Python OpenCV:Rubiks立方体解算器颜色提取

Question

描述:

我正在使用Python和OpenCV解决rubiks cube.为此我试图提取立方体的所有颜色(单个立方体块),然后应用适当的算法(我设计的,没有问题).

问题:

假设我已经提取了立方体的所有颜色,我如何找到提取的立方体的位置.就像我怎么知道它是在上中下层还是它的角落 - 中间边缘的一块.

我做了什么:

这里我刚刚提取出黄色.

颜色提取后:

原始图像

代码

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()

请提供一些建议,我如何找到立方体的位置.这里有4个黄色立方体:右上角,中心,右边缘,左下角.如何识别这些位置,例如:通过为每个位置分配数字(此处:3,4,5,7)

感谢任何帮助/想法:)谢谢.

PS:OpenCV新手:)

Answer 1

这是一个简单的方法：

• 将图像转换为 HSV 格式
• 使用颜色阈值检测正方形 cv2.inRange
• 执行形态学操作并在蒙版上绘制正方形
• 查找蒙版上的轮廓并从上到下或从下到上排序
• 取每行三个正方形并从左向右或从右向左排序

转换为 HSV 格式后，我们使用颜色阈值cv2.inRange()检测方块。我们将检测到的正方形绘制到掩码上

从这里我们找到面具上的轮廓，并利用imutils.contours.sort_contours()从上到下或从下到上对轮廓进行排序。接下来，我们取每行 3 个正方形，并从左到右或从右到左对这一行进行排序。这是排序（上-下，左）或（下-上，右）的可视化

现在我们已经对轮廓进行了排序，我们只需将矩形绘制到我们的图像上。这是结果

从左到右和从上到下（左），从右到左和从上到下

从左到右和从下到上（左）、从右到左和从下到上

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()

Answer 2

这是找到的黄色方块的原始代码和位置。

来源

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()

输出

185 307
185 189
307 185
431 55