页面比较

警告
此对象不会返回 This object does not return a RESPONSE, 只返回图像识别结果only the results of image recognition.

信息
如果是截图，请仔细检查您上传的图片是否完整，如果不完整会影响识别效果

创建任务

通过 createTask方法创建识别任务

...

If it's a screenshot, please carefully check if the uploaded image is complete. Incomplete images can affect the recognition accuracy.

Create Task

Create recognition task using the method of Create Task.

Request node：
状态
colour Green
title 国际节点INTERNATIONAL NODE
https://api.yescaptcha.com
状态
colour Green
title 国内节点CHINA NODE
https://cn.yescaptcha.com
请求地址： Method address:： https://api.yescaptcha.com/createTask
请求格式：Request format：POST application/json

...

Object structure

属性

类型

必须

说明

type

string

是

FunCaptchaClassification

状态

colour	Green
title	4 点数

image

string

是

Base64 编码的图片，可以是截图（只传六宫格图片部份，其他内容不要传，上面标题之类的东西不要传，只传图片！）

question

String

是

问题名称，请传全称，如：Pick the lion 仅支持英文要求大小写准确，其他语言请自行转换为英文

服务器会根据问题自动判断不同的图片类型，所以请确保问题正确。

全部图片类型与对应的英文问题请看文档最后列表

请求示例

代码块

language	json

{
    "clientKey": "cc9c18d3e263515c2c072b36a7125eecc078618f", // 填你的密钥
    "task": {
        "type": "FunCaptchaClassification",
        "image": "data:image/jpeg;base64,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",
        // 仅支持英文要求大小写准确，其他语言请自行转换为英文
        // 服务器会根据问题自动判断不同的图片类型，所以请确保问题正确。
        "question": "Pick the bread"  
    }
}

响应示例

代码块

language	json

{
    "errorId": 0,
    "errorCode": "",
    "status": "ready",
    "solution": {
        "objects": [4], // 目标所处的位置，第5张图片（从0开始）
        "labels": [
            "parrot",
            "panda",
            "octopus",
            "owl",
            "bread",
            "dog"
        ]
    },
    "taskId": "04a3fa76-11a0-11ed-a58c-0ef20f419382"
}

图片格式与问题说明：

如果遇到新的类型，请联系客服增加支持。

PS 如果遇到竖的6宫格，直接获取原图上传，服务端可以自动识别。

类型	英文问题	中文问题	说明
	`Pick the bread`	选出面包	目前有52种不同的分类，请直接传完整问题
	`Pick one square that shows two identical objects`	选出两个相同的图形	还有一种3个黑白扭曲变形图标，请看下面。
	`Pick one square that shows two identical objects`	选出两个相同的图形	问题与上面的相同，区别是黑暗版本的尺寸为450300，请确保图片尺寸正确*
	`Pick the penguin`	选出企鹅
	`Pick the shadow with a different object silhouette`	选择具有不同对象轮廓的阴影
	`Pick the dice pair with the same icon facing up`	选择相同图标的骰子对
	`Pick the dice pair whose top sides add up to 14`	选择顶边加起来为 14 的骰子对	准确率98.8%，更新于2023/04/20
	`Pick the matching cards`	选择匹配的卡片
	`Pick the animal looking at the shape that matches the shape it's standing on`	选择动物看向的形状与它站立的形状相匹配的
	`Pick one square that shows three of the same object.`	选出三个相同图形的方块
	`Pick the mouse that can reach all the cheese in the maze`	选出能够拿到迷宫里所有奶酪的老鼠
Image Added	`Pick the mouse that can't reach the cheese`	选出不能够拿到迷宫里所有奶酪的老鼠	准确率98.19%，更新于2023/06/20
	`Pick the image where the darts add up to 10`	选择飞镖加起来为 10 的图像
	`Pick the image that is the correct way up`	选择正确向上的图像
	`Pick the dice pair whose top sides add up to 5`	选择顶边加起来为 5 的骰子对	（5/6/7/8/9都有）
	`Pick the wrong shadow`	选择错误的阴影
	`Select the animal with the wrong head`	选择头部错误的动物
	`Pick the shadow that mathes the icons at the top of the image`	选择与图像顶部的图标匹配的阴影
	`Pick the image of 2 striped shapes`	选择2个条纹形状的图像
	`Pick the image of 2 checkered shapes`	选择 2 个方格形状的图像
	`Pick the spiral galaxy`	选择螺旋星系
	`Use the arrows to rotate the animal to face in the direction of the hand`	选择与手指方向相同的动物图片	准确率96.06%，更新于2023/04/25
	`Change the dice until the count matches the image on the left`	相加等于目标点数	准确率98.52%，更新于2023/06/18
	`Use the arrows to change the number of objects until it matches the left image`	选择物体数量正确的图片	准确率99.31%，更新于2023/06/12
	`Using the arrows, move the person to the indicated seat`	选择坐在指定座位的人物图片	准确率98.21%，更新于2023/06/04
	`Use the arrows to move the person to the spot indicated by the cross`	选择站在指示位置上的人	准确率99.47%，更新于2023/05/24
	`Use the arrows to move the person to the icon indicated by the colored circle`	选择站在指示位置上的人	准确率97.37%，更新于2023/05/15
	`Pick the image where all animals are walking in the same direction as the arrow`	选出所有动物都沿着箭头方向行走的图像
	`Select the image with the icon order Chair then Fence`	选择先椅子再围栏的图片
	`Using the arrows, connect the same two icons with the dotted line as shown on the left`	选择虚线连接相同的两个图标
	`Select the image where the total fingers add up to 4`	选择总共有4根手指的图片	准确率100.00%，更新于2023/04/16
	`Select the image where the total fingers add up to 3`	选择总共有3根手指的图片	准确率100.00%，更新于2023/06/16
	`Use the arrows to place the train on the coordinate point shown in the picture on the left`	选择停在指定位置的火车	准确率98.06%，更新于2023/06/07
	`use the arrows to move the train to the coordinates shown in the picture on the left`	选择停在指定位置的火车	准确率99.89%，更新于2023/06/19
	`Use the arrows to move the train into the position indicated in the left image`	选择停在指定位置的火车
	`Pick any square`	选择任何正方形
	`Select an image of three circles in any direction on a straight line`	选取任意方向上三个圆圈在一条直线上的图片
	`Pick the image with 3 crosses in a row in any direction`	选择任何方向连续 3 个叉的图像
	`Pick the image where the number matches the amount of animals`	选择数字与动物数量相匹配的图像
	`Use the arrows to rotate the animal with the same icon to face where the hand is pointing`	选择具有相同图标的动物面向手指方向的图片	准确率94.45%，更新于2023/06/13
	`Use the buttons to place the indicated car, in the correct position in the race`	选择在比赛中正确位置的汽车
	`Pick the distorted object`	选择扭曲的物体	准确率67.00%，更新于2023/06/09
	`Pick the object that is not distorted`	选择没有扭曲的物体	准确率54.00%，更新于2023/06/09
	`Select the animal with the wrong head`	选择头和身体匹配错误的动物	准确率100.00%，更新于2023/06/14
更多未列出类型，请直接提交英文问题。