My approach for the problem is to perform color-segmentation to get a binary mask. Next, using binary mask to remove the background to make the board visible, removed from artifacts. Finally output the chess border features in an accurate way.

• 1. Performing color-segmentation: We convert the loaded image to the HSV format define lower/upper ranges and perform color segmentation using cv2.inRange to obtain a binary mask.
• 2. Extracting chess-board: After obtaining binary mask we will use it to remove the background and separate chess part from the rest of the image using cv2.bitwise_and. Arithmetic operation and is highly useful for defining roi in hsv colored images.
• 3. Displaying chess-board features. After extracting the chessboard from the image, we will set the patternSizeto (7, 7) and flags to adaptive_thresh + fast_check + normalize image inspired from the source.

Steps:

• Color-segmentation to get the binary mask.

  • 

  • lwr = np.array([0, 0, 143])
    upr = np.array([179, 61, 252])
    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
    msk = cv2.inRange(hsv, lwr, upr)
    

• Removing background using mask

  • 

  • krn = cv2.getStructuringElement(cv2.MORPH_RECT, (50, 30))
    dlt = cv2.dilate(msk, krn, iterations=5)
    res = 255 - cv2.bitwise_and(dlt, msk)
    

• Displaying Chess-board features

  • 

  • res = np.uint8(res)
    ret, corners = cv2.findChessboardCorners(res, (7, 7),
                                             flags=cv2.CALIB_CB_ADAPTIVE_THRESH +
                                                   cv2.CALIB_CB_FAST_CHECK +
                                                   cv2.CALIB_CB_NORMALIZE_IMAGE)
    if ret:
        print(corners)
        fnl = cv2.drawChessboardCorners(img, (7, 7), corners, ret)
        cv2.imshow("fnl", fnl)
        cv2.waitKey(0)
    else:
        print("No Checkerboard Found")
    

Code:

import cv2
import numpy as np

# Load the image
img = cv2.imread("kFM1C.jpg")

# Color-segmentation to get binary mask
lwr = np.array([0, 0, 143])
upr = np.array([179, 61, 252])
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
msk = cv2.inRange(hsv, lwr, upr)

# Extract chess-board
krn = cv2.getStructuringElement(cv2.MORPH_RECT, (50, 30))
dlt = cv2.dilate(msk, krn, iterations=5)
res = 255 - cv2.bitwise_and(dlt, msk)

# Displaying chess-board features
res = np.uint8(res)
ret, corners = cv2.findChessboardCorners(res, (7, 7),
                                         flags=cv2.CALIB_CB_ADAPTIVE_THRESH +
                                               cv2.CALIB_CB_FAST_CHECK +
                                               cv2.CALIB_CB_NORMALIZE_IMAGE)
if ret:
    print(corners)
    fnl = cv2.drawChessboardCorners(img, (7, 7), corners, ret)
    cv2.imshow("fnl", fnl)
    cv2.waitKey(0)
else:
    print("No Checkerboard Found")

To find lower and upper boundaries of the mask, you may find useful: HSV-Threshold-script

The source code of OpenCV is available, so I'd recommend just taking some time going through the code. The relevant file for this particular function is:

calib3d/src/calibinit.cpp

I've not looked into it in detail, but it looks like

CV_IMPL
int cvFindChessboardCorners( const void* arr, CvSize pattern_size,
                             CvPoint2D32f* out_corners, int* out_corner_count,
                             int flags )

is the main implementation of this method. In here they

1. Use cvCheckChessboard to determine if a chessboard is in the image
2. Convert to binary (B&W) and dilate to split the corners apart
3. Use icvGenerateQuads to find the squares.

The code then seems to go though a set of checks to condense these quads to chessboard corners, including icvFindConnectedQuads, icvCleanFoundConnectedQuads to remove extra corners, icvCheckQuadGroup, and icvCheckBoardMonotony.

All of these functions are implemented in the same file, apart from cvCheckChessboard which is in calib3d/src/checkchessboard.cpp. Depending how well you wanted to understand the code, there seem to be a number of debugging lines, which can be included if you #define DEBUG_CHESSBOARD, that may help you see what's going on.

I was able to obtain a satisfactory result using cv2.goodFeaturesToTrack().

CODE:

corners = cv2.goodFeaturesToTrack(gray_img,25,0.01,10)
corners = np.int0(corners)

for i in corners:
    x,y = i.ravel()
    cv2.circle(img,(x,y),3,(0,0,255),-1)

cv2.imshow('Corners',img)

I know it is not accurate, but with some pre-processing you should be able to obtain a better result.

:D