### Applying pystackreg Transformation Matrices with skimage

Source: https://github.com/glichtner/pystackreg/blob/master/docs/tutorial.rst

This example illustrates how to reuse transformation matrices generated by pystackreg with the `skimage` library. It iterates through the saved `tmats`, applies each transformation to individual images in the stack using `skimage.transform.AffineTransform` and `skimage.transform.warp`, and then displays the averaged results. This highlights the interoperability of pystackreg's output.

```ipython3
f, ax = plt.subplots(2, int(np.ceil((len(transformations)+1)/2)), figsize=(20, 12))
ax = ax.ravel()

ax[0].imshow(overlay_images(unreg, aggregator=np.mean), cmap='gray')
ax[0].set_title('Original (overlay)')
ax[0].axis('off')

for i, (name, tf) in enumerate(transformations.items()):
    
    if name == 'BILINEAR':
        # Bilinear transformation is not an affine transformation, we can't use the transformation matrix here
        continue
    
    # copy the unregistered image
    reg = unreg.copy()
    
    for i_img in range(unreg.shape[0]):
        # get skimage's AffineTransform object
        tform = transform.AffineTransform(matrix=tmats[i][i_img, :, :])
        
        # transform image using the saved transformation matrix
        reg[i_img, :, :] = transform.warp(reg[i_img, :, :], tform)
    
    
    ax[i+1].imshow(overlay_images(reg, aggregator=np.mean), cmap='gray')
    ax[i+1].set_title(name + ' (overlay)')
    ax[i+1].axis('off')

# turn off axis in remaining plots
for i in range(len(transformations), len(ax)):
    ax[i].axis('off')
```

--------------------------------

### Apply Various Image Transformations with pyStackReg

Source: https://github.com/glichtner/pystackreg/blob/master/README.rst

This example demonstrates how to load two images and apply all five supported transformation types (translation, rigid body, scaled rotation, affine, and bilinear) using the `StackReg` class. It shows how to initialize `StackReg` with a specific transformation type and then use `register_transform` to apply it.

```python
from pystackreg import StackReg
from skimage import io

#load reference and "moved" image
ref = io.imread('some_original_image.tif')
mov = io.imread('some_changed_image.tif')

#Translational transformation
sr = StackReg(StackReg.TRANSLATION)
out_tra = sr.register_transform(ref, mov)

#Rigid Body transformation
sr = StackReg(StackReg.RIGID_BODY)
out_rot = sr.register_transform(ref, mov)

#Scaled Rotation transformation
sr = StackReg(StackReg.SCALED_ROTATION)
out_sca = sr.register_transform(ref, mov)

#Affine transformation
sr = StackReg(StackReg.AFFINE)
out_aff = sr.register_transform(ref, mov)

#Bilinear transformation
sr = StackReg(StackReg.BILINEAR)
out_bil = sr.register_transform(ref, mov)
```

--------------------------------

### Check pystackreg Version

Source: https://github.com/glichtner/pystackreg/blob/master/docs/tutorial.rst

Prints the installed version of the pystackreg library to confirm its availability and version number. This is useful for debugging and ensuring compatibility.

```python
print(pystackreg.__version__)
```

--------------------------------

### Install pyStackReg via Pip

Source: https://github.com/glichtner/pystackreg/blob/master/README.rst

This snippet provides the command to install the pyStackReg library using the Pip package manager.

```python
pip install pystackreg
```

--------------------------------

### Install pyStackReg via Conda

Source: https://github.com/glichtner/pystackreg/blob/master/README.rst

This snippet provides the command to install the pyStackReg library using the Conda package manager from the conda-forge channel.

```python
conda install pystackreg -c conda-forge
```

--------------------------------

### Separate Registration from Transformation in pyStackReg

Source: https://github.com/glichtner/pystackreg/blob/master/README.rst

This example illustrates how to perform image registration and transformation as separate steps. It shows how to register one image to another using `sr.register()` and then implicitly use those calculated parameters for subsequent operations, which is useful for applying transformations across different image channels or complex workflows.

```python
from pystackreg import StackReg
from skimage import io

img0 = io.imread('some_multiframe_image.tif')
img1 = io.imread('another_multiframe_image.tif')
# img0.shape: frames x width x height (3D)

sr = StackReg(StackReg.RIGID_BODY)

# register 2nd image to 1st
sr.register(img0[0, :, :], img0[1,:,:])
```

