class Tester:
def __init__(self):
self.explorer = Explorer(True)
def test(self):
file = open('resultsfile.txt', 'w')
i = 0
for file_name in os.listdir('labelme-luna16-Data/test'):
print(file_name)
image = cv2.imread(
f'labelme-luna16-Data/test/{file_name}/images/img.png', 0)
image_label = cv2.imread(
f'labelme-luna16-Data/test/{file_name}/label_viz.png')
label = cv2.imread(
f'labelme-luna16-Data/test/{file_name}/masks/label.png')
label = cv2.cvtColor(label, cv2.COLOR_BGR2GRAY)
ret, label = cv2.threshold(label, 20, 255, cv2.THRESH_BINARY)
boxes, mask = self.explorer.explore(image)
image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
if not boxes.size == 0:
for box in boxes:
cls = str(box[0])
x1 = int(box[1])
y1 = int(box[2])
x2 = int(box[3])
y2 = int(box[4])
image = cv2.rectangle(image, (x1, y1), (x2, y2),
(0, 0, 255), 1)
image = cv2.putText(image, cls[:5], (x1, y1),
cv2.FONT_HERSHEY_SIMPLEX, 0.5,
(0, 0, 255))
file.write(file_name + '\n')
file.write(str(int(boxes.size // 5)) + '\n')
if not boxes.size == 0:
for box in boxes:
cls = str(box[0])
x1 = int(box[1])
y1 = int(box[2])
w = int(box[3] - box[1])
h = int(box[4] - box[2])
file.write(f'{x1} {y1} {w} {h} {cls}\n')
# image = cv2.resize(image,None,fx=2,fy=2)
cv2.imshow('JK', image)
cv2.imshow('mask', mask)
cv2.imshow('labelviz', image_label)
cv2.imwrite(f'biaozhu/{i}.png', label)
cv2.imwrite(f'yuche/{i}.png', mask)
i += 1
#if cv2.waitKey(1) == ord('c'):
#break
file.close()
def sample(self, keep=None):
"""
Sample the posterior and return the weighted average result of the
Model.
The more sensible result of this method is a SampleList which contains
samples taken from the posterior distribution.
Parameters
----------
keep : None or dict of {int:float}
Dictionary of indices (int) to be kept at a fixed value (float)
Hyperparameters follow model parameters
The values will override those at initialization.
They are only used in this call of fit.
"""
if keep is None:
keep = self.keep
fitlist = self.makeFitlist(keep=keep)
self.initWalkers(fitlist=fitlist)
for eng in self.engines:
eng.walkers = self.walkers
self.distribution.ncalls = 0 # reset number of calls
self.plotData()
if self.verbose >= 1:
print("Fit", ("all" if keep is None else fitlist), "parameters of")
print(" ", self.model._toString(" "))
print("Using a", self.distribution, "with")
np = self.model.npchain
for name, hyp in zip(self.distribution.PARNAMES,
self.distribution.hyperpar):
print(" %-7.7s " % name, end="")
if np in fitlist:
print("unknown")
else:
print(" (fixed) ", hyp.hypar)
# print( "%7.2f (fixed)" % hyp.hypar )
np += 1
print("Moving the walkers with")
for eng in self.engines:
print(" ", eng)
if self.verbose >= 2:
print("Iteration logZ H LowL npar parameters")
explorer = Explorer(self)
self.logZ = -sys.float_info.max
self.info = 0
logWidth = math.log(1.0 -
math.exp((-1.0 * self.discard) / self.ensemble))
if self.optionalRestart():
logWidth -= self.iteration * (1.0 * self.discard) / self.ensemble
while self.iteration < self.getMaxIter():
# find worst walker(s) in ensemble
worst = self.findWorst()
worstLogW = logWidth + self.walkers[worst[-1]].logL
# Keep posterior samples
self.storeSamples(worst, worstLogW - math.log(self.discard))
# Update Evidence Z and Information H
logZnew = numpy.logaddexp(self.logZ, worstLogW)
self.info = (math.exp(worstLogW - logZnew) * self.lowLhood +
math.exp(self.logZ - logZnew) *
(self.info + self.logZ) - logZnew)
self.logZ = logZnew
if self.verbose >= 3 or (self.verbose >= 2
and self.iteration % 100 == 0):
kw = worst[0]
pl = self.walkers[kw].parlist[self.walkers[kw].fitIndex]
np = len(pl)
print(
"%8d %8.1f %8.1f %8.1f %6d " %
(self.iteration, self.logZ, self.info, self.lowLhood, np),
fmt(pl))
self.plotResult(worst[0], self.iteration)
self.samples.weed(self.maxsize) # remove overflow in samplelist
self.copyWalker(worst)
# Explore the copied walker(s)
explorer.explore(worst, self.lowLhood, fitlist)
# Shrink the interval
logWidth -= (1.0 * self.discard) / self.ensemble
self.iteration += 1
self.optionalSave()
# End of Sampling
self.addEnsembleToSamples(logWidth)
# Calculate weighted average and stdevs for the parameters;
self.samples.LogZ = self.logZ
self.samples.info = self.info
self.samples.normalize()
# put the info into the model
self.model.parameters = self.samples.parameters
self.model.stdevs = self.samples.stdevs
if self.verbose >= 1:
self.report()
return self.samples.average(self.xdata)
