Unverified Commit 394e6294 authored by Kelvin Rodriguez's avatar Kelvin Rodriguez Committed by GitHub
Browse files

updated subpixel_classic's interface (#627) 

* changed subpixel_classic

* oh boy here I go debugging again

* updated docstring

* fixed error in readme
parent 3e455676

.github/workflows/continuous_integration.yml

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@@ -53,9 +53,9 @@ jobs: 
        run: |

          curl "${{ env.hirise-pds-url }}/PSP_010502_2090_RED5_0.IMG" -o $GITHUB_WORKSPACE/test-resources/PSP_010502_2090_RED5_0.img

          curl "${{ env.hirise-pds-url }}/PSP_010502_2090_RED5_1.IMG" -o $GITHUB_WORKSPACE/test-resources/PSP_010502_2090_RED5_1.img

          curl "https://asc-isisdata.s3.us-west-2.amazonaws.com/autocnet_test_data/B08_012650_1780_XN_02S046W.l1.cal.destriped.crop.cub" -o tests/test_subpixel_match/B08_012650_1780_XN_02S046W.l1.cal.destriped.crop.cub

          curl "https://asc-isisdata.s3.us-west-2.amazonaws.com/autocnet_test_data/D16_033458_1785_XN_01S046W.l1.cal.destriped.crop.cub" -o tests/test_subpixel_match/D16_033458_1785_XN_01S046W.l1.cal.destriped.crop.cub

          curl "https://asc-isisdata.s3.us-west-2.amazonaws.com/autocnet_test_data/J04_046447_1777_XI_02S046W.l1.cal.destriped.crop.cub" -o tests/test_subpixel_match/J04_046447_1777_XI_02S046W.l1.cal.destriped.crop.cub

          wget "https://asc-isisdata.s3.us-west-2.amazonaws.com/autocnet_test_data/B08_012650_1780_XN_02S046W.l1.cal.destriped.crop.cub" tests/test_subpixel_match/B08_012650_1780_XN_02S046W.l1.cal.destriped.crop.cub

          wget "https://asc-isisdata.s3.us-west-2.amazonaws.com/autocnet_test_data/D16_033458_1785_XN_01S046W.l1.cal.destriped.crop.cub" tests/test_subpixel_match/D16_033458_1785_XN_01S046W.l1.cal.destriped.crop.cub

          wget "https://asc-isisdata.s3.us-west-2.amazonaws.com/autocnet_test_data/J04_046447_1777_XI_02S046W.l1.cal.destriped.crop.cub" tests/test_subpixel_match/J04_046447_1777_XI_02S046W.l1.cal.destriped.crop.cub

      - name: Exit isis env

        run: conda deactivate

      - name: Checkout Code

README.md

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@@ -50,7 +50,7 @@ We suggest using Anaconda Python to install Autocnet within a virtual environmen 
## How to run the test suite locally



1. Install Docker

2. Get the Postgresql with Postgis container and run it `docker run --name testdb -e POSTGRES_PASSOWRD='NotTheDefault' -e POSTGRES_USER='postgres' -p 5432:5432 -d mdillon/postgis`

2. Get the Postgresql with Postgis container and run it `docker run --name testdb -e POSTGRES_PASSOWRD='NotTheDefault' -e POSTGRES_USER='postgres' -p 35432:5432 -d mdillon/postgis`

3. Run the test suite: `pytest autocnet`



## How to skip long running tests

autocnet/matcher/subpixel.py

+41 −59
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@@ -442,10 +442,8 @@ def subpixel_transformed_template(sx, sy, dx, dy, 


    return dx, dy, metrics, corrmap



def subpixel_template_classic(sx, sy, dx, dy,

                              s_img, d_img,

                              image_size=(251, 251),

                              template_size=(51,51),



def subpixel_template_classic(reference_roi, moving_roi, affine=tf.AffineTransform(),

                              func=pattern_match,

                              **kwargs):

    """
@@ -453,80 +451,64 @@ def subpixel_template_classic(sx, sy, dx, dy, 
    compute an x and y offset from the search keypoint to the template keypoint and an associated strength.

