Botanik bir numpy.array haritasındaki 1'lerin grup sayısını sayma

Şu anda PIL (Python Image Library) aracılığıyla Python'da bazı görüntü işleme ile uğraşıyorum. Ana amacım bir immünohistokimya görüntüsünde renkli hücre sayısını saymak. Bununla ilgili programlar, kütüphaneler, fonksiyonlar ve öğreticiler olduğunu biliyorum ve neredeyse hepsini kontrol ettim. Asıl amacım kodu mümkün olduğunca sıfırdan manuel olarak yazmak. Bu yüzden çok sayıda dış kütüphane ve fonksiyon kullanmaktan kaçınmaya çalışıyorum. Programın çoğunu yazdım. İşte adım adım neler oluyor:

Program görüntü dosyasını alır:

Ve kırmızı hücreler için işler (temel olarak, kırmızı için belirli bir eşiğin altındaki RGB değerlerini kapatır):

Ve bunun boolean haritasını oluşturur, (büyük olduğu için bir kısmını yapıştıracaktır), temelde yukarıdaki işlenmiş ikinci görüntüde kırmızı bir pikselle karşılaştığı her yere 1 koyar.

22222222222222222222222222222222222222222
20000000111111110000000000000000000000002
20000000111111110000000000000000000000002
20000000111111110000000000000000000000002
20000000011111100000000000000000001100002
20000000001111100000000000000000011111002
20000000000110000000000000000000011111002
20000000000000000000000000000000111111002
20000000000000000000000000000000111111102
20000000000000000000000000000001111111102
20000000000000000000000000000001111111102
20000000000000000000000000000000111111002
20000000000000000000000000000000010000002
20000000000000000000000000000000000000002
22222222222222222222222222222222222222222

Boolean haritasındaki 1'lerin grup sayısını saymama yardım etmek için bilerek bu çerçeveyi 2'ler ile sınırlarda bir şey gibi oluşturdum.

Size soruyorum, nasıl olur da bu tür boole haritasındaki hücre sayısını (1'lik gruplar) verimli bir şekilde sayabilirim? Son derece ilgili ve benzer görünen http://en.wikipedia.org/wiki/Connected-component_labeling bulduk , ancak gördüğüm kadarıyla piksel seviyesinde. Benimki boole seviyesinde. Sadece 1s ve 0s.

Çok teşekkürler.

Kaba kuvvet yaklaşımı bir şey, ancak dizi üzerinde rasterleştirme yerine, sorunları bulmak için piksel koleksiyonları üzerinde dizin oluşturmak için sorunu ters çevirerek yapılır.

data = """\
000000011111111000000000000000000000000
000000011111111000000000000000000000000
000000011111111000000000000000000000000
000000001111110000000001000000000110000
000000000111110000000011000000001111100
000000000011100000000000100000011111100
000000000000000000000000000000011111100
000000000000000000000000000000011111110
000000000000000000000000000000111111110
000000000000000000000000000000111111110
000000000000000000000000000000011111100
000000000000000000000000000000001000000
000000000000000000000000000000000000000"""

from collections import namedtuple
Point = namedtuple('Point', 'x y')

def points_adjoin(p1, p2):
    # to accept diagonal adjacency, use this form
    #return -1 <= p1.x-p2.x <= 1 and -1 <= p1.y-p2.y <= 1
    return (-1 <= p1.x-p2.x <= 1 and p1.y == p2.y or
             p1.x == p2.x and -1 <= p1.y-p2.y <= 1)

def adjoins(pts, pt):
    return any(points_adjoin(p,pt) for p in pts)

def locate_regions(datastring):
    data = map(list, datastring.splitlines())
    regions = []
    datapts = [Point(x,y) 
                for y,row in enumerate(data) 
                    for x,value in enumerate(row) if value=='1']
    for dp in datapts:
        # find all adjoining regions
        adjregs = [r for r in regions if adjoins(r,dp)]
        if adjregs:
            adjregs[0].add(dp)
            if len(adjregs) > 1:
                # joining more than one reg, merge
                regions[:] = [r for r in regions if r not in adjregs]
                regions.append(reduce(set.union, adjregs))
        else:
            # not adjoining any, start a new region
            regions.append(set([dp]))
    return regions

def region_index(regs, p):
    return next((i for i,reg in enumerate(regs) if p in reg), -1)

def print_regions(regs):
    maxx = max(p.x for r in regs for p in r)
    maxy = max(p.y for r in regs for p in r)
    allregionpts = reduce(set.union, regs)
    for y in range(-1,maxy+2):
        line = []
        for x in range(-1,maxx+2):
            p = Point(x, y)
            if p in allregionpts:
                line.append(str(region_index(regs, p)))
            else:
                line.append('.')
        print ''.join(line)
    print


