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 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")
image = trim_borders(image,150)