particleCharacterization.py 6.74 KB
Newer Older
JosefBrandt's avatar
JosefBrandt committed
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
#!/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
25
from copy import deepcopy
JosefBrandt's avatar
JosefBrandt committed
26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45

def getContourStatsWithPixelScale(cnt, pixelscale):
        long, short, longellipse, shortellipse, area = getContourStats(cnt)
        return long*pixelscale, short*pixelscale, longellipse*pixelscale, shortellipse*pixelscale, area*pixelscale**2

def getContourStats(cnt):
    ##characterize particle
        longellipse, shortellipse = np.nan, np.nan
        
        if cnt.shape[0] >= 5:       ##at least 5 points required for ellipse fitting...
            ellipse = cv2.fitEllipse(cnt)
            shortellipse, longellipse = ellipse[1]

        rect = cv2.minAreaRect(cnt)
        long, short = rect[1]
        if short>long:
            long, short = short, long
        
        area = cv2.contourArea(cnt)
    
46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195
        return long, short, longellipse, shortellipse, area

class ColorRangeHSV(object):
    def __init__(self, name, hue, hue_tolerance, min_sat, max_sat):
        self.name = name
        self.minHue = hue-hue_tolerance/2
        self.maxHue = hue+hue_tolerance/2
        self.minSat = min_sat
        self.maxSat = max_sat
    
    def containsHSV(self, hsv):
        hue = hsv[0]
        sat = hsv[1]
        
        if self.minHue <= hue <= self.maxHue and self.minSat <= sat <= self.maxSat:
            return True
        else:
            if self.name != 'white':
                return False
            else:
                if sat < 128 and hsv[2] > 70:
                    return True

class ColorClassifier(object):
    def __init__(self):
        hue_tolerance = 30
        self.colors = [ColorRangeHSV('yellow', 30, hue_tolerance, 30, 255),
                  ColorRangeHSV('blue', 120, hue_tolerance, 80, 255),
                  ColorRangeHSV('red', 180, hue_tolerance, 50, 255),
                  ColorRangeHSV('red', 0, hue_tolerance, 50, 255),
                  ColorRangeHSV('green', 70, hue_tolerance, 50, 255),
                  ColorRangeHSV('white', 128, 256, 0, 50)]
    
    def classifyColor(self, meanHSV):
        result = 'non-determinable'
        for color in self.colors:
            if color.containsHSV(meanHSV):
                result = color.name
                break
        
        return result

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

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]
    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
    
def contoursToImg(contours, padding=2):
    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))

    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

def imgToCnt(img, xmin, ymin, padding):
    def getSimpleContour(img):
        if cv2.__version__ > '3.5':
            contour, hierarchy = cv2.findContours(img, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE)
        else:
            temp, contour, hierarchy = cv2.findContours(img, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE)
        
        if len(contour)>1:
            raise NotConnectedContoursError
        return contour
    
    def getFullContour(img):
        if cv2.__version__ > '3.5':
            contour, hierarchy = cv2.findContours(img, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE)
        else:
            temp, contour, hierarchy = cv2.findContours(img, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE)
        
        if len(contour)>1:
            raise NotConnectedContoursError
        return contour
    
    contour = getSimpleContour(img)
    
    if len(contour[0]) < 5:
        contour = getFullContour(img)
    
    newContour = contour[0]
    for i in range(len(newContour )):
        newContour [i][0][0] += xmin-padding
        newContour [i][0][1] += ymin-padding
        
    return newContour 

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()
        ymin, ymax = cnt[:, 1].min(), cnt[:, 1].max()        
    return xmin, xmax, ymin, ymax

class NotConnectedContoursError(Exception):
    pass

if __name__ == '__main__':
    colors = {'white': (41, 25, 66),
              "red": (128, 121, 57),
              "red2": (23, 88, 49), 
              "yellow": (25, 121, 91),
              "pink": (11, 79, 51),
              "brown": (32, 38, 64),
              "green": (54, 99, 53)}
    classifier= ColorClassifier()
#    print(classifier.hsv)
    for name, mean in colors.items():
        print(name, classifier.classifyColor(mean))