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pmascalchipmascalchi
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New recipe
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‎Recipes/ProcessImages/Arithmetics_SingleChannel.py‎

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import os.path
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import sys
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import numpy as np
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from tifffile import imread, imsave
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import ctypes
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"""
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Various arithmetics to be applied to one channel.
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Requirements
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------------
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numpy (comes with Aivia installer)
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scikit-image (comes with Aivia installer)
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tifffile (installed with scikit-image)
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Parameters
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----------
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Input Image : Aivia channel
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Input channel to use for the processing.
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Operation : int
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Number-coded arithmetics action (0=multiply, 1=divide, 2=add, 3=subtract).
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Threshold : int
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Grayvalue for the arithmetics.
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Returns
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-------
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Aivia objects
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Result of the process.
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"""
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# [INPUT Name:inputImagePath Type:string DisplayName:'Input Image']
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# [INPUT Name:value Type:int DisplayName:'Value' Default:0 Min:0 Max:65535]
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# [INPUT Name:actionType Type:int DisplayName:'Operation (0=multiply, 1=divide, 2=add, 3=subtract)' Default:0 Min:0 Max:65535]
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# [OUTPUT Name:resultImagePath Type:string DisplayName:'Processed Image']
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def run(params):
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image_location = params['inputImagePath']
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result_location = params['resultImagePath']
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operation_type = int(params['actionType'])
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value = float(params['value'])
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tCount = int(params['TCount'])
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zCount = int(params['ZCount'])
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if not os.path.exists(image_location):
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print(f'Error: {image_location} does not exist')
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return
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image_data = imread(image_location)
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d_type = image_data.dtype
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dims = image_data.shape
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output_data = np.empty(image_data.shape, dtype=d_type)
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axes = ''
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# 3D+T
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if tCount > 1 and zCount > 1:
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print(f"Applying to 3D+T case with dims: {image_data.shape}")
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for t in range(0, dims[0]):
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output_data[t, :, :, :] = process_data(image_data[t, :, :, :], operation_type, value).astype(d_type)
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axes = 'TZYX'
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# 2D +/- T and 3D
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else:
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print(f"Applying to 2D/3D case with dims: {image_data.shape}")
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output_data = process_data(image_data, operation_type, value).astype(d_type)
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if zCount > 1:
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axes = 'ZYX'
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else:
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axes = 'TYX' if tCount > 1 else 'YX'
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imsave(result_location, output_data, metadata={'axes': axes}, ome=True)
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def process_data(data, op_type: int, value: int):
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out_data = None
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if op_type == 0: # MULTIPLY
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out_data = np.float32(data) * value
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elif op_type == 1: # DIVIDE
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if value == 0:
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sys.exit('Division by zero is not possible.')
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out_data = np.float32(data) / value
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elif op_type == 2: # ADD
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out_data = np.float32(data) + value
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elif op_type == 3: # SUBTRACT
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out_data = np.float32(data) - value
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elif op_type > 3:
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sys.exit('Wrong selection of operation type')
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# Clipping data only if 8 or 16 bit
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if any(data.dtype == dtp for dtp in [np.uint8, np.uint16]):
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if np.max(out_data) > np.iinfo(data.dtype).max or np.min(out_data) < 0:
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out_data = out_data.clip(0, np.iinfo(data.dtype).max)
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print(f'Clipping data to fit the {data.dtype} range.')
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return out_data
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def Mbox(title, text):
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return ctypes.windll.user32.MessageBoxW(0, text, title, 0)
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if __name__ == '__main__':
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# Commandline arguments: image_path,
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params = {'inputImagePath': r'D:\PythonCode\_tests\3D Object Analysis From Seeds_test_seeds.aivia.tif',
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'resultImagePath': r'D:\PythonCode\_tests\test.tif',
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'Calibration': 'XYZT: 0.3225 Micrometers, 0.3225 Micrometers, 1 Micrometers, 1 Default',
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'ZCount': 8, 'TCount': 1, 'actionType': 2, 'value': 2}
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run(params)
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# CHANGELOG
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# v1_00: - From Threshold_for_3DObjects.py. Compatible with 32 bit images
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# v1_10: - Adding ome=True tag with imwrite for Aivia 13.0.0 compatibility

‎Recipes/ProcessImages/Boundaries_From_3D_Labeled_Mask_1_00.py‎

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import ctypes
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import sys
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import os.path
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import numpy as np
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from skimage.io import imread, imsave
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from skimage import segmentation
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"""
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Detects boundaries of 3D labeled objects and creates measurable objects in Aivia.
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Works only when there is no time dimension (yet).
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Requirements
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------------
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numpy (comes with Aivia installer)
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scikit-image (comes with Aivia installer)
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ctypes
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Parameters
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----------
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Input channel:
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Input channel with labeled mask and touching objects.
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Returns
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-------
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Object Set in Aivia
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Note: replace output with one of the line below to change output type (objects or mask)
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# [OUTPUT Name:resultPath Type:string DisplayName:'Labeled Boundaries Mask']
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# [OUTPUT Name:resultPath Type:string DisplayName:’Boundaries from labels’ Objects:3D MinSize:0.5 MaxSize:50000.0]
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"""
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# [INPUT Name:inputImagePath Type:string DisplayName:'Labeled Mask']
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# [OUTPUT Name:resultPath Type:string DisplayName:’Boundaries from labels’ Objects:3D MinSize:0.5 MaxSize:50000.0]
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def run(params):
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image_location = params['inputImagePath']
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result_location = params['resultPath']
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tCount = int(params['TCount'])
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if not os.path.exists(image_location):
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print(f"Error: {image_location} does not exist")
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return
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image_data = imread(image_location)
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dims = image_data.shape
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print('-- Input dimensions (expected (Z), Y, X): ', np.asarray(dims), ' --')
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# Checking image is not 2D/2D+t or 3D+t
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if len(dims) == 2 or (len(dims) == 3 and tCount > 1):
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message = 'Error: Cannot be applied to timelapses or 2D images.'
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Mbox('Error', message, 0)
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sys.exit(message)
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output_data = np.empty_like(image_data)
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# Detecting boundaries (mode = ‘thick’, ‘inner’, ‘outer’, ‘subpixel’)
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boundaries = segmentation.find_boundaries(image_data, mode='inner')
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output_data = np.where(boundaries, image_data, 0)
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imsave(result_location, output_data)
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print('Successfully saved output channel')
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def Mbox(title, text, style):
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return ctypes.windll.user32.MessageBoxW(0, text, title, style)
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if __name__ == '__main__':
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params = {'inputImagePath': r'D:\PythonCode\_tests\3D-image.aivia.tif',
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'resultPath': r'D:\PythonCode\_tests\test.tif',
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'TCount': 1}
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run(params)
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# CHANGELOG
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# v1_00: - From Objects_From_3D_Labeled_Mask_1_00.py

‎Recipes/ProcessImages/Dilate_2D_Labeled_Mask_1_00.py‎

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import ctypes
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import os.path
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import numpy as np
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from skimage.io import imread, imsave
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from skimage.segmentation import expand_labels
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"""
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Dilate 2D labeled masks.
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Requirements
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------------
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numpy (comes with Aivia installer)
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scikit-image (comes with Aivia installer)
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ctypes
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Parameters
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----------
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Input channel:
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Input channel to be dilated. Prefer a binary mask.
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Returns
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-------
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Channel in Aivia
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"""
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# [INPUT Name:inputImagePath Type:string DisplayName:'Labeled Mask']
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# [INPUT Name:dilation Type:int DisplayName:'Dilate distance (pixels)' Default:1 Min:0 Max:65535]
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# [OUTPUT Name:resultPath Type:string DisplayName:'Dilated Labeled Mask']
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def run(params):
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image_location = params['inputImagePath']
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result_location = params['resultPath']
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zCount = int(params['ZCount'])
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tCount = int(params['TCount'])
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dilation = int(params['dilation'])
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if not os.path.exists(image_location):
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print(f"Error: {image_location} does not exist")
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return
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if zCount > 1:
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Mbox('Error', 'This recipes currently only supports 2D images.', 0)
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return
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lbl_data = imread(image_location)
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dims = lbl_data.shape
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print('-- Input dimensions (expected (T,) (Z,) Y, X): ', np.asarray(dims), ' --')
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output_data = expand_labels(lbl_data, dilation)
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imsave(result_location, output_data)
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def Mbox(title, text, style):
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return ctypes.windll.user32.MessageBoxW(0, text, title, style)
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if __name__ == '__main__':
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params = {'inputImagePath': r'D:\PythonCode\_tests\3D-image.aivia.tif',
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'resultPath': r'D:\PythonCode\_tests\test.tif',
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'ZCount': 51, 'TCount': 1,
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'threshold': 1, 'dilation': 3}
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run(params)
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# CHANGELOG
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# v1_00: - Comes from Dilate_3D_v1_00.py

‎Recipes/ProcessImages/Objects_From_3D_Labeled_Mask_1_00.py‎

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import ctypes
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import sys
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import os.path
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import numpy as np
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from skimage.io import imread, imsave
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from skimage import segmentation
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"""
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Separates labeled objects in a 3D mask and creates measurable objects in Aivia.
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For instance, CellPose and StarDist output masks where objects can touch each other.
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In Aivia, objects need to be separated to be measurable.
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WARNING: the boundary detection leads to a large cut (~3 pixels wide) between objects.
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Works only when there is no time dimension (yet).
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Requirements
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------------
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numpy (comes with Aivia installer)
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scikit-image (comes with Aivia installer)
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ctypes
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Parameters
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----------
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Input channel:
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Input channel with labeled mask and touching objects.
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Returns
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-------
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Object Set in Aivia
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Note: replace output with one of the line below to change output type (objects or mask)
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# [OUTPUT Name:resultPath Type:string DisplayName:'Split Labeled Mask']
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# [OUTPUT Name:resultPath Type:string DisplayName:’Objects from labels’ Objects:3D MinSize:0.5 MaxSize:50000.0]
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"""
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# [INPUT Name:inputImagePath Type:string DisplayName:'Labeled Mask']
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# [OUTPUT Name:resultPath Type:string DisplayName:’Objects from labels’ Objects:3D MinSize:0.5 MaxSize:50000.0]
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def run(params):
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image_location = params['inputImagePath']
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result_location = params['resultPath']
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tCount = int(params['TCount'])
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if not os.path.exists(image_location):
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print(f"Error: {image_location} does not exist")
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return
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image_data = imread(image_location)
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dims = image_data.shape
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print('-- Input dimensions (expected (Z), Y, X): ', np.asarray(dims), ' --')
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# Checking image is not 2D/2D+t or 3D+t
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if len(dims) == 2 or (len(dims) == 3 and tCount > 1):
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message = 'Error: Cannot be applied to timelapses or 2D images.'
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Mbox('Error', message, 0)
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sys.exit(message)
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output_data = np.empty_like(image_data)
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# Detecting boundaries to be subtracted to original image
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boundaries = segmentation.find_boundaries(image_data, mode='outer')
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output_data = np.where(boundaries, 0, image_data)
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imsave(result_location, output_data)
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print('Successfully saved output channel')
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def Mbox(title, text, style):
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return ctypes.windll.user32.MessageBoxW(0, text, title, style)
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if __name__ == '__main__':
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params = {'inputImagePath': r'D:\PythonCode\_tests\3D-image.aivia.tif',
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'resultPath': r'D:\PythonCode\_tests\test.tif',
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'TCount': 1}
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run(params)
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# CHANGELOG
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# v1_00: - From Split_3D_Labeled_Mask_1_10.py

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