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计算机影像

从图像中提取丰富的信息,以便对视觉数据进行分类和处理,在计算机的辅助下审查图像,为策展服务提供帮助。

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、特定于域的模型和描述来识别内容并标为可信。应用成人/不雅内容设置,帮助你检测可能的成人内容。识别图片中的图像类型和配色方案。

在实际操作中查看

特征名称:
说明 { "tags": [ "train", "platform", "station", "building", "indoor", "subway", "track", "walking", "waiting", "pulling", "board", "people", "man", "luggage", "standing", "holding", "large", "woman", "yellow", "suitcase" ], "captions": [ { "text": "people waiting at a train station", "confidence": 0.833099365 } ] }
标记 [ { "name": "train", "confidence": 0.9975446 }, { "name": "platform", "confidence": 0.995543063 }, { "name": "station", "confidence": 0.9798007 }, { "name": "indoor", "confidence": 0.927719653 }, { "name": "subway", "confidence": 0.838939846 }, { "name": "pulling", "confidence": 0.431715637 } ]
图像格式 "Jpeg"
图像尺寸 462 x 600
剪贴画类型 0
线条绘制类型 0
黑色和白色 false
成人内容 false
成人评分 0.0147124995
猥亵内容 false
猥亵内容评分 0.0162802152
类别 [ { "name": "trans_trainstation", "score": 0.98828125 } ]
人脸 []
背景主色
"Black"
前景主色
"Black"
主题色
#484C83

想要生成它?

读取图像中的文本

使用光学字符识别 (OCR) 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。通过获取文本的照片而非进行复制来节省时间和精力。

开始使用正式发布版的 OCR 服务,并快速浏览下方的新预览版 OCR 引擎(通过“识别文本”API 操作),了解面向英文的优化文本识别结果。

在实际操作中查看

  1. 预览
  2. JSON

Sorry!

Have a

nice day !

Oops!

See you soon !

bye!

{
  "status": "Succeeded",
  "succeeded": true,
  "failed": false,
  "finished": true,
  "recognitionResult": {
    "lines": [
      {
        "boundingBox": [
          122,
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          233
        ],
        "text": "Sorry!",
        "words": [
          {
            "boundingBox": [
              121,
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              81,
              442,
              223,
              140,
              263
            ],
            "text": "Sorry!"
          }
        ]
      },
      {
        "boundingBox": [
          586,
          160,
          917,
          120,
          929,
          221,
          599,
          262
        ],
        "text": "Have a",
        "words": [
          {
            "boundingBox": [
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              138,
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              232,
              593,
              265
            ],
            "text": "Have"
          },
          {
            "boundingBox": [
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              135,
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              224,
              833,
              230
            ],
            "text": "a"
          }
        ]
      },
      {
        "boundingBox": [
          577,
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          204,
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          335,
          590,
          374
        ],
        "text": "nice day !",
        "words": [
          {
            "boundingBox": [
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              342,
              583,
              353
            ],
            "text": "nice"
          },
          {
            "boundingBox": [
              774,
              234,
              934,
              211,
              941,
              339,
              782,
              341
            ],
            "text": "day"
          },
          {
            "boundingBox": [
              934,
              211,
              991,
              204,
              997,
              339,
              941,
              339
            ],
            "text": "!"
          }
        ]
      },
      {
        "boundingBox": [
          210,
          367,
          436,
          347,
          435,
          499,
          192,
          483
        ],
        "text": "Oops!",
        "words": [
          {
            "boundingBox": [
              192,
              368,
              452,
              345,
              466,
              495,
              205,
              519
            ],
            "text": "Oops!"
          }
        ]
      },
      {
        "boundingBox": [
          167,
          622,
          686,
          588,
          693,
          684,
          174,
          719
        ],
        "text": "See you soon !",
        "words": [
          {
            "boundingBox": [
              165,
              624,
              307,
              614,
              308,
              711,
              168,
              722
            ],
            "text": "See"
          },
          {
            "boundingBox": [
              300,
              614,
              442,
              606,
              441,
              702,
              302,
              712
            ],
            "text": "you"
          },
          {
            "boundingBox": [
              448,
              605,
              622,
              597,
              619,
              690,
              448,
              701
            ],
            "text": "soon"
          },
          {
            "boundingBox": [
              622,
              597,
              686,
              594,
              683,
              687,
              619,
              690
            ],
            "text": "!"
          }
        ]
      },
      {
        "boundingBox": [
          824,
          491,
          1010,
          482,
          1013,
          611,
          808,
          603
        ],
        "text": "bye!",
        "words": [
          {
            "boundingBox": [
              811,
              491,
              1034,
              480,
              1040,
              609,
              817,
              620
            ],
            "text": "bye!"
          }
        ]
      }
    ]
  }
}

想要生成它?