--------------------------------

### Python Project Dependencies List

Source: https://github.com/glichtner/pystackreg/blob/master/docs/requirements.txt

Specifies the Python packages and their minimum required versions. These are typically installed via `pip` from a `requirements.txt` file to set up the project's development or runtime environment.

```Python
sphinx>=1.3
sphinx-rtd-theme
sphinx_autodoc_typehints
```

--------------------------------

### Visualize Original Image Stack Overlay

Source: https://github.com/glichtner/pystackreg/blob/master/docs/tutorial.rst

Generates and displays an overlay image by averaging all frames in the original, unregistered image stack. This visualization highlights the blurriness and misalignment present across the entire stack before any registration is applied.

```python
f, ax = plt.subplots(1, 1, figsize=(10, 9))
    
ax.imshow(overlay_images(unreg), cmap='gray')
ax[0].set_title('overlay (original)')
ax[0].axis('off');
```

--------------------------------

### Registering Image Stacks with pystackreg Transformations

Source: https://github.com/glichtner/pystackreg/blob/master/docs/tutorial.rst

This snippet demonstrates applying various transformation types (e.g., BILINEAR, RIGID_BODY) provided by pystackreg to register images in a stack. It iterates through predefined transformations, registers the stack using `sr.register_stack`, and visualizes the averaged registered images. The transformation matrices are saved in `tmats` for subsequent use.

```ipython3
f, ax = plt.subplots(2, int(np.ceil((len(transformations)+1)/2)), figsize=(20, 12))
ax = ax.ravel()

ax[0].imshow(overlay_images(unreg, aggregator=np.mean), cmap='gray')
ax[0].set_title('Original (overlay)')
ax[0].axis('off')

# store transformation matrices for later use in this variable
tmats = []

for i, (name, tf) in enumerate(transformations.items()):
    sr = StackReg(tf)
    
    reference = 'first' if name == 'BILINEAR' else 'previous'
    
    tmat = sr.register_stack(unreg, axis=0, reference=reference, verbose=True)
    reg = sr.transform_stack(unreg)
    
    tmats.append(tmat)
    
    ax[i+1].imshow(overlay_images(reg, aggregator=np.mean), cmap='gray')
    ax[i+1].set_title(name + ' (overlay)')
    ax[i+1].axis('off')
```

--------------------------------

### Registering and Transforming Entire Image Stacks with PyStackReg

Source: https://github.com/glichtner/pystackreg/blob/master/README.rst

Illustrates various methods for registering and transforming entire image stacks using `StackReg`. It covers different reference options like 'previous', 'first', 'mean', and using a subset of frames or a moving average for registration. This functionality requires the `pystackreg` and `skimage` libraries.

```Python
from pystackreg import StackReg
from skimage import io

img0 = io.imread('some_multiframe_image.tif') # 3 dimensions : frames x width x height

sr = StackReg(StackReg.RIGID_BODY)

# register each frame to the previous (already registered) one
# this is what the original StackReg ImageJ plugin uses
out_previous = sr.register_transform_stack(img0, reference='previous')

# register to first image
out_first = sr.register_transform_stack(img0, reference='first')

# register to mean image
out_mean = sr.register_transform_stack(img0, reference='mean')

# register to mean of first 10 images
out_first10 = sr.register_transform_stack(img0, reference='first', n_frames=10)

# calculate a moving average of 10 images, then register the moving average to the mean of
# the first 10 images and transform the original image (not the moving average)
out_moving10 = sr.register_transform_stack(img0, reference='first', n_frames=10, moving_average = 10)
```

--------------------------------

### Visualize Original Reference and Shifted Images

Source: https://github.com/glichtner/pystackreg/blob/master/docs/tutorial.rst

Generates a matplotlib figure to visually compare the reference image, the shifted image, and an RGB composite overlay of both. This visualization clearly demonstrates the initial misalignment between the two images before any registration is applied.

```python
f, ax = plt.subplots(1, 3, figsize=(20, 9))
    
before_reg = composite_images([ref, mov])
    
ax[0].imshow(ref, cmap='gray')
ax[0].set_title('reference image')
ax[0].axis('off')
    
ax[1].imshow(mov, cmap='gray')
ax[1].set_title('shifted image')
ax[1].axis('off')
    
ax[2].imshow(before_reg)
ax[2].set_title('overlay (reference=red, shifted=green)')
ax[2].axis('off');
```

