backend/image_processing.py at main · TheNewReducers/backend · GitHub

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from google.cloud import vision
import io
import json

from typing import Sequence


def analyze_image_from_uri(
        path: str,
        feature_types: Sequence,
) -> vision.AnnotateImageResponse:
    client = vision.ImageAnnotatorClient()

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

    image = vision.Image(content=content)
    features = [vision.Feature(type_=feature_type) for feature_type in feature_types]
    request = vision.AnnotateImageRequest(image=image, features=features)

    response = client.annotate_image(request=request)

    return response


def print_text(response: vision.AnnotateImageResponse):
    print("=" * 80)
    for annotation in response.text_annotations:
        vertices = [f"({v.x},{v.y})" for v in annotation.bounding_poly.vertices]
        print(
            f"{repr(annotation.description):42}",
            ",".join(vertices),
            sep=" | ",
        )


def detect_text(path):
    """Detects text in the file."""

    client = vision.ImageAnnotatorClient()

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

    image = vision.Image(content=content)

    response = client.text_detection(image=image)
    texts = response.text_annotations
    print('Texts:')

    for text in texts:
        print('\n"{}"'.format(text.description))

        vertices = (['({},{})'.format(vertex.x, vertex.y)
                     for vertex in text.bounding_poly.vertices])

        print('bounds: {}'.format(','.join(vertices)))

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


def detect_labels(path):
    import io
    import os

    # Imports the Google Cloud client library
    from google.cloud import vision

    # Instantiates a client
    client = vision.ImageAnnotatorClient()

    # The name of the image file to annotate
    file_name = os.path.abspath(path)

    # Loads the image into memory
    with io.open(file_name, 'rb') as image_file:
        content = image_file.read()

    image = vision.Image(content=content)

    # Performs label detection on the image file
    response = client.label_detection(image=image)
    labels = response.label_annotations

    print('Labels:')
    for label in labels:
        print(label.description)


def detect_text_from_file(file) -> str:
    client = vision.ImageAnnotatorClient()
    content = file.read()
    image = vision.Image(content=content)
    response = client.text_detection(image=image)
    texts = response.text_annotations

    single_string_output: str = texts[0].description

    print(single_string_output)

    return single_string_output


if __name__ == '__main__':
    path = "./test-data/kassenbon-1.jpg"
    analyze_image_from_uri(path, [vision.Feature.Type.TEXT_DETECTION])

    # detect_labels(path)
    # detect_text(path)