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Added ability to bypass the manual selection of particles #14

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Added ability to bypass the manual selection of particles #14

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0eaefdc
Skip particle selection
coltenelkin Jan 23, 2024
71d0364
Handle greyscale images
coltenelkin Jan 23, 2024
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76 changes: 72 additions & 4 deletions PebbleCounts.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,6 +2,12 @@
# Developed by Ben Purinton (purinton[at]uni-potsdam.de)
# 04 April 2019
# See the help manual for instructions on use at: https://github.com/bpurinton/PebbleCounts
#
# 23 Jan 2024
# modifications by Colten Elkin to bypass the manual clicking selection process. Needed to streamline
# the processing of many images. Potential degredation in confidence in particle distribution if
# the skip_manual_clicking input is set to True. Contact via email provided on github.com/coltenelkin
# with questions

# =============================================================================
# Load the modules
Expand All @@ -24,9 +30,13 @@
import matplotlib.pyplot as plt
from matplotlib.path import Path
from shapely.geometry.polygon import Polygon
from datetime import datetime
from datetime import timedelta

# ignore some warnings thrown by sci-kit
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
warnings.filterwarnings("ignore")

# use a different backend for matplotlib on MacOS
from sys import platform as sys_pf
Expand Down Expand Up @@ -80,6 +90,9 @@
help="Canny filtering sigma value for edge detection. DEFAULT=2", default=2)
parser.add_argument("-resize", type=float,
help="Value to resize windows by should be between 0 and 1. DEFAULT=0.8", default=0.8)
parser.add_argument("-skip_manual_clicking", type=bool,
help="Value to optionally skip manually clicking good particles. A value of True assumes that all k-means areas are well-delineated particles. DEFAULT=False", default=False)

args = parser.parse_args()

# =============================================================================
Expand Down Expand Up @@ -107,6 +120,7 @@
sobel_th = args.sobel_th
input_resolution = args.input_resolution
subset = args.subset
skip_manual_clicking = args.skip_manual_clicking

# exit if there was no image supplied or the file doesn't exist
if im == None:
Expand Down Expand Up @@ -212,6 +226,7 @@
else:
bgr = cv2.imread(im)


# do strong nonlocal means denoising
print("\nNon-local means filtering of color image")
bgr = cv2.fastNlMeansDenoisingColored(bgr, None, first_nl_denoise, 1, 7, 21)
Expand Down Expand Up @@ -472,6 +487,10 @@
X = np.column_stack((a_blur_.reshape(-1, 1), b_blur_.reshape(-1, 1),
rgrid_scaled.reshape(-1, 1), cgrid_scaled.reshape(-1, 1)))

if np.isnan(X).any(): # test if a_blur, b_blur are NaNs (in this case the image would be colorless)
print('There is reason to believe that this image is singleband (greyscale).\nThis could affect the quality of k-means clustering...')
X = np.column_stack((rgrid_scaled.reshape(-1, 1), cgrid_scaled.reshape(-1, 1))) # if the image is greyscale, just use cgrid and rgrid

# run kmeans
print("Running k-means")
# dummy variables for looping
Expand Down Expand Up @@ -532,16 +551,22 @@

# eliminate any regions that are smaller than cutoff value
tmp, num = ndi.label(master_mask[:,:,0])
centroidSaverX = [] # Add a list of tuples of each of the kept centroids so we can just use all of them automatically.
centroidSaverY = []
for region in meas.regionprops(tmp):
grain_dil_ = morph.dilation(region.image, footprint=square(2)).astype(int)
grain_dil_ = np.pad(grain_dil_, ((1, 1), (1,1)), 'constant')
b_ = meas.regionprops(grain_dil_)[0].minor_axis_length
a_ = meas.regionprops(grain_dil_)[0].major_axis_length
centroidTuple = region.centroid
if b_ < float(cutoff) or a_ < float(cutoff):
idxs = region.coords
idxs = [tuple(i) for i in idxs]
for idx in idxs:
tmp[idx] = 0
else: # save centroids of all particles for later. This will be used if we don't click the good particles in the next section below
centroidSaverX = np.append(centroidSaverX, centroidTuple[0])
centroidSaverY = np.append(centroidSaverY, centroidTuple[1])
idx = np.where(tmp == 0)
master_mask[idx] = [0, 0, 0]

