Python image file formats

_includes/datatypes/activities/datatypes_skimage_napari.py

@@ -1,19 +1,21 @@
 # Import modules
-import napari
+from load_from_url import load_from_url
+from napari.viewer import Viewer
 import numpy as np
 
 # Open napari
-viewer = napari.Viewer()
+viewer = Viewer()
 
 # Open the image
 # - Uncomment the image that you want to read
-from skimage.io import imread
-image = imread('https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__nuclei_noisy_different_intensity.tif')
-#image = imread('https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__nuclei_intensity_clipping_issue_a.tif')
-#image = imread('https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__nuclei_intensity_clipping_issue_b.tif')
-#image = imread('https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit_binary__two_objects.tif')
-#image = imread('https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_16bit__autophagosomes.tif')
-#image = imread('https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_16bit__scanR_datatype_issue.tif')
+image_url = load_from_url('https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__nuclei_noisy_different_intensity.tif')
+#image_url = imread('https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__nuclei_intensity_clipping_issue_a.tif')
+#image_url = imread('https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__nuclei_intensity_clipping_issue_b.tif')
+#image_url = imread('https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit_binary__two_objects.tif')
+#image_url = imread('https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_16bit__autophagosomes.tif')
+#image_url = imread('https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_16bit__scanR_datatype_issue.tif')
+print(image_url.dims)
+image = image_url.data
 
 # View the image
 viewer.add_image(image)

_includes/image_file_formats/activities/image_file_formats_skimage_napari.py

@@ -0,0 +1,46 @@
+from load_from_url import load_from_url
+from napari.viewer import Viewer
+
+### NOTE: xml/h5 currently not working!
+
+# image_url = load_from_url('https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__nuclei_PLK1_control.tif')
+# image_url = load_from_url('https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_xyc__two_images.lif')
+# image_url = load_from_url('https://github.com/NEUBIAS/training-resources/raw/master/image_data/xyz__multiple_images.czi')
+image_url = load_from_url('https://github.com/NEUBIAS/training-resources/raw/master/image_data/xyzct_16bit__mitosis.ics')
+### image_url = load_from_url('https://github.com/NEUBIAS/training-resources/raw/master/image_data/xyzct_16bit__mitosis.xml')
+
+
+# Visualize and explore available metadata
+print(image_url.metadata)
+
+# Explore some useful metadata
+# Appreciate that accessing the metadata is not always straightforward
+print(image_url.dims)
+print(image_url.physical_pixel_sizes)
+print(image_url.metadata.images[0].pixels.time_increment)
+
+# Visualize the dataset in napari viewer
+napari_viewer = Viewer()
+napari_viewer.add_image(image_url.data, scale = image_url.physical_pixel_sizes)
+
+# Resave the dataset as ome tif with pixel size
+from aicsimageio.writers.ome_tiff_writer import OmeTiffWriter
+import numpy as np
+OmeTiffWriter.save(image_url.data.astype(np.uint16), 
+                   "file.ome.tif", 
+                   dim_order="TCZYX",
+                   physical_pixel_sizes=image_url.physical_pixel_sizes)
+
+# Resave dataset as gif
+from aicsimageio.writers.timeseries_writer import TimeseriesWriter
+OmeTiffWriter.save(image_url.data.astype(np.uint16), 
+                   "file.gif", 
+                   dim_order="TCZYX",
+                   physical_pixel_sizes=image_url.physical_pixel_sizes)
+
+# Reload ome tif
+from aicsimageio import AICSImage
+resaved = AICSImage('file.ome.tif')
+
+# Appreciate that metadata has changed
+print(resaved.metadata)

_includes/lut/activities/explore_luts_skimage_napari.py

@@ -1,38 +1,41 @@
-# Instantiate the napari viewer
-import napari
-viewer = napari.Viewer()
+from load_from_url import load_from_url
+from napari.viewer import Viewer
+import numpy as np
 
