FunCaptchaClassification: Funcaptcha 图像识别

此对象不会返回 RESPONSE, 只返回图像识别结果

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

识别流程

  1. 获取验证码图片,并转成base64(需要您自己处理,底部有一个canvas转base64参考方法)

  2. 获取验证码问题,直接取网页上的问题即可,不需要翻译

  3. 使用本接口提交识别任务(发送到 /createTask 创建识别任务,见本文档处理)

  4. 获得返回的识别结果(创建任务时返回的内容就是结果,见下方返回示例,如"objects": [4]

  5. 根据返回的结果,自己点击网页上相应的位置(结果从0开始计算)

创建任务

通过 createTask方法 创建识别任务

请求节点: 国际节点 https://api.yescaptcha.com 国内节点 https://cn.yescaptcha.com

请求地址: https://api.yescaptcha.com/createTask

请求格式:POST application/json

对象结构

属性

类型

必须

说明

属性

类型

必须

说明

type

string

FunCaptchaClassification 4 点数3点数

image

string

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

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

{ "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

{ "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:

  1. 请直接获取网页上显示的问题全文+图片提交,全部可以识别

  2. 如果遇到新的类型,请联系客服增加支持。

  3. 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

 

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450*300,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

 

 

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

准确率99.94%,更新于2023/07/04

 

Select the animal with the wrong head

选择头部错误的动物

准确率99.94%,更新于2023/06/14

 

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

准确率98.80%,更新于2023/06/23

 

Pick the spiral galaxy

选择螺旋星系

 

 

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

 

 

 

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2023/04/03

 

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2023/04/03

 

 

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

准确率100.00%,更新于2023/04/03

 

 

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

准确率100.00%,更新于2023/04/03

 

 

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

 

 

 

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

 

Pick the image with only one rope

选择只有一根绳子的图片

准确率98.20%,更新于2023/06/28

 

Pick the image where the darts add up to 8

选择飞镖总数为 8 的图片

准确率100.00%,更新于2023/06/28

 

Pick the image of the brick cube and the striped heart

选择砖块立方体和条纹心形的图像

准确率100.00%,更新于2023/06/28

 

Pick the image of the brick cube and the striped sphere

选择砖块立方体和条纹球体的图像

准确率100.00%,更新于2023/06/29

 

Pick the image of the brick heart and the striped heart

选择砖块心形和条纹心形的图像

准确率97.00%,更新于2023/06/28

 

Pick the image of the brick sphere and the checkered heart

选择砖块球体和方格心形的图像

准确率100.00%,更新于2023/06/29

 

Pick the image of the checkered cone and the checkered sphere

选择方格圆锥体和方格球体的图像

准确率99.50%,更新于2023/06/29

 

Pick the image of the fuzzy cone and the brick sphere

选择毛绒锥体和砖块球体图像

准确率100.00%,更新于2023/06/29

 

Pick the image of the fuzzy cube and the checkered cube

选择毛绒立方体和方格立方体的图像

准确率100.00%,更新于2023/06/29

 

Pick the image of the fuzzy heart and the brick heart

选择毛绒心形和砖块心形的图像

准确率100.00%,更新于2023/06/29

 

Pick the image of the fuzzy heart and the striped heart

选择毛绒心形和条纹心形的图像

准确率100.00%,更新于2023/06/29

 

Pick the image of the fuzzy cube and the striped cube

选择毛绒立方体和条纹立方体的图像

准确率100.00%,更新于2023/06/29

 

Pick the image of the striped cone and the checkered cube

选择条纹圆锥体和方格立方体的图像

准确率96.00%,更新于2023/06/29

 

Pick the image of the brick cone and the striped cone

选择砖块锥体和条纹锥体的图像

准确率100.00%,更新于2023/06/29

 

Pick the image of the striped cube and the checkered cube

选择条纹立方体和方格立方体的图像

准确率100.00%,更新于2023/06/29

 

Pick the image of the fuzzy sphere and the striped cone

选择毛绒球体和条纹锥体的图像

准确率100.00%,更新于2023/06/29

 

Pick the image of the striped cube and the checkered sphere

选择条纹立方体和方格球体的图像

准确率100.00%,更新于2023/06/29

 

Pick the image of the striped cube and the checkered heart

选择条纹立方体和方格心形的图像

准确率100.00%,更新于2023/06/29

 

Pick the image of the brick cone and the checkered cube

选择砖块锥体和方格立方体的图像

准确率100.00%,更新于2023/06/29

 

