Computer Vision

從影像擷取豐富資訊,進而分類及處理視覺化資料,並對影像進行機器輔助審核,以協助籌備您的服務。

分析影像

這項功能會傳回在影像中找到的視覺化內容資訊。使用標記、特定領域模型及四種語言描述,從容地識別內容並將其加上標籤。使用物件偵測來找出影像內的數千個物件。套用成人/猥褻設定,以協助您偵測潛在成人內容。識別圖片中的影像類型及色彩配置。

觀看影片

person
person
subway train
功能名稱:
物件數 [ { "rectangle": { "x": 93, "y": 178, "w": 115, "h": 237 }, "object": "person", "confidence": 0.764 }, { "rectangle": { "x": 0, "y": 229, "w": 101, "h": 206 }, "object": "person", "confidence": 0.624 }, { "rectangle": { "x": 161, "y": 31, "w": 439, "h": 423 }, "object": "subway train", "parent": { "object": "train", "parent": { "object": "Land vehicle", "parent": { "object": "Vehicle", "confidence": 0.926 }, "confidence": 0.923 }, "confidence": 0.917 }, "confidence": 0.801 } ]
標籤 [ { "name": "train", "confidence": 0.9975446 }, { "name": "platform", "confidence": 0.9955431 }, { "name": "station", "confidence": 0.979800761 }, { "name": "indoor", "confidence": 0.9277198 }, { "name": "subway", "confidence": 0.8389395 }, { "name": "clothing", "confidence": 0.5043765 }, { "name": "pulling", "confidence": 0.4317162 } ]
描述 { "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": 0.8330992 } ] }
影像格式 "Jpeg"
影像尺寸 462 x 600
美工圖案類型 0
線條繪圖類型 0
黑與白 false
成人內容 false
成人分數 0.009112834
辛辣 false
辛辣分數 0.0143244695
類別 [ { "name": "trans_trainstation", "score": 0.98828125 } ]
臉部 []
主要色彩背景
"Black"
主要色彩前景
"Black"
輔色
#484C83

想要建置這個嗎?

現已正式推出:閱讀影像中的印刷和手寫文字

在讀取作業中利用最先進的光學字元辨識 (OCR) 來偵測內嵌的印刷和手寫文字、將辨識到的文字擷取至電腦可讀取的字元資料流,以便搜尋。透過替文字照相,來省下複製文字所需的時間及精力。

觀看影片

  1. 預覽
  2. JSON

Sorry!

Have a

Oops!

nice day !

See you soon !

Bye !

{
  "status": "Succeeded",
  "succeeded": true,
  "failed": false,
  "finished": true,
  "recognitionResults": [
    {
      "page": 1,
      "clockwiseOrientation": 353.71,
      "width": 1138,
      "height": 825,
      "unit": "pixel",
      "lines": [
        {
          "boundingBox": [
            124,
            126,
            399,
            90,
            407,
            199,
            140,
            229
          ],
          "text": "Sorry!",
          "words": [
            {
              "boundingBox": [
                137,
                121,
                397,
                89,
                410,
                198,
                150,
                229
              ],
              "text": "Sorry!"
            }
          ]
        },
        {
          "boundingBox": [
            591,
            173,
            908,
            124,
            921,
            207,
            604,
            256
          ],
          "text": "Have a",
          "words": [
            {
              "boundingBox": [
                598,
                173,
                812,
                140,
                824,
                224,
                610,
                256
              ],
              "text": "Have"
            },
            {
              "boundingBox": [
                834,
                136,
                894,
                127,
                906,
                212,
                846,
                221
              ],
              "text": "a"
            }
          ]
        },
        {
          "boundingBox": [
            199,
            379,
            424,
            365,
            423,
            476,
            209,
            488
          ],
          "text": "Oops!",
          "words": [
            {
              "boundingBox": [
                205,
                377,
                420,
                364,
                426,
                475,
                212,
                488
              ],
              "text": "Oops!"
            }
          ]
        },
        {
          "boundingBox": [
            583,
            267,
            973,
            224,
            982,
            305,
            592,
            348
          ],
          "text": "nice day !",
          "words": [
            {
              "boundingBox": [
                584,
                271,
                762,
                251,
                771,
                330,
                593,
                344
              ],
              "text": "nice"
            },
            {
              "boundingBox": [
                810,
                245,
                940,
                229,
                949,
                310,
                819,
                325
              ],
              "text": "day"
            },
            {
              "boundingBox": [
                954,
                227,
                973,
                225,
                982,
                306,
                963,
                308
              ],
              "text": "!"
            }
          ]
        },
        {
          "boundingBox": [
            166,
            628,
            662,
            599,
            667,
            683,
            170,
            712
          ],
          "text": "See you soon !",
          "words": [
            {
              "boundingBox": [
                172,
                628,
                295,
                624,
                300,
                704,
                178,
                712
              ],
              "text": "See"
            },
            {
              "boundingBox": [
                312,
                623,
                446,
                618,
                449,
                692,
                316,
                702
              ],
              "text": "you"
            },
            {
              "boundingBox": [
                463,
                617,
                620,
                611,
                620,
                680,
                465,
                691
              ],
              "text": "soon"
            },
            {
              "boundingBox": [
                636,
                610,
                659,
                609,
                658,
                677,
                636,
                679
              ],
              "text": "!"
            }
          ]
        },
        {
          "boundingBox": [
            824,
            498,
            1003,
            489,
            1014,
            594,
            834,
            607
          ],
          "text": "Bye !",
          "words": [
            {
              "boundingBox": [
                830,
                497,
                961,
                489,
                967,
                598,
                837,
                606
              ],
              "text": "Bye"
            },
            {
              "boundingBox": [
                982,
                488,
                1004,
                486,
                1011,
                595,
                989,
                597
              ],
              "text": "!"
            }
          ]
        }
      ]
    }
  ]
}

示範結果僅供參考 - 因為套用的影像操作較少,實際的 API 結果可能會有所不同。

想要建置這個嗎?