 # Read the image
-from skimage.io import imread
-image = imread('https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__nuclei_high_dynamic_range.tif')
+image_url = load_from_url('https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__nuclei_high_dynamic_range.tif')
+print(image_url.dims)
+image = image_url.data
 
 # Check image type and values
-import numpy as np
 print(image.dtype, np.min(image), np.max(image))
 
+# Create a new napari viewer.
+napari_viewer = Viewer()
 # View the intensity image as grayscale
-viewer.add_image(image, name='image_grayscale', colormap='gray')
+napari_viewer.add_image(image, name='image_grayscale', colormap='gray')
 
 # Change brightness and contrast
-viewer.layers['image_grayscale'].contrast_limits=(100, 175)
+napari_viewer.layers['image_grayscale'].contrast_limits=(100, 175)
 # Napari GUI: explore different contrast limits
 
 # View the intensity image as grayscale
-viewer.add_image(image, name='image_grayscale2', colormap='gray')
+napari_viewer.add_image(image, name='image_grayscale2', colormap='gray')
 # Napari GUI: visualize images side by side
 # Napari GUI: change brightness and contrast to visualize dim nuclei
 
 # Check available colormap
-print(list(napari.utils.colormaps.AVAILABLE_COLORMAPS))
+from napari.utils import colormaps
+print(list(colormaps.AVAILABLE_COLORMAPS))
 # Change colormap
-viewer.add_image(image, name='image_turbo', colormap='turbo')
+napari_viewer.add_image(image, name='image_turbo', colormap='turbo')
 # Napari GUI: explore the LUTs
 
 # Extract image data from the layers
-image_grayscale = viewer.layers['image_grayscale'].data
-image_grayscale2 = viewer.layers['image_grayscale2'].data
+image_grayscale = napari_viewer.layers['image_grayscale'].data
+image_grayscale2 = napari_viewer.layers['image_grayscale2'].data
 
 # Compare raw data
 print(image_grayscale[0:5,0:5])
 print(image_grayscale2[0:5,0:5])
-print((image_grayscale == image_grayscale2).all())
+print((image_grayscale == image_grayscale2).all())

_includes/pixels/activities/pixels_skimage_napari.py

@@ -3,13 +3,16 @@
 ## Install them into your activated conda environment.
 ## conda activate skimage-napari-tutorial
 ## pip install napari-plot-profile
-## pip install matplotlib
 #######################################################
 
-from skimage.io import imread
+from load_from_url import load_from_url
+
 # Load the image.
 # You can also load a local image by providing the path to the file.
-image = imread("https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__nuclei_noisy_different_intensity.tif")
+image_url = load_from_url("https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__nuclei_noisy_different_intensity.tif")
+print(image_url.dims)
+
+image = image_url.data
 
 # Print image shape
 print(image.shape)
@@ -18,13 +21,13 @@
 print(image)
 
 # Top left corner is [y, x] = [r, c] = [0, 0]
-print(image[0, 0])
+print(image[0, 0, 0, 0, 0])
 
 # [y, x] = [r, c] = [1, 0]
-print(image[1, 0])
+print(image[0, 0, 0, 1, 0])
 
 # [y, x] = [r, c] = [0, 2]
-print(image[0, 2])
+print(image[0, 0, 0, 0, 2])
 
 from napari.viewer import Viewer
 # Create a new napari viewer.

_includes/spatial_calibration/activities/spatial_calibration_skimage_napari.py

@@ -3,26 +3,29 @@
 ## installation activity for skimage napari.
 #######################################################
 
-# Import python packages.
+# Import python packages
+from load_from_url import load_from_url
 from napari.viewer import Viewer
 
-from load_from_url import load_from_url
-# Read image with AICSImage.
-image = load_from_url("https://github.com/NEUBIAS/training-resources/raw/master/image_data/xyz_8bit__mitotic_plate_calibrated.tif")
+# Read image with AICSImage
+image_url = load_from_url("https://github.com/NEUBIAS/training-resources/raw/master/image_data/xyz_8bit__mitotic_plate_calibrated.tif")
+image = image_url.data
 
