OpenCV：如何去除图像中不需要的部分

Question

我正在尝试获取图像中蓝色区域的轮廓，然后计算长度和面积，如图所示（我有许多分辨率相同但蓝色区域大小不同的相似图像） .

这是我使用的代码：

import cv2
import numpy as np

# read image as grayscale
img = cv2.imread('VF2.jpg', cv2.IMREAD_GRAYSCALE)

# threshold to binary
thresh = cv2.threshold(img, 210, 255, cv2.THRESH_BINARY)[1]  # the 2nd parameter should be changed.

# apply morphology
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5,5))
morph = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)

# find contours - write black over all small contours
letter = morph.copy()
cntrs = cv2.findContours(morph, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)  
# cntrs = cv2.findContours(morph, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)  

cntrs = cntrs[0] if len(cntrs) == 2 else cntrs[1]
# cntrs = cntrs[0]

for c in cntrs:
    area = cv2.contourArea(c)
    print(area)
    if area < 100:
        cv2.drawContours(letter,[c],0,(0,0,0),-1)

# do canny edge detection
edges = cv2.Canny(letter, 200, 200) # the result for edges is good.
length = cv2.arcLength(cntrs[0], False)  # not closed curves
print('length = ',length)  # both length and area need calibration


area = cv2.contourArea(cntrs[0])
print('area = ',area)

# Outputs
print(np.squeeze(cntrs[0]), '\n')                    # Contour
print('Contour points:', cntrs[0].shape[0], '\n')
print('arcLength:', cv2.arcLength(cntrs[0], True))  # closed curves

# write results
# cv2.imwrite("K_thresh.png", thresh)    

# show results
# cv2.imshow("K_thresh", thresh)
# cv2.imshow("K_morph", morph)
cv2.imshow("K_letter", letter)
cv2.imshow("K_edges", edges)
cv2.waitKey(0)
cv2.destroyAllWindows()

我使用上面的代码并获得了轮廓，但有一些额外的部分，如下图突出显示。任何人都可以帮助删除额外的部分并关闭大纲吗？非常感谢。

Answer 1

将内核大小更改为 (4, 4) 并执行侵蚀而不是打开，此处：

import cv2
import numpy as np

img = cv2.imread("images/flower.jpg", cv2.IMREAD_GRAYSCALE)
scale_percent = 60 # percent of original size
width = int(img.shape[1] * scale_percent / 100)
height = int(img.shape[0] * scale_percent / 100)
dim = (width, height)
  
# resize image
resized = cv2.resize(img, dim, interpolation = cv2.INTER_AREA)

# threshold to binary
thresh = cv2.threshold(resized, 210, 255, cv2.THRESH_BINARY_INV)[1]  # the 2nd parameter should be changed.

# apply morphology
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (4,4))
morph = cv2.morphologyEx(thresh, cv2.MORPH_ERODE, kernel, 1)

letter = morph.copy()
cntrs = cv2.findContours(morph, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)  
# cntrs = cv2.findContours(morph, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)  

cntrs = cntrs[0] if len(cntrs) == 2 else cntrs[1]
# cntrs = cntrs[0]

for c in cntrs:
    area = cv2.contourArea(c)
    print(area)
    if area < 100:
        cv2.drawContours(letter,[c],0,(0,0,0),-1)


# do canny edge detection
edges = cv2.Canny(letter, 200, 200) # the result for edges is good.
length = cv2.arcLength(cntrs[0], False)  # not closed curves
print('length = ',length)  # both length and area need calibration

area = cv2.contourArea(cntrs[0])
print('area = ',area)

# Outputs
print(np.squeeze(cntrs[0]), '\n')                    # Contour
print('Contour points:', cntrs[0].shape[0], '\n')
print('arcLength:', cv2.arcLength(cntrs[0], True))  # closed curves

# write results
# cv2.imwrite("K_thresh.png", thresh)    

# show results
# cv2.imshow("K_thresh", thresh)
# cv2.imshow("K_morph", morph)
cv2.imshow("K_letter", letter)
cv2.imshow("K_edges", edges)
cv2.waitKey(0)
cv2.destroyAllWindows()

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