arnaucode 9adf62a5df | 7 years ago | |
---|---|---|
web | 7 years ago | |
.gitignore | 7 years ago | |
LICENSE | 7 years ago | |
README.md | 7 years ago | |
captcha.go | 7 years ago | |
demo01.png | 7 years ago | |
errors.go | 7 years ago | |
goCaptcha | 7 years ago | |
imageOperations.go | 7 years ago | |
log.go | 7 years ago | |
main.go | 7 years ago | |
mongoConfig.go | 7 years ago | |
mongodbConfig.json | 7 years ago | |
readDataset.go | 7 years ago | |
serverConfig.go | 7 years ago | |
serverConfig.json | 7 years ago | |
serverRoutes.go | 7 years ago |
captcha server, with own datasets, to train own machine learning AI
Insert this lines in the html file:
<link rel="stylesheet" href="goCaptcha.css">
<div id="goCaptcha"></div>
<script>//define the url where is placed the server of goCaptcha
var goCaptchaURL = "http://127.0.0.1:3025";
</script>
<script src="goCaptcha.js"></script>
It will place the goCaptcha box in the div:
{
"serverIP": "127.0.0.1",
"serverPort": "3025",
"imgsFolder": "imgs",
"numImgsCaptcha": 9
}
./goCaptcha
It will show:
user@laptop:~/goCaptcha$ ./goCaptcha
goCaptcha started
dataset read
num of dataset categories: 4
server running
port: 3025
GET/ 127.0.0.1:3025/captcha
Server response:
{
"id": "881c6083-0643-4d1c-9987-f8cc5bb9d5b1",
"imgs": [
"7cf6f630-e78f-469c-85dd-2d677996fea1.png",
"d4014318-f875-4b42-b704-4f5bf5e5e00c.png",
"2dd69b44-903d-4e78-bb7b-f8b07877c9e5.png",
"2954fc38-819d-40c9-ae3e-7b6fbb68ddbe.png",
"b060f58a-d44b-4e05-b466-92aa801a2aa1.png",
"1b838c46-b784-471e-b143-48be058c39a7.png"
],
"question": "leopard",
"date": "1502274893"
}
(in this case, 'leopard')
The selection is stored in an array:
selection=[0,0,1,0,1,0];
Where the '1' are the images selected, in the images array order.
POST/ 127.0.0.1:3025/answer
Post example:
{
"captchaid": "881c6083-0643-4d1c-9987-f8cc5bb9d5b1",
"selection": [0,0,1,0,1,0]
}
Server response:
true
First, server reads all dataset. Dataset is a directory with subdirectories, where each subdirectory contains images of one element.
For example:
imgs/
leopard/
img01.png
img02.png
img03.png
...
laptop/
img01.png
img02.png
...
house/
img01.png
img02.png
...
Then, stores all the filenames corresponding to each subdirectory. So, we have each image and to which element category is (the name of subdirectory).
When server recieves a GET /captcha, generates a captcha, getting random images from the dataset.
For each captcha generated, generates two mongodb models:
Captcha Model
{
"id" : "881c6083-0643-4d1c-9987-f8cc5bb9d5b1",
"imgs" : [
"7cf6f630-e78f-469c-85dd-2d677996fea1.png",
"d4014318-f875-4b42-b704-4f5bf5e5e00c.png",
"2dd69b44-903d-4e78-bb7b-f8b07877c9e5.png",
"2954fc38-819d-40c9-ae3e-7b6fbb68ddbe.png",
"b060f58a-d44b-4e05-b466-92aa801a2aa1.png",
"1b838c46-b784-471e-b143-48be058c39a7.png"
],
"question" : "leopard"
}
CaptchaSolution Model
{
"id" : "881c6083-0643-4d1c-9987-f8cc5bb9d5b1",
"imgs" : [
"image_0022.jpg",
"image_0006.jpg",
"image_0050.jpg",
"image_0028.jpg",
"image_0119.jpg",
"image_0092.jpg"
],
"imgssolution" : [
"camera",
"camera",
"laptop",
"crocodile",
"leopard",
"leopard"
],
"question" : "leopard",
"date": "1502274893"
}
Both models are stored in the MongoDB.
The Captcha Model 'imgs' parameter, are UUIDs generated to set 'random' names to images. The server stores into MongoDB the relation between the 'random' name of each image and the real path of the image:
{
"captchaid" : "881c6083-0643-4d1c-9987-f8cc5bb9d5b1",
"real" : "leopard/image_0092.jpg",
"fake" : "1b838c46-b784-471e-b143-48be058c39a7.png"
}
When the server recieves a petition to get an image, recieves the petition with the fake image name, then, gets the real path of the image, gets it and serves the image content under the fake image name:
127.0.0.1:3025/image/1b838c46-b784-471e-b143-48be058c39a7.png
Captcha Model contains the captcha that server returns to the petition. And CaptchaSolution contains the solution of the captcha. Both have the same Id.
When server recieves POST /answer, gets the answer, search for the CaptchaSolution based on the CaptchaId in the MongoDB, and then compares the answer 'selection' parameter with the CaptchaSolution.
If the selection is correct, returns 'true', if the selection is not correct, returns 'false'.