我使用来自 Github 的 Spencer Whitt 的 CCL 算法从噪声中删除检测到的道路标记。
首先，我在图像上运行 CCL 算法，以便它识别连续元素。现在我扩展了算法，使其忽略具有少量像素的元素。所以我尝试对CCL算法创建的字典进行排序和过滤。因此，在附加代码中，不够用的键将被删除，并带有相应的值。
不幸的是，附加代码不仅删除了小元素，而且还分割了“大”的连续道路标记。为什么？
图片：
覆铜板算法：
from PIL import Image
import PIL
from PIL import ImageOps
import collections
import operator
import numpy as np
import sys
import math, random
from itertools import product
from ufarray import *
Image.MAX_IMAGE_PIXELS = 1000000000
import cv2
def run(img):
data = img.load()
width, height = img.size
# Union find data structure
uf = UFarray()
#
# First pass
#
# Dictionary of point:label pairs
labels = {}
for y, x in product(range(height), range(width)):
#
# Pixel names were chosen as shown:
#
# -------------
# | a | b | c |
# -------------
# | d | e | |
# -------------
# | | | |
# -------------
#
# The current pixel is e
# a, b, c, and d are its neighbors of interest
#
# 255 is white, 0 is black
# White pixels part of the background, so they are ignored
# If a pixel lies outside the bounds of the image, it default to white
#
# If the current pixel is white, it's obviously not a component...
if data[x, y] == 255:
pass
# If pixel b is in the image and black:
# a, d, and c are its neighbors, so they are all part of the same component
# Therefore, there is no reason to check their labels
# so simply assign b's label to e
elif y > 0 and data[x, y-1] == 0:
labels[x, y] = labels[(x, y-1)]
# If pixel c is in the image and black:
# b is its neighbor, but a and d are not
# Therefore, we must check a and d's labels
elif x+1 < width and y > 0 and data[x+1, y-1] == 0:
c = labels[(x+1, y-1)]
labels[x, y] = c
# If pixel a is in the image and black:
# Then a and c are connected through e
# Therefore, we must union their sets
if x > 0 and data[x-1, y-1] == 0:
a = labels[(x-1, y-1)]
uf.union(c, a)
# If pixel d is in the image and black:
# Then d and c are connected through e
# Therefore we must union their sets
elif x > 0 and data[x-1, y] == 0:
d = labels[(x-1, y)]
uf.union(c, d)
# If pixel a is in the image and black:
# We already know b and c are white
# d is a's neighbor, so they already have the same label
# So simply assign a's label to e
elif x > 0 and y > 0 and data[x-1, y-1] == 0:
labels[x, y] = labels[(x-1, y-1)]
# If pixel d is in the image and black
# We already know a, b, and c are white
# so simpy assign d's label to e
elif x > 0 and data[x-1, y] == 0:
labels[x, y] = labels[(x-1, y)]
# All the neighboring pixels are white,
# Therefore the current pixel is a new component
else:
labels[x, y] = uf.makeLabel()
#
# Second pass
#
uf.flatten()
colors = {}
# Image to display the components in a nice, colorful way
output_img = Image.new("RGB", (width, height))
outdata = output_img.load()
for (x, y) in labels:
# Name of the component the current point belongs to
component = uf.find(labels[(x, y)])
# Update the labels with correct information
labels[(x, y)] = component
# Associate a random color with this component
if component not in colors:
colors[component] = (random.randint(0,255), random.randint(0,255),random.randint(0,255))
# Colorize the image
outdata[x, y] = colors[component]
return (labels, output_img)
附加代码：
def main():
# Open the image
img = Image.open("files/motorway/gabor/motorway_gabor_S.tiff")
img = PIL.ImageOps.invert(img)
# Threshold the image, this implementation is designed to process b+w
# images only
img = img.point(lambda p: p > 190 and 255)
img = img.convert('1')
# labels is a dictionary of the connected component data in the form:
# (x_coordinate, y_coordinate) : component_id
#
# if you plan on processing the component data, this is probably what you
# will want to use
#
# output_image is just a frivolous way to visualize the components.
(labels, output_img) = run(img)
# output_img.show()
output_img.save("files/motorway/gabor/motorway_gabor_S_cc1.tiff")
################################################################################
sorted_x = sorted(labels.items(), key=operator.itemgetter(1))
str_labels = str(sorted_x) # Convert Dictionary to String, remove unnecessary characters
str_labels = str_labels.replace("{", "[")
str_labels = str_labels.replace("}", "]")
str_labels = str_labels.replace("(", "")
str_labels = str_labels.replace(")", "")
str_labels = str_labels.replace(":", ",")
# print str_labels[:100]
int_labels = eval(str_labels) # Transforming back into an integer list
i = 0
key = []
value = []
#int_labels2 = list(int_labels)
while i < len(int_labels)/3:
key.append(int_labels[3*i]) # x (keys)
key.append(int_labels[3 * i + 1]) # y (keys)
value.append(int_labels[3 * i + 2]) # values
i = i + 1
counter = collections.Counter(value) # Counting the frequency of "values"
counter_values = counter.values()
################################################################################
# Selection
# Sorting out "keys" and "values" that are too big/small
# key2 = relevant "keys"
# value2 = to key2 appropriate relevant values
i = 0
k = 0
v = 0
key2 = []
key3 = []
value2 = []
value3 = []
help = []
while i < len(counter_values):
if counter_values[i] >= 80:# and counter_values[i] <= 71:
value2.append(counter_values[v]*2)
v = v + 1
while k < counter_values[i]*2 + sum(help)*2:
key2.append(key[k])
k = k + 1
else:
value3.append(counter_values[v]*2)
v = v + 1
while k < counter_values[i]*2 + sum(help)*2:
key3.append(key[k])
k = k + 1
help.append(counter_values[i])
i = i + 1
#####################################################################
# Image to display the components in a nice, colorful way
width, height = img.size
output_img = Image.new("RGB", (width, height))
outdata = output_img.load()
# list (key2) to x- and y-tuple-pairs
i = 0
key2t = []
while i < len(key2):
key2t.append(tuple(key2[i:i + 2]))
i = i + 2
# transform value2-list
i = 0
j = 0
k = 0
value2_full = []
while i < len(value2):
while j < value2[i]:
value2_full.append(k)
j = j + 1
j = 0
i = i + 1
k = k + 1
d = dict(zip(key2t, value2_full))
colors = {}
i = 0
for (x, y) in d:
if value2_full[i] not in colors:
colors[value2_full[i]] = (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))
# Colorize the image
outdata[x, y] = colors[value2_full[i]]
output_img.save("files/motorway/gabor/motorway_gabor_S_cc2.tiff")