Python OpenCV排序轮廓 [英] Python opencv sorting contours
问题描述
我正在关注这个问题:
从左到右和从上到下对轮廓进行排序.但是，可以使用此(OpenCV 3)找到我的轮廓:
im2, contours, hierarchy = cv2.findContours(threshold,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
，它们的格式如下:
array([[[ 1, 1]],
[[ 1, 36]],
[[63, 36]],
[[64, 35]],
[[88, 35]],
[[89, 34]],
[[94, 34]],
[[94, 1]]], dtype=int32)]
运行代码
max_width = max(contours, key=lambda r: r[0] + r[2])[0]
max_height = max(contours, key=lambda r: r[3])[3]
nearest = max_height * 1.4
contours.sort(key=lambda r: (int(nearest * round(float(r[1])/nearest)) * max_width + r[0]))
我遇到错误
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
所以我将其更改为:
max_width = max(contours, key=lambda r: np.max(r[0] + r[2]))[0]
max_height = max(contours, key=lambda r: np.max(r[3]))[3]
nearest = max_height * 1.4
contours.sort(key=lambda r: (int(nearest * round(float(r[1])/nearest)) * max_width + r[0]))
但是现在我得到了错误:
TypeError: only length-1 arrays can be converted to Python scalars
阅读下面的答案后，我修改了代码:
编辑2
这是我用来放大"字符并找到轮廓的代码
kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(35,35))
# dilate the image to get text
# binaryContour is just the black and white image shown below
dilation = cv2.dilate(binaryContour,kernel,iterations = 2)
编辑结束2
im2, contours, hierarchy = cv2.findContours(dilation,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
myContours = []
# Process the raw contours to get bounding rectangles
for cnt in reversed(contours):
epsilon = 0.1*cv2.arcLength(cnt,True)
approx = cv2.approxPolyDP(cnt,epsilon,True)
if len(approx == 4):
rectangle = cv2.boundingRect(cnt)
myContours.append(rectangle)
max_width = max(myContours, key=lambda r: r[0] + r[2])[0]
max_height = max(myContours, key=lambda r: r[3])[3]
nearest = max_height * 1.4
myContours.sort(key=lambda r: (int(nearest * round(float(r[1])/nearest)) * max_width + r[0]))
i=0
for x,y,w,h in myContours:
letter = binaryContour[y:y+h, x:x+w]
cv2.rectangle(binaryContour,(x,y),(x+w,y+h),(255,255,255),2)
cv2.imwrite("pictures/"+str(i)+'.png', letter) # save contour to file
i+=1
排序前的轮廓:
[(1, 1, 94, 36), (460, 223, 914, 427), (888, 722, 739, 239), (35,723, 522, 228),
(889, 1027, 242, 417), (70, 1028, 693, 423), (1138, 1028, 567, 643),
(781, 1030, 98, 413), (497, 1527, 303, 132), (892, 1527, 168, 130),
(37, 1719, 592, 130), (676, 1721, 413, 129), (1181, 1723, 206, 128),
(30, 1925, 997, 236), (1038, 1929, 170, 129), (140, 2232, 1285, 436)]
排序后的轮廓:
(注意:，这不是我希望对轮廓进行排序的顺序.请参阅底部的图像)
[(1, 1, 94, 36), (460, 223, 914, 427), (35, 723, 522, 228), (70,1028, 693, 423),
(781, 1030, 98, 413), (888, 722, 739, 239), (889, 1027, 242, 417),
(1138, 1028, 567, 643), (30, 1925, 997, 236), (37, 1719, 592, 130),
(140, 2232, 1285, 436), (497, 1527, 303, 132), (676, 1721, 413, 129),
(892, 1527, 168, 130), (1038, 1929, 170, 129), (1181, 1723, 206, 128)]
我正在使用的图片
我要按以下顺序查找轮廓:
用于查找轮廓的膨胀图像
您真正需要的是设计一个公式，将轮廓信息转换为等级，并使用该等级对轮廓进行排序，因为您需要对轮廓进行排序从上到下，从左到右，因此您的公式必须包含给定轮廓的origin才能计算其等级.例如，我们可以使用以下简单方法:
def get_contour_precedence(contour, cols):
origin = cv2.boundingRect(contour)
return origin[1] * cols + origin[0]
根据轮廓的原点为每个轮廓指定等级.当两个连续的轮廓垂直放置时，它变化很大，但是当轮廓水平堆叠时，它变化很小.因此，以这种方式，首先将轮廓从上到下进行分组，并且在发生碰撞"的情况下，将使用水平放置的轮廓中变化较小的值.
import cv2
def get_contour_precedence(contour, cols):
tolerance_factor = 10
origin = cv2.boundingRect(contour)
return ((origin[1] // tolerance_factor) * tolerance_factor) * cols + origin[0]
img = cv2.imread("/Users/anmoluppal/Downloads/9VayB.png", 0)
_, img = cv2.threshold(img, 70, 255, cv2.THRESH_BINARY)
im, contours, h = cv2.findContours(img.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours.sort(key=lambda x:get_contour_precedence(x, img.shape[1]))
# For debugging purposes.
for i in xrange(len(contours)):
img = cv2.putText(img, str(i), cv2.boundingRect(contours[i])[:2], cv2.FONT_HERSHEY_COMPLEX, 1, [125])
如果仔细观察，放置3, 4, 5, 6轮廓的第三行6位于3到5之间，原因是第6个轮廓略低于3, 4, 5轮廓线. /p>
告诉我，您想以其他方式输出，我们可以调整get_contour_precedence以获得3, 4, 5, 6轮廓校正等级.
