def standardiser(d, path):
specname = d.split("/")[-2]
print(specname)
dest = d + "/3/maskedSamplesNormSize/"
dirMaker(dest, True)
d += path
imgs = glob(d + "*.png")
shapes = []
for i in imgs:
shape = cv2.imread(i).shape
shapes.append(shape)
maxShape = np.max(np.array(shapes), axis=0)
print(specname + " has max shape " + str(maxShape))
plate = np.zeros(maxShape).astype(np.uint8)
for i in imgs:
sampname = nameFromPath(i, 3)
img = cv2.imread(i)
if sampname.lower().find("c") > -1:
pass #img = cv2.rotate(img, cv2.ROTATE_180)
s = img.shape
imgR = plate.copy()
imgR[:s[0], :s[1], :] = img
cv2.imwrite(dest + sampname + ".png", imgR)
def getFiles(s, size, dataHome):
# copy the tif files from HPC
print("Starting " + s)
path = '/Volumes/USB/' + s + str(size) + '/tifFiles/'
dirMaker(path)
imgs = sorted(glob(dataHome + s + '/3/tifFiles/*.tif'))
for i in imgs:
print(" Copying " + nameFromPath(i))
os.system('scp -r ' + i + ' ' + path)
print("Finished " + s)
def processErrors(dataHome, size, datasrc, plot):
'''
Get the per feature error as a csv, save it and provide some quick stats
'''
annosrc = dataHome + "landmark/"
names = nameFromPath(sorted(glob(annosrc + "S*")))
# get the features and calculate the error between them
featureErrors = getFeatureError(dataHome, size, datasrc, plot)
# save as a csv
dirMaker(dataHome + "landmark/")
featureErrors.to_csv(dataHome + "landmark/" + datasrc + ".csv")
def pushFiles(s, size, dataHome):
# send the featExtracted files to
name = s.split("/")[-2]
print("Starting " + name)
dataDest = dataHome + "SpecimenSections/" + name + "/"
dirMaker(dataDest)
feats = s + str(size) + "/FeatureSectionsFinal/linearSect/"
os.system('nohup scp -r ' + feats + '* ' + dataDest + " &")
while True:
dirsMade = len(os.listdir(dataDest))
dirsThere = len(os.listdir(feats))
print(name + ": " + str(dirsMade) + "/" + str(dirsThere))
time.sleep(20)
if dirsMade == dirsThere:
break
print("Finished " + s)
import numpy as np
from glob import glob
import matplotlib.pyplot as plt
src = '/Volumes/USB/H653A_11.3/3/'
imgsrc = src + "SegmentationManual/"
model = "ResNet101"
model = "VGG16"
segsrc = src + "Segmentations" + model + "/"
imgDest = src + "SegmentationsEvals" + model + "/"
masks = sorted(glob(segsrc + "*.png"))
imgs = sorted(glob(imgsrc + "*anno*.png"))
dirMaker(imgDest)
maskToUse = getMatchingList(imgs, masks)
for n, (m, i) in enumerate(zip(maskToUse, imgs)):
if m is None:
continue
name = nameFromPath(i, 3)
print(name + " Processing")
img = cv2.imread(i)
mask = cv2.imread(m)