Python OpenCV: 魔方求解的颜色提取

Question

描述：

我正在用Python和OpenCV来解决魔方的问题。为此，我想提取出魔方小块（也就是每个独立的魔方块）的所有颜色，然后应用我设计的合适算法（这部分没有问题）。

问题：

假设我已经提取了所有小块的颜色，我该如何找到这些小块的位置呢？我怎么知道它们是在上层、中层还是下层，或者它们是角块、中块还是边块呢？

我做了什么：

这里我只提取了黄色。

提取颜色后：

原始图片

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()

请给我一些建议，如何找到这些小块的位置。在这里，我发现了4个黄色小块：右上角、中心、右边缘和左下角。我该如何识别这些位置，比如给每个位置分配一个数字（这里是：3、4、5、7）呢？

任何帮助或想法都非常感谢 :) 谢谢。

附注：OpenCV新手 :)

Answer 1

下面是原始代码和找到的黄色方块的位置。

源代码

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()

输出结果

185 307
185 189
307 185
431 55

Answer 2

这里有一个简单的方法：

• 把图片转换成HSV格式
• 使用颜色阈值来检测方块，方法是用 cv2.inRange()
• 进行一些形态学操作，并在一个遮罩上画出方块
• 在遮罩上 找到轮廓，并从上到下或从下到上进行排序
• 把每一行的三个方块从左到右或从右到左排序

---

在转换成HSV格式后，我们使用 cv2.inRange() 进行颜色阈值处理来检测方块。然后我们把检测到的方块画到一个遮罩上。

接下来，我们在遮罩上找到轮廓，并利用 imutils.contours.sort_contours() 从上到下或从下到上对轮廓进行排序。然后我们把每一行的三个方块从左到右或从右到左进行排序。下面是排序的可视化效果（上到下，左）或（下到上，右）。

现在我们已经把轮廓排序好了，只需把矩形画到我们的图片上。下面是结果：

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()

要获取HSV颜色范围，你可以使用这个简单的HSV颜色阈值脚本来确定颜色的上下范围。记得在 cv2.imread() 中更改图片路径。

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()