Python OpenCV:在特定轮廓内绘制外部轮廓
我是OpenCV的新手，我正在尝试在特定轮廓内绘制外部轮廓.这是我用来澄清的图像(已经是灰度，阈值等)
I'm new to OpenCV and I'm trying to draw the outer contours inside a specific contour. Here's the image I'm using to clarify (already grayscaled, thresholded, etc.)
我想要的是在外部矩形内找到所有圆的轮廓(共120个).
What I want is to find all the contours of the circles (120 in total), inside the outer rectangle.
contours = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
因此，我基本上使用了RETR_EXTERNAL，但是它仅返回外部矩形.我尝试使用RETR_TREE，但是在那种情况下，由于某些我不理解的原因，它使我返回的轮廓多于圆形.需要说明的是:我只想每个圆1个轮廓.
So I basically used RETR_EXTERNAL for this but it only returns the outer rectangle. I tried using RETR_TREE but in that case it's returning me way more contours than there are circles, for some reason I don't understand. To clarify: I just want 1 contour per circle.
如何使用RETR_EXTERNAL并忽略外部轮廓(矩形)，使其仅返回圆?
How can I use RETR_EXTERNAL and ignore the outer contour (rectangle), so that it only returns the circles?
按区域过滤轮廓:
我按区域过滤了轮廓，以隔离圆.我认为您可能需要对图像进行阈值处理多一点帮助从圆圈中划出圆圈.我使用了以下代码:
Filter the contours by area:
I filtered the contours by area to isolate the circles. I think you may need to work on thresholding the image a bit more to help delineate the circles from the border in the image. I used the following code:
import cv2
import numpy as np
img = cv2.imread("/your/path/C03eN.jpg")
def find_contours_and_centers(img_input):
img_gray = cv2.cvtColor(img_input, cv2.COLOR_BGR2GRAY)
img_gray = cv2.bilateralFilter(img_gray, 3, 27,27)
#(T, thresh) = cv2.threshold(img_input, 0, 100, 0)
_, contours_raw, hierarchy = cv2.findContours(img_gray, cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
contours = [i for i in contours_raw if cv2.contourArea(i) > 20]
contour_centers = []
for idx, c in enumerate(contours):
M = cv2.moments(c)
cX = int(M["m10"] / M["m00"])
cY = int(M["m01"] / M["m00"])
samp_bounds = cv2.boundingRect(c)
contour_centers.append(((cX,cY), samp_bounds))
print("{0} contour centers and bounds found".format(len(contour_centers)))
contour_centers = sorted(contour_centers, key=lambda x: x[0])
return (contours, contour_centers)
conts, cents = find_contours_and_centers(img.copy())
circles = [i for i in conts if np.logical_and((cv2.contourArea(i) > 650),(cv2.contourArea(i) < 4000))]
cv2.drawContours(img, circles, -1, (0,255,0), 2)
cv2.imwrite("/your/path/tester.jpg", img)
结果:
如果您只想使用cv2.RETR_EXTERNAL提取较大的外部矩形内的图像部分，则可以专注于内圆，您可以执行以下操作:
If you just want to extract the portion of the image that is inside the larger outer rectangle, using cv2.RETR_EXTERNAL, allowing you to focus on the inner circles you can do something like the following:
import cv2
import numpy as np
img = cv2.imread("/your/path/C03eN.jpg")
def find_contours_and_centers(img_input):
img_gray = cv2.cvtColor(img_input, cv2.COLOR_BGR2GRAY)
img_gray = cv2.bilateralFilter(img_gray, 3, 27,27)
#(T, thresh) = cv2.threshold(img_input, 0, 100, 0)
#_, contours_raw, hierarchy = cv2.findContours(img_gray, cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
_, contours_raw, hierarchy = cv2.findContours(img_gray, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = [i for i in contours_raw if cv2.contourArea(i) > 20]
contour_centers = []
for idx, c in enumerate(contours):
M = cv2.moments(c)
cX = int(M["m10"] / M["m00"])
cY = int(M["m01"] / M["m00"])
samp_bounds = cv2.boundingRect(c)
contour_centers.append(((cX,cY), samp_bounds))
print("{0} contour centers and bounds found".format(len(contour_centers)))
contour_centers = sorted(contour_centers, key=lambda x: x[0])
return (contours, contour_centers)
conts, cents = find_contours_and_centers(img.copy())
x,y,w,h = cv2.boundingRect(conts[0])
cropped = img[y+10:y+(h-10),x+10:x+(w-10)]
cv2.imwrite("/your/path/cropped.jpg", cropped)
结果: