From 2047c61767a466e01fc39497dd76088aa9fa7932 Mon Sep 17 00:00:00 2001 From: arnaucode Date: Tue, 8 Aug 2017 22:06:59 +0200 Subject: [PATCH] added explanation to README.md --- README.md | 121 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 121 insertions(+) diff --git a/README.md b/README.md index 49e413f..8da722b 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,123 @@ # goCaptcha captcha server, with own datasets, to train own machine learning AI + + +### How to use? + +1. Get the captcha: +``` +GET/ 127.0.0.1:3025/captcha +``` +Server response: +```json +{ + "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": "" +} +``` + +2. User selects the images that fit in the 'question' parameter +(in this case, 'leopard') + +3. Post the answer. The answer contains the CaptchaId, and an array with the selected images +``` +POST/ 127.0.0.1:3025/answer +``` +Post example: +```json +{ + "captchaid": "881c6083-0643-4d1c-9987-f8cc5bb9d5b1", + "selection": [0,0,0,0,1,1] +} +``` +Server response: +``` +true +``` + +### How this works? + +###### Server reads dataset +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). + + +###### Server generates captcha +When server recieves a GET /captcha, generates a captcha, getting random images from the dataset. + +For each captcha generated, generates two mongodb models: +```json +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" +} +``` + +```json +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" +} +``` +Both models are stored in the MongoDB. + +Captcha Model contains the captcha that server returns to the petition. And CaptchaSolution contains the solution of the captcha. Both have the same Id. + + +###### Server validates captcha +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'.