--------------------------------

### Create and Reshape Test Data for pystackreg

Source: https://github.com/glichtner/pystackreg/blob/master/docs/tutorial.rst

Illustrates how to create a sample stack and then reshape it using `numpy.moveaxis` to place the frames axis at a non-default position (axis 2) for testing `pystackreg`'s axis handling. It includes verifying the initial and final shapes of the array.

```python
unreg.shape
```

```python
unreg_axis = np.moveaxis(unreg, 0, 2)
unreg_axis.shape
```

--------------------------------

### Apply and Visualize Single Image Transformations

Source: https://github.com/glichtner/pystackreg/blob/master/docs/tutorial.rst

Iterates through various pystackreg transformation types, applying each to register the 'moved' image to the reference image. For each transformation, it displays the resulting overlay of the registered image on the reference, and for non-BILINEAR types, visualizes the effect of the transformation matrix on a rectangle.

```python
f, ax = plt.subplots(5, 2, figsize=(16, 18))
    
for i, (name, tf) in enumerate(transformations.items()):
        sr = StackReg(tf)
        reg = sr.register_transform(ref, mov)
        reg = reg.clip(min=0)
            
        after_reg =  composite_images([ref, reg])
        
        ax[i][0].imshow(after_reg, cmap='gray', vmin=0, vmax=1)
        ax[i][0].set_title(name + ' (overlay on reference)')
        ax[i][0].axis('off')
        
        if name != 'BILINEAR':
            show_transformation(sr.get_matrix(), ax[i][1])
            ax[i][1].set_title(name + ' (applied on a rectangle)')
        else:
            ax[i][1].axis('off')
```

--------------------------------

### Convert Stack Data to uint16

Source: https://github.com/glichtner/pystackreg/blob/master/docs/tutorial.rst

Demonstrates converting the data type of a registered stack to `uint16` using the `to_uint16` function and verifies the resulting data type and minimum value, showing the effect of the conversion on data range.

```python
reg_int = to_uint16(reg)

print(f"After using the to_uint16 function, the output has a datatype {reg_int.dtype} with a minimal value of {reg_int.min()}")
```

--------------------------------

### Separating Registration and Transformation for Image Stacks in PyStackReg

Source: https://github.com/glichtner/pystackreg/blob/master/README.rst

Shows how to perform registration and transformation as separate steps, which is useful for applying transformations calculated from one image stack to another (e.g., different color channels). It also demonstrates saving and loading transformation matrices using NumPy and applying them with `skimage.transform`. This functionality requires `pystackreg`, `skimage`, and `numpy`.

```Python
from pystackreg import StackReg
from skimage import io

img0 = io.imread('some_multiframe_image.tif') # 3 dimensions : frames x width x height
img1 = io.imread('another_multiframe_image.tif') # same shape as img0

# both stacks must have the same shape
assert img0.shape == img1.shape

sr = StackReg(StackReg.RIGID_BODY)

# register each frame to the previous (already registered) one
# this is what the original StackReg ImageJ plugin uses
tmats = sr.register_stack(img0, reference='previous')
out = sr.transform_stack(img1)

# tmats contains the transformation matrices -> they can be saved
# and loaded at another time
import numpy as np
np.save('transformation_matrices.npy', tmats)

tmats_loaded = np.load('transformation_matrices.npy')

# make sure you use the correct transformation here!
sr = StackReg(StackReg.RIGID_BODY)

# transform stack using the tmats loaded from file
sr.transform_stack(img1, tmats=tmats_loaded)

# with the transformation matrices at hand you can also
# use the transformation algorithms from other packages:
from skimage import transform as tf

out = np.zeros(img0.shape).astype(np.float)

for i in range(tmats.shape[0]):
    out[i, :, :] = tf.warp(img1[i, :, :], tmats[i, :, :], order=3)
```

--------------------------------

### Import pystackreg and Dependencies

Source: https://github.com/glichtner/pystackreg/blob/master/docs/tutorial.rst

Imports necessary libraries including os, sys, numpy, matplotlib, and skimage for general utilities, numerical operations, plotting, and image processing. It also imports the core StackReg class and the pystackreg module itself.

```python
import os
import sys
import numpy as np
from matplotlib import pyplot as plt
from skimage import transform, io, exposure

from pystackreg import StackReg
import pystackreg
```