-# Display information about dimensions.
-print(image.dims)
+# Display information about dimensions
+print(image_url.dims)
+print(image.shape)
+# Note that dims of image_url gives the same info as numpy array shape
 
-# Display physical pixel size read from the metadata.
-print(image.physical_pixel_sizes)
+# Display physical pixel size read from the metadata
+print(image_url.physical_pixel_sizes)
 
 # Create a new napari_viewer
 napari_viewer = Viewer()
 
-# Add image with scaling to the viewer.
+# Add image with scaling to the viewer
 napari_viewer.add_image(image.data, scale=image.physical_pixel_sizes)
-# Napari GUI: Change order of visible axes.
-# Napari GUI: Explor 3D visualization.
+# Napari GUI: Change order of visible axes
+# Napari GUI: Explor 3D visualization
 
-# Note: As of now napari lacks funcitonality to easily measure distances between points in an open image.
+# Note: As of now napari lacks funcitonality to easily measure distances between points in an open image
 # Currently, no orthogonal view plugin exists for napari.

_includes/tool_installation/activities/tool_installation_napari.md

@@ -1,16 +1,14 @@
-### Install skimage napari
-
 Essentially, follow these instructions: https://alisterburt.github.io/napari-workshops/SciPy-0722/scipy_installation.html
 
 #### Installation (only once)
 
 1. install miniconda (TODO)
-1. `conda create -n skimage-napari-tutorial python=3.9`
+1. `conda create -y -n skimage-napari-tutorial -c conda-forge python=3.9`
 1. `conda activate skimage-napari-tutorial`
-1. `conda install -c conda-forge notebook matplotlib`
+1. `pip install "napari[all]"`
+1. `pip install notebook matplotlib`
 1. `pip install aicsimageio`
 1. `conda install -c conda-forge bioformats_jar`
-1. `pip install "napari[all]"`
 1. `pip install napari-brightness-contrast`
 1. `pip install napari-plot-profile`
 1. create a directory called `skimage-napari-tutorial` (e.g. on your Desktop)

functions/load_from_url.py

@@ -1,24 +1,46 @@
 import os, tempfile
-import urllib
+import requests
 from aicsimageio import AICSImage
 from aicsimageio.readers import BioformatsReader
 
-def load_from_url(url):
+def load_from_url(url, verbose=False):
 
     with tempfile.TemporaryDirectory() as tempdir:
         # extract image name
-        fname = os.path.join(tempdir, url.rsplit('/', 1)[1])
-        # download image from url and save it with the same name
-        r = urllib.request.urlretrieve(url, fname)
+        url_fname = url.rsplit('/', 1)[1]
+        file_name, file_ext = os.path.splitext(url_fname)
+        url_file_name, _ = os.path.splitext(url)
+        fname = os.path.join(tempdir, file_name+file_ext) 
+
+        if verbose:
+            print('Saving file\n', fname, '\nin:\n', tempdir)
+
+        # download url file and save it with the same name
+        r = requests.get(url)
+        open(fname, "wb").write(r.content)
+
+        # if file is ics, download corresponding ids
+        if file_ext=='.ics':
+            r = requests.get(url_file_name+'.ids')
+            ids_name = os.path.join(tempdir, file_name+'.ids')
+            open(ids_name, "wb").write(r.content)
+
+        # if file is xml, download corresponding h5
+        if file_ext=='.xml':
+            r = requests.get(url_file_name+'.h5')
+            ids_name = os.path.join(tempdir, file_name+'.h5')
+            open(ids_name, "wb").write(r.content)
+
         # load image using AICSImage to retrieve metadata
         image = AICSImage(fname, reader=BioformatsReader)
         _ = image.data # this is to make sure the image is loaded into memory
 
     return image
 
+
 if __name__=='__main__':
     url = 'https://github.com/NEUBIAS/training-resources/raw/master/image_data/xyz_8bit_calibrated__mri_full_head.tif'
-    image = load_from_url(url)
+    image = load_from_url(url, verbose=True)
     print(image.data)
     print(image.physical_pixel_sizes)