From d947c880e256d5a93236067343e84549e91dee77 Mon Sep 17 00:00:00 2001
From: Stephan Seitz <stephan.seitz@fau.de>
Date: Wed, 8 Jan 2020 14:28:52 +0100
Subject: [PATCH] Create ill-formed lenna in test_superresolution
---
tests/test_superresolution.py | 179 +++++++++++++++++++++++++++++++++-
1 file changed, 175 insertions(+), 4 deletions(-)
diff --git a/tests/test_superresolution.py b/tests/test_superresolution.py
index 322c205..43cb7b1 100644
--- a/tests/test_superresolution.py
+++ b/tests/test_superresolution.py
@@ -8,13 +8,18 @@
"""
from os.path import dirname, join
+from time import sleep
import numpy as np
import skimage.io
import sympy
import pystencils
-from pystencils_reco.resampling import downsample, resample, scale_transform, translate
+import pystencils_reco.transforms
+from pystencils_reco.filters import gauss_filter
+from pystencils_reco.resampling import (
+ downsample, resample, resample_to_shape, scale_transform, translate)
+from pystencils_reco.stencils import BallStencil
try:
import pyconrad.autoinit
@@ -70,13 +75,179 @@ def test_polar_transform():
x.set_coordinate_origin_to_field_center()
y.set_coordinate_origin_to_field_center()
- y.coordinate_transform = lambda x: sympy.Matrix((x.norm(), sympy.atan2(*x) / (2 * sympy.pi) * y.shape[1]))
lenna_file = join(dirname(__file__), "test_data", "lenna.png")
lenna = skimage.io.imread(lenna_file, as_gray=True).astype(np.float32)
- transformed = np.zeros((500, 500), np.float32)
+ transformed = np.zeros((400, 400), np.float32)
resample(x, y).compile()(x=lenna, y=transformed)
- pyconrad.imshow(transformed)
+ pyconrad.show_everything()
+ while True:
+ sleep(100)
+
+
+def test_polar_transform2():
+ x, y = pystencils.fields('x, y: float32[2d]')
+
+ class PolarTransform(sympy.Function):
+ def eval(args):
+ return sympy.Matrix(
+ (args.norm(), sympy.atan2(args[1]-x.shape[1]/2, args[0]-x.shape[0]/2) / sympy.pi * x.shape[1]/2))
+
+ x.set_coordinate_origin_to_field_center()
+ y.coordinate_transform = PolarTransform
+ y.set_coordinate_origin_to_field_center()
+
+ lenna_file = join(dirname(__file__), "test_data", "lenna.png")
+ lenna = skimage.io.imread(lenna_file, as_gray=True).astype(np.float32)
+
+ transformed = np.zeros((400, 400), np.float32)
+
+ resample(x, y).compile()(x=lenna, y=transformed)
+
+ pyconrad.show_everything()
+ while True:
+ sleep(100)
+
+
+def test_polar_inverted_transform():
+ x, y = pystencils.fields('x, y: float32[2d]')
+
+ class PolarTransform(sympy.Function):
+ def eval(args):
+ return sympy.Matrix(
+ (args.norm(), sympy.atan2(args[1]-x.shape[1]/2, args[0]-x.shape[0]/2) / sympy.pi * x.shape[1]/2))
+
+ def inv():
+ return lambda l: sympy.Matrix((sympy.cos(l[1] * sympy.pi / x.shape[1]*2) * l[0],
+ sympy.sin(l[1] * sympy.pi / x.shape[1]*2) * l[0])) + sympy.Matrix(x.shape) * 0.5
+
+ lenna_file = join(dirname(__file__), "test_data", "lenna.png")
+ lenna = skimage.io.imread(lenna_file, as_gray=True).astype(np.float32)
+
+ # transformed = np.zeros((400, 400), np.float32)
+ # back_transformed = np.zeros((400, 400), np.float32)
+ transformed = np.zeros_like(lenna)
+ back_transformed = np.zeros_like(lenna)
+
+ x.set_coordinate_origin_to_field_center()
+ y.coordinate_transform = PolarTransform
+ y.set_coordinate_origin_to_field_center()
+ resample(x, y).compile()(x=lenna, y=transformed)
+ resample(y, x).compile()(x=back_transformed, y=transformed)
+
+ pyconrad.show_everything()
+ while True:
+ sleep(100)
+
+
+def test_shift():
+ x, y = pystencils.fields('x, y: float32[2d]')
+
+ class ShiftTransform(sympy.Function):
+ def eval(args):
+ return args + sympy.Matrix((5, 5))
+
+ def inv():
+ return lambda l: l - sympy.Matrix((5, 5))
+
+ lenna_file = join(dirname(__file__), "test_data", "lenna.png")
+ lenna = skimage.io.imread(lenna_file, as_gray=True).astype(np.float32)
+
+ transformed = np.zeros_like(lenna)
+ back_transformed = np.zeros_like(lenna)
+
+ x.set_coordinate_origin_to_field_center()
+ y.coordinate_transform = ShiftTransform
+ y.set_coordinate_origin_to_field_center()
+ resample(x, y).compile()(x=lenna, y=transformed)
+ resample(y, x).compile()(x=back_transformed, y=transformed)
+
+ diff = lenna - back_transformed
+ assert diff is not None
+
+ pyconrad.show_everything()
+ while True:
+ sleep(100)
+
+
+def test_motion_model():
+ x, y = pystencils.fields('x, y: float32[2d]')
+ transform_field = pystencils.fields('t_x, t_y: float32[2d]')
+
+ lenna_file = join(dirname(__file__), "test_data", "lenna.png")
+ lenna = skimage.io.imread(lenna_file, as_gray=True).astype(np.float32)
+
+ # transformed = np.zeros((400, 400), np.float32)
+ # back_transformed = np.zeros((400, 400), np.float32)
+ transformed = np.zeros_like(lenna)
+ back_transformed = np.zeros_like(lenna)
+ translate_x = np.zeros((10, 10), np.float32)
+ translate_y = np.zeros((10, 10), np.float32)
+
+ pystencils_reco.transforms.extend_to_size_of_other_field(transform_field[0], x)
+ pystencils_reco.transforms.extend_to_size_of_other_field(transform_field[1], x)
+
+ shift = sympy.Matrix(sympy.symbols('s:2'))
+ shift_val = sympy.Matrix([transform_field[i].interpolated_access(
+ transform_field[i].physical_to_index(x.physical_coordinates))
+ for i in range(x.ndim)])
+
+ class ShiftTransform(sympy.Function):
+ def eval(args):
+ return args + shift
+
+ def inv():
+ return lambda args: args - shift
+
+ y.coordinate_transform = ShiftTransform
+ pystencils_reco.AssignmentCollection([*resample(x, y),
+ *[pystencils.Assignment(shift[i], shift_val[i]) for i in range(2)]]
+ ).compile()(x=lenna, y=transformed, t_x=translate_x, t_y=translate_y)
+
+ pystencils_reco.AssignmentCollection([*resample(x, y),
+ *[pystencils.Assignment(shift[i], shift_val[i]) for i in range(2)]]
+ ).compile()(x=back_transformed,
+ y=transformed,
+ t_x=translate_x,
+ t_y=translate_y)
+
+ pyconrad.show_everything()
+ while True:
+ sleep(100)
+
+
+def test_motion_model2():
+ x, y = pystencils.fields('x, y: float32[2d]')
+ transform_field = pystencils.fields('t_x, t_y: float32[2d]')
+
+ lenna_file = join(dirname(__file__), "test_data", "lenna.png")
+ lenna = skimage.io.imread(lenna_file, as_gray=True).astype(np.float32)
+
+ # transformed = np.zeros((400, 400), np.float32)
+ # back_transformed = np.zeros((400, 400), np.float32)
+ transformed = np.zeros_like(lenna)
+ blurred = np.zeros_like(lenna)
+
+ translate_x = np.zeros_like(lenna)
+ translate_y = np.zeros_like(lenna)
+ amplitude = 20
+
+ resample_to_shape(amplitude * np.random.randn(10, 10).astype(np.float32), lenna.shape).compile()(output=translate_x)
+ resample_to_shape(amplitude * np.random.randn(10, 10).astype(np.float32), lenna.shape).compile()(output=translate_y)
+
+ translate(x, y, sympy.Matrix((transform_field[0].center, transform_field[1].center))
+ ).compile()(x=lenna, y=transformed, t_x=translate_x, t_y=translate_y)
+
+ # resample(x, y).compile()(x=back_transformed