--------------------------------

### Define Transformation Visualization Helper

Source: https://github.com/glichtner/pystackreg/blob/master/docs/tutorial.rst

Defines a helper function `show_transformation` to visualize the effect of a given transformation matrix on a simple rectangle. It plots both the original rectangle and its transformed counterpart, illustrating how the image coordinates are altered by the matrix.

```python
def show_transformation(tmat, ax=None):
        if ax is None:
            _, ax = plt.subplots()
        p = np.array([[1,120,1], [1,1,1], [250, 1, 1], [250,120,1], [1,120,1]])
        ax.plot(p[:, 0], p[:,1])
        q=np.dot(p, tmat.T)
        ax.plot(q[:, 0], q[:,1])
        ax.invert_xaxis()
        ax.invert_yaxis()
        ax.legend(['Original image', 'transformed image'])
```

--------------------------------

### Define pystackreg Transformation Types

Source: https://github.com/glichtner/pystackreg/blob/master/docs/tutorial.rst

Creates a dictionary that maps descriptive string names to the corresponding transformation constants provided by the `StackReg` class. This allows for easy selection and iteration over different registration transformation models like TRANSLATION, RIGID_BODY, SCALED_ROTATION, AFFINE, and BILINEAR.

```python
transformations = {
        'TRANSLATION': StackReg.TRANSLATION,
        'RIGID_BODY': StackReg.RIGID_BODY,
        'SCALED_ROTATION': StackReg.SCALED_ROTATION,
        'AFFINE': StackReg.AFFINE,
        'BILINEAR': StackReg.BILINEAR
    }
```

--------------------------------

### Load Sample Image Data

Source: https://github.com/glichtner/pystackreg/blob/master/docs/tutorial.rst

Loads a multi-frame TIFF image stack from a specified relative path ('../data/pc12-unreg.tif'). The loaded image data is then converted to a float type to prevent potential overflow issues during subsequent image processing and difference calculations.

```python
data_path = os.path.join(os.getcwd(), '..', 'data')
unreg = io.imread(os.path.join(data_path, 'pc12-unreg.tif'))

# convert to float to prevent overflows when calculating differences images
unreg = unreg.astype(np.float)
```

--------------------------------

### Define Image Overlay Helper Function

Source: https://github.com/glichtner/pystackreg/blob/master/docs/tutorial.rst

Defines a utility function `overlay_images` to combine multiple images into a single overlay. It can optionally apply histogram equalization to each image and uses a specified aggregator (defaulting to numpy.mean) to combine the stacked images.

```python
def overlay_images(imgs, equalize=False, aggregator=np.mean):
        
        if equalize:
            imgs = [exposure.equalize_hist(img) for img in imgs]
        
        imgs = np.stack(imgs, axis=0)
        
        return aggregator(imgs, axis=0)
```

--------------------------------

### Implementing Custom Progress Callback for pystackreg

Source: https://github.com/glichtner/pystackreg/blob/master/docs/tutorial.rst

This snippet demonstrates how to provide a custom callback function to `pystackreg`'s registration methods (e.g., `register_transform_stack`) to monitor progress. The `show_progress` function accepts `current_iteration` and `end_iteration` parameters, allowing real-time feedback during the registration process. This is useful for long-running operations.

```ipython3
def show_progress(current_iteration, end_iteration):
    print(f"Registering {current_iteration} of {end_iteration} images")

sr = StackReg(StackReg.RIGID_BODY)
reg = sr.register_transform_stack(unreg, axis=0, progress_callback=show_progress)
```

--------------------------------

### Converting pystackreg Output to uint16 and Handling Negative Values

Source: https://github.com/glichtner/pystackreg/blob/master/docs/tutorial.rst

This snippet addresses the `float` output and potential negative values from `pystackreg`'s transformation functions. It demonstrates how to use the `pystackreg.util.to_uint16()` utility function to convert the registered image stack to an unsigned 16-bit integer format, suitable for image display and storage, after ensuring non-negative values.

```ipython3
from pystackreg.util import to_uint16

sr = StackReg(StackReg.RIGID_BODY)
reg = sr.register_transform_stack(unreg - unreg.min())

print(f"The output of the transform function has the datatype {reg.dtype} with a minimal value of {reg.min()}")
```

