MaitaiMaintenance update

HISTORY.rst

@@ -2,6 +2,11 @@
 History
 =======
 
+0.3.6 (2022-01-13)
+-------------------
+* Include Open3d library for point downsampling
+* Maintenance update
+
 0.3.5 (2022-01-03)
 -------------------
 * HotFix for folder selection on MacOS

requirements.txt

@@ -1,10 +1,10 @@
+click>=8.0.3
 setuptools>=57.1.0
-click>=8.0.1
-scikit-image>=0.18.2
-numpy>=1.21.0
+scikit-image>=0.19.1
+numpy>=1.20.0
 tifffile>=2021.7.2
-tqdm>=4.61.1
-imagecodecs>=2021.6.8
-opencv-python>=4.5.0
-scipy>=1.7.0
+tqdm>=4.62.3
+imagecodecs>=2021.8.26
+opencv-python>=4.5.4.60
+scipy>=1.7.3
 open3d>=0.14.1

setup.py

@@ -8,16 +8,16 @@
 with open('HISTORY.rst') as history_file:
     history = history_file.read()
 
-requirements = ['click>=8.0.1',
+requirements = ['click>=8.0.3',
                 'setuptools>=57.1.0',
-                'scikit-image>=0.18.2',
-                'numpy>=1.21.0',
+                'scikit-image>=0.19.1',
+                'numpy>=1.20.0',
                 'tifffile>=2021.7.2',
-                'tqdm>=4.61.1',
-                'imagecodecs>=2021.6.8',
-                'edt>=2.1.1',
-                'opencv-python>=4.5.0',
-                'scipy>=1.7.0']
+                'tqdm>=4.62.3',
+                'imagecodecs>=2021.8.26',
+                'opencv-python>=4.5.4.60',
+                'scipy>=1.7.3',
+                'open3d>=0.14.1']
 
 setup(author="Robert Kiewisz",
       author_email='[email protected]',

slcpy/build_graph.py

@@ -1,9 +1,7 @@
-from os import listdir, getcwd
+from os import getcwd, listdir
 from os.path import join
-from typing import Optional
 
 import click
-from click.core import Option
 import numpy as np
 from tifffile import tifffile
 from tqdm import tqdm
@@ -25,10 +23,6 @@
               default=6,
               help='Filter size matrix for denoising.',
               show_default=True)
-@click.option('-c', '--clean_graph',
-              default=10,
-              help='Clean graph from neighborhood points.',
-              show_default=True)
 @click.option('-d', '--down_sampling',
               default=0,
               help='Down-sample point cloud by the factor of...',
@@ -42,41 +36,60 @@
 def main(dir_path: str,
          output: str,
          filter: int,
-         clean_graph: int,
          save: str,
-         down_sampling: Optional[int]=None):
+         down_sampling: bool):
     """
     MAIN MODULE FOR EXTRACTING POINT CLOUD FROM SEMANTIC LABEL
 
     Args:
-        -dir / dir_path: Directory to the folder with image dataset.
-        -o / output: Output directory for saving transformed files.
-        -f / filter: filter size matrix for denoising
-        -c /clean_graph: Clean graph from neighborhood points.
-        -s / save: select type of saved data other numpy .npy or .csv
+        dir_path: Directory to the folder with image dataset.
+        output: Output directory for saving transformed files.
+        filter: filter size matrix for denoising
+        clean_graph: Clean graph from neighborhood points.
+        save: select type of saved data other numpy .npy or .csv
     """
 
     for file in tqdm(listdir(dir_path)):
         if file.endswith('.tif'):
-            img, coords = slcpy_graph(dir_path=join(dir_path, file),
-                                      filter_img=filter,
-                                      down_sampling=down_sampling)
+            if down_sampling:
+                img, coords_HD, coords_LD = slcpy_graph(dir_path=join(dir_path, file),
+                                                        filter_img=filter,
+                                                        down_sampling=down_sampling)
+            else:
+                img, coords_HD = slcpy_graph(dir_path=join(dir_path, file),
+                                             filter_img=filter,
+                                             down_sampling=down_sampling)
+
             if save == "numpy":
-                np.save(join(output, file[:-4]),
-                        coords)
+                np.save(join(output, file[:-4] + '_HD'),
+                        coords_HD)
+                if down_sampling:
+                    np.save(join(output, file[:-4] + '_LD'),
+                            coords_LD)
             elif save == "csv":
-                np.savetxt(join(output, str(file[:-4] + ".csv")),
-                           coords,
+                np.savetxt(join(output, str(file[:-4] + '_HD' + ".csv")),
+                           coords_HD,
                            delimiter=",")
+                if down_sampling:
+                    np.savetxt(join(output, str(file[:-4] + '_LD' + ".csv")),
+                               coords_LD,
+                               delimiter=",")
             elif save == "all":
                 tifffile.imwrite(join(output, file[:-4] + '_denoise.tif'),
                                  np.array(img, 'int8'))
                 np.save(join(output, file[:-4]),
-                        coords)
+                        coords_HD)
                 np.savetxt(join(output, str(file[:-4] + ".csv")),
-                           coords,
+                           coords_HD,
                            delimiter=",")
 
