我已经在嘈杂的声学图像中执行了预处理步骤，现在我需要检测窄黑线。
你能想出更好的方法来检测这些线条吗？
我的目标是检测此图像中红色框中的线。
最佳答案
失败的答案:- 这不是一个完美的解决方案，但需要进一步的工作以使其对各种图像具有鲁棒性。我注意到黑色线条中的噪点非常少，因此 Canny 在该区域内没有发现很多边缘。代码和结果如下:-
import numpy as np
import cv2
gray = cv2.imread('2.png')
edges = cv2.Canny(gray,10,60,apertureSize = 7)
cv2.imwrite('2-1-edges-10-60.jpg',edges)
kernel = np.ones((5,5),np.uint8)
closeEdges = cv2.morphologyEx(edges, cv2.MORPH_CLOSE, kernel)
cv2.imwrite('2-2-edges-10-60-dilated-1.jpg',closeEdges)
invertEdges = 255 - closeEdges
cv2.imwrite('2-3-invertedges-10-60.jpg',invertEdges)
minLineLength=100
lines = cv2.HoughLinesP(image=invertEdges,rho=1,theta=np.pi/180, threshold=200,lines=np.array([]), minLineLength=minLineLength,maxLineGap=80)
a,b,c = lines.shape
for i in range(a):
cv2.line(gray, (lines[i][0][0], lines[i][0][1]), (lines[i][0][2], lines[i][0][3]), (0, 0, 255), 1, cv2.LINE_AA)
cv2.imwrite('2-4-houghlines.jpg',gray)
在输出图像的反转上使用连通分量并找到最大尺寸的元素可能会有所帮助。
解决这个问题的另一种方法是使用梯度图像并直接找到梯度幅度小范围的区域。这种方法会更加灵活，因为它不需要使用固定阈值 - 如上所述的 10 和 60。阈值可以根据图像梯度进行自适应/您可以在使用硬编码阈值之前对图像梯度进行归一化。
更好的答案(准确率 30-40%)
import numpy as np
import cv2
import os
# Store all images in this folder
path='images-1'
def autocrop(image, threshold=0):
if len(image.shape) == 3:
flatImage = np.max(image, 2)
else:
flatImage = image
rows = np.where(np.max(flatImage, 0) > threshold)[0]
if rows.size:
cols = np.where(np.max(flatImage, 1) > threshold)[0]
image = image[cols[0]: cols[-1] + 1, rows[0]: rows[-1] + 1]
else:
image = image[:1, :1]
return image
def skeleton(img):
size = np.size(img)
skel = np.zeros(img.shape,np.uint8)
element = cv2.getStructuringElement(cv2.MORPH_CROSS,(3,3))
done = False
while( not done):
eroded = cv2.erode(img,element)
temp = cv2.dilate(eroded,element)
temp = cv2.subtract(img,temp)
skel = cv2.bitwise_or(skel,temp)
img = eroded.copy()
zeros = size - cv2.countNonZero(img)
if zeros==size:
done = True
return skel
def gamma_correction(img, correction):
img = img/255.0
img = cv2.pow(img, correction)
return np.uint8(img*255)
def auto_canny(image, sigma=0.33):
# compute the median of the single channel pixel intensities
v = np.median(image)
# apply automatic Canny edge detection using the computed median
lower = int(max(0, (1.0 - sigma) * v))
upper = int(min(255, (1.0 + sigma) * v))
edged = cv2.Canny(image, lower, upper)
# return the edged image
return edged
for file in os.listdir(path):
if file.endswith(".png"):
current = os.path.join(path, file)
img = cv2.imread(current, 0)
print 'processing ' + current
img = autocrop(img, 0)
cv2.imwrite(current + '-0-cropped.jpg', img)
height, width = img.shape[:2]
img = cv2.resize(img, (width, width))
cv2.imwrite(current + '-0-resized.jpg', img)
# cv2.imwrite(current +'-2-auto_canny_default.jpg', auto_canny(img))
# img = cv2.medianBlur(img,5)
# cv2.imwrite(current +'-0-medianBlur.jpg',img)
# th3 = cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY,11,2)
# cv2.imwrite(current +'-1-threshold_gaussian.jpg',th3)
# laplacian = cv2.Laplacian(img,cv2.CV_64F)
# cv2.imwrite(current + '-3-threshold_gaussian.jpg', laplacian)
#img = cv2.bilateralFilter(img, 3, 3, 5)
edges = cv2.Canny(img,10,20,apertureSize = 5)
cv2.imwrite(current +'-1-edges-10-60.jpg',edges)
kernel = np.ones((3,3),np.uint8)
edges = cv2.morphologyEx(edges, cv2.MORPH_CLOSE, kernel)
cv2.imwrite(current +'-1-edgesClosed-10-60.jpg', edges)
edges = 255-edges
cv2.imwrite(current +'-2-edgesClosedInverted-10-60.jpg', edges)
im2, contours, hierarchy = cv2.findContours(edges, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
imgColor = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
maxArea = 0
for cnt in contours:
if maxArea < cv2.contourArea(cnt):
maxArea = cv2.contourArea(cnt)
for cnt in contours:
rect = cv2.minAreaRect(cnt) #I have used min Area rect for better result
width = rect[1][0]
height = rect[1][1]
if cv2.contourArea(cnt) > int(maxArea/2.5) and ( width < height/2 or height < width/2):
cv2.drawContours(imgColor, cnt, -1, (0,255,0), 1)
cv2.imwrite(current+'-5-Contours.jpg',imgColor)
# edges = skeleton(255-edges)
# cv2.imwrite(current +'-2-skeleton.jpg', edges)
# edges = 255-edges
# minLineLength=int(width/4)
# threshold = 20
# maxLineGap = 1
# rho = 1
# lines = cv2.HoughLinesP(image=edges,rho=rho,theta=np.pi/180, threshold=threshold,lines=np.array([]), minLineLength=minLineLength,maxLineGap=maxLineGap)
# if lines is not None:
# a,b,c = lines.shape
# for i in range(a):
# cv2.line(img, (lines[i][0][0], lines[i][0][1]), (lines[i][0][2], lines[i][0][3]), (0, 0, 255), 1, cv2.LINE_AA)
# cv2.line(edges, (lines[i][0][0], lines[i][0][1]), (lines[i][0][2], lines[i][0][3]), (0, 0, 255), 1, cv2.LINE_AA)
# cv2.imwrite(current+'-5-houghlines.jpg',img)
# cv2.imwrite(current+'-6-houghlines.jpg',edges)
# print 'cool'
# else:
# cv2.imwrite(current+'-5-houghlines.jpg',img)
此外，请检查以下链接:
关于opencv - 在噪声很大的图像中检测窄线，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/42788536/