOGNIZER_KEY - your Form Recognizer API key
+"""
+
+import os
+import json
+
+def convert_to_and_from_dict():
+    path_to_sample_forms = os.path.abspath(
+        os.path.join(
+            os.path.abspath(__file__),
+            "..",
+            "..",
+            "./sample_forms/id_documents/license.jpg",
+        )
+    )
+
+    from azure.core.serialization import AzureJSONEncoder
+    from azure.core.credentials import AzureKeyCredential
+    from azure.ai.formrecognizer import FormRecognizerClient
+    from azure.ai.formrecognizer import RecognizedForm
+
+    endpoint = os.environ["AZURE_FORM_RECOGNIZER_ENDPOINT"]
+    key = os.environ["AZURE_FORM_RECOGNIZER_KEY"]
+
+    form_recognizer_client = FormRecognizerClient(
+        endpoint=endpoint, credential=AzureKeyCredential(key)
+    )
+    with open(path_to_sample_forms, "rb") as f:
+        poller = form_recognizer_client.begin_recognize_identity_documents(identity_document=f)
+
+    id_documents = poller.result()
+
+    # convert the received model to a dictionary
+    recognized_form_dict = [doc.to_dict() for doc in id_documents]
+
+    # save the dictionary as a JSON content in a JSON file
+    with open('data.json', 'w') as f:
+        json.dump(recognized_form_dict, f, cls=AzureJSONEncoder)
+
+    # convert the dictionary back to the original model
+    model = [RecognizedForm.from_dict(doc) for doc in recognized_form_dict]
+
+    # use the model as normal
+    for idx, id_document in enumerate(model):
+        print("--------Recognizing converted ID document #{}--------".format(idx+1))
+        first_name = id_document.fields.get("FirstName")
+        if first_name:
+            print("First Name: {} has confidence: {}".format(first_name.value, first_name.confidence))
+        last_name = id_document.fields.get("LastName")
+        if last_name:
+            print("Last Name: {} has confidence: {}".format(last_name.value, last_name.confidence))
+        document_number = id_document.fields.get("DocumentNumber")
+        if document_number:
+            print("Document Number: {} has confidence: {}".format(document_number.value, document_number.confidence))
+
+    print("----------------------------------------")
+
+
+if __name__ == "__main__":
+    convert_to_and_from_dict()

sdk/formrecognizer/azure-ai-formrecognizer/samples/v3.2-beta/async_samples/sample_convert_to_and_from_dict_async.py

@@ -0,0 +1,83 @@
+# coding: utf-8
+
+# -------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for
+# license information.
+# --------------------------------------------------------------------------
+
+"""
+FILE: sample_convert_to_and_from_dict_async.py
+
+DESCRIPTION:
+    This sample demonstrates how to convert models returned from an analyze operation
+    to and from a dictionary. The dictionary in this sample is then converted to a
+    JSON file, then the same dictionary is converted back to its original model.
+
+USAGE:
+    python sample_convert_to_and_from_dict_async.py
+
+    Set the environment variables with your own values before running the sample:
+    1) AZURE_FORM_RECOGNIZER_ENDPOINT - the endpoint to your Cognitive Services resource.
+    2) AZURE_FORM_RECOGNIZER_KEY - your Form Recognizer API key
+"""
+
+import os
+import json
+import asyncio
+
+async def convert_to_and_from_dict_async():
+    path_to_sample_documents = os.path.abspath(
+        os.path.join(
+            os.path.abspath(__file__),
+            "..",
+            "..",
+            "..",
+            "./sample_forms/forms/Form_1.jpg",
+        )
+    )
+
+    from azure.core.serialization import AzureJSONEncoder
+    from azure.core.credentials import AzureKeyCredential
+    from azure.ai.formrecognizer.aio import DocumentAnalysisClient
+    from azure.ai.formrecognizer import AnalyzeResult
+
+    endpoint = os.environ["AZURE_FORM_RECOGNIZER_ENDPOINT"]
+    key = os.environ["AZURE_FORM_RECOGNIZER_KEY"]
+
+    document_analysis_client = DocumentAnalysisClient(
+        endpoint=endpoint, credential=AzureKeyCredential(key)
+    )
+    async with document_analysis_client:
+        with open(path_to_sample_documents, "rb") as f:
+            poller = await document_analysis_client.begin_analyze_document(
+                "prebuilt-document", document=f
+            )
+        result = await poller.result()
+
+    # convert the received model to a dictionary
+    analyze_result_dict = result.to_dict()
+
+    # save the dictionary as a JSON content in a JSON file
+    with open('data.json', 'w') as f:
+        json.dump(analyze_result_dict, f, cls=AzureJSONEncoder)
+
+    # convert the dictionary back to the original model
+    model = AnalyzeResult.from_dict(analyze_result_dict)
+
+    # use the model as normal
+    print("----Converted from dictionary AnalyzeResult----")
+    print("Model ID: '{}'".format(model.model_id))
+    print("Number of pages analyzed {}".format(len(model.pages)))
+    print("API version used: {}".format(model.api_version))
+
+    print("----------------------------------------")
+
+
+async def main():
+    await convert_to_and_from_dict_async()
+
+
+if __name__ == '__main__':
+    loop = asyncio.get_event_loop()
+    loop.run_until_complete(main())

