I have been trying to segment biological cells in an image using watershed algorithm. I found an excellent article on pyimagesearch which clearly gives an overview of the algorithm and its implementation in python. The code uses both opencv and scikit-image for processing the image.
My goal is to convert the whole code into pure opencv. But the issues is that there's a function called scipy.feature.peak_local_max in scikit-image which does the job of finding local peaks in an image very efficiently. I couldn't find or devise such function in OpenCV.
Original Code(I have documented this snippet according to my understanding, please correct if am wrong):
import the necessary packages
from skimage.feature import peak_local_max
from skimage.morphology import watershed
from scipy import ndimage
import numpy as np
import argparse
import imutils
import cv2
from matplotlib import pyplot as plt
# load the image and perform pyramid mean shift filtering
# to aid the thresholding step
image = cv2.imread("test2.png")
shifted = cv2.pyrMeanShiftFiltering(image, 21, 51)
# Apply grayscale
gray = cv2.cvtColor(shifted, cv2.COLOR_BGR2GRAY)
# Convert to binary
thresh = cv2.threshold(gray, 0, 255,cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
# Watershed starts from here
# compute the exact Euclidean distance from every binary
# pixel to the nearest zero pixel, then find peaks in this
# distance map
D = ndimage.distance_transform_edt(thresh)
localMax = peak_local_max(D, indices=False, min_distance=10,labels=thresh)
# perform a connected component analysis on the local peaks,
# using 8-connectivity, then appy the Watershed algorithm
markers = ndimage.label(localMax, structure=np.ones((3, 3)))[0]
# Apply segmentation
labels = watershed(-D, markers, mask=thresh)
print("[INFO] {} unique segments found".format(len(np.unique(labels)) - 1))
cv2.imwrite("labels.png",labels)
# Contouring
for label in np.unique(labels):
# if the label is zero, we are examining the 'background'
# so simply ignore it
if label == 0:
continue
# otherwise, allocate memory for the label region and draw
# it on the mask
mask = np.zeros(gray.shape, dtype="uint8")
mask[labels == label] = 255
cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
c = max(cnts, key=cv2.contourArea)
# draw a circle enclosing the object
peri = cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c, 0.018 * peri, True)
cv2.drawContours(image, [approx], -1, (0,0,255), 2)
cv2.imwrite("output.jpg",image)
Pure OpenCV Code till finding distance map:
# import the necessary packages
import numpy as np
import cv2
# load the image and perform pyramid mean shift filtering
# to aid the thresholding step
image = cv2.imread("1.png")
shifted = cv2.pyrMeanShiftFiltering(image, 21, 51)
# Apply grayscale
gray = cv2.cvtColor(shifted, cv2.COLOR_BGR2GRAY)
# Convert to binary
thresh = cv2.threshold(gray, 0, 255,cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
# Watershed starts from here
# compute the exact Euclidean distance from every binary
# pixel to the nearest zero pixel, then find peaks in this
# distance map
D = cv2.distanceTransform(thresh,cv2.DIST_L2,0)
The point till D, both the original code and the pure opencv code which I have tried have exactly the same outputs, the issue is I dont exactly have a clear idea on how to implement peak_local_max in opencv which would give identical result as scikit's function.
It would be really helpful if someone who has relavent knowledge could explain how this function works in finding those peaks in such a fine grained manner.
Input Image:
Peak Local max output in scikit-image(BGR format image):
Required output:
