Python中噪声图像中的多曲线检测

如何解决Python中噪声图像中的多曲线检测

我有与下面类似的图像。首先，我试图检测这些图像中的曲线。我想捕捉的曲线在图像上有标记。接下来，我想将这些曲线拟合到圆圈中。我将使用这些圆的半径作为结果。 但是我在检测图像中的曲线时遇到问题。非常感谢任何帮助。提前致谢。

Input Image

Expected

这是我用来检测和绘制曲线的代码：

import cv2
import numpy as np
from skimage.feature import peak_local_max
from skimage.morphology import watershed
from scipy import ndimage

image = cv2.imread("croppedImage.png")

img = cv2.medianBlur(image,13)

gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

thresh = cv2.adaptiveThreshold(gray,255,cv2.ADAPTIVE_THRESH_MEAN_C,\
            cv2.THRESH_BINARY,45,0)

kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(5,3))
kernel1 = np.ones((3,3),np.uint8)
kernel2 = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3))
dilate = cv2.dilate(thresh,kernel1,iterations=1)
erode = cv2.erode(dilate,kernel,iterations=1)

# Remove small noise by filtering using contour area
cnts = cv2.findContours(erode,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]

for c in cnts:
    if cv2.contourArea(c) < 800:
        if len(c)>0:
            cv2.drawContours(thresh,[c],(0,0),-1)
        
# Compute Euclidean distance from every binary pixel
# to the nearest zero pixel then find peaks
distance_map = ndimage.distance_transform_edt(erode)
local_max = peak_local_max(distance_map,indices=False,min_distance=1,labels=thresh)

# Perform connected component analysis then apply Watershed
markers = ndimage.label(local_max,structure=np.ones((3,3)))[0]
labels = watershed(-distance_map,markers,mask=erode)

# Iterate through unique labels
for label in np.unique(labels):
    if label == 0:
        continue

    # Create a mask
    mask = np.zeros(thresh.shape,dtype="uint8")
    mask[labels == label] = 255

    # Find contours and determine contour area
    cnts = cv2.findContours(mask.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
    cnts = cnts[0] if len(cnts) == 2 else cnts[1]
    c = max(cnts,key=cv2.contourArea)
    
    cv2.drawContours(image,-1,(36,12),-1)

        

cv2.imwrite('Results/drawedImage.png',image)

thresh = 155
im_bw = cv2.threshold(image,thresh,cv2.THRESH_BINARY)[1]

cv2.imwrite("Results/binary.png",im_bw)

Result Image

Binary Result

从下面的图像中，我可以拟合圆。但我没有像这样干净的图像。

gray_blurred = cv2.GaussianBlur(img,(11,11),0)

ret3,thresh= cv2.threshold(gray_blurred,100,cv2.THRESH_BINARY+cv2.THRESH_OTSU)

# Apply Hough transform on the blurred image. 
detected_circles = cv2.HoughCircles(thresh,cv2.HOUGH_GRADIENT,1,80,param1 = 20,param2 = 9,minRadius = 120,maxRadius = 200) 

  
# Draw circles that are detected. 
if detected_circles is not None: 
  
    # Convert the circle parameters a,b and r to integers. 
    detected_circles = np.uint16(np.around(detected_circles)) 

    for pt in detected_circles[0,:]: 
        a,b,r = pt[0],pt[1],pt[2] 
        
        # Draw the circumference of the circle. 
        cv2.circle(img,(a,b),r,2) 
  
        # Draw a small circle (of radius 1) to show the center. 
        cv2.circle(img,255),3) 
 
else:
    print("Circle is not found")