计算机影像 API

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、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。

在实际操作中查看


性别 Male
年龄 36
特征名称:
说明 { "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": 0.8909298 } ] }
标记 [ { "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 } ]
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 0.07518345
猥亵内容 false
猥亵内容评分 0.1814024
类别 [ { "Name": "people_swimming", "Score": 0.98046875 } ]
人脸 [ { "Age": 36, "Gender": "Male", "FaceRectangle": { "Top": 133, "Left": 298, "Width": 121, "Height": 121 } } ]
背景主色
"White"
前景主色
"Grey"
主题色
#19A4B2

想要生成它?

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."
            }
          ]
        }
      ]
    }
  ]
}

上传此演示的数据即表示你同意 Microsoft 对其进行存储并将其用于改进 Microsoft 服务(例如优化此 API)。为了保护用户隐私,我们会采取措施对数据进行反识别处理并确保它的安全。我们不会公开用户数据或让其他人使用这些数据。

想要生成它?

预览: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