+                if down_sampling:
+                    np.save(join(output, file[:-4] + '_LD'),
+                            coords_LD)
+                    np.savetxt(join(output, str(file[:-4] + + '_LD' + ".csv")),
+                               coords_LD,
+                               delimiter=",")
+
 
 if __name__ == '__main__':
     main()

slcpy/build_semantic.py

@@ -1,7 +1,6 @@
-from os import mkdir, rename, listdir, getcwd
+from os import getcwd, listdir, mkdir, rename
 from os.path import isdir, join
 from shutil import rmtree
-from time import sleep
 
 import click
 import numpy as np
@@ -52,7 +51,7 @@
               default=None,
               help='Define size in pixels of output images in z.',
               show_default=True)
-@click.option('-a', '--filter_empty_patches',
+@click.option('-f', '--filter_empty_patches',
               default=False,
               help='If True only images containing any data are saved.',
               show_default=True)
@@ -75,22 +74,22 @@ def main(dir_path,
     MAIN MODULE FOR COMPOSING SEMANTIC LABEL FROM GIVEN POINT CLOUD
 
     Args:
-        -dir / dir_path: Directory to the folder with image dataset.
-        -o / output: Output directory for saving transformed files.
-        -m / build_mask: Define if the semantic mask should be build and saved.
-        -px / pixel_size: Pixel size for all images. Note that if images has
+        dir_path: Directory to the folder with image dataset.
+        output: Output directory for saving transformed files.
+        build_mask: Define if the semantic mask should be build and saved.
+        pixel_size: Pixel size for all images. Note that if images has
             different pixel size set to None to automatically calculate it
             for each image.
-       -d / circle_size: Size of drawn circle in Angstrom.
-       -l / multi_classification: If True as an output each line is drawn
+        circle_size: Size of drawn circle in Angstrom.
+        multi_classification: If True as an output each line is drawn
             with unique label.
-       -t / pretrim_mask: If True the image mask will be trimmed before
+        pretrim_mask: If True the image mask will be trimmed before
             building label
             mask. It's helpful for big files to speed up computation.
-       -xy / trim_size_xy: Final XY dimension of output images.
-       -z / time_size_z: Final Z dimension of output images.
-       -a / filter_empty_patches: Use whole image for trimming
-       -s / stride: stride for patch step size with overlay
+        trim_size_xy: Final XY dimension of output images.
+        time_size_z: Final Z dimension of output images.
+        filter_empty_patches: Use whole image for trimming
+        stride: stride for patch step size with overlay
     """
 
     if isdir(output):
@@ -126,7 +125,7 @@ def main(dir_path,
 
     for file in batch_iter:
         img_name = file
-        mask_name = file[:-4] + r'_mask.tif'
+        mask_name = file[:-4] + '_mask.tif'
         image_counter += 1
 
         if file.endswith('.tif'):
@@ -153,15 +152,15 @@ def main(dir_path,
                                      np.array(label_mask, 'int8'))
             else:
                 if filter_empty_patches:
-                    idx = trim_images(image=image, 
+                    idx = trim_images(image=image,
                                       label_mask=label_mask,
                                       trim_size_xy=trim_size_xy,
                                       trim_size_z=trim_size_z,
                                       multi_layer=multi_classification,
                                       output=output,
                                       image_counter=idx)
                 else:
-                    idx = trim_to_patches(image=image, 
+                    idx = trim_to_patches(image=image,
                                           label_mask=label_mask,
                                           trim_size_xy=trim_size_xy,
                                           trim_size_z=trim_size_z,

slcpy/main.py

@@ -30,6 +30,7 @@ def trim_label_mask(points: np.ndarray,
     image_trim = image[int(min_z):int(max_z),
                        int(min_y):int(max_y),
                        int(min_x):int(max_x)]
+
     label_mask_trim = label_mask[int(min_z):int(max_z),
                                  int(min_y):int(max_y),
                                  int(min_x):int(max_x)]
@@ -44,19 +45,19 @@ def trim_label_mask(points: np.ndarray,
 def slcpy_semantic(dir_path: str,
                    mask: bool,
                    pixel_size=None,
-                   circle_size=125,
+                   circle_size=250,
                    multi_layer=False,
-                   trim_mask=True):
+                   trim_mask=False):
     """
     MODULE TO LOAD 3D .tif FILES WITH CORRESPONDING .am FILES
 
