from segmentation import process_image from aggregate import aggregateData import skimage.io import matplotlib.pyplot as plt image = skimage.io.imread( "Data/Unearthed Cape Town/De Beers Particle Size Challenge/ParticleSegmentationImages/original3.png" ) truth = skimage.io.imread( "Data/Unearthed Cape Town/De Beers Particle Size Challenge/ParticleSegmentationImages/original3.png" ) # trim borders border_width = 50 truth = truth[:, border_width:-border_width] image, labelledFeat = process_image(image) colorData, sizeData = aggregateData(image, labelledFeat) fig, (ax1, ax2, ax3) = plt.subplots(3, 1, sharex=True, sharey=True, figsize=(30, 10)) ax1.imshow(image, cmap=plt.cm.gray) ax1.imshow(truth, cmap=plt.cm.gray) ax1.imshow(labelledFeat, cmap=plt.cm.gray) ax1.axis("off") ax2.axis("off") ax3.axis("off")
import os import time import scipy.ndimage as ndi DIR = "Data/Unearthed Cape Town/De Beers Particle Size Challenge/Originals" TRUTHDIR = "Data/Unearthed Cape Town/De Beers Particle Size Challenge/Truth" for file in os.listdir(DIR): if file.endswith(".png"): FILEPATH = DIR+"/"+file TRUTHPATH = TRUTHDIR+"/truth"+file[-5:] print(FILEPATH) image = skimage.io.imread(FILEPATH) truth = skimage.io.imread(TRUTHPATH) border_width = 150 image = trim_borders(image,border_width) image, labelledFeat, procTime = process_image(image) colorData, sizeData, featTime = aggregateData(image,labelledFeat) truth = trim_borders(truth, border_width) # filter truth image truth2 = np.zeros_like(truth) truth2[truth < 100] = 1 truth2[truth > 100] = 0 truth_particles, truth_features = ndi.label(truth2) view = viewer.viewerClass(image, labelledFeat, colorData, sizeData) view.view(selectSize=True, sizeValue=200) image = skimage.io.imread("Data/Unearthed Cape Town/De Beers Particle Size Challenge/Originals/original1.png")