I am trying to write an algorithm to systematically determine how many different "curves" are in an image. Example Image. I'm specifically interested in the white lines here, so I've used a color threshold to mask the rest of the image and only get the white pixels. These lines represent a path run by a player (wide receivers in the NFL), so I'm interested in the x and y coordinates that the path represents - and each "curve" represents a different path that the player took (or "route"). All curves should start on or behind the blue line.
However, while I can get just the white pixels, I can't figure out how to systematically identify the separate curves. In this example image, there are 8 white curves (or routes) present. I've identified those curves in this image. I tried edge detection, and then using scipy ndimage to get the number of connected components, but because the curves overlap it counts them as connected and only gives me 3 labeled components for this image as opposed to eight. Here's what the edge detection output looks like. Is there a better way to go about this? Here is my sample code.
import cv2
from skimage.morphology import skeletonize
import numpy as np
from scipy import ndimage
#Read in image
image = cv2.imread('example_image.jpeg')
#Color boundary to get white pixels
lower_white = np.array([230, 230, 230])
upper_white = np.array([255, 255, 255])
#mask image for white pixels
mask = cv2.inRange(image, lower_white, upper_white)
c_pixels = cv2.bitwise_and(image, image, mask=mask)
#make pixels from 0 to 1 form to use in skeletonize
c_pixels = c_pixels.clip(0,1)
ske_c = skeletonize(c_pixels[:,:,1]).astype(np.uint8)
#Edge Detection
inputImage =ske_c*255
edges = cv2.Canny(inputImage,100,200,apertureSize = 7)
#Show edges
cv2.imshow('edges', edges)
cv2.waitKey(0)
cv2.destroyAllWindows()
#Find number of components
# smooth the image (to remove small objects); set the threshold
edgesf = ndimage.gaussian_filter(edges, 1)
T = 50 # set threshold by hand to avoid installing `mahotas` or
# `scipy.stsci.image` dependencies that have threshold() functions
# find connected components
labeled, nr_objects = ndimage.label(edgesf > T) # `dna[:,:,0]>T` for red-dot case
print("Number of objects is %d " % nr_objects)