--------------------------------

### Select Reference and Moved Images

Source: https://github.com/glichtner/pystackreg/blob/master/docs/tutorial.rst

Extracts two specific frames from the loaded `unreg` image stack to be used for single-image registration. The first frame (`unreg[0, :, :]`) is designated as the reference image, and the fifth frame (`unreg[4, :, :]`) is designated as the 'moved' image.

```python
ref = unreg[0, :, :]
mov = unreg[4, :, :]
```

--------------------------------

### Define Image Composite Helper Function

Source: https://github.com/glichtner/pystackreg/blob/master/docs/tutorial.rst

Defines a utility function `composite_images` to create an RGB composite from multiple grayscale images. It normalizes pixel values to 0-1 and pads with black channels if fewer than three images are provided, making them suitable for display as an RGB image.

```python
def composite_images(imgs, equalize=False, aggregator=np.mean):
        
        if equalize:
            imgs = [exposure.equalize_hist(img) for img in imgs]
        
        imgs = [img / img.max() for img in imgs]
        
        if len(imgs) < 3:
            imgs += [np.zeros(shape=imgs[0].shape)] * (3-len(imgs))
      
        imgs = np.dstack(imgs)
        
        return imgs
```

--------------------------------

### pystackreg.StackReg Class API

Source: https://github.com/glichtner/pystackreg/blob/master/docs/reference/pystackreg.rst

API documentation for the `StackReg` class, which is central to pystackreg for performing image registration and transformation operations. This class exposes methods for various registration algorithms.

```APIDOC
Class: pystackreg.StackReg
Description: Handles image stack registration and transformation.
Members: (Detailed methods and properties would be listed here, e.g., register_stack, transform_image, etc.)
```

--------------------------------

### Register Stack with Default pystackreg Parameters

Source: https://github.com/glichtner/pystackreg/blob/master/docs/tutorial.rst

Registers a stack using `pystackreg` with default parameters, demonstrating how `pystackreg` automatically identifies the frames axis and raises a warning if it differs from the expected default (axis 0). This highlights the library's axis detection capability.

```python
sr = StackReg(StackReg.RIGID_BODY)
sr.register_stack(unreg_axis);
```

--------------------------------

### Transforming a Single Image Frame with PyStackReg

Source: https://github.com/glichtner/pystackreg/blob/master/README.rst

Demonstrates how to apply a previously calculated transformation to a single image frame using the `transform` method of a StackReg instance. This assumes `sr` (StackReg instance) and `img1` are already defined and a transformation has been registered.

```Python
out = sr.transform(img1[1,:,:])
```

--------------------------------

### Transform Stack with Explicit Frames Axis

Source: https://github.com/glichtner/pystackreg/blob/master/docs/tutorial.rst

Transforms the registered stack using `pystackreg`, emphasizing the necessity of supplying the `axis` parameter again during transformation to match the axis used during registration and prevent errors. This ensures consistency across registration and transformation steps.

```python
reg_axis = sr.transform_stack(unreg_axis, axis=2)
```

--------------------------------

### Check Image Stack Length

Source: https://github.com/glichtner/pystackreg/blob/master/docs/tutorial.rst

Prints the total number of images (frames) present in the loaded `unreg` image stack. This confirms the dimensions of the dataset before proceeding with stack-level operations.

```python
print(f"Number of images in the stack: {len(unreg)}")
```

--------------------------------

### Register Stack with Explicit Frames Axis

Source: https://github.com/glichtner/pystackreg/blob/master/docs/tutorial.rst

Registers the stack using `pystackreg`, explicitly specifying the correct frames axis (axis 2) to avoid warnings and ensure proper registration when the frames axis is not the first dimension. This demonstrates how to override default axis detection.

```python
sr.register_stack(unreg_axis, axis=2);
```

--------------------------------

### pystackreg.util.to_uint16 Function API

Source: https://github.com/glichtner/pystackreg/blob/master/docs/reference/pystackreg.rst

API documentation for the `to_uint16` utility function within the `pystackreg.util` module. This function is typically used for converting numerical data to an unsigned 16-bit integer format, which is common in image processing.

```APIDOC
Function: pystackreg.util.to_uint16
Description: Converts input data to an unsigned 16-bit integer type.
Signature: to_uint16(data: numpy.ndarray) -> numpy.ndarray
  data: The input array to be converted.
Returns: A new array with the data converted to uint16.
```

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