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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Pass in image of online chessboard screenshot, returns corners of chessboard
# usage: chessboard_finder.py [-h] urls [urls ...]
# Find orthorectified chessboard corners in image
# positional arguments:
# urls Input image urls
# optional arguments:
# -h, --help show this help message and exit
# sudo apt-get install libatlas-base-dev for numpy error, see https://github.com/Kitt-AI/snowboy/issues/262
import numpy as np
# sudo apt-get install libopenjp2-7 libtiff5
import PIL.Image
import argparse
from time import time
from .helper_image_loading import *
def nonmax_suppress_1d(arr, winsize=5):
"""Return 1d array with only peaks, use neighborhood window of winsize px"""
_arr = arr.copy()
for i in range(_arr.size):
if i == 0:
left_neighborhood = 0
else:
left_neighborhood = arr[max(0,i-winsize):i]
if i >= _arr.size-2:
right_neighborhood = 0
else:
right_neighborhood = arr[i+1:min(arr.size-1,i+winsize)]
if arr[i] < np.max(left_neighborhood) or arr[i] <= np.max(right_neighborhood):
_arr[i] = 0
return _arr
def findChessboardCorners(img_arr_gray, noise_threshold = 8000):
# Load image grayscale as an numpy array
# Return None on failure to find a chessboard
#
# noise_threshold: Ratio of standard deviation of hough values along an axis
# versus the number of pixels, manually measured bad trigger images
# at < 5,000 and good chessboards values at > 10,000
# Get gradients, split into positive and inverted negative components
gx, gy = np.gradient(img_arr_gray)
gx_pos = gx.copy()
gx_pos[gx_pos<0] = 0
gx_neg = -gx.copy()
gx_neg[gx_neg<0] = 0
gy_pos = gy.copy()
gy_pos[gy_pos<0] = 0
gy_neg = -gy.copy()
gy_neg[gy_neg<0] = 0
# 1-D ampltitude of hough transform of gradients about X & Y axes
num_px = img_arr_gray.shape[0] * img_arr_gray.shape[1]
hough_gx = gx_pos.sum(axis=1) * gx_neg.sum(axis=1)
hough_gy = gy_pos.sum(axis=0) * gy_neg.sum(axis=0)
# Check that gradient peak signal is strong enough by
# comparing normalized standard deviation to threshold
if min(hough_gx.std() / hough_gx.size,
hough_gy.std() / hough_gy.size) < noise_threshold:
return None
# Normalize and skeletonize to just local peaks
hough_gx = nonmax_suppress_1d(hough_gx) / hough_gx.max()
hough_gy = nonmax_suppress_1d(hough_gy) / hough_gy.max()
# Arbitrary threshold of 20% of max
hough_gx[hough_gx<0.2] = 0
hough_gy[hough_gy<0.2] = 0
# Now we have a set of potential vertical and horizontal lines that
# may contain some noisy readings, try different subsets of them with
# consistent spacing until we get a set of 7, choose strongest set of 7
pot_lines_x = np.where(hough_gx)[0]
pot_lines_y = np.where(hough_gy)[0]
pot_lines_x_vals = hough_gx[pot_lines_x]
pot_lines_y_vals = hough_gy[pot_lines_y]
# Get all possible length 7+ sequences
seqs_x = getAllSequences(pot_lines_x)
seqs_y = getAllSequences(pot_lines_y)
if len(seqs_x) == 0 or len(seqs_y) == 0:
return None
# Score sequences by the strength of their hough peaks
seqs_x_vals = [pot_lines_x_vals[[v in seq for v in pot_lines_x]] for seq in seqs_x]
seqs_y_vals = [pot_lines_y_vals[[v in seq for v in pot_lines_y]] for seq in seqs_y]
# shorten sequences to up to 9 values based on score
# X sequences
for i in range(len(seqs_x)):
seq = seqs_x[i]
seq_val = seqs_x_vals[i]
# if the length of sequence is more than 7 + edges = 9
# strip weakest edges
if len(seq) > 9:
# while not inner 7 chess lines, strip weakest edges
while len(seq) > 7:
if seq_val[0] > seq_val[-1]:
seq = seq[:-1]
seq_val = seq_val[:-1]
else:
seq = seq[1:]
seq_val = seq_val[1:]
seqs_x[i] = seq
seqs_x_vals[i] = seq_val
# Y sequences
for i in range(len(seqs_y)):
seq = seqs_y[i]
seq_val = seqs_y_vals[i]
while len(seq) > 9:
if seq_val[0] > seq_val[-1]:
seq = seq[:-1]
seq_val = seq_val[:-1]
else:
seq = seq[1:]
seq_val = seq_val[1:]
seqs_y[i] = seq
seqs_y_vals[i] = seq_val
# Now that we only have length 7-9 sequences, score and choose the best one
scores_x = np.array([np.mean(v) for v in seqs_x_vals])
scores_y = np.array([np.mean(v) for v in seqs_y_vals])
# Keep first sequence with the largest step size
# scores_x = np.array([np.median(np.diff(s)) for s in seqs_x])
# scores_y = np.array([np.median(np.diff(s)) for s in seqs_y])
#TODO(elucidation): Choose heuristic score between step size and hough response
best_seq_x = seqs_x[scores_x.argmax()]
best_seq_y = seqs_y[scores_y.argmax()]
# print(best_seq_x, best_seq_y)
# Now if we have sequences greater than length 7, (up to 9),
# that means we have up to 9 possible combinations of sets of 7 sequences
# We try all of them and see which has the best checkerboard response
sub_seqs_x = [best_seq_x[k:k+7] for k in range(len(best_seq_x) - 7 + 1)]
sub_seqs_y = [best_seq_y[k:k+7] for k in range(len(best_seq_y) - 7 + 1)]
dx = np.median(np.diff(best_seq_x))
dy = np.median(np.diff(best_seq_y))
corners = np.zeros(4, dtype=int)
# Add 1 buffer to include the outer tiles, since sequences are only using
# inner chessboard lines
corners[0] = int(best_seq_y[0]-dy)
corners[1] = int(best_seq_x[0]-dx)
corners[2] = int(best_seq_y[-1]+dy)
corners[3] = int(best_seq_x[-1]+dx)
# Generate crop image with on full sequence, which may be wider than a normal
# chessboard by an extra 2 tiles, we'll iterate over all combinations
# (up to 9) and choose the one that correlates best with a chessboard
gray_img_crop = PIL.Image.fromarray(img_arr_gray).crop(corners)
# Build a kernel image of an idea chessboard to correlate against
k = 8 # Arbitrarily chose 8x8 pixel tiles for correlation image
quad = np.ones([k,k])
kernel = np.vstack([np.hstack([quad,-quad]), np.hstack([-quad,quad])])
kernel = np.tile(kernel,(4,4)) # Becomes an 8x8 alternating grid (chessboard)
kernel = kernel/np.linalg.norm(kernel) # normalize
# 8*8 = 64x64 pixel ideal chessboard
k = 0
n = max(len(sub_seqs_x), len(sub_seqs_y))
final_corners = None
best_score = None
# Iterate over all possible combinations of sub sequences and keep the corners
# with the best correlation response to the ideal 64x64px chessboard
for i in range(len(sub_seqs_x)):
for j in range(len(sub_seqs_y)):
k = k + 1
# [y, x, y, x]
sub_corners = np.array([
sub_seqs_y[j][0]-corners[0]-dy, sub_seqs_x[i][0]-corners[1]-dx,
sub_seqs_y[j][-1]-corners[0]+dy, sub_seqs_x[i][-1]-corners[1]+dx],
dtype=np.int)
# Generate crop candidate, nearest pixel is fine for correlation check
sub_img = gray_img_crop.crop(sub_corners).resize((64,64))
# Perform correlation score, keep running best corners as our final output
# Use absolute since it's possible board is rotated 90 deg
score = np.abs(np.sum(kernel * sub_img))
if best_score is None or score > best_score:
best_score = score
final_corners = sub_corners + [corners[0], corners[1], corners[0], corners[1]]