sdk/formrecognizer/azure-ai-formrecognizer/samples/v3.2-beta/sample_convert_to_and_from_dict.py

@@ -0,0 +1,74 @@
+# coding: utf-8
+
+# -------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for
+# license information.
+# --------------------------------------------------------------------------
+
+"""
+FILE: sample_convert_to_and_from_dict.py
+
+DESCRIPTION:
+    This sample demonstrates how to convert models returned from an analyze operation
+    to and from a dictionary. The dictionary in this sample is then converted to a
+    JSON file, then the same dictionary is converted back to its original model.
+
+USAGE:
+    python sample_convert_to_and_from_dict.py
+
+    Set the environment variables with your own values before running the sample:
+    1) AZURE_FORM_RECOGNIZER_ENDPOINT - the endpoint to your Cognitive Services resource.
+    2) AZURE_FORM_RECOGNIZER_KEY - your Form Recognizer API key
+"""
+
+import os
+import json
+
+def convert_to_and_from_dict():
+    path_to_sample_documents = os.path.abspath(
+        os.path.join(
+            os.path.abspath(__file__),
+            "..",
+            "..",
+            "./sample_forms/forms/Form_1.jpg",
+        )
+    )
+
+    from azure.core.serialization import AzureJSONEncoder
+    from azure.core.credentials import AzureKeyCredential
+    from azure.ai.formrecognizer import DocumentAnalysisClient, AnalyzeResult
+
+    endpoint = os.environ["AZURE_FORM_RECOGNIZER_ENDPOINT"]
+    key = os.environ["AZURE_FORM_RECOGNIZER_KEY"]
+
+    document_analysis_client = DocumentAnalysisClient(
+        endpoint=endpoint, credential=AzureKeyCredential(key)
+    )
+    with open(path_to_sample_documents, "rb") as f:
+        poller = document_analysis_client.begin_analyze_document(
+            "prebuilt-document", document=f
+        )
+    result = poller.result()
+
+    # convert the received model to a dictionary
+    analyze_result_dict = result.to_dict()
+
+    # save the dictionary as a JSON content in a JSON file
+    with open('data.json', 'w') as f:
+        json.dump(analyze_result_dict, f, cls=AzureJSONEncoder)
+
+    # convert the dictionary back to the original model
+    model = AnalyzeResult.from_dict(analyze_result_dict)
+
+    # use the model as normal
+    print("----Converted from dictionary AnalyzeResult----")
+    print("Model ID: '{}'".format(model.model_id))
+    print("Number of pages analyzed {}".format(len(model.pages)))
+    print("API version used: {}".format(model.api_version))
+
+    print("----------------------------------------")
+
+
+if __name__ == "__main__":
+    convert_to_and_from_dict()