def __init__(self,
axis,
one_axis,
two_axis,
corr_axis,
imgSrc,
rootPath,
figure=None,
load_callback=None):
"""initialization function which will plot the imagematrix passed in and set the bounds according the bounds specified by extent
keywords)
axis)the matplotlib axis to plot the image into
one_axis) the matplotlib axis to plot the cutout of the fixed point when using the corresponding point functionality
two_axis) the matplotlib axis to plot the cutout of the point that should be moved when using the corresponding point functionality
corr_axis) the matplotlib axis to plot out the matrix of correlation values found when using the corresponding point functionality
imagefile) a string with the path of the file which contains the full resolution image that should be used when calculating the corresponding point funcationality
currently the reading of the image is using PIL so the path specified must be an image which is PIL readable
imagematrix) a numpy 2d matrix containing a low resolution varsion of the full resolution image, for the purposes of faster plotting/memory management
extent) a list [minx,maxx,miny,maxy] of the corners of the image. This will specify the scale of the image, and allow the corresponding point functionality
to specify how much the movable point should be shifted in the units given by this extent. If omitted the units will be in pixels and extent will default to
[0,width,height,0].
"""
#define the attributes of this class
self.axis = axis
self.one_axis = one_axis
self.two_axis = two_axis
self.corr_axis = corr_axis
#initialize the images for the various subplots as None
self.oneImage = None
self.twoImage = None
self.corrImage = None
self.imgSrc = imgSrc
self.imgCollection = ImageCollection(rootpath=rootPath,
imageSource=imgSrc,
axis=self.axis)
(x, y) = imgSrc.get_xy()
bbox = imgSrc.calc_bbox(x, y)
self.imgCollection.set_view_home()
self.imgCollection.load_image_collection(load_callback=load_callback)
self.maxvalue = 512
self.currentPosLine2D = Line2D([x], [y],
marker='o',
markersize=7,
markeredgewidth=1.5,
markeredgecolor='r',
zorder=100)
self.axis.add_line(self.currentPosLine2D)
self.axis.set_title('Mosaic Image')
self.fig = figure
self.update_pos_cursor()
def run(self):
full_executor = ThreadPoolExecutor(max_workers=self.max_workers)
# Split available worker threads between training data preparation and post
# processing (if possible).
if self.max_workers > 1:
workers = min(self.max_workers - 1,
round(self.max_workers * self.train_workers_ratio))
train_executor = ThreadPoolExecutor(max_workers=workers)
post_executor = ThreadPoolExecutor(max_workers=self.max_workers -
workers)
else:
train_executor = full_executor
post_executor = full_executor
images = ImageCollection(self.images, executor=full_executor)
images.prune_corrupt_images()
total_images = len(images)
if total_images == 0:
print('No image files to process.')
return
elif total_images < self.clusters:
print(
'{} clusters were requested but only {} images are there. Reducing clusters to {}.'
.format(self.clusters, total_images, total_images))
self.clusters = total_images
if self.clusters > 1:
clusters = images.make_clusters(number=self.clusters)
else:
clusters = [images]
detector = AutoencoderSaliencyDetector(self.patch_size,
stride=self.detector_stride,
hidden=self.latent_size)
jobs = []
for i, cluster in enumerate(clusters):
cluster.set_executor(train_executor)
print("Cluster {} of {}".format(i + 1, self.clusters))
threshold = self.process_cluster(detector, cluster)
jobs.extend([
post_executor.submit(self.postprocess_map, image, threshold)
for image in cluster
])
wait(jobs)
#tetra_fn='/home/carlos/PycharmProjects/margreet_code_review/rough_ave.tif'
#image_fn ='/home/carlos/PycharmProjects/margreet_code_review/hel4.pma'
tmp=Mapping(tetra_fn)
if 1:
root=os.path.normpath("N:/tnw/BN/CMJ/Shared/Margreet/20181203_HJ_training/#3.10_0.1mg-ml_streptavidin_50pM_HJC_G_movies")
name='hel4.pma'
else:
root=os.path.normpath("N:/tnw/BN/CMJ/Shared/Margreet/181218 - First single-molecule sample (GattaQuant)/RawData")
name='Spooled files.sifx'
image_fn=os.path.join(root,name)
imc = ImageCollection(tetra_fn, image_fn)
# better: give image directory, and search for specific name or extension
img = imc.get_image(1)
#tmp._tf2_matrix
#imc.mapping._tf2_matrix
#imc.pts_number # is too 19 for the pma
#import matplotlib.pyplot as plt; plt.imshow(imc.im_mean20_correct)
traces_fn=os.path.join(root,name[:-4]+'-P.traces')
Ncolours=2
if os.path.isfile(traces_fn):
with open(traces_fn, 'r') as infile:
Nframes = np.fromfile(infile, dtype = np.int32, count = 1).item()
Ntraces = np.fromfile(infile, dtype = np.int16, count = 1).item()
rawData = np.fromfile(infile, dtype = np.int16, count = Ncolours*Nframes * Ntraces)