return final_corners
def getAllSequences(seq, min_seq_len=7, err_px=5):
"""Given sequence of increasing numbers, get all sequences with common
spacing (within err_px) that contain at least min_seq_len values"""
# Sanity check that there are enough values to satisfy
if len(seq) < min_seq_len:
return []
# For every value, take the next value and see how many times we can step
# that falls on another value within err_px points
seqs = []
for i in range(len(seq)-1):
for j in range(i+1, len(seq)):
# Check that seq[i], seq[j] not already in previous sequences
duplicate = False
for prev_seq in seqs:
for k in range(len(prev_seq)-1):
if seq[i] == prev_seq[k] and seq[j] == prev_seq[k+1]:
duplicate = True
if duplicate:
continue
d = seq[j] - seq[i]
# Ignore two points that are within error bounds of each other
if d < err_px:
continue
s = [seq[i], seq[j]]
n = s[-1] + d
while np.abs((seq-n)).min() < err_px:
n = seq[np.abs((seq-n)).argmin()]
s.append(n)
n = s[-1] + d
if len(s) >= min_seq_len:
s = np.array(s)
seqs.append(s)
return seqs
def getChessTilesColor(img, corners):
# img is a color RGB image
# corners = (x0, y0, x1, y1) for top-left corner to bot-right corner of board
height, width, depth = img.shape
if depth !=3:
print("Need RGB color image input")
return None
# corners could be outside image bounds, pad image as needed
padl_x = max(0, -corners[0])
padl_y = max(0, -corners[1])
padr_x = max(0, corners[2] - width)
padr_y = max(0, corners[3] - height)
img_padded = np.pad(img, ((padl_y,padr_y),(padl_x,padr_x), (0,0)), mode='edge')
chessboard_img = img_padded[
(padl_y + corners[1]):(padl_y + corners[3]),
(padl_x + corners[0]):(padl_x + corners[2]), :]
# 256x256 px RGB image, 32x32px individual RGB tiles, normalized 0-1 floats
chessboard_img_resized = np.asarray( \
PIL.Image.fromarray(chessboard_img) \
.resize([256,256], PIL.Image.BILINEAR), dtype=np.float32) / 255.0
# stack deep 64 tiles with 3 channesl RGB each
# so, first 3 slabs are RGB for tile A1, then next 3 slabs for tile A2 etc.
tiles = np.zeros([32,32,3*64], dtype=np.float32) # color
# Assume A1 is bottom left of image, need to reverse rank since images start
# with origin in top left
for rank in range(8): # rows (numbers)
for file in range(8): # columns (letters)
# color
tiles[:,:,3*(rank*8+file):3*(rank*8+file+1)] = \
chessboard_img_resized[(7-rank)*32:((7-rank)+1)*32,file*32:(file+1)*32]
return tiles
def getChessBoardGray(img, corners):
# img is a grayscale image
# corners = (x0, y0, x1, y1) for top-left corner to bot-right corner of board
height, width = img.shape
# corners could be outside image bounds, pad image as needed
padl_x = max(0, -corners[0])
padl_y = max(0, -corners[1])
padr_x = max(0, corners[2] - width)
padr_y = max(0, corners[3] - height)
img_padded = np.pad(img, ((padl_y,padr_y),(padl_x,padr_x)), mode='edge')
chessboard_img = img_padded[
(padl_y + corners[1]):(padl_y + corners[3]),
(padl_x + corners[0]):(padl_x + corners[2])]
# 256x256 px image, 32x32px individual tiles
# Normalized
chessboard_img_resized = np.asarray( \
PIL.Image.fromarray(chessboard_img) \
.resize([256,256], PIL.Image.BILINEAR), dtype=np.uint8) / 255.0
return chessboard_img_resized
def getChessTilesGray(img, corners):
chessboard_img_resized = getChessBoardGray(img, corners)
return getTiles(chessboard_img_resized)
def getTiles(processed_gray_img):
