CHESS CONFIGURATION

FEN Generator for Chess Configuration Chalenge

This trained model may be used for solution others puzzles of the chelenge.😉

Chess FEN Generator¶

Original: \ https://www.kaggle.com/koryakinp/chess-fen-generator

Download Data¶

In [1]:

!pip install --upgrade fastai git+https://gitlab.aicrowd.com/yoogottamk/aicrowd-cli.git >/dev/null
%load_ext aicrowd.magic

  Running command git clone -q https://gitlab.aicrowd.com/yoogottamk/aicrowd-cli.git /tmp/pip-req-build-a3selx8e

In [2]:

API_KEY = '' #Please enter your API Key [https://www.aicrowd.com/participants/me]
%aicrowd login --api-key $API_KEY

Verifying API Key...
API Key valid
Saved API Key successfully!

In [3]:

%aicrowd dataset download --challenge chess-configuration -j 3

In [4]:

!rm -rf data
!mkdir data

!unzip -q train.zip  -d data/ 
!unzip -q val.zip -d data/ 
!unzip -q test.zip  -d data/ 

!mv train.csv data/train.csv
!mv val.csv data/val.csv
!mv sample_submission.csv data/sample_submission.csv

Import packages¶

In [5]:

import numpy as np
import pandas as pd
import os
import glob
import re
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from random import shuffle
from skimage.util.shape import view_as_blocks
from skimage import io, transform
from sklearn.model_selection import train_test_split
from keras.models import Sequential, load_model
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Convolution2D
import warnings
warnings.filterwarnings('ignore')

Data Import¶

In [6]:

#DATADIR = 'D:/Datasets/CHESS/CONFIGURATION/'
DATADIR = './data/'

In [7]:

train_df = pd.read_csv(DATADIR+"train.csv")
val_df = pd.read_csv(DATADIR+"val.csv")

test_df = pd.read_csv(DATADIR+"sample_submission.csv")

In [8]:

train_size = 10000
val_size = 4000
test_size = 10000

train = [f'{DATADIR}train/{i}.jpg' for i in train_df.ImageID]
val = [f'{DATADIR}val/{i}.jpg' for i in val_df.ImageID]
test = [f'{DATADIR}test/{i}.jpg' for i in test_df.ImageID]

train = train[:train_size]
val =  val[:val_size]

In [9]:

mpimg.imread(test[0]).shape

Out[9]:

(254, 254, 3)

Function to extract FEN¶

In [10]:

def fen_from_filename(filename):
    base = os.path.basename(filename)
    image_id = int(os.path.splitext(base)[0])
    #print(image_id)
    fen = train_df.iloc[image_id].label
    return fen

Examples:¶

In [11]:

print(fen_from_filename(train[0]))
print(fen_from_filename(train[1]))
print(fen_from_filename(train[2]))

1rbqkb1r/p1p1n1pp/1pn1p3/1P1p1p2/3P4/N3B2P/P1PNPPP1/R2QKB1R
2bk4/2q1p3/3p3P/5r2/r5nP/P2K2N1/8/2q1NB1R
3rnq2/3k1p2/5rP1/pppp1P2/P1BP2P1/RPP1K2N/3B3P/1N5R

Plotting samples¶

In [12]:

f, axarr = plt.subplots(1,3, figsize=(120, 120))

for i in range(0,3):
    axarr[i].set_title(fen_from_filename(train[i]), fontsize=70, pad=30)
    axarr[i].imshow(mpimg.imread(train[i]))
    axarr[i].axis('off')