vision/snippets/detect/beta_snippets.py

#!/usr/bin/env python

# Copyright 2018 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.

"""
Google Cloud Vision API Python Beta Snippets

Example Usage:
python beta_snippets.py -h
python beta_snippets.py object-localization INPUT_IMAGE
python beta_snippets.py object-localization-uri gs://...
python beta_snippets.py handwritten-ocr INPUT_IMAGE
python beta_snippets.py handwritten-ocr-uri gs://...
python beta_snippets.py batch-annotate-files INPUT_PDF
python beta_snippets.py batch-annotate-files-uri gs://...
python beta_snippets.py batch-annotate-images-uri gs://... gs://...


For more information, the documentation at
https://cloud.google.com/vision/docs.
"""

import argparse


# [START vision_localize_objects_beta]
def localize_objects(path):
    """Localize objects in the local image.

    Args:
    path: The path to the local file.
    """
    from google.cloud import vision_v1p3beta1 as vision

    client = vision.ImageAnnotatorClient()

    with open(path, "rb") as image_file:
        content = image_file.read()
    image = vision.Image(content=content)

    objects = client.object_localization(image=image).localized_object_annotations

    print(f"Number of objects found: {len(objects)}")
    for object_ in objects:
        print(f"\n{object_.name} (confidence: {object_.score})")
        print("Normalized bounding polygon vertices: ")
        for vertex in object_.bounding_poly.normalized_vertices:
            print(f" - ({vertex.x}, {vertex.y})")


# [END vision_localize_objects_beta]


# [START vision_localize_objects_gcs_beta]
def localize_objects_uri(uri):
    """Localize objects in the image on Google Cloud Storage

    Args:
    uri: The path to the file in Google Cloud Storage (gs://...)
    """
    from google.cloud import vision_v1p3beta1 as vision

    client = vision.ImageAnnotatorClient()

    image = vision.Image()
    image.source.image_uri = uri

    objects = client.object_localization(image=image).localized_object_annotations

    print(f"Number of objects found: {len(objects)}")
    for object_ in objects:
        print(f"\n{object_.name} (confidence: {object_.score})")
        print("Normalized bounding polygon vertices: ")
        for vertex in object_.bounding_poly.normalized_vertices:
            print(f" - ({vertex.x}, {vertex.y})")


# [END vision_localize_objects_gcs_beta]


# [START vision_handwritten_ocr_beta]
def detect_handwritten_ocr(path):
    """Detects handwritten characters in a local image.

    Args:
    path: The path to the local file.
    """
    from google.cloud import vision_v1p3beta1 as vision

    client = vision.ImageAnnotatorClient()

    with open(path, "rb") as image_file:
        content = image_file.read()

    image = vision.Image(content=content)

    # Language hint codes for handwritten OCR:
    # en-t-i0-handwrit, mul-Latn-t-i0-handwrit
    # Note: Use only one language hint code per request for handwritten OCR.
    image_context = vision.ImageContext(language_hints=["en-t-i0-handwrit"])

    response = client.document_text_detection(image=image, image_context=image_context)

    print(f"Full Text: {response.full_text_annotation.text}")
    for page in response.full_text_annotation.pages:
        for block in page.blocks:
            print(f"\nBlock confidence: {block.confidence}\n")

            for paragraph in block.paragraphs:
                print("Paragraph confidence: {}".format(paragraph.confidence))

                for word in paragraph.words:
                    word_text = "".join([symbol.text for symbol in word.symbols])
                    print(
                        "Word text: {} (confidence: {})".format(
                            word_text, word.confidence
                        )
                    )

                    for symbol in word.symbols:
                        print(
                            "\tSymbol: {} (confidence: {})".format(
                                symbol.text, symbol.confidence
                            )
                        )

    if response.error.message:
        raise Exception(
            "{}\nFor more info on error messages, check: "
            "https://cloud.google.com/apis/design/errors".format(response.error.message)
        )


# [END vision_handwritten_ocr_beta]


# [START vision_handwritten_ocr_gcs_beta]
def detect_handwritten_ocr_uri(uri):
    """Detects handwritten characters in the file located in Google Cloud
    Storage.

