def remove_small_clusters(sample,
min_cluster_size = 7000,
save_folder_tag="labeled"):
for ranges in chunks(range(0, 2119), 100):
ranges = ranges if ranges[1]<2120 else [2000, 2120]
print("processing: ", ranges)
masks3d = dm.assemble_3d_database(sample, ranges)
masks3d_filtered = ds.find_big_ones_clusters(masks3d,
min_cluster_length=min_cluster_size,
min_cluster_order=None)
masks3d_filtered = masks3d_filtered.astype(np.uint8)
for n, mask2d in zip(range(*ranges), masks3d_filtered):
dm.save_tif(mask2d, sample+"_"+save_folder_tag, dm.generate_tif_file_name(n))
def preview(sample_name, n, contrast=True, server=True):
if server:
img = dm.get_img2d_from_server(sample_name, n)
else:
file_name = dm.generate_tif_file_name(n, True)
img = dm.get_img2d_from_database(file_name,
folder_name="crop_"+sample_name)
if contrast:
clip_limit = 0.1
img = exposure.equalize_adapthist(img, clip_limit=clip_limit)
fig, ax = plt.subplots(figsize=(7, 7))
ax.imshow(img, cmap="gray")
dm.save_plot(fig, "sections", sample_name+"_"+str(n))
import numpy as np
from tqdm import tqdm
import configures
import data_manager as dm
if __name__ == "__main__":
params = configures.InputParameters()
sample = params.sample
coords = params.coords_2d
z_range = params.z_range
masks3d = dm.assemble_3d_database(sample, z_range)
image3d = dm.assemble_3d_server(sample, z_range)
print(len(masks3d))
print(len(image3d))
new_folder = "gecko_123438_plus"
for n, mask, image in zip(np.arange(*z_range), masks3d, image3d):
mask_wide = np.zeros(image.shape, dtype=bool)
mask_wide[coords] = mask
dm.save_tif(mask_wide, new_folder, dm.generate_tif_file_name(n))
print("count", count, " of ", max_iter, f" %: {count/max_iter:.2f}",
"| time (min): ", (time.time() - start_time) / 60)
count += 1
return t < 2
if __name__ == "__main__":
sample = "gecko_123438"
params = configures.InputParameters()
z_range = params.z_range
coords = params.coords_3d
start_time = time.time()
shot_names = [dm.generate_tif_file_name(n) for n in range(*z_range)]
image_3d = dm.assemble_3d_server(sample, z_range)
print(image_3d.shape)
image_3d = image_3d[coords]
print(image_3d.shape)
thrs1 = 0.000266 #np.percentile(image_3d.flat[::5], 95)
thrs2 = -1.54e-05 # np.percentile(image_3d.flat[::5], 35)
image_3d = median(image_3d, selem=ball(1))
print(image_3d.shape)
divs = np.array(image_3d.shape) // np.array([20, 40, 40])
image_3d = chunked_func(func=binarize_img,