def run_all_embryos():
for embryo in TRAIN_EMBRYOS.keys():
tiff = fm.get_tiff_file(embryo)
rf = extract_region_with_cells(tiff)
features = rf.extract_features()
try:
mc = Multiclusterer(features)
labels_count = mc.count_labels()
fm.save_results(
labels_count,
file_name="Labels_counts_multiclustering_emb{}".format(embryo),
timestamped=True)
mc.plot_results()
except:
try:
features = features.drop('centroid_3D', axis=1)
mc = Multiclusterer(features)
labels_count = mc.count_labels()
fm.save_results(
labels_count,
file_name="Labels_counts_multiclustering_emb{}".format(
embryo),
timestamped=True)
except Exception as e:
print(repr(e))
plt.show()
print("Data processing is now finished")
print("I am ready to save the results.")
file_name = input(
'''Which file name do you want to use (press enter to use "classification_results") ?'''
)
return file_name
if __name__ == '__main__':
logging.basicConfig(level=logging.INFO)
print("Hello my friend,")
# get a file from its embryo code
#tiff = fm.get_tiff_file(EMBRYO)
#get a file from path
path = get_path_input()
tiff = fm.get_tiff_file(path)
# Extract a RegionFrame including all the regions 3D of the image
rf = extract_region_with_cells(tiff, filter=100)
# Extract features from the regions
features = rf.extract_features()
# Classify features using tail filtering, PCA and MeanShift
f_label = classify(features)
file_name = get_res_file_name()
if file_name == "":
file_name = "classification_results"
save_results_classif(features, f_label, file_name=file_name)
#Plot results
viewer = MultiLayerViewer(tiff)
viewer.plot_imgs(features=f_label)
viewer.show()
"Embryo {} : {} points (final)".format(
emb, len(f_data)))
sns.distplot(mean_int, ax=ax[i][3])
# plot the threshold with a red cross
if thresh is not None:
ax[i][1].plot(thresh, 0, marker="x", color='r')
ax[i][3].plot(thresh, 0, marker="x", color='r')
i += 1
plt.tight_layout()
plt.show()
if __name__ == '__main__':
dir = "features"
files = fm.get_files(dir=dir)
dataset = {}
for file in files:
emb = int(file.split('emb')[1].split('.')[0])
print(file)
dataset[emb] = fm.get_data_from_file(file)
break
tiff = fm.get_tiff_file(emb)
for ds in dataset.values():
f_label = classify(ds)
viewer = MultiLayerViewer(tiff)
viewer.plot_imgs(features=f_label)
plt.show()
else:
ax.imshow(image, cmap=cmap)
if __name__ == '__main__':
"""FILE_NAME = "C10DsRedlessxYw_emb11_Center_Out.tif"
PATH_TO_CURRENT_DIR = os.path.abspath(os.path.dirname(__file__))
PATH_TO_ROOT_DIR = os.path.normpath(os.path.join(PATH_TO_CURRENT_DIR, '..'))
sys.path.append(PATH_TO_ROOT_DIR)
DATA_PATH = os.path.join(PATH_TO_ROOT_DIR, 'data')
EMB_PATH = os.path.join(DATA_PATH, 'embryos')
file_path = os.path.join(EMB_PATH, FILE_NAME)"""
emb = fm.get_tiff_file(8)
ch1 = emb[:, :, :, 0]
lbl_img = label_filter(ch1[0])[0]
props = [
region_properties(label_image=label_filter(img, filter=100)[0],
image=img,
properties=[
'extent', 'max_intensity', 'area',
"mean_intensity", "bbox"
]) for img in ch1
]
viewer = MultiLayerViewer(emb, channel=0)
viewer.plot_imgs(properties=props)
plt.show()
# display_file(file_path)
"""