# test against data set
regs = locate_regions(data)
print len(regs)
print_regions(regs)

Baskılar:

4
........................................
........00000000........................
........00000000........................
........00000000........................
.........000000.........1.........33....
..........00000........11........33333..
...........000...........2......333333..
................................333333..
................................3333333.
...............................33333333.
...............................33333333.
................................333333..
.................................3......
........................................

Bunu kullanabilirsiniz ndimage.label, bu da bunu yapmanın güzel bir yoludur. Her özellik benzersiz bir değere ve özellik sayısına sahip yeni bir dizi döndürür. Bir bağlantı öğesi de belirleyebilirsiniz.

import scipy
from scipy import ndimage
import matplotlib.pyplot as plt

#flatten to make greyscale, using your second red-black image as input.
im = scipy.misc.imread('blobs.jpg',flatten=1)
#smooth and threshold as image has compression artifacts (jpg)
im = ndimage.gaussian_filter(im, 2)
im[im<10]=0
blobs, number_of_blobs = ndimage.label(im)
print 'Number of blobls:', number_of_blobs

plt.imshow(blobs)
plt.show()

#Output is:
Number of blobls: 30

İşte O (toplam piksel sayısı + hücre pikseli sayısı) olan bir algoritma. Resmi yalnızca hücre pikselleri için tararız ve birini bulduğumuzda, silmek için hücreyi suyla doldururuz.

Common Lisp'te uygulama, ancak önemsiz bir şekilde Python'a çevirebileceksiniz.

(defun flood-fill (picture i j target-color replacement-color)
  ;; http://en.wikipedia.org/wiki/Flood_fill
  (when (= (aref picture i j) target-color)
    (setf (aref picture i j) replacement-color)
    (when (plusp i)
      (flood-fill picture (1- i) j target-color replacement-color))
    (when (< (1+ i) (array-dimension picture 0))
      (flood-fill picture (1+ i) j target-color replacement-color))
    (when (plusp j)
      (flood-fill picture i (1- j) target-color replacement-color))
    (when (< (1+ j) (array-dimension picture 1))
      (flood-fill picture i (1+ j) target-color replacement-color)))
  picture)


(defun count-cells (picture)
  (loop
    :with cell-count = 0
    :for i :from 0 :below (array-dimension picture 0)
    :do (loop
          :for j :from 0 :below (array-dimension picture 1)
          :unless (zerop (aref picture i j))
          :do (progn (incf cell-count)
                     (flood-fill picture i j 1 0)))
    :finally (return cell-count)))




(count-cells
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--> 30

Bir cevaptan daha uzun bir yorum daha:

@İnterjay'in ima ettiği gibi, bir ikili görüntüde, yani sadece 2 rengin mevcut olduğu bir pikselde, pikseller 1 veya 0 değerini alır. Bu, kullandığınız görüntü gösterim biçiminde doğru olabilir veya olmayabilir, ancak doğrudur imajınızın 'kavramsal' sunumunda; uygulama ayrıntılarının sizi bu konuda karıştırmasına izin vermeyin. Bu uygulama ayrıntılarından biri, görüntünün sınırındaki 2'leri kullanmanızdır - görüntünün çevresindeki ölü bölgeyi tanımlamanın mükemmel bir şekilde mantıklı bir yoludur, ancak görüntünün ikiliğini niteliksel olarak etkilemez.

N, NE, NW ve W piksellerinin incelenmesine gelince: bu, bileşenin oluşumunda piksellerin bağlantısı ile ilgilidir. Her pikselde (sınır özel durumları çubuğu) 8 komşu vardır (N, S, E, W, NE, NW, SE, SW) ama hangileri aynı bileşene dahil edilmek için aday? Bazen sadece köşelerde buluşan bileşenler (NE, NW, SE, SW) bağlı olarak kabul edilmez, bazen de.

Başvurunuz için neyin uygun olduğuna karar vermelisiniz. Neler olup bittiğini hissetmek için her bir piksel için farklı komşuları kontrol ederek sıralı algoritmanın birkaç işlemini elle yapmanızı öneririm.