    Args:
    uri: The path to the file in Google Cloud Storage (gs://...)
    """
    from google.cloud import vision_v1p3beta1 as vision

    client = vision.ImageAnnotatorClient()
    image = vision.Image()
    image.source.image_uri = uri

    # Language hint codes for handwritten OCR:
    # en-t-i0-handwrit, mul-Latn-t-i0-handwrit
    # Note: Use only one language hint code per request for handwritten OCR.
    image_context = vision.ImageContext(language_hints=["en-t-i0-handwrit"])

    response = client.document_text_detection(image=image, image_context=image_context)

    print(f"Full Text: {response.full_text_annotation.text}")
    for page in response.full_text_annotation.pages:
        for block in page.blocks:
            print(f"\nBlock confidence: {block.confidence}\n")

            for paragraph in block.paragraphs:
                print("Paragraph confidence: {}".format(paragraph.confidence))

                for word in paragraph.words:
                    word_text = "".join([symbol.text for symbol in word.symbols])
                    print(
                        "Word text: {} (confidence: {})".format(
                            word_text, word.confidence
                        )
                    )

                    for symbol in word.symbols:
                        print(
                            "\tSymbol: {} (confidence: {})".format(
                                symbol.text, symbol.confidence
                            )
                        )

    if response.error.message:
        raise Exception(
            "{}\nFor more info on error messages, check: "
            "https://cloud.google.com/apis/design/errors".format(response.error.message)
        )


# [END vision_handwritten_ocr_gcs_beta]


# [START vision_batch_annotate_files_beta]
def detect_batch_annotate_files(path):
    """Detects document features in a PDF/TIFF/GIF file.

    While your PDF file may have several pages,
    this API can process up to 5 pages only.

    Args:
    path: The path to the local file.
    """
    from google.cloud import vision_v1p4beta1 as vision

    client = vision.ImageAnnotatorClient()

    with open(path, "rb") as pdf_file:
        content = pdf_file.read()

    # Other supported mime_types: image/tiff' or 'image/gif'
    mime_type = "application/pdf"
    input_config = vision.InputConfig(content=content, mime_type=mime_type)

    feature = vision.Feature(type_=vision.Feature.Type.DOCUMENT_TEXT_DETECTION)
    # Annotate the first two pages and the last one (max 5 pages)
    # First page starts at 1, and not 0. Last page is -1.
    pages = [1, 2, -1]

    request = vision.AnnotateFileRequest(
        input_config=input_config, features=[feature], pages=pages
    )

    response = client.batch_annotate_files(requests=[request])

    for image_response in response.responses[0].responses:
        for page in image_response.full_text_annotation.pages:
            for block in page.blocks:
                print(f"\nBlock confidence: {block.confidence}\n")
                for par in block.paragraphs:
                    print(f"\tParagraph confidence: {par.confidence}")
                    for word in par.words:
                        symbol_texts = [symbol.text for symbol in word.symbols]
                        word_text = "".join(symbol_texts)
                        print(
                            "\t\tWord text: {} (confidence: {})".format(
                                word_text, word.confidence
                            )
                        )
                        for symbol in word.symbols:
                            print(
                                "\t\t\tSymbol: {} (confidence: {})".format(
                                    symbol.text, symbol.confidence
                                )
                            )


# [END vision_batch_annotate_files_beta]


# [START vision_batch_annotate_files_gcs_beta]
def detect_batch_annotate_files_uri(gcs_uri):
    """Detects document features in a PDF/TIFF/GIF file.

    While your PDF file may have several pages,
    this API can process up to 5 pages only.

    Args:
    uri: The path to the file in Google Cloud Storage (gs://...)
    """
    from google.cloud import vision_v1p4beta1 as vision

    client = vision.ImageAnnotatorClient()

    # Other supported mime_types: image/tiff' or 'image/gif'
    mime_type = "application/pdf"
    input_config = vision.InputConfig(
        gcs_source=vision.GcsSource(uri=gcs_uri), mime_type=mime_type
    )

    feature = vision.Feature(type_=vision.Feature.Type.DOCUMENT_TEXT_DETECTION)
    # Annotate the first two pages and the last one (max 5 pages)
    # First page starts at 1, and not 0. Last page is -1.
    pages = [1, 2, -1]

    request = vision.AnnotateFileRequest(
        input_config=input_config, features=[feature], pages=pages
    )

    response = client.batch_annotate_files(requests=[request])

    for image_response in response.responses[0].responses:
        for page in image_response.full_text_annotation.pages:
            for block in page.blocks:
                print(f"\nBlock confidence: {block.confidence}\n")
                for par in block.paragraphs:
                    print(f"\tParagraph confidence: {par.confidence}")
                    for word in par.words:
                        symbol_texts = [symbol.text for symbol in word.symbols]
                        word_text = "".join(symbol_texts)
                        print(
                            "\t\tWord text: {} (confidence: {})".format(
                                word_text, word.confidence
                            )
                        )
                        for symbol in word.symbols:
                            print(
                                "\t\t\tSymbol: {} (confidence: {})".format(
                                    symbol.text, symbol.confidence
                                )
                            )


# [END vision_batch_annotate_files_gcs_beta]


# [START vision_async_batch_annotate_images_beta]
def async_batch_annotate_images_uri(input_image_uri, output_uri):
    """Batch annotation of images on Google Cloud Storage asynchronously.

