Zählen der Anzahl der Einergruppen in einer booleschen Karte von numpy.array

16

Ich beschäftige mich gerade mit der Bildverarbeitung in Python über PIL (Python Image Library). Mein Hauptziel ist es, die Anzahl der gefärbten Zellen in einem immunhistochemischen Bild zu zählen. Ich weiß, dass es relevante Programme, Bibliotheken, Funktionen und Tutorials gibt, und ich habe fast alle überprüft. Mein Hauptziel ist es, den Code so weit wie möglich manuell von Grund auf neu zu schreiben. Daher versuche ich, viele externe Bibliotheken und Funktionen zu vermeiden. Ich habe den größten Teil des Programms geschrieben. Also hier ist, was Schritt für Schritt vor sich geht:

Programm nimmt die Bilddatei auf:Beispiel

Und verarbeitet es für die roten Zellen (im Grunde werden die RGB-Werte unter einem bestimmten Schwellenwert für Rot deaktiviert): Bildbeschreibung hier eingeben

Und erstellt die boolesche Karte davon (ich werde einen Teil davon einfügen, da es groß ist), die im Grunde genommen nur 1 setzt, wo immer es auf ein rotes Pixel im verarbeiteten zweiten Bild oben stößt. 

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

Ich habe dieses rahmenartige Element absichtlich an den Rändern mit 2 generiert, um die Anzahl der Einergruppen in dieser booleschen Karte zu ermitteln.

Meine Frage an euch ist, wie kommt es, dass ich die Anzahl der Zellen (Einergruppen) in einer solchen booleschen Karte effizient zählen kann? Ich habe http://en.wikipedia.org/wiki/Connected-component_labeling gefunden, das sehr ähnlich und ähnlich aussieht, aber soweit ich das sehe, liegt es auf Pixelebene. Meins ist auf der booleschen Ebene. Nur 1s und 0s.

Danke vielmals.

python Ibrahim C. Kurt
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Die Beschriftung der angeschlossenen Komponenten ist genau das, was Sie brauchen. Ich weiß nicht, warum Sie denken, dass es anders ist, da der Wikipedia-Artikel auch Beispiele enthält, die mit Arrays von 1s und 0s beginnen.
Ich weiß, dass es ähnlich aussieht (oder vielleicht auch gleich), ich kann die gesamte Wikipedia-Seite einfach nicht vollständig erfassen, da Englisch nicht meine Muttersprache ist. Der Teil "Sequentieller Algorithmus" auf der Seite sieht aus wie der Umgang mit Einsen und Nullen, aber ich habe die Logik dahinter immer noch nicht gesehen. Warum beginnt Forex mit der Überprüfung von Norden, Nordosten, Nordwesten und Westen?
Dieses Problem ist gelöst.
Was ist, wenn sich interessierende Zellen überlappen? Sollten Sie nicht speziell nach kreisförmigen Features suchen, um zwei Zellen zu unterscheiden, die verbunden aussehen, und nicht nur fortlaufende Blobs zu finden?
Endolith
Die Dinge werden viel komplizierter, wenn die immunhistochemischen Bilder, die Sie haben, eine schlechte Qualität in Bezug auf Zellüberlappung, Auflösung, Standardabweichung der Pixelwerte usw. aufweisen. Ich habe versucht, ein kleines Programm zu schreiben, das funktioniert gut für solche Zwecke, aber es sieht aus wie das Eingabebild und seine Bedingungen sind sehr wichtig für ein perfektes Ergebnis ..
Ibrahim C. Kurt

Antworten:

6

Etwas wie ein Brute-Force-Ansatz, aber durch Invertieren des Problems, um über Sammlungen von Pixeln zu indizieren, um Regionen zu finden, anstatt über das Array zu rastern.

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)

Drucke:

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

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wow ... ich weiß nicht was ich sagen soll. Sehr cool. Und funktioniert total. Vielen Dank, Paul.
So viel Arbeit dabei, wenn es bereits eine Funktion Scipygibt, die wahrscheinlich auch schneller ist ^^ 'Aber wahrscheinlich trotzdem eine gute Übung und es zeigt, wie man das im Allgemeinen macht. Ich werde dann abstimmen.
Zelphir Kaltstahl
13

Sie können verwenden ndimage.label, was eine schöne Möglichkeit ist, dies zu tun. Es wird ein neues Array zurückgegeben, wobei jedes Feature einen eindeutigen Wert und die Anzahl der Features hat. Sie können auch ein Verbindungselement angeben.