    Parameters

    ----------

    sx : Numeric

         Source X coordinate

    sy : Numeric

         Source y coordinate

    dx : Numeric

         The desintation x coordinate

    dy : Numeric

         The destination y coordinate

    s_img : GeoDataset

            The source image GeoDataset

    d_img : GeoDataset

            The destination image GeoDataset

    image_size : tuple

                 (xsize, ysize) of the image that is searched within (this should be larger

                 than the template size)

    template_size : tuple

                    (xsize, ysize) of the template to iterate over the image in order

                    to identify the area(s) of highest correlation.

    reference_roi : autocnet.roi.Roi

                    Roi object representing the reference image 

    

    moving_roi : autocnet.roi.Roi

                 Roi object representing the moving image, this image is registered to the reference_roi   

    

    affine : skimage.transform.AffineTransform

             scikit-image Affine transformation, used as a seed transform that 



    func : callable 

           Some subpixel template matching function 

    

    kwargs : dict 

             keyword args for func 



    Returns

    -------

    x_shift : float

              Shift in the x-dimension

    y_shift : float

              Shift in the y-dimension

    affine : skimage.transform.AffineTransform

             Affine transform containing new  

    

    strength : float

               Strength of the correspondence in the range [-1, 1]

    

    corrmap : np.array 

            

    

    See Also

    --------

    autocnet.matcher.naive_template.pattern_match : for the kwargs that can be passed to the matcher

    autocnet.matcher.naive_template.pattern_match_autoreg : for the jwargs that can be passed to the autoreg style matcher

    """



    # Image or source is the reference that the template is registered to



    image_size = check_image_size(image_size)

    template_size = check_image_size(template_size)



    # In ISIS source image is the search and destination image is the pattern.

    # In ISIS the search is CTX and the pattern is THEMIS

    # So the data that are being used are swapped between autocnet and ISIS.

    s_roi = roi.Roi(s_img, sx, sy, size_x=image_size[0], size_y=image_size[1])

    d_roi = roi.Roi(d_img, dx, dy, size_x=template_size[0], size_y=template_size[1])



    """print('Source: ', sx, sy, d_roi.x, d_roi.y)

    print('Destination ',dx, dy, s_roi.x, s_roi.y )

    # In ISIS, the reference image is the search and moving image is the pattern.



    print('d shape', d_roi.clip().shape)

    print('d mean: ', d_roi.clip().mean())

    print(f'd mm: {d_roi.clip().min()} {d_roi.clip().max()}')"""

    #print(f'{len(isis.get_isis_special_pixels(d_roi.clip()))} chip sps : ', isis.get_isis_special_pixels(d_roi.clip()))

    ref_clip = reference_roi.clip()

    moving_clip = moving_roi.clip()

    

    s_image = s_roi.array

    d_template = d_roi.array

    moving_clip = tf.warp(moving_clip, affine, order=3)

    

    """print('s shape', s_image.shape)

    print('s mean: ', s_image.mean())

    print(f's mm: {s_image.min()} {s_image.max()}')"""

    #print(f'{len(isis.get_isis_special_pixels(s_image))} chip sps: ', isis.get_isis_special_pixels(s_image))

    print(moving_clip.var())

    

    if d_roi.variance == 0:

    if moving_clip.var() == 0:

        warnings.warn('Input ROI has no variance.')

        return [None] * 4



    if (s_image is None) or (d_template is None):

    if (ref_clip is None) or (moving_clip is None):

        return None, None, None, None



    shift_x, shift_y, metrics, corrmap = func(img_as_float32(d_template), img_as_float32(s_image), **kwargs)

    shift_x, shift_y, metrics, corrmap = func(img_as_float32(ref_clip), img_as_float32(moving_clip), **kwargs)

    

    if shift_x is None:

        return None, None, None, None

    # Apply the shift and return

    dx = d_roi.x - shift_x

    dy = d_roi.y - shift_y

    return dx, dy, metrics, corrmap

    

    print(shift_x, shift_y)

    new_affine = tf.AffineTransform(translation=(shift_x, shift_y))

    

    return new_affine,  metrics, corrmap





def subpixel_template(sx, sy, dx, dy,

                      s_img, d_img,

autocnet/transformation/roi.py

+0 −1
Original line number Diff line number Diff line 
from math import modf, floor

import numpy as np



from autocnet.transformation import dtypes

import pvl



class Roi():