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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# TensorFlow Chessbot
# This contains ChessboardPredictor, the class responsible for loading and
# running a trained CNN on chessboard screenshots. Used by chessbot.py.
# A CLI interface is provided as well.
#
# $ ./tensorflow_chessbot.py -h
# usage: tensorflow_chessbot.py [-h] [--url URL] [--filepath FILEPATH]
#
# Predict a chessboard FEN from supplied local image link or URL
#
# optional arguments:
# -h, --help show this help message and exit
# --url URL URL of image (ex. http://imgur.com/u4zF5Hj.png)
# --filepath FILEPATH filepath to image (ex. u4zF5Hj.png)
#
# This file is used by chessbot.py, a Reddit bot that listens on /r/chess for
# posts with an image in it (perhaps checking also for a statement
# "white/black to play" and an image link)
#
# It then takes the image, uses some CV to find a chessboard on it, splits it up
# into a set of images of squares. These are the inputs to the tensorflow CNN
# which will return probability of which piece is on it (or empty)
#
# Dataset will include chessboard squares from chess.com, lichess
# Different styles of each, all the pieces
#
# Generate synthetic data via added noise:
# * change in coloration
# * highlighting
# * occlusion from lines etc.
#
# Take most probable set from TF response, use that to generate a FEN of the
# board, and bot comments on thread with FEN and link to lichess analysis.
#
# A lot of tensorflow code here is heavily adopted from the
# [tensorflow tutorials](https://www.tensorflow.org/versions/0.6.0/tutorials/pdes/index.html)
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1' # Ignore Tensorflow INFO debug messages
import tensorflow as tf
import numpy as np
from .helper_functions import *
from .helper_image_loading import *
from .chessboard_finder import *
def load_graph(frozen_graph_filepath):
# Load and parse the protobuf file to retrieve the unserialized graph_def.
with tf.io.gfile.GFile(frozen_graph_filepath, "rb") as f:
graph_def = tf.compat.v1.GraphDef()
graph_def.ParseFromString(f.read())
# Import graph def and return.
with tf.Graph().as_default() as graph:
# Prefix every op/nodes in the graph.
tf.import_graph_def(graph_def, name="tcb")
return graph
class ChessboardPredictor(object):
"""ChessboardPredictor using saved model"""
def __init__(self, frozen_graph_path='./saved_models/frozen_graph.pb'):
# Restore model using a frozen graph.
print("\t Loading model '%s'" % frozen_graph_path)
graph = load_graph(frozen_graph_path)
self.sess = tf.compat.v1.Session(graph=graph)
# Connect input/output pipes to model.
self.x = graph.get_tensor_by_name('tcb/Input:0')
self.keep_prob = graph.get_tensor_by_name('tcb/KeepProb:0')
self.prediction = graph.get_tensor_by_name('tcb/prediction:0')
self.probabilities = graph.get_tensor_by_name('tcb/probabilities:0')
print("\t Model restored.")