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

Bildbeschreibung hier eingeben

j08lue
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Danke, Fraxel. Das funktioniert total als schnelle und schmutzige Lösung, aber vielleicht sollte ich die Qualität des Bildes verbessern, denn wie Sie sehen können, gibt es viele zusammengeführte Zellen. Die Antwort sollte 30 Zellen sein. Nochmals vielen Dank. (edit: Ich habe versucht, die Auflösungsqualität des Bildes zu verbessern und es dann mit Ihrem Code zu verschmelzen, aber es verschmilzt immer noch viele Zellen. Es muss ungefähr so ​​sein, wie Abflachen = 1 oder Ungelesen?)
1
@ Ibrahim C. Kurt - Ich habe aktualisiert, um das zu korrigieren (ich habe es gerade erst bemerkt!). Das Problem war, dass das hochgeladene Bild jpg war und viele Artefakte aufwies. Kleine Menge an Glättung und Schwellenwert behebt das. Sollte perfekt für PNG-Bilder funktionieren (ich denke ...)
ohne die Notwendigkeit der Glättung und Schwellenwertbildung.
Es funktioniert total. Du hast recht. Ohne weitere Modifikationen ist es ausreichend, diese von jpg auf png zu ändern. Vielen Dank. Ich habe jetzt mehr als 2 perfekte Antworten. Ich weiß nicht, was ich tun und sagen soll: D
@ Ibrahim C. Kurt - Glück;)
6

Hier ist ein Algorithmus, der 0 ist (Gesamtzahl der Pixel + Anzahl der Zellenpixel). Wir scannen das Bild einfach nach Zellpixeln und wenn wir eines finden, füllen wir die Zelle mit einer Flut, um es zu löschen.

Implementierung in Common Lisp, aber Sie können es trivial in Python übersetzen.

(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
  (make-array '(128 171) :element-type 'bit
              :initial-contents
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                #171*000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000111111111000000000000000000000000000
                #171*000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000111111111000000000000000000001110000
                #171*000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000011111110000000000000000000011111100
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                #171*000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000111111100
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                #171*000000000000000000000000001111111100011111110000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000)))
--> 30

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Hey Pascal, vielen Dank für die Antwort. Ich sehe, dein Programm findet die richtige Antwort sehr sauber. Das Problem ist, dass ich keine Ahnung von dieser Common-Lisp-Sprache habe, aber ich werde versuchen, das herauszufinden und eine ähnliche Skripty in Python zu schreiben.
3

Mehr ein erweiterter Kommentar als eine Antwort:

Wie @interjay angedeutet hat, nehmen in einem Binärbild, dh einem Bild, in dem nur 2 Farben vorhanden sind, die Pixel den Wert 1 oder 0 an. Dies kann in dem von Ihnen verwendeten Bilddarstellungsformat wahr sein oder nicht, ist aber wahr in der "konzeptuellen" Darstellung Ihres Bildes; Lassen Sie sich in diesem Punkt nicht von Implementierungsdetails verwirren. Eine dieser Implementierungsdetails ist die Verwendung von 2s um den Bildrand - eine absolut sinnvolle Methode, um die tote Zone um das Bild herum zu identifizieren, ohne die Binärheit des Bildes qualitativ zu beeinflussen.

Zur Untersuchung der Pixel N, NE, NW und W: Dies hängt mit der Konnektivität der Pixel bei der Bildung der Komponente zusammen. Jedes Pixel (abgesehen von den Rand-Sonderfällen) hat 8 Nachbarn (N, S, E, W, NE, NW, SE, SW). Welche sind jedoch Kandidaten für die Aufnahme in dieselbe Komponente? Manchmal werden Komponenten, die sich nur an Ecken treffen (NE, NW, SE, SW), nicht als verbunden angesehen, manchmal sind sie es.

Sie müssen entscheiden, was für Ihre Anwendung geeignet ist. Ich schlage vor, Sie arbeiten einige Operationen des sequentiellen Algorithmus von Hand aus, indem Sie für jedes Pixel verschiedene Nachbarn überprüfen, um ein Gefühl dafür zu bekommen, was vor sich geht.


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