    Args:
    input_image_uri: The path to the image in Google Cloud Storage (gs://...)
    output_uri: The path to the output path in Google Cloud Storage (gs://...)
    """
    import re

    from google.cloud import storage

    from google.cloud import vision_v1p4beta1 as vision

    client = vision.ImageAnnotatorClient()

    # Construct the request for the image(s) to be annotated:
    image_source = vision.ImageSource(image_uri=input_image_uri)
    image = vision.Image(source=image_source)
    features = [
        vision.Feature(type_=vision.Feature.Type.LABEL_DETECTION),
        vision.Feature(type_=vision.Feature.Type.TEXT_DETECTION),
        vision.Feature(type_=vision.Feature.Type.IMAGE_PROPERTIES),
    ]
    requests = [
        vision.AnnotateImageRequest(image=image, features=features),
    ]

    gcs_destination = vision.GcsDestination(uri=output_uri)
    output_config = vision.OutputConfig(gcs_destination=gcs_destination, batch_size=2)

    operation = client.async_batch_annotate_images(
        requests=requests, output_config=output_config
    )

    print("Waiting for the operation to finish.")
    operation.result(timeout=10000)

    # Once the request has completed and the output has been
    # written to Google Cloud Storage, we can list all the output files.
    storage_client = storage.Client()

    match = re.match(r"gs://([^/]+)/(.+)", output_uri)
    bucket_name = match.group(1)
    prefix = match.group(2)

    bucket = storage_client.get_bucket(bucket_name)

    # Lists objects with the given prefix.
    blob_list = list(bucket.list_blobs(prefix=prefix))
    print("Output files:")
    for blob in blob_list:
        print(blob.name)

    # Processes the first output file from Google Cloud Storage.
    # Since we specified batch_size=2, the first response contains
    # annotations for the first two annotate image requests.
    output = blob_list[0]

    json_string = output.download_as_bytes().decode("utf-8")
    response = vision.BatchAnnotateImagesResponse.from_json(json_string)

    # Prints the actual response for the first annotate image request.
    print(
        "The annotation response for the first request: {}".format(
            response.responses[0]
        )
    )


# [END vision_async_batch_annotate_images_beta]


if __name__ == "__main__":
    parser = argparse.ArgumentParser(
        description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter
    )
    subparsers = parser.add_subparsers(dest="command")

    object_parser = subparsers.add_parser(
        "object-localization", help=localize_objects.__doc__
    )
    object_parser.add_argument("path")

    object_uri_parser = subparsers.add_parser(
        "object-localization-uri", help=localize_objects_uri.__doc__
    )
    object_uri_parser.add_argument("uri")

    handwritten_parser = subparsers.add_parser(
        "handwritten-ocr", help=detect_handwritten_ocr.__doc__
    )
    handwritten_parser.add_argument("path")

    handwritten_uri_parser = subparsers.add_parser(
        "handwritten-ocr-uri", help=detect_handwritten_ocr_uri.__doc__
    )
    handwritten_uri_parser.add_argument("uri")

    batch_annotate_parser = subparsers.add_parser(
        "batch-annotate-files", help=detect_batch_annotate_files.__doc__
    )
    batch_annotate_parser.add_argument("path")

    batch_annotate_uri_parser = subparsers.add_parser(
        "batch-annotate-files-uri", help=detect_batch_annotate_files_uri.__doc__
    )
    batch_annotate_uri_parser.add_argument("uri")

    batch_annotate__image_uri_parser = subparsers.add_parser(
        "batch-annotate-images-uri", help=async_batch_annotate_images_uri.__doc__
    )
    batch_annotate__image_uri_parser.add_argument("uri")
    batch_annotate__image_uri_parser.add_argument("output")

    args = parser.parse_args()

    if "uri" in args.command:
        if "object-localization-uri" in args.command:
            localize_objects_uri(args.uri)
        elif "handwritten-ocr-uri" in args.command:
            detect_handwritten_ocr_uri(args.uri)
        elif "batch-annotate-files-uri" in args.command:
            detect_batch_annotate_files_uri(args.uri)
        elif "batch-annotate-images-uri" in args.command:
            async_batch_annotate_images_uri(args.uri, args.output)
    else:
        if "object-localization" in args.command:
            localize_objects(args.path)
        elif "handwritten-ocr" in args.command:
            detect_handwritten_ocr(args.path)
        elif "batch-annotate-files" in args.command:
            detect_batch_annotate_files(args.path)