added explanation to README.md

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'.