     Args:
         dir_path: path direction of the input file *.tif
-        mask: If True 
+        mask: If True semantic mask is created
         pixel_size: pixel size in Angstrom
         circle_size: size of a circle the label mask in Angstrom
-        trim_mask: True/False statement for trimming input data
         multi_layer: single, or unique value for each lines
+        trim_mask: If True input data are trimed to the point cloud
     """
     print(" Converting image {}".format(dir_path))
     img = ImportDataFromAmira(
@@ -136,15 +137,14 @@ def slcpy_stitch(dir_path: str,
 
 def slcpy_graph(dir_path: str or np.ndarray,
                 filter_img: int,
-                down_sampling: int):
+                down_sampling: bool):
     """
     MODULE TO BUILD POINT CLOUD FROM 3D .tiff FILES
 
     Args:
         dir_path: Path direction of the input file *.tif with semantic masks.
-        filter_img: Filter size for cleaning lonely pixels.
-        clean_graph: Min number of pixels between each coord can be picked
-        down_sampling: Number of downsample iterations.
+        filter_img: Remove object that are smaller then given pixel size.
+        down_sampling: If True downsampling is perform on build point cloud.
     """
 
     img = ImportSemanticMask(src_tiff=dir_path)

slcpy/stitch_images.py

@@ -45,12 +45,12 @@ def main(dir_path: str,
     Main module for stitch individual images into montaged image
 
     Args:
-        -dir / dir_path: Directory to the folder with image dataset.
-        -o / output: Output directory for saving transformed files.
-        -m / mask: Indicate if stitched images are mask or images.
-        -pf / prefix: if not None, indicate additional file prefix.
-        -b / binary: If True transform date to binary format.
-        -dt /dtype: Data format type.
+        dir_path: Directory to the folder with image dataset.
+        output: Output directory for saving transformed files.
+        mask: Indicate if stitched images are mask or images.
+        prefix: if not None, indicate additional file prefix.
+        binary: If True transform date to binary format.
+        dtype: Data format type.
     """
 
     stitched_image = slcpy_stitch(dir_path,

slcpy/utils/import_data.py

@@ -13,11 +13,12 @@
 
 class ImportDataFromAmira:
     """
-    Class module to load 3D .tif file
+    MAIN CLASS TO HANDLE 3D .TIF AND .AM DATA
 
     Args:
         src_tiff: source of the 3D .tif file
         src_am: source of the spatial graph for corresponding 3D .tif
+        mask: If True output semnantic mask
         pixel_size: numeric value of pixel size
     """
 
@@ -205,13 +206,12 @@ def get_points(self):
 
 class ImportSemanticMask:
     """
-    Class module to load 3D .tif file
+    MAIN MODULE TO LOAD SEMANTIC MASK
 
     Args:
         src_tiff: source of the 3D .tif file
         filter_small_object: Filter size to remove small object .
         clean_close_point: If True, close point will be removed.
-
     """
 
     def __init__(self,
@@ -261,12 +261,12 @@ def find_maximas(self,
 
         x, y, z = [], [], []
         sk_image = skeletonize_3d(denoise_img)
-        
+
         z_iter = tqdm(range(denoise_img.shape[0]),
                       'Building point cloud..',
                       total=denoise_img.shape[0],
                       leave=False)
-        
+
         """ Compute euclidean transformation for labels and build point cloud """
         for i in z_iter:
             picked_maxima = peak_local_max(sk_image[i, :],
@@ -282,6 +282,9 @@ def find_maximas(self,
         if down_sampling:
             pcd = o3d.geometry.PointCloud()
             pcd.points = o3d.utility.Vector3dVector(coordinates)
-            coordinates = np.asarray(pcd.voxel_down_sample(voxel_size=25).points)
+            coordinates_ds = np.asarray(
+                pcd.voxel_down_sample(voxel_size=25).points)
+
+            return denoise_img, coordinates, coordinates_ds
 
         return denoise_img, coordinates

slcpy/utils/interpolation.py

@@ -5,7 +5,7 @@ def interpolation_1D(start: int,
                      stop: int,
                      max_len: int):
     """
-    Module to build a continues segment in given 3D frame
+    1D INTERPOLATION FOR BUILDING SEMANTIC MASK
 
     Args:
         start: 1D single coordinate to start interpolation
@@ -25,7 +25,7 @@ def interpolation_1D(start: int,
 
 def interpolation_3D(points: np.ndarray):
     """
-    Module: interpolation_3D
+    3D INTERPOLATION FOR BUILDING SEMANTIC MASK
 
     Args:
         points: numpy array with points belonging to individual segments given