计算机影像 API

从图像提取丰富信息,对视觉对象数据进行分类和处理,并屏蔽用户不想要的内容。

分析图像

此功能可返回图像中找到的视觉对象内容的相关信息。使用标记、描述和特定于域的模型来识别内容并标为可信。应用“成人/不雅”设置,启用成人内容自动限制。识别图片中的图像类型和配色方案。
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Gender Male
Age 36
Feature Name: Value
Description { "Tags": [ "water", "swimming", "sport", "pool", "person", "man", "frisbee", "ocean", "blue", "bird", "riding", "top", "standing", "wave", "young", "body", "large", "game", "glass", "pond", "playing", "board", "catch", "clear", "boat", "white" ], "Captions": [ { "Text": "a man swimming in a pool of water", "Confidence": 0.8206323 } ] }
Tags [ { "Name": "water", "Confidence": 0.9997857 }, { "Name": "swimming", "Confidence": 0.955619633 }, { "Name": "sport", "Confidence": 0.953807831 }, { "Name": "pool", "Confidence": 0.9515978 }, { "Name": "person", "Confidence": 0.889862537 }, { "Name": "water sport", "Confidence": 0.664259 } ]
Image format "Jpeg"
Image dimensions 462 x 600
Clip art type 0
Line drawing type 0
Black and white false
Adult content false
Adult score 0.07518345
Racy false
Racy score 0.1814024
Categories [ { "Name": "people_swimming", "Score": 0.98046875 } ]
Faces [ { "Age": 36, "Gender": "Male", "FaceRectangle": { "Top": 133, "Left": 298, "Width": 121, "Height": 121 } } ]
Dominant color background
"White"
Dominant color foreground
"Grey"
Accent Color
#19A4B2

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近乎实时地分析视频

近乎实时地分析视频:通过从设备中提取视频的帧,并将这些帧发送到你选择的 API 调用,可将任意计算机视觉 API 用于视频文件。更快速地从视频中获取结果。

通过 GitHub 上的示例开始使用并构建你自己的应用。

了解更多
在实际操作中查看

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生成缩略图

基于任何输入图像生成充分利用存储空间的高质量缩略图。使用缩略图生成来修改图像,从而最好地满足你的大小、形状和风格需要。应用智能裁剪来生成与原始图像纵横比不同却保留感兴趣区域的缩略图。
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读取图像中的文本

光学字符识别技术 (OCR) 可检测图像中的文本,并将所识别的字词提取到计算机可识别的字符流中。分析图像以检测嵌入的文本、生成字符流和启用搜索。获取文本的照片而非进行复制,节省时间和精力。
在实际操作中查看

  1. 预览
  2. JSON

IF WE DID

ALL

THE THINGS

WE ARE

CAPABLÉ•

OF DOING,

WE WOULD

LITERALLY

ASTOUND

QURSELV*S.

{
  "TextAngle": 0.0,
  "Orientation": "NotDetected",
  "Language": "en",
  "Regions": [
    {
      "BoundingBox": "316,47,284,340",
      "Lines": [
        {
          "BoundingBox": "319,47,182,24",
          "Words": [
            {
              "BoundingBox": "319,47,42,24",
              "Text": "IF"
            },
            {
              "BoundingBox": "375,47,44,24",
              "Text": "WE"
            },
            {
              "BoundingBox": "435,47,66,23",
              "Text": "DID"
            }
          ]
        },
        {
          "BoundingBox": "316,74,204,69",
          "Words": [
            {
              "BoundingBox": "316,74,204,69",
              "Text": "ALL"
            }
          ]
        },
        {
          "BoundingBox": "318,147,207,24",
          "Words": [
            {
              "BoundingBox": "318,147,63,24",
              "Text": "THE"
            },
            {
              "BoundingBox": "397,147,128,24",
              "Text": "THINGS"
            }
          ]
        },
        {
          "BoundingBox": "316,176,125,23",
          "Words": [
            {
              "BoundingBox": "316,176,44,23",
              "Text": "WE"
            },
            {
              "BoundingBox": "375,176,66,23",
              "Text": "ARE"
            }
          ]
        },
        {
          "BoundingBox": "319,194,281,44",
          "Words": [
            {
              "BoundingBox": "319,194,281,44",
              "Text": "CAPABLÉ•"
            }
          ]
        },
        {
          "BoundingBox": "318,243,181,29",
          "Words": [
            {
              "BoundingBox": "318,243,43,23",
              "Text": "OF"
            },
            {
              "BoundingBox": "376,243,123,29",
              "Text": "DOING,"
            }
          ]
        },
        {
          "BoundingBox": "316,271,170,24",
          "Words": [
            {
              "BoundingBox": "316,272,44,23",
              "Text": "WE"
            },
            {
              "BoundingBox": "375,271,111,24",
              "Text": "WOULD"
            }
          ]
        },
        {
          "BoundingBox": "317,300,200,24",
          "Words": [
            {
              "BoundingBox": "317,300,200,24",
              "Text": "LITERALLY"
            }
          ]
        },
        {
          "BoundingBox": "316,328,157,24",
          "Words": [
            {
              "BoundingBox": "316,328,157,24",
              "Text": "ASTOUND"
            }
          ]
        },
        {
          "BoundingBox": "318,357,214,30",
          "Words": [
            {
              "BoundingBox": "318,357,214,30",
              "Text": "QURSELV*S."
            }
          ]
        }
      ]
    }
  ]
}

