particleCharacterization.py 8.56 KB
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
GEPARD - Gepard-Enabled PARticle Detection
Copyright (C) 2018  Lars Bittrich and Josef Brandt, Leibniz-Institut für 
Polymerforschung Dresden e. V. <bittrich-lars@ipfdd.de>    

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with this program, see COPYING.  
If not, see <https://www.gnu.org/licenses/>.
"""

import numpy as np
import cv2
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from copy import deepcopy
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from .particleClassification.colorClassification import ColorClassifier
from .particleClassification.shapeClassification import ShapeClassifier
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from ..segmentation import closeHolesOfSubImage
from ..errors import InvalidParticleError
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from ..helperfunctions import cv2imread_fix
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class ParticleStats(object):
    longSize = None
    shortSize = None
    height = None
    area = None
    shape = None
    color = None

def particleIsValid(particle):
    if particle.longSize == 0 or particle.shortSize == 0:
        return False
    
    if cv2.contourArea(particle.contour) == 0:
        return False
    return True
    
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def updateStatsOfParticlesIfNotManuallyEdited(particleContainer):
    dataset = particleContainer.datasetParent
    fullimage = loadFullimageFromDataset(dataset)
    zimg = loadZValImageFromDataset(dataset)
    
    for particle in particleContainer.particles:
        if not hasattr(particle, "wasManuallyEdited"):
            particle.wasManuallyEdited = False
        
        if not particle.wasManuallyEdited:
            newStats = getParticleStatsWithPixelScale(particle.contour, dataset, fullimage, zimg)
            particle.__dict__.update(newStats.__dict__)

def getParticleStatsWithPixelScale(contour, dataset, fullimage=None, zimg=None):
    if fullimage is None:
        fullimage = loadFullimageFromDataset(dataset)
    if zimg is None:
        zimg = loadZValImageFromDataset(dataset)
        
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    cnt = deepcopy(contour)
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    pixelscale = dataset.getPixelScale()
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    newStats = ParticleStats()
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    newStats.longSize, newStats.shortSize, newStats.area = getContourStats(cnt)
    newStats.longSize *= pixelscale
    newStats.shortSize *= pixelscale
    newStats.area *= (pixelscale**2)
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    if 0 in [newStats.longSize, newStats.shortSize, newStats.area]:
        raise InvalidParticleError
    
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    newStats.height = getParticleHeight(cnt, zimg, dataset)    
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    newStats.shape = getParticleShape(cnt, newStats.height)
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    if newStats.shape == 'fibre':
        newStats.longSize, newStats.shortSize = getFibreDimension(cnt)
        newStats.longSize *= pixelscale
        newStats.shortSize *= pixelscale
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    partImg = getParticleImageFromFullimage(cnt, fullimage)
    newStats.color = getParticleColor(partImg)
    return newStats
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def getFibreDimension(contour):
    longSize = cv2.arcLength(contour, True)/2
    img = contoursToImg([contour])[0]
    dist = cv2.distanceTransform(img, cv2.DIST_L2, 3)
    maxThickness = np.max(dist)*2
    return longSize, maxThickness
    
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def getParticleColor(imgRGB, colorClassifier=None):
    img = cv2.cvtColor(imgRGB, cv2.COLOR_RGB2HSV_FULL)
    meanHSV = cv2.mean(img)
    if colorClassifier is None:
        colorClassifier = ColorClassifier()
    color = colorClassifier.classifyColor(meanHSV)
    return color

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def getParticleShape(contour, particleHeight, shapeClassifier=None):
    if shapeClassifier is None:
        shapeClassifier = ShapeClassifier()
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    shape = shapeClassifier.classifyShape(contour, particleHeight)
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    return shape

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def getParticleHeight(contour, fullZimg, dataset):
    zimg = getParticleImageFromFullimage(contour, fullZimg)
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    if zimg.shape[0] == 0 or zimg.shape[1] == 0:
        raise InvalidParticleError
        
    zimg = cv2.medianBlur(zimg, 5)
    avg_ZValue = np.mean(zimg[zimg > 0])
    if np.isnan(avg_ZValue):  #i.e., only zeros in zimg
        avg_ZValue = 0
    z0, z1 = dataset.zpositions.min(), dataset.zpositions.max()
    height = avg_ZValue/255.*(z1-z0) + z0
    return height

def getContourStats(cnt):
    ##characterize particle
        if cnt.shape[0] >= 5:       ##at least 5 points required for ellipse fitting...
            ellipse = cv2.fitEllipse(cnt)
            short, long = ellipse[1]
        else:
            rect = cv2.minAreaRect(cnt)
            long, short = rect[1]
            
