watershed_algo.py

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

image = cv2.imread(r"D:\computer_vision\data\watershed_coins_01.jpg")
shifted = cv2.pyrMeanShiftFiltering(image, 21, 51)
cv2.imshow("Input", shifted)
cv2.waitKey(0)
cv2.destroyAllWindows()

# convert the mean shift image to grayscale, then apply
# Otsu's thresholding
gray = cv2.cvtColor(shifted, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255,cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
cv2.imshow("Thresh", thresh)

# 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=20,
	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]
labels = watershed(-D, markers, mask=thresh)
print("[INFO] {} unique segments found".format(len(np.unique(labels)) - 1))


# loop over the unique labels returned by the Watershed
# algorithm
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

	# detect contours in the mask and grab the largest one
	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
	((x, y), r) = cv2.minEnclosingCircle(c)
	cv2.circle(image, (int(x), int(y)), int(r), (0, 255, 0), 2)
	cv2.putText(image, "#{}".format(label), (int(x) - 10, int(y)),
		cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)

# show the output image
cv2.imshow("Output", image)
cv2.waitKey(0)