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从图像读取手写文本

此技术(手写 OCR)使你可以检测和提取笔记、信件、文章、白板、表格等对象中的手写文本。它适用于不同的图面和背景,如白纸、黄色便签和白板等。

手写文本识别既省时又省力,并且可通过允许拍摄文本图像,而不必进行转录,使你工作更高效。它使得数字化笔记变为可能,届时你变可以实现快速而轻视的搜索。它还减少了纸张乱堆的情况。

注意:此技术当前处于预览状态,且仅适用于英语文本。

若要试用此光学字符识别演示版,请上传本地存储的图像或提供图像 URL。对于此演示版,除非授予操作权限,否则我们不会存储提供的图像。

在实际操作中查看

  1. 预览
  2. JSON
Our greatest glory is not 	
in never failing , 	
but in rising every time we fall 	
    {
  "status": "Succeeded",
  "recognitionResult": {
    "lines": [
      {
        "boundingBox": [
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        "text": "Our greatest glory is not",
        "words": [
          {
            "boundingBox": [
              204,
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            "text": "Our"
          },
          {
            "boundingBox": [
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            "text": "greatest"
          },
          {
            "boundingBox": [
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            "text": "glory"
          },
          {
            "boundingBox": [
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            "text": "is"
          },
          {
            "boundingBox": [
              1822,
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            ],
            "text": "not"
          }
        ]
      },
      {
        "boundingBox": [
          420,
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        ],
        "text": "but in rising every time we fall",
        "words": [
          {
            "boundingBox": [
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            ],
            "text": "but"
          },
          {
            "boundingBox": [
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            "text": "in"
          },
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            "boundingBox": [
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            ],
            "text": "rising"
          },
          {
            "boundingBox": [
              1331,
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              1502,
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            ],
            "text": "every"
          },
          {
            "boundingBox": [
              1812,
              1263,
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              2179,
              1500,
              1813,
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            ],
            "text": "time"
          },
          {
            "boundingBox": [
              2219,
              1261,
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              1498,
              2220,
              1500
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            "text": "we"
          },
          {
            "boundingBox": [
              2551,
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            "text": "fall"
          }
        ]
      },
      {
        "boundingBox": [
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        ],
        "text": "in never failing ,",
        "words": [
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          {
            "boundingBox": [
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            "text": "never"
          },
          {
            "boundingBox": [
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            ],
            "text": "failing"
          },
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              2705,
              1140
            ],
            "text": ","
          }
        ]
      }
    ]
  }
}

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了解其他认知服务 API

人脸 API

检测、分析、组织和标记照片中的人脸

内容审查器

自动化图像、文本和视频审查

情感 API 预览版

通过情绪识别实现个性化用户体验

计算机影像 API

从图像中提取可操作信息

语言理解智能服务 预览版

教会应用理解用户发出的命令

必应拼写检查 API

检测并更正应用中的拼写错误

Web 语言模型 API 预览版

利用就 Web 上的数据进行训练的预测语言模型的功能

文本分析 API 预览版

轻松评估观点和主题以理解用户的需求

Translator 文本 API

通过简单的 REST API 调用即可轻松实现自动文本翻译

必应语音 API 预览版

将语音转换为文本,再转回语音,并理解用户的意图

自定义语音服务 预览版

克服语音识别障碍,如说话风格、背景噪音和词汇

必应拼写检查 API

检测并更正应用中的拼写错误

说话人识别 API 预览版

使用语音以识别并对单个说话人进行身份验证

Translator 语音 API

通过简单的 REST API 调用即可轻松实现实时语音翻译

建议 API 预览版

预测和推荐客户所需的商品

学术知识 API 预览版

利用 Microsoft Academic Graph 中丰富的学术内容

准备好好利用你的应用了吗?