Pick the image of the striped heart and the checkered cube

选择条纹心形和方格立方体的图像

准确率100.00%,更新于2023/06/29

 

Pick the image of the brick cone and the checkered sphere

选择砖块锥体和方格球体的图像

 

 

Pick the image of the fuzzy sphere and the striped cube

选择毛绒球体和条纹立方体的图像

准确率100.00%,更新于2023/06/30

 

Pick the image of 2 striped shapes

选择2个条纹形状的图像

准确率100.00%,更新于2023/07/06

 

Pick the image of 2 checkered shapes

选择2个方格形状的图像

准确率100.00%,更新于2023/07/06

 

Pick the image with the matching reflection

选择动物和水中倒影相匹配的图像

准确率100.00%,更新于2023/07/06

 

Pick the image of the person walking up the stairs

选择爬上楼梯的人

准确率99.50%,更新于2023/07/07

 

Pick the image of the person walking down the stairs

选择走下楼梯的人

准确率99.00%,更新于2023/07/13

 

Pick the cube with icons split in half

选择图标分成两半的立方体

准确率99.63%,更新于2023/07/10

 

Pick the puzzle with the wrong pieces

选出错误的拼图

准确率100.00%,更新于2023/07/09

 

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99.36%-last24h,更新于2024/05/30/9:00

numericalmatch

 

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100.00%,更新于2023/04/16

 

 

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100.00%,更新于2023/04/16

 

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99.77%-last24h,更新于2024/05/30/9:00

coordinatesmatch

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

hopscotch_v2

 

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

hopscotch_highsec

 

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

准确率99.53%-last24h,更新于2024/05/30/9:00

icon_connect

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

准确率100.00%,更新于2023/04/03

trains_simple

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

准确率100.00%,更新于2023/06/26

 

 

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100.00%-last24h,更新于2024/05/30/9:00

3d_rollball_objects_multi

Use the arrows to rotate the object to face in the direction of the hand

使用箭头来旋转物体,使其朝向手指向的方向

准确率99.83%-last24h,更新于2024/05/29/9:00

 

Use the arrows to find the room that matches the left image

使用箭头来找到与左图匹配的房间

准确率96.27%,更新于2023/11/20

 

 

Using the arrows, pick the group of rocks that has the amount indicated on the left

将石块的数量与左边的数字匹配

准确率99.60%-last24h,更新于2024/05/30/9:00

 

 

using the arrows, find the image where one of the towers of rocks contains the exact amount shown on the left

使用箭头,找到正确的图片,要求图中堆叠起的石头数量与左侧所示的完全相同

准确率100.00%,更新于2024/09/16

 

Use the arrows to rotate the object to face in the direction of the hand

使用箭头来旋转物体,使其朝向手指向的方向

准确率98.81%-last24h,更新于2024/05/30/9:00

 

use the arrows to find the distance between the two cars that matches the left image

使用箭头找到与左图匹配的车距

准确率100.00%,更新于2023/12/25

 

use the arrows to choose the image where all the darts add up to the number in the left image

使用箭头选择图像,要求图像中所有飞镖相加的总和等于左侧图像中的数字

准确率100.00%,更新于2023/04/03

 

 

use the arrows to find the image where the number on each ring adds up to the number on the left

使用箭头找图像,图像中各戒指上的数值加起来等于左边的数值。

准确率100.00%-last24h,更新于2024/05/30/9:00

 

use the arrows to move the icon into the indicated orbit

使用箭头将所示图标移动到指定的轨道中

准确率98.75%-last24h,更新于2024/08/10/9:00

orbit_match_game

click on the arrows to view different images when the image matches the example on the left, click submit!

使用箭头找出缺失左边所示拼块的拼图

准确率100.00%-last24h,更新于2024/05/30/9:00

 

 

use the arrows to find the jigsaw puzzle that is missing the puzzle piece shown on the left

使用箭头找出缺失左边所示拼块的拼图。

准确率100.00%-last24h,更新于2024/05/30/9:00

 

use the arrows to pick the image where the object directly below the arrow matches the left image

 

准确率98.58%,更新于2024/02/14

 

 

match the icon on the left with the icon on the top of the dice

请将左侧的图标与骰子顶面的图标相匹配

准确率99.96% ,更新于2024/04/3

 

 

find the image that has the prize on the left held by the claw in the machine on the right

找出正确的图片,要求图中右侧抓娃娃机中的爪子抓住的物品即左侧所示奖品。

准确率92.89%-last24h,更新于2024/05/30/9:00

 

 

click on the arrows to view different images when the image matches the example on the left, click submit!