预览:从图像读取手写文本

检测和提取笔记、信件、文章、白板、表格和其他源中的手写文本。通过获取手写笔记的照片而非进行誊写来减少纸张杂乱的情况和提高效率,然后通过执行搜索来轻松查找数字笔记。手写 OCR 适用于不同的图面和背景,如白纸、黄色便签和白板。

注意:此技术当前处于预览状态,且仅适用于英语文本。

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",
  "succeeded": true,
  "failed": false,
  "finished": true,
  "recognitionResult": {
    "lines": [
      {
        "boundingBox": [
          67,
          204,
          668,
          210,
          667,
          272,
          66,
          267
        ],
        "text": "Our greatest glory is not",
        "words": [
          {
            "boundingBox": [
              47,
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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": [
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            ],
            "text": "not"
          }
        ]
      },
      {
        "boundingBox": [
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        ],
        "text": "in never failing ,",
        "words": [
          {
            "boundingBox": [
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            ],
            "text": "in"
          },
          {
            "boundingBox": [
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            "text": "never"
          },
          {
            "boundingBox": [
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            ],
            "text": "failing"
          },
          {
            "boundingBox": [
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            ],
            "text": ","
          }
        ]
      },
      {
        "boundingBox": [
          139,
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        ],
        "text": "but in rising every",
        "words": [
          {
            "boundingBox": [
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            ],
            "text": "but"
          },
          {
            "boundingBox": [
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            "text": "in"
          },
          {
            "boundingBox": [
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            "text": "rising"
          },
          {
            "boundingBox": [
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            ],
            "text": "every"
          }
        ]
      },
      {
        "boundingBox": [
          622,
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        ],
        "text": "time we fall",
        "words": [
          {
            "boundingBox": [
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              470,
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            ],
            "text": "time"
          },
          {
            "boundingBox": [
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              471,
              743,
              470
            ],
            "text": "we"
          },
          {
            "boundingBox": [
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              472,
              853,
              471
            ],
            "text": "fall"
          }
        ]
      }
    ]
  }
}

想要生成它?

识别名人和地标

识别全球商业、政治和体育领域的 200,000 多位名人和 9,000 处自然和人造地标。

在实际操作中查看

{
  "categories": [
    {
      "name": "people_",
      "score": 0.86328125,
      "detail": {
        "celebrities": [
          {
            "name": "Satya Nadella",
            "faceRectangle": {
              "left": 240,
              "top": 294,
              "width": 135,
              "height": 135
            },
            "confidence": 0.99999833106994629
          }
        ],
        "landmarks": null
      }
    }
  ],
  "adult": null,
  "tags": [
    {
      "name": "person",
      "confidence": 0.99956613779067993
    },
    {
      "name": "suit",
      "confidence": 0.98934584856033325
    },
    {
      "name": "man",
      "confidence": 0.98844343423843384
    },
    {
      "name": "outdoor",
      "confidence": 0.860062301158905
    }
  ],
  "description": {
    "tags": [
      "person",
      "suit",
      "man",
      "necktie",
      "outdoor",
      "building",
      "clothing",
      "standing",
      "wearing",
      "business",
      "looking",
      "holding",
      "black",
      "front",
      "hand",
      "dressed",
      "phone",
      "field"
    ],
    "captions": [
      {
        "text": "Satya Nadella wearing a suit and tie",
        "confidence": 0.9903275009959599
      }
    ]
  },
  "requestId": "c7127100-22f8-45cd-b6bb-d823fd48b4dd",
  "metadata": {
    "width": 600,
    "height": 900,
    "format": "Jpeg"
  },
  "faces": [
    {
      "age": 49,
      "gender": "Male",
      "faceRectangle": {
        "left": 240,
        "top": 294,
        "width": 135,
        "height": 135
      }
    }
  ],
  "color": {
    "dominantColorForeground": "Black",
    "dominantColorBackground": "Black",
    "dominantColors": [
      "Black",
      "Grey"
    ],
    "accentColor": "7B5E50",
    "isBWImg": false
  },
  "imageType": {
    "clipArtType": 0,
    "lineDrawingType": 0
  }
}

想要生成它?

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

想要生成它?

生成缩略图

基于任何图像生成节约存储空间的高质量缩略图,然后修改图像以充分满足大小、形状和样式需求。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。

在实际操作中查看

想要生成它?

了解认知服务 API

计算机影像

从图像中提取可操作信息

人脸

检测、识别、分析、组织和标记照片中的人脸

视频索引器 预览版

解锁视频见解

内容审查器

自动化图像、文本和视频审查

自定义视觉 预览版

为你独一无二的用例轻松自定义最先进的计算机影像模型

文本分析

轻松评估观点和主题以理解用户的需求

文本翻译

通过简单的 REST API 调用即可轻松进行机器翻译

必应拼写检查

检测并更正应用中的拼写错误

内容审查器

自动化图像、文本和视频审查

语言理解

教会应用理解用户发出的命令

必应语音

将语音转换为文本,再转回语音,并理解用户的意图

说话人识别 预览版

使用语音辨识和验证各个说话人的身份

语音翻译

通过简单的 REST API 调用即可轻松实现实时语音翻译

自定义语音 预览版

克服语音识别障碍,如说话风格、背景噪音和词汇

语音服务 预览版

针对语音转文本、文本转语音和语音翻译的统一语音服务

QnA Maker

提取信息,并将其转化为一目了然的对话式答案

准备好好利用你的应用了吗?