def test_compute_rts(self, retina, angle_scale_translation, expected_err,
rtol):
angle, scale, translation = angle_scale_translation
mod_img = ImageMethods.compute_rts(retina,
angle=angle,
scale=scale,
translation=translation)
rec_img = ImageMethods.compute_rts(mod_img,
angle=angle,
scale=scale,
translation=translation,
inverse=True)
assert m.isclose(np.linalg.norm(retina - rec_img),
expected_err,
rel_tol=rtol)
def image_save_back_tf(rot_tr_arr: np.ndarray, fnames: Sequence[str],
src_path: str, dest_path: str):
"""
Creates backtransformed images
For an external program that compares images `*_A.*` with `*_B.*` this prepares images
such that::
test00001.jpg -> test00001_A.png
test00021.jpg -> TR -> test00001_B.png, test00021_A.png
test00221.jpg -> TR -> test00201_B.png, test00221_A.png
test00241.jpg -> TR -> test00221_B.png
where TR denotes the backtransformation.
"""
for i, fname in enumerate(fnames):
if i == 0:
pass
src_arr = np.array(Image.open(src_path + "/" + fname))
dest_arr = ImageMethods.compute_rts(
src_arr,
angle=rot_tr_arr[i][3],
translation=rot_tr_arr[i][:2],
inverse=True,
)
dest_img = Image.fromarray(dest_arr).convert("L")
if i != len(fnames) - 1:
out_a = dest_path + "/" + fname.split(".")[0] + "_A.png"
dest_img.save(out_a)
if i != 0:
out_b = dest_path + "/" + fnames[i - 1].split(".")[0] + "_B.png"
dest_img.save(out_b)
def _value_function_handle(params: Dict[Enum, Parameter]) -> np.ndarray:
return ImageMethods.compute_rts(
params[LogPolParams.MOD_IMG].value,
angle=params[LogPolParams.RECOVERED_ROTATION].value[0],
scale=params[LogPolParams.RECOVERED_SCALE].value[0],
translation=params[LogPolParams.RECOVERED_TRANSLATION].value[:-1],
inverse=True,
)
def _value_function_handle(params: Dict[Enum, Parameter]) -> np.ndarray:
return ImageMethods.compute_rts(
params[AngleSelectParams.IMG].value,
angle=params[AngleSelectParams.ANGLE_B].value,
scale=1,
translation=params[AngleSelectParams.TRANSLATION_B].value[:-1],
inverse=True,
preserve_range=True,
)
import numpy as np import imgreg.data as data from imgreg.models.validator import Validator from imgreg.util.methods import ImageMethods ref_img = np.array(data.ref_img()) # modify the image using an affine transformation img = ImageMethods.compute_rts(ref_img, angle=2, translation=(6, 2)) # Create the model: val = Validator(img, ref_img) # The ImageParameters of the model have matplotlib support via the display function: val.display([val.ABSOLUTE_DIFFERENCE_IMG, val.SQUARED_DIFFERENCE_IMG]) # Increase the overlap to the reference image val.IMG.value = ImageMethods.compute_rts(ref_img, angle=1, translation=(1, 2)) # Note how the difference images show less pronounced differences with increased overlap val.display([val.ABSOLUTE_DIFFERENCE_IMG, val.SQUARED_DIFFERENCE_IMG])
def request_rts(request, ref_image):
angle, translation = request.param
return ImageMethods.compute_rts(ref_image,
angle=angle,
translation=translation)