I am following this question:
How can I sort contours from left to right and top to bottom?
to sort contours from left-to-right and top-to-bottom. However, my contours are found using this (OpenCV 3):
im2, contours, hierarchy = cv2.findContours(threshold,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
and they are formatted like this:
array([[[ 1, 1]],
[[ 1, 36]],
[[63, 36]],
[[64, 35]],
[[88, 35]],
[[89, 34]],
[[94, 34]],
[[94, 1]]], dtype=int32)]
When I run the code
max_width = max(contours, key=lambda r: r[0] + r[2])[0]
max_height = max(contours, key=lambda r: r[3])[3]
nearest = max_height * 1.4
contours.sort(key=lambda r: (int(nearest * round(float(r[1])/nearest)) * max_width + r[0]))
I am getting the error
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
so I changed it to this:
max_width = max(contours, key=lambda r: np.max(r[0] + r[2]))[0]
max_height = max(contours, key=lambda r: np.max(r[3]))[3]
nearest = max_height * 1.4
contours.sort(key=lambda r: (int(nearest * round(float(r[1])/nearest)) * max_width + r[0]))
but now I am getting the error:
TypeError: only length-1 arrays can be converted to Python scalars
EDIT:
After reading the answer below I modified my code:
EDIT 2
This is the code that I use to "dilate" the characters and find the contours
kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(35,35))
# dilate the image to get text
# binaryContour is just the black and white image shown below
dilation = cv2.dilate(binaryContour,kernel,iterations = 2)
END OF EDIT 2
im2, contours, hierarchy = cv2.findContours(dilation,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
myContours = []
# Process the raw contours to get bounding rectangles
for cnt in reversed(contours):
epsilon = 0.1*cv2.arcLength(cnt,True)
approx = cv2.approxPolyDP(cnt,epsilon,True)
if len(approx == 4):
rectangle = cv2.boundingRect(cnt)
myContours.append(rectangle)
max_width = max(myContours, key=lambda r: r[0] + r[2])[0]
max_height = max(myContours, key=lambda r: r[3])[3]
nearest = max_height * 1.4
myContours.sort(key=lambda r: (int(nearest * round(float(r[1])/nearest)) * max_width + r[0]))
i=0
for x,y,w,h in myContours:
letter = binaryContour[y:y+h, x:x+w]
cv2.rectangle(binaryContour,(x,y),(x+w,y+h),(255,255,255),2)
cv2.imwrite("pictures/"+str(i)+'.png', letter) # save contour to file
i+=1
Contours before sorting:
[(1, 1, 94, 36), (460, 223, 914, 427), (888, 722, 739, 239), (35,723, 522, 228),
(889, 1027, 242, 417), (70, 1028, 693, 423), (1138, 1028, 567, 643),
(781, 1030, 98, 413), (497, 1527, 303, 132), (892, 1527, 168, 130),
(37, 1719, 592, 130), (676, 1721, 413, 129), (1181, 1723, 206, 128),
(30, 1925, 997, 236), (1038, 1929, 170, 129), (140, 2232, 1285, 436)]
Contours after sorting:
(NOTE: This is not the order I want the contours to be sorted in. Refer to image at the bottom)
[(1, 1, 94, 36), (460, 223, 914, 427), (35, 723, 522, 228), (70,1028, 693, 423),
(781, 1030, 98, 413), (888, 722, 739, 239), (889, 1027, 242, 417),
(1138, 1028, 567, 643), (30, 1925, 997, 236), (37, 1719, 592, 130),
(140, 2232, 1285, 436), (497, 1527, 303, 132), (676, 1721, 413, 129),
(892, 1527, 168, 130), (1038, 1929, 170, 129), (1181, 1723, 206, 128)]
Image I am working with
I want to find the contours in the following order:
Dilation image used for finding contours
What you actually need is to devise a formula to convert your contour information to a rank and use that rank to sort the contours, Since you need to sort the contours from top to Bottom and left to right so your formula must involve the origin of a given contour to calculate its rank. For example we can use this simple method:
def get_contour_precedence(contour, cols):
origin = cv2.boundingRect(contour)
return origin[1] * cols + origin[0]
It gives a rank to each contour depending upon the origin of contour. It varies largely when two consecutive contours lie vertically but varies marginally when contours are stacked horizontally. So in this way, First the contours would be grouped from Top to Bottom and in case of Clash the less variant value among the horizontal laid contours would be used.
import cv2
def get_contour_precedence(contour, cols):
tolerance_factor = 10
origin = cv2.boundingRect(contour)
return ((origin[1] // tolerance_factor) * tolerance_factor) * cols + origin[0]
img = cv2.imread("/Users/anmoluppal/Downloads/9VayB.png", 0)
_, img = cv2.threshold(img, 70, 255, cv2.THRESH_BINARY)
im, contours, h = cv2.findContours(img.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours.sort(key=lambda x:get_contour_precedence(x, img.shape[1]))
# For debugging purposes.
for i in xrange(len(contours)):
img = cv2.putText(img, str(i), cv2.boundingRect(contours[i])[:2], cv2.FONT_HERSHEY_COMPLEX, 1, [125])
If you see closely, the third row where 3, 4, 5, 6 contours are placed the 6 comes between 3 and 5, The reason is that the 6th contour is slightly below the line of 3, 4, 5 contours.
Tell me is you want the output in other way around we can tweak the get_contour_precedence to get 3, 4, 5, 6 ranks of contour corrected.
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