辨識品牌、名人和地標

辨識超過 1,500 種全球品牌和標誌、100 萬名來自商業界、政界、體壇及娛樂圈的名人,以及世界各地 9,000 個自然與人工地標。

觀看影片

{
  "categories": [
    {
      "name": "people_",
      "score": 0.86328125,
      "detail": {
        "celebrities": [
          {
            "name": "Satya Nadella",
            "faceRectangle": {
              "left": 240,
              "top": 294,
              "width": 135,
              "height": 135
            },
            "confidence": 0.99984323978424072
          }
        ],
        "landmarks": null
      }
    }
  ],
  "adult": null,
  "tags": [
    {
      "name": "person",
      "confidence": 0.99956607818603516
    },
    {
      "name": "suit",
      "confidence": 0.98934578895568848
    },
    {
      "name": "man",
      "confidence": 0.98844337463378906
    },
    {
      "name": "tie",
      "confidence": 0.959053635597229
    },
    {
      "name": "human face",
      "confidence": 0.95430314540863037
    },
    {
      "name": "clothing",
      "confidence": 0.860575795173645
    },
    {
      "name": "smile",
      "confidence": 0.8601078987121582
    },
    {
      "name": "outdoor",
      "confidence": 0.86006265878677368
    },
    {
      "name": "glasses",
      "confidence": 0.68438893556594849
    }
  ],
  "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": 0.99032749706305134
      }
    ]
  },
  "requestId": "fe522d83-a0e3-42d5-9e87-d1071b8f9d00",
  "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
  },
  "brands": []
}

想要建置這個嗎?

近乎即時分析影片

近乎即時分析影片:從裝置擷取視訊框架,然後將其傳送至您所選的 API 呼叫,即可對您的視訊檔案使用任何一項電腦視覺 API。更快從影片取得結果。

使用 GitHub 上的範例,開始使用並建置您專屬的應用程式。

深入了解

觀看影片

想要建置這個嗎?

產生縮圖

依據任何影像,產生高品質且具儲存體效益的縮圖,並修改影像以最符合您的大小、圖形和樣式需求。套用智慧裁剪,產生出外觀比例與原始影像不同的縮圖,同時保留所關注的區域。

觀看影片

想要建置這個嗎?

"We can use the Computer Vision API to prove to our clients the reliability of the data, so they can be confident making important business decisions based on that information"

Leendert de Voogd:CEO | Vigiglobe
vigiglobe

"It didn't take us long to realize Microsoft Cognitive Services had handed us a powerful set of computer-vision and artificial-intelligence tools that we could use to create great apps and new features for our customers in just a few hours"

John Fan:共同創立者暨執行長 | Cardinal Blue Software
Pic Collage

"Because the Cognitive Services APIs harness the power of machine learning, we were able to bring advanced intelligence into our product without the need to have a team of data scientists on hand"

Aaron Edell:產品負責人 | GrayMeta
GrayMeta

"We found Cognitive Services to be the missing piece in the equation, the one that we needed to bring this solution to market and really revolutionize the way people look at video"

Katie McCann:產品和工程部門副總裁 | Prism Skylabs
Prism Skylabs

"Microsoft Cognitive Services gives us a huge range of opportunities. It's a perfect match for us now, and in the future when we want to add more features to our app"

Jaan Apajalahti:執行長 | Blucup
Blucup

"Using the Cognitive Services APIs, it took us three months to develop a test pair of glasses that can translate text and images into speech, identify emotions, and describe scenery.If we had been working full time, we could have done it in two weeks"

Benoit Chirouter:研發主任 | Pivothead
Pivothead

探索認知服務 API

Computer Vision

從影像擷取可操作的資訊

臉部

偵測、識別、分析、組織和標記相片中的臉孔

筆跡辨識器 預覽

能夠辨識數位筆跡內容的 AI 服務,例如手寫、圖形及手寫文件的版面配置

影片索引器

深入探索影片

自訂視覺

輕鬆自訂先進且適合您獨特使用案例的電腦視覺模型

表單辨識器 預覽

具 AI 功能的文件擷取服務,能夠理解您的表單

文字分析

輕鬆解讀意見與話題,從而了解使用者的需求

Translator Text

使用簡單的 REST API 呼叫,輕鬆進行機器翻譯

製作問與答的人員

將資訊整理成易於導覽的交談式回答

語言理解

教導您的應用程式理解使用者發出的命令

沈浸式閱讀程式 預覽

讓年齡層和活動能力不同的使用者們都能閱讀和理解文字

語音服務

語音轉換文字、文字轉換語音和語音翻譯的統一語音服務

說話者辨識 預覽

使用語音來辨識及驗證各個說話者

內容仲裁

自動審核影像、文字及影片

Anomaly Detector 預覽

輕鬆為應用程式賦予異常偵測功能。

個人化工具 預覽

提供個人化使用者體驗的 AI 服務

準備好大幅提升應用程式的效能了嗎?