        if short>long:
            long, short = short, long
        
        area = cv2.contourArea(cnt)
    
        return long, short, area

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def mergeContours(contours):
    img, xmin, ymin, padding = contoursToImg(contours)
    return imgToCnt(img, xmin, ymin, padding)

def getParticleImageFromFullimage(contour, fullimage):
    contourCopy = deepcopy(contour)
    xmin, xmax, ymin, ymax = getContourExtrema(contourCopy)

    img = fullimage[ymin:ymax, xmin:xmax]
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    img = img.copy()
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    mask = np.zeros(img.shape[:2])
    
    for i in range(len(contourCopy)):
        contourCopy[i][0][0] -= xmin
        contourCopy[i][0][1] -= ymin
        
    cv2.drawContours(mask, [contourCopy], -1, (255, 255, 255), -1)
    cv2.drawContours(mask, [contourCopy], -1, (255, 255, 255), 1)
    
    img[mask == 0] = 0
    img = np.array(img, dtype = np.uint8)
    return img
    
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def contoursToImg(contours, padding=0):
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    contourCopy = deepcopy(contours)
    xmin, xmax, ymin, ymax = getContourExtrema(contourCopy)
    
    padding = padding   #pixel in each direction
    rangex = int(np.round((xmax-xmin)+2*padding))
    rangey = int(np.round((ymax-ymin)+2*padding))
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    if rangex == 0 or rangey == 0:
        raise InvalidParticleError
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    img = np.zeros((rangey, rangex))
    for curCnt in contourCopy:
        for i in range(len(curCnt)):
            curCnt[i][0][0] -= xmin-padding
            curCnt[i][0][1] -= ymin-padding
        cv2.drawContours(img, [curCnt], -1, 255, -1)
        cv2.drawContours(img, [curCnt], -1, 255, 1)
    img = np.uint8(cv2.morphologyEx(img, cv2.MORPH_CLOSE, np.ones((3, 3))))
    return img, xmin, ymin, padding

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def imgToCnt(img, xmin, ymin, padding=0):
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    def getContour(img, contourMode):
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        if cv2.__version__ > '3.5':
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            contours, hierarchy = cv2.findContours(img, cv2.RETR_EXTERNAL, contourMode)
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        else:
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            temp, contours, hierarchy = cv2.findContours(img, cv2.RETR_EXTERNAL, contourMode)
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        if len(contours) == 0:   #i.e., no contour found
            raise InvalidParticleError
        elif len(contours) == 1:   #i.e., exactly one contour found
            contour = contours[0]
        else:       #i.e., multiple contours found
            contour = getLargestContour(contours)
            
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        return contour
    
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    def getLargestContour(contours):
        areas = []
        for contour in contours:
            areas.append(cv2.contourArea(contour))
        maxIndex = areas.index(max(areas))
        print(f'{len(contours)} contours found, getting the largest one. Areas are: {areas}, taking contour at index {maxIndex}')
        return contours[maxIndex]

    img = closeHolesOfSubImage(img)
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    contour = getContour(img, contourMode=cv2.CHAIN_APPROX_NONE)
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    for i in range(len(contour)):
        contour [i][0][0] += xmin-padding
        contour [i][0][1] += ymin-padding
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    return contour
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def getContourExtrema(contours):
    try:
        cnt = np.vstack(tuple(contours))
        xmin, xmax = cnt[:,0,:][:, 0].min(), cnt[:,0,:][:, 0].max()
        ymin, ymax = cnt[:,0,:][:, 1].min(), cnt[:,0,:][:, 1].max()        
    except IndexError: #i.e., not a list of contours was passed, but an individual contour. Hence, the above indexing does not work
        xmin, xmax = cnt[:, 0].min(), cnt[:, 0].max()
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        ymin, ymax = cnt[:, 1].min(), cnt[:, 1].max()
        
    xmin, xmax = int(round(xmin)), int(round(xmax))
    ymin, ymax = int(round(ymin)), int(round(ymax))
    
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    return xmin, xmax, ymin, ymax

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def getParticleCenterPoint(contour):
    img, xmin, ymin, padding = contoursToImg(contour)
    dist = cv2.distanceTransform(img, cv2.DIST_L2, 3)
    ind = np.argmax(dist)
    y = ind//dist.shape[1]-1
    x = ind%dist.shape[1]-1
    x += xmin
    y += ymin
    return x, y
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def loadFullimageFromDataset(dataset):
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    return cv2imread_fix(dataset.getImageName())
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def loadZValImageFromDataset(dataset):
    return cv2imread_fix(dataset.getZvalImageName(), cv2.IMREAD_GRAYSCALE)