# Given 256x256 px normalized grayscale image of a chessboard (32x32px per tile)
# NOTE (values must be in range 0-1)
# Return a 32x32x64 tile array
#
# stack deep 64 tiles
# so, first slab is tile A1, then A2 etc.
tiles = np.zeros([32,32,64], dtype=np.float32) # grayscale
# Assume A1 is bottom left of image, need to reverse rank since images start
# with origin in top left
for rank in range(8): # rows (numbers)
for file in range(8): # columns (letters)
tiles[:,:,(rank*8+file)] = \
processed_gray_img[(7-rank)*32:((7-rank)+1)*32,file*32:(file+1)*32]
return tiles
def findGrayscaleTilesInImage(img):
""" Find chessboard and convert into input tiles for CNN """
if img is None:
return None, None
# Convert to grayscale numpy array
img_arr = np.asarray(img.convert("L"), dtype=np.float32)
# Use computer vision to find orthorectified chessboard corners in image
corners = findChessboardCorners(img_arr)
if corners is None:
return None, None
# Pull grayscale tiles out given image and chessboard corners
tiles = getChessTilesGray(img_arr, corners)
# Return both the tiles as well as chessboard corner locations in the image
return tiles, corners
# DEBUG
# from matplotlib import pyplot as plt
# def plotTiles(tiles):
# """Plot color or grayscale tiles as 8x8 subplots"""
# plt.figure(figsize=(6,6))
# files = "ABCDEFGH"
# for rank in range(8): # rows (numbers)
# for file in range(8): # columns (letters)
# plt.subplot(8,8,(7-rank)*8 + file + 1) # Plot rank reverse order to match image
# if tiles.shape[2] == 64:
# # Grayscale
# tile = tiles[:,:,(rank*8+file)] # grayscale
# plt.imshow(tile, interpolation='None', cmap='gray', vmin = 0, vmax = 1)
# else:
# #Color
# tile = tiles[:,:,3*(rank*8+file):3*(rank*8+file+1)] # color
# plt.imshow(tile, interpolation='None',)
# plt.axis('off')
# plt.title('%s %d' % (files[file], rank+1), fontsize=6)
# plt.show()
def main(url):
print("Loading url %s..." % url)
color_img, url = loadImageFromURL(url)
# Fail if can't load image
if color_img is None:
print('Couldn\'t load url: %s' % url)
return
if color_img.mode != 'RGB':
color_img = color_img.convert('RGB')
print("Processing...")
a = time()
img_arr = np.asarray(color_img.convert("L"), dtype=np.float32)
corners = findChessboardCorners(img_arr)
print("Took %.4fs" % (time()-a))
# corners = [x0, y0, x1, y1] where (x0,y0)
# is top left and (x1,y1) is bot right
if corners is not None:
print("\tFound corners for %s: %s" % (url, corners))
link = getVisualizeLink(corners, url)
print(link)
# tiles = getChessTilesColor(np.array(color_img), corners)
# tiles = getChessTilesGray(img_arr, corners)
# plotTiles(tiles)
# plt.imshow(color_img, interpolation='none')
# plt.plot(corners[[0,0,2,2,0]]-0.5, corners[[1,3,3,1,1]]-0.5, color='red', linewidth=1)
# plt.show()
else:
print('\tNo corners found in image')
if __name__ == '__main__':
np.set_printoptions(suppress=True, precision=2)
parser = argparse.ArgumentParser(description='Find orthorectified chessboard corners in image')
parser.add_argument('urls', default=['https://i.redd.it/1uw3h772r0fy.png'],
metavar='urls', type=str, nargs='*', help='Input image urls')
# main('http://www.chessanytime.com/img/jeudirect/simplechess.png')
# main('https://i.imgur.com/JpzfV3y.jpg')
# main('https://i.imgur.com/jsCKzU9.jpg')
# main('https://i.imgur.com/49htmMA.png')
# main('https://i.imgur.com/HHdHGBX.png')
# main('http://imgur.com/By2xJkO')
# main('http://imgur.com/p8DJMly')
# main('https://i.imgur.com/Ns0iBrw.jpg')
# main('https://i.imgur.com/KLcCiuk.jpg')
args = parser.parse_args()
for url in args.urls:
main(url)
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