def getPrediction(self, tiles):
"""Run trained neural network on tiles generated from image"""
if tiles is None or len(tiles) == 0:
print("Couldn't parse chessboard")
return None, 0.0
# Reshape into Nx1024 rows of input data, format used by neural network
validation_set = np.swapaxes(np.reshape(tiles, [32*32, 64]),0,1)
# Run neural network on data
guess_prob, guessed = self.sess.run(
[self.probabilities, self.prediction],
feed_dict={self.x: validation_set, self.keep_prob: 1.0})
# Prediction bounds
a = np.array(list(map(lambda x: x[0][x[1]], zip(guess_prob, guessed))))
tile_certainties = a.reshape([8,8])[::-1,:]
# Convert guess into FEN string
# guessed is tiles A1-H8 rank-order, so to make a FEN we just need to flip the files from 1-8 to 8-1
labelIndex2Name = lambda label_index: ' KQRBNPkqrbnp'[label_index]
pieceNames = list(map(lambda k: '1' if k == 0 else labelIndex2Name(k), guessed)) # exchange ' ' for '1' for FEN
fen = '/'.join([''.join(pieceNames[i*8:(i+1)*8]) for i in reversed(range(8))])
return fen, tile_certainties
## Wrapper for chessbot
def makePrediction(self, url):
"""Try and return a FEN prediction and certainty for URL, return Nones otherwise"""
img, url = helper_image_loading.loadImageFromURL(url, max_size_bytes=2000000)
result = [None, None, None]
# Exit on failure to load image
if img is None:
print('Couldn\'t load URL: "%s"' % url)
return result
# Resize image if too large
img = helper_image_loading.resizeAsNeeded(img)
# Exit on failure if image was too large teo resize
if img is None:
print('Image too large to resize: "%s"' % url)
return result
# Look for chessboard in image, get corners and split chessboard into tiles
tiles, corners = chessboard_finder.findGrayscaleTilesInImage(img)
# Exit on failure to find chessboard in image
if tiles is None:
print('Couldn\'t find chessboard in image')
return result
# Make prediction on input tiles
fen, tile_certainties = self.getPrediction(tiles)
# Use the worst case certainty as our final uncertainty score
certainty = tile_certainties.min()
# Get visualize link
visualize_link = helper_image_loading.getVisualizeLink(corners, url)
# Update result and return
result = [fen, certainty, visualize_link]
return result
def close(self):
print("Closing session.")
self.sess.close()
###########################################################
# MAIN CLI
def main(args):
# Load image from filepath or URL
if args.filepath:
# Load image from file
img = helper_image_loading.loadImageFromPath(args.filepath)
args.url = None # Using filepath.
else:
img, args.url = helper_image_loading.loadImageFromURL(args.url)
# Exit on failure to load image
if img is None:
raise Exception('Couldn\'t load URL: "%s"' % args.url)
# Resize image if too large
# img = helper_image_loading.resizeAsNeeded(img)
# Look for chessboard in image, get corners and split chessboard into tiles
tiles, corners = chessboard_finder.findGrayscaleTilesInImage(img)
# Exit on failure to find chessboard in image
if tiles is None:
raise Exception('Couldn\'t find chessboard in image')
# Create Visualizer url link
if args.url:
viz_link = helper_image_loading.getVisualizeLink(corners, args.url)
print('---\nVisualize tiles link:\n %s\n---' % viz_link)
if args.url:
print("\n--- Prediction on url %s ---" % args.url)
else:
print("\n--- Prediction on file %s ---" % args.filepath)
# Initialize predictor, takes a while, but only needed once
predictor = ChessboardPredictor()
fen, tile_certainties = predictor.getPrediction(tiles)
predictor.close()
if args.unflip:
fen = unflipFEN(fen)
short_fen = shortenFEN(fen)
# Use the worst case certainty as our final uncertainty score
certainty = tile_certainties.min()
print('Per-tile certainty:')
print(tile_certainties)
print("Certainty range [%g - %g], Avg: %g" % (
tile_certainties.min(), tile_certainties.max(), tile_certainties.mean()))
active = args.active
print("---\nPredicted FEN:\n%s %s - - 0 1" % (short_fen, active))
print("Final Certainty: %.1f%%" % (certainty*100))
if __name__ == '__main__':
np.set_printoptions(suppress=True, precision=3)
import argparse
parser = argparse.ArgumentParser(description='Predict a chessboard FEN from supplied local image link or URL')
parser.add_argument('--url', default='http://imgur.com/u4zF5Hj.png', help='URL of image (ex. http://imgur.com/u4zF5Hj.png)')
parser.add_argument('--filepath', help='filepath to image (ex. u4zF5Hj.png)')
parser.add_argument('--unflip', default=False, action='store_true', help='revert the image of a flipped chessboard')
parser.add_argument('--active', default='w')
args = parser.parse_args()
main(args)
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