 

准确率97.59%,更新于2023/04/03

 

 

find the image where the rat can reach the exact amount of cheese as the image on the left.

找到正确的图片,让老鼠能拿到和左侧图片中相同数量的奶酪。

准确率100.00%,更新于2023/05/07

 

 

use the arrows to find the basket picture with the same content as shown on the left

 

准确率100.00%,更新于2024/06/02

 

 

use the arrows to match the number of pins that fall with the number shown on the left

 

准确率100.00%,更新于2024/09/23

 

 

use the arrows to choose the image where the winner has the icon  shown in the image on the left

使用箭头选择这样的图片:获胜者有左侧图片中显示的图标

准确率76.50%,更新于2024/10/31

 

 

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,如下图:

 

样式说明:

如果您的类型是这种六宫格的,直接网页上取原图(或截图,推荐原图)一张图片上传就可以了,具体看上面的内容。

如果是下面这种左右选择的类型,请根据下面的说明内容开发。

 

使用方法:

一、如果您是移动端调用,直接截图下方红色框框区域图片即可,一共六张,拼成一个数组提交到后端进行识别

  • 请注意问题一定正确,下面附所有已支持的问题,没列出来的可以直接传英文问题

  • 请注意:如果是截图拼成数组,请确保不少于实际显示的图片数量

  • 请注意:请确保单张图片大小不超过120Kb,超过会报错,推荐尺寸386x200

  • 请注意:请传下图中红色部份的内容,不要多传其他区域内容

{ "clientKey": "cc9c18d3e263515c2c072b36a7125eecc078618f", // 填你的密钥 "task": { "type": "FunCaptchaClassification", "image": [ // 六张截图 "data:image/jpeg;base64,/9j/2wCEAAoHB...", "data:image/jpeg;base64,/9j/2wCEAAoHB...", …… ], "question": "Use the arrows to rotate the animal to face in the direction of the hand" } }

二、如果您是PC端调用,可以直接在网页上获取原图,如下所示,按默认的1张图片提交方式,正常提交到后端即可。

请注意:如果是提交一张原图,请确保图片宽度不小于1200px

 

注意,如果在被风控的情况下,可能会遇见超过6张图片的情况,如果是截图请截到每一张,如果是网页还是一样传原始图片即可。

12张

20张

请确保问题正确,其他语言请转换为下列英文问题(请勿自行翻译)

直接获取网页上的问题提交也是可以的。

如果出现新样式我们可能不会同步在文档上,您直接获取图片和问题提交即可。

 

针对新出现的点击样式,我们已经增加了识别

目前处于样本积攒阶段,识别速度与准确率会略差,

当样本数量足够时会训练模型提高准确率与识别速度

 

 

附注:

Pick the xxx的50个问题列表

{

"Pick the ant",
"Pick the apple",
"Pick the banana",
"Pick the bat",
"Pick the bear",
"Pick the bee",
"Pick the bread",
"Pick the butterfly",
"Pick the camel",
"Pick the cat",
"Pick the chicken",
"Pick the cow",
"Pick the crab",
"Pick the deer",
"Pick the dinosaur",
"Pick the dog",
"Pick the dolphin",
"Pick the donut",
"Pick the duck",
"Pick the elephant",
"Pick the frog",
"Pick the giraffe",
"Pick the goat",
"Pick the grapes",
"Pick the icecream",
"Pick the ice cream",
"Pick the kangaroo",
"Pick the koala",
"Pick the ladybug",
"Pick the lion",
"Pick the lobster",
"Pick the monkey",
"Pick the mouse",
"Pick the octopus",
"Pick the owl",
"Pick the parrot",
"Pick the panda",
"Pick the pig",
"Pick the pineapple",
"Pick the pizza",
"Pick the rabbit",
"Pick the rhino",
"Pick the seal",
"Pick the shark",
"Pick the sheep",
"Pick the snail",
"Pick the snake",
"Pick the starfish",
"Pick the turtle",
"Pick the zebra"

}

Selenium如何获取原始图片

我们可以通过源代码获取到这个背景图片

得到这个url之后,可以通过Js的方式转为base64

定义转换函数:

获取结果:

使用效果:

把刚才获取到的url传入,就可以得到图片的base64值

那么在selenium中,可以执行类似的代码来获取base64