google vision API返回空的边界框顶点，而是返回normalized_vertexes

问题描述

我正在使用 vision.enums.Feature.Type.DOCUMENT_TEXT_DETECTION 提取pdf文档中的一些密集文本.这是我的代码:

I am using vision.enums.Feature.Type.DOCUMENT_TEXT_DETECTION to extract some dense text in a pdf document. Here is my code:

from google.cloud import vision

def extract_text(bucket, filename, mimetype):
    print('Looking for text in PDF {}'.format(filename))
    # BATCH_SIZE; How many pages should be grouped into each json output file.
    # """OCR with PDF/TIFF as source files on GCS"""

    # Detect text
    feature = vision.types.Feature(
        type=vision.enums.Feature.Type.DOCUMENT_TEXT_DETECTION)
    # Extract text from source bucket
    gcs_source_uri = 'gs://{}/{}'.format(bucket, filename)
    gcs_source = vision.types.GcsSource(uri=gcs_source_uri)
    input_config = vision.types.InputConfig(
        gcs_source=gcs_source, mime_type=mimetype)

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

    print('Waiting for the ORC operation to finish.')
    ocr_response = vision_client.batch_annotate_files(requests=[request])

    print('OCR completed.')

在回复中，我希望找到 ocr_response.responses [1 ... n] .pages [1 ... n] .blocks [1 ... n] .bounding_box 填写的顶点列表，但此列表为空.而是有一个 normalized_vertices 列表，它们是介于0和1之间的归一化顶点.为什么会这样呢?为什么 vertices 结构为空?我正在关注此文章，那里的作者使用顶点，但是我不明白为什么我没有得到它们.要将它们转换为非规范化的形式，我将规范化的顶点乘以高度和宽度，但是结果很糟糕，盒子的位置不好.

In the response, I am expecting to find into ocr_response.responses[1...n].pages[1...n].blocks[1...n].bounding_box a list of vertices filled in, but this list is empty. Instead, there is a normalized_vertices list which are the normalised vertices between 0 and 1. Why is that so? why the vertices structure is empty?I am following this article, and the author there uses vertices, but I don't understand why I don't get them.To convert them to the non normalised form, I am multiplying the normalised vertex by height and width, but the result is awful, the boxes are not well positioned.

推荐答案

要将标准化顶点转换为顶点，您应该将NormalizedVertex的x字段乘以width值以获得顶点的x字段并乘以的y字段高度值来获取NormalizedVertex即可获得顶点的y.

To convert Normalized Vertex to Vertex you should multiply the x field of your NormalizedVertex with the width value to get the x field of the Vertex and multiply the y field of your NormalizedVertex with the height value to get the y of the Vertex.

原因为什么要获得归一化顶点和作者中篇文章获得顶点"的原因是因为2020年5月15日以来TEXT_DETECTION和DOCUMENT_TEXT_DETECTION模型已升级到较新版本，中篇文章撰写于2018年12月25日.

The reason why you get Normalized Vertex, and the author of Medium article get Vertex is because the TEXT_DETECTION and DOCUMENT_TEXT_DETECTION models have been upgraded to newer versions since May 15, 2020, and medium article was written on Dec 25, 2018.

要使用旧模型生成结果，必须在Feature对象的model字段中指定"builtin/legacy_20190601"以获取旧模型结果.

To use legacy models for results, you must specify "builtin/legacy_20190601" in the model field of a Feature object to get the old model results.

但是Google的文档提到，到2020年11月15日之后，将不再提供旧型号.

But the Google's doc mention that after November 15, 2020 the old models will not longer be offered.

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