Expand All @@ -558,9 +583,10 @@
# instantiate coordinate class for storing the clicks
coords = func.select_grains()
# create a window, call it open and click through it
win_name = "KMeans ('r' see image, 'q' close)"
win_name = "KMeans ('r' see image, 'left-click' on all good particles, then 'q' close. Close before clicking to select all colored particles)" #
cv2.namedWindow(win_name, cv2.WINDOW_NORMAL)
cv2.setMouseCallback(win_name, coords.clicker, param=[img])
startNow = datetime.now() # secondary time variables for skipping manual clicking. Ignored if default value or if set to False
while cv2.getWindowProperty(win_name, 0) >= 0:
cv2.imshow(win_name, img)
cv2.moveWindow(win_name, 0, 0)
Expand All @@ -583,11 +609,16 @@
if k == ord('q') & 0xFF:
cv2.destroyAllWindows()
break
now = datetime.now() # test time. Want the window to stay up for 5 seconds even if we're skipping manual clicking
if skip_manual_clicking: # block that automatically moves onto next section if the user set the input to skip manual clicking
if now - startNow >= timedelta(seconds=5):
cv2.destroyAllWindows()
break

# get the region properties of clicked areas
print("Getting properties of clicked grains")
master_mask = master_mask[:,:,0]
if len(coords.clicks) != 0:
if len(coords.clicks) != 0: # clicked on good particles
master_mask, _ = ndi.label(master_mask)
labels = np.zeros(master_mask.shape).astype(np.uint8)
for click in coords.clicks:
Expand Down Expand Up @@ -625,8 +656,45 @@
if np.sum(labels) != 0:
all_labels[ulx:lrx, uly:lry] = all_labels[ulx:lrx, uly:lry] + labels

else:
pass
else: #if we didn't click on any particles, we assume that all particles are good and use their centroids as the click location
master_mask, _ = ndi.label(master_mask)
labels = np.zeros(master_mask.shape).astype(np.uint8) # initialize binary storage array same size as photo
for indexer in range(len(centroidSaverX)): # call the tuples from when we saved the region centroids
centroidX, centroidY = centroidSaverX[indexer], centroidSaverY[indexer] # grab centroid of n-th particle
# print(centroidX, centroidY)
if not master_mask[int(centroidX), int(centroidY)] == 0: # make sure the centroid isn't in the mask = 0 area
labels[master_mask==master_mask[int(centroidX), int(centroidY)]] = 1 # where the region around the centroid is all the same value, set the labeller to 1
labels, _ = ndi.label(labels)
for grain in meas.regionprops(labels):
# dilate the grains
grain_dil = morph.dilation(grain.image, footprint=square(2)).astype(int)
grain_dil = np.pad(grain_dil, ((1, 1), (1,1)), 'constant')
b = meas.regionprops(grain_dil)[0].minor_axis_length
a = meas.regionprops(grain_dil)[0].major_axis_length
# area of ellipse
y0, x0 = grain.centroid[0]+ulx, grain.centroid[1]+uly
orientation = grain.orientation - np.pi/2
phi = np.linspace(0,2*np.pi,50)
X = x0 + a/2 * np.cos(phi) * np.cos(-orientation) - b/2 * np.sin(phi) * np.sin(-orientation)
Y = y0 + a/2 * np.cos(phi) * np.sin(-orientation) + b/2 * np.sin(phi) * np.cos(-orientation)
tupVerts = list(zip(X, Y))
p = Path(tupVerts)
x, y = zip(*p.vertices)
poly = Polygon([(i[0], i[1]) for i in list(zip(x, y))])
# percent difference in area (misfit)
perc_diff_area = ((poly.area-grain.filled_area)/poly.area)*100
# append the grain
grains.append((y0, x0, b, a, orientation, grain.filled_area, poly.area, perc_diff_area))

# add the chosen grains to the ignore mask
labels[labels != 0] = 1
labels = labels.astype(bool)
ignore_mask[ulx:lrx, uly:lry] = np.logical_and(ignore_mask[ulx:lrx, uly:lry],
np.invert(labels))

# also add it to an "all labels" mask if there are any labels in it
if np.sum(labels) != 0:
all_labels[ulx:lrx, uly:lry] = all_labels[ulx:lrx, uly:lry] + labels

# =============================================================================
# Output a final plot, .csv of grain-size data, and label mask
Expand Down