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Author: pmascalchi

Recipes/TransformImages/StackReg_ImageAlignment.py

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+# -------- Activate virtual environment -------------------------
+import os
+import ctypes
+import sys
+from pathlib import Path
+
+def search_activation_path():
+    for i in range(5):
+        final_path = str(Path(__file__).parents[i]) + '\\env\\Scripts\\activate_this.py'
+        if os.path.exists(final_path):
+            return final_path
+    return ''
+
+activate_path = search_activation_path()
+if os.path.exists(activate_path):
+    exec(open(activate_path).read(), {'__file__': activate_path})
+    print(f'Aivia virtual environment activated\nUsing python: {activate_path}')
+else:
+    error_mess = f'Error: {activate_path} was not found.\n\nPlease check that:\n' \
+                 f'   1/ The \'FirstTimeSetup.py\' script was already run in Aivia,\n' \
+                 f'   2/ The current python recipe is in one of the "\\PythonEnvForAivia\\" subfolders.'
+    ctypes.windll.user32.MessageBoxW(0, error_mess, 'Error', 0)
+    sys.exit(error_mess)
+# ---------------------------------------------------------------
+
+from magicgui import magicgui
+from skimage.io import imread, imsave
+import numpy as np
+from pystackreg import StackReg
+from pystackreg.util import to_uint16
+import imagecodecs
+
+# Manual input parameters (only used if 'use' is True below)
+noGUI_params = {'use': False,
+                'reg_type': 'Rigid Body',
+                'reg_method': 'previous'
+                }
+
+"""
+Performs a 2D registration for timelapses, using PyStackReg. No parameters available (default ones only).
+Methods:
+- Previous = Use previous image to calculate registration
+- First = First timepoint is used as the fixed reference.
+
+Documentation: https://pypi.org/project/pystackreg/
+Paper for citation available at the bottom of page above.
+
+Requirements
+------------
+scikit-image
+numpy
+imagecodecs
+pystackreg
+PySide2
+magicgui
+
+Parameters
+----------
+Input:
+    Channel/image in Aivia to be aligned
+  
+Output
+------
+New channel with registered images
+"""
+
+reg_methods = {
+    'Previous image is the reference (original StackReg ImageJ plugin)': 'previous',
+    'First image is the reference': 'first',
+    'Mean of all images is the reference': 'mean'
+    }
+reg_types = {
+    'Translation only': StackReg.TRANSLATION,
+    'Rigid Body (translation + rotation)': StackReg.RIGID_BODY,
+    'Affine (translation + rotation + scaling + shearing)': StackReg.AFFINE,
+    'Bilinear (non-linear transformation; does not preserve straight lines)': StackReg.BILINEAR
+}
+
+# [INPUT Name:inputRawImagePath Type:string DisplayName:'Unregistered stack']
+# [OUTPUT Name:resultPath Type:string DisplayName:'Registered stack']
+def run(params):
+    global reg_methods, reg_types
+
+    rawImageLocation = params['inputRawImagePath']
+    resultLocation = params['resultPath']
+    calibration = params['Calibration']
+    tCount = int(params['TCount'])
+    zCount = int(params['ZCount'])
+
+    if tCount < 2:
+        show_error(f'Error: detected dimensions do not contain time. (t={tCount})')
+    if zCount > 1 and tCount > 1:
+        show_error(f'Error: detected dimensions contain time and depth. This script is for 2D only. (t={tCount}, z={zCount})')
+
+    # Checking existence of temporary files (individual channels)
+    if not os.path.exists(rawImageLocation):
+        show_error(f'Error: {rawImageLocation} does not exist')
+
+    # Parsing calibration string
+    # calib_values = calibration[calibration.find(':') + 2:].split(', ')
+    # calib_indiv_values = [single.split(' ') for single in calib_values]
+
+    # pix_res_XY = float(calib_indiv_values[0][0])
+    # pix_res_Z = float(calib_indiv_values[2][0])
+
+    # Loading input image
+    raw_npimg = imread(rawImageLocation)
+    raw_dims = np.asarray(raw_npimg.shape)
+    print('-- Input dimensions (expected T, Z, Y, X): ', raw_dims, ' --')
+
+    # Preparing output
+    final_img = np.zeros(raw_npimg.shape).astype(raw_npimg.dtype)
+
+    # Check manual inputs
+    if noGUI_params['use']:
+        reg_type = noGUI_params['reg_type']
+        reg_method = noGUI_params['reg_method']
+
+    else:  # Choose csv/xlsx table (Aivia format) with GUI
+        gui.called.connect(lambda x: gui.close())
+        gui.show(run=True)
+
+        # Parameters collected from the GUI
+        reg_type = reg_types[gui.reg_typ.value]
+        reg_method = reg_methods[gui.reg_meth.value]
+
+    # Prepare parameters for registration
+    sr = StackReg(reg_type)
+
+    # Register 2D timelapse
+    out_npimg = to_uint16(sr.register_transform_stack(raw_npimg, reference=reg_method))          # 16-bit
+
+    # Formatting result array
+    print(raw_npimg.dtype)
+    if raw_npimg.dtype is np.dtype('uint8'):
+        final_img[...] = out_npimg[...]
+        print('Note: Output converted from 16-bit to 8-bit.')
+    else:
+        final_img = out_npimg
+
+    # Save result
+    imsave(resultLocation, final_img)
+
+
+@magicgui(layout='vertical',
+          reg_typ={'label': 'Registration type: ', 'choices': reg_types.keys()},
+          reg_meth={'label': 'Registration reference: ', 'choices': reg_methods.keys()},
+          call_button="Continue")
+def gui(reg_typ=[*reg_types][0], reg_meth=[*reg_methods][0]):
+    pass
+
+
+def show_error(message):
+    ctypes.windll.user32.MessageBoxW(0, message, 'Error', 0)
+    sys.exit(message)
+
+
+if __name__ == '__main__':
+    params = {}
+    params['inputRawImagePath'] = r'D:\PythonCode\_tests\2D-TL-toalign_8b.aivia.tif'
+    # params['inputRawImagePath'] = r'D:\Data\__Leica Images\UK\Alain Stewart\23-03-13 Tara Spires-Jones' \
+    #                               r'\211027_SD001_16_ba17_b1_1_Dapi_405_spyh_488_T22_555_PSD95_647.lif - Series003-1.tif'
+    params['resultPath'] = r'D:\PythonCode\_tests\2D-TL-aligned.tif'
+    params['Calibration'] = '  : 0.4 microns, 0.4 microns, 1.2 microns, 2 seconds'
+    params['TCount'] = 16
+    params['ZCount'] = 1
+
+    run(params)
+
+# CHANGELOG
+# v1_00 PM: - Registration with default parameters of PyStackReg
+# v1_01 PM: - New virtual env code for auto-activation