Python OpenCV: Rubik's cube solver color extraction

Question

Description:

I am working on solving rubiks cube using Python & OpenCV. For this purpose I am trying to extract all the colors of the cubies(individual cube pieces) and then applying appropriate algorithm(which I've designed, no issues there).

The problem:

Suppose if I've extracted all the colors of the cubies, how I can locate the position of the extracted cubies? How will I know whether it is in top-middle-lower layer or whether its a corner-middle-edge piece?

What I've done:

Here I have just extracted yellow color.

After color extraction:

Original Image

The Code

import numpy as np
import cv2
from cv2 import *

im = cv2.imread('v123.bmp')
im = cv2.bilateralFilter(im,9,75,75)
im = cv2.fastNlMeansDenoisingColored(im,None,10,10,7,21)
hsv_img = cv2.cvtColor(im, cv2.COLOR_BGR2HSV)   # HSV image


COLOR_MIN = np.array([20, 100, 100],np.uint8)       # HSV color code lower and upper bounds
COLOR_MAX = np.array([30, 255, 255],np.uint8)       # color yellow 

frame_threshed = cv2.inRange(hsv_img, COLOR_MIN, COLOR_MAX)     # Thresholding image
imgray = frame_threshed
ret,thresh = cv2.threshold(frame_threshed,127,255,0)
contours, hierarchy = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
print type(contours)
for cnt in contours:
    x,y,w,h = cv2.boundingRect(cnt)
    print x,
    print y
    cv2.rectangle(im,(x,y),(x+w,y+h),(0,255,0),2)
cv2.imshow("Show",im)
cv2.imwrite("extracted.jpg", im)
cv2.waitKey()
cv2.destroyAllWindows()

Please give some suggestions on how can I locate the positions of the cubies. Here 4 yellow cubies are spotted: top-right-corner, center, right-edge, bottom-left-corner. How can I identify these positions for eg: by assigning digits to each position (here: 3, 4, 5, 7)

Any help/idea is appreciated :) Thanks.

P.S.: OpenCV newbie :)

Answer 1

Here's a simple approach:

• Convert image to HSV format
• Use color thresholding to detect the squares with cv2.inRange()
• Perform morphological operations and draw squares onto a mask
• Find contours on mask and sort from top-bottom or bottom-top
• Take each row of three squares and sort from left-right or right-left

---

After converting to HSV format, we perform color thresholding using cv2.inRange() to detect the squares. We draw the detected squares onto a mask

From here we find contours on the mask and utilize imutils.contours.sort_contours() to sort the contours from top-to-bottom or bottom-to-top. Next we take each row of 3 squares and sort this row from left-to-right or right-to-left. Here's a visualization of the sorting (top-bottom, left) or (bottom-top, right)

Now that we have the contours sorted, we simply draw the rectangles onto our image. Here's the results

Left-to-right and top-to-bottom (left), right-to-left and top-to-bottom

Left-to-right and bottom-to-top (left), right-to-left and bottom-to-top

import cv2
import numpy as np
from imutils import contours

image = cv2.imread('1.png')
original = image.copy()
image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
mask = np.zeros(image.shape, dtype=np.uint8)

colors = {
    'gray': ([76, 0, 41], [179, 255, 70]),        # Gray
    'blue': ([69, 120, 100], [179, 255, 255]),    # Blue
    'yellow': ([21, 110, 117], [45, 255, 255]),   # Yellow
    'orange': ([0, 110, 125], [17, 255, 255])     # Orange
    }

# Color threshold to find the squares
open_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (7,7))
close_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5,5))
for color, (lower, upper) in colors.items():
    lower = np.array(lower, dtype=np.uint8)
    upper = np.array(upper, dtype=np.uint8)
    color_mask = cv2.inRange(image, lower, upper)
    color_mask = cv2.morphologyEx(color_mask, cv2.MORPH_OPEN, open_kernel, iterations=1)
    color_mask = cv2.morphologyEx(color_mask, cv2.MORPH_CLOSE, close_kernel, iterations=5)

    color_mask = cv2.merge([color_mask, color_mask, color_mask])
    mask = cv2.bitwise_or(mask, color_mask)

gray = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)
cnts = cv2.findContours(gray, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
# Sort all contours from top-to-bottom or bottom-to-top
(cnts, _) = contours.sort_contours(cnts, method="top-to-bottom")

# Take each row of 3 and sort from left-to-right or right-to-left
cube_rows = []
row = []
for (i, c) in enumerate(cnts, 1):
    row.append(c)
    if i % 3 == 0:  
        (cnts, _) = contours.sort_contours(row, method="left-to-right")
        cube_rows.append(cnts)
        row = []

# Draw text
number = 0
for row in cube_rows:
    for c in row:
        x,y,w,h = cv2.boundingRect(c)
        cv2.rectangle(original, (x, y), (x + w, y + h), (36,255,12), 2)

        cv2.putText(original, "#{}".format(number + 1), (x,y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255,255,255), 2)
        number += 1

cv2.imshow('mask', mask)
cv2.imwrite('mask.png', mask)
cv2.imshow('original', original)
cv2.waitKey()

To get the HSV color ranges, you can use this simple HSV color thresholder script to determine the lower/upper color ranges. Change the image path in cv2.imread()

import cv2
import numpy as np

def nothing(x):
    pass

# Load image
image = cv2.imread('1.jpg')

# Create a window
cv2.namedWindow('image')

# Create trackbars for color change
# Hue is from 0-179 for Opencv
cv2.createTrackbar('HMin', 'image', 0, 179, nothing)
cv2.createTrackbar('SMin', 'image', 0, 255, nothing)
cv2.createTrackbar('VMin', 'image', 0, 255, nothing)
cv2.createTrackbar('HMax', 'image', 0, 179, nothing)
cv2.createTrackbar('SMax', 'image', 0, 255, nothing)
cv2.createTrackbar('VMax', 'image', 0, 255, nothing)

# Set default value for Max HSV trackbars
cv2.setTrackbarPos('HMax', 'image', 179)
cv2.setTrackbarPos('SMax', 'image', 255)
cv2.setTrackbarPos('VMax', 'image', 255)

# Initialize HSV min/max values
hMin = sMin = vMin = hMax = sMax = vMax = 0
phMin = psMin = pvMin = phMax = psMax = pvMax = 0

while(1):
    # Get current positions of all trackbars
    hMin = cv2.getTrackbarPos('HMin', 'image')
    sMin = cv2.getTrackbarPos('SMin', 'image')
    vMin = cv2.getTrackbarPos('VMin', 'image')
    hMax = cv2.getTrackbarPos('HMax', 'image')
    sMax = cv2.getTrackbarPos('SMax', 'image')
    vMax = cv2.getTrackbarPos('VMax', 'image')

    # Set minimum and maximum HSV values to display
    lower = np.array([hMin, sMin, vMin])
    upper = np.array([hMax, sMax, vMax])

    # Convert to HSV format and color threshold
    hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
    mask = cv2.inRange(hsv, lower, upper)
    result = cv2.bitwise_and(image, image, mask=mask)

    # Print if there is a change in HSV value
    if((phMin != hMin) | (psMin != sMin) | (pvMin != vMin) | (phMax != hMax) | (psMax != sMax) | (pvMax != vMax) ):
        print("(hMin = %d , sMin = %d, vMin = %d), (hMax = %d , sMax = %d, vMax = %d)" % (hMin , sMin , vMin, hMax, sMax , vMax))
        phMin = hMin
        psMin = sMin
        pvMin = vMin
        phMax = hMax
        psMax = sMax
        pvMax = vMax

    # Display result image
    cv2.imshow('image', result)
    if cv2.waitKey(10) & 0xFF == ord('q'):
        break

cv2.destroyAllWindows()

Answer 2

Here's the original code and location of the found yellow squares.

source

import numpy as np

import sys; sys.path.append('/usr/lib/pyshared/python2.7')

import cv2
from cv2 import *

im = cv2.imread('rubik.png')
im = cv2.bilateralFilter(im,9,75,75)
im = cv2.fastNlMeansDenoisingColored(im,None,10,10,7,21)
hsv_img = cv2.cvtColor(im, cv2.COLOR_BGR2HSV)   # HSV image


COLOR_MIN = np.array([20, 100, 100],np.uint8)       # HSV color code lower and upper bounds
COLOR_MAX = np.array([30, 255, 255],np.uint8)       # color yellow 

frame_threshed = cv2.inRange(hsv_img, COLOR_MIN, COLOR_MAX)     # Thresholding image
imgray = frame_threshed
ret,thresh = cv2.threshold(frame_threshed,127,255,0)
contours, hierarchy = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
# print type(contours)
for cnt in contours:
    x,y,w,h = cv2.boundingRect(cnt)
    print x,y
    cv2.rectangle(im,(x,y),(x+w,y+h),(0,255,0),2)
cv2.imshow("Show",im)
cv2.imwrite("extracted.jpg", im)
cv2.waitKey()
cv2.destroyAllWindows()

output

185 307
185 189
307 185
431 55