範例說明
本文為參考下面的文章:
Image Segmentation with Distance Transform and Watershed Algorithm
這篇文章是OpenCV官方網站上的一篇教程，介紹了如何使用distanceTransform函數進行圖像分割。在這篇教程中，作者首先介紹了distanceTransform函數的基本概念和用法，然後通過一個實例演示了如何使用distanceTransform函數對圖像進行分割。
範例程式碼
以下為程式範例
from __future__ import print_function
import cv2 as cv
import numpy as np
import argparse
import random as rng
rng.seed(12345)
parser = argparse.ArgumentParser(description='Code for Image Segmentation with Distance Transform and Watershed Algorithm.\
Sample code showing how to segment overlapping objects using Laplacian filtering, \
in addition to Watershed and Distance Transformation')
parser.add_argument('--input', help='Path to input image.', default='cards.png')
args = parser.parse_args()
src = cv.imread(cv.samples.findFile(args.input))
if src is None:
print('Could not open or find the image:', args.input)
exit(0)
# Show source image
cv.imshow('Source Image', src)
src[np.all(src == 255, axis=2)] = 0
# Show output image
cv.imshow('Black Background Image', src)
kernel = np.array([[1, 1, 1], [1, -8, 1], [1, 1, 1]], dtype=np.float32)
# do the laplacian filtering as it is
# well, we need to convert everything in something more deeper then CV_8U
# because the kernel has some negative values,
# and we can expect in general to have a Laplacian image with negative values
# BUT a 8bits unsigned int (the one we are working with) can contain values from 0 to 255
# so the possible negative number will be truncated
imgLaplacian = cv.filter2D(src, cv.CV_32F, kernel)
sharp = np.float32(src)
imgResult = sharp - imgLaplacian
# convert back to 8bits gray scale
imgResult = np.clip(imgResult, 0, 255)
imgResult = imgResult.astype('uint8')
imgLaplacian = np.clip(imgLaplacian, 0, 255)
imgLaplacian = np.uint8(imgLaplacian)
#cv.imshow('Laplace Filtered Image', imgLaplacian)
cv.imshow('New Sharped Image', imgResult)
bw = cv.cvtColor(imgResult, cv.COLOR_BGR2GRAY)
_, bw = cv.threshold(bw, 40, 255, cv.THRESH_BINARY | cv.THRESH_OTSU)
cv.imshow('Binary Image', bw)
dist = cv.distanceTransform(bw, cv.DIST_L2, 3)
# Normalize the distance image for range = {0.0, 1.0}
# so we can visualize and threshold it
cv.normalize(dist, dist, 0, 1.0, cv.NORM_MINMAX)
cv.imshow('Distance Transform Image', dist)
_, dist = cv.threshold(dist, 0.4, 1.0, cv.THRESH_BINARY)
# Dilate a bit the dist image
kernel1 = np.ones((3,3), dtype=np.uint8)
dist = cv.dilate(dist, kernel1)
cv.imshow('Peaks', dist)
dist_8u = dist.astype('uint8')
# Find total markers
_, contours, _ = cv.findContours(dist_8u, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
# Create the marker image for the watershed algorithm
markers = np.zeros(dist.shape, dtype=np.int32)
# Draw the foreground markers
for i in range(len(contours)):
cv.drawContours(markers, contours, i, (i+1), -1)
# Draw the background marker
cv.circle(markers, (5,5), 3, (255,255,255), -1)
markers_8u = (markers * 10).astype('uint8')
cv.imshow('Markers', markers_8u)
cv.watershed(imgResult, markers)
#mark = np.zeros(markers.shape, dtype=np.uint8)
mark = markers.astype('uint8')
mark = cv.bitwise_not(mark)
# uncomment this if you want to see how the mark
# image looks like at that point
#cv.imshow('Markers_v2', mark)
# Generate random colors
colors = []
for contour in contours:
colors.append((rng.randint(0,256), rng.randint(0,256), rng.randint(0,256)))
# Create the result image
dst = np.zeros((markers.shape[0], markers.shape[1], 3), dtype=np.uint8)
# Fill labeled objects with random colors
for i in range(markers.shape[0]):
for j in range(markers.shape[1]):
index = markers[i,j]
if index > 0 and index <= len(contours):
dst[i,j,:] = colors[index-1]
# Visualize the final image
cv.imshow('Final Result', dst)
cv.waitKey()
distanceTransform
distanceTransform函數是OpenCV中的一個函數，用於計算圖像中每個非零點到最近背景像素的距離。distanceTransform函數的第二個Mat矩陣參數dst保存了每個點與最近的零點的距離信息，圖像上越亮的點，代表了離零點的距離越遠。在這篇文章中，作者通過一個實例演示了如何使用distanceTransform函數對圖像進行分割。
在這個實例中，作者首先讀取了一張灰度圖像，然後使用threshold函數對圖像進行二值化處理。接著，作者使用distanceTransform函數計算了圖像中每個非零點到最近背景像素的距離，並將結果保存在了一個Mat矩陣中。最後，作者使用threshold函數對Mat矩陣進行二值化處理，得到了一張分割後的圖像。
需要注意的是，在使用distanceTransform函數時，需要先將圖像進行二值化處理。此外，在計算距離時，可以選擇歐氏距離、L1距離或L-infinity距離等不同的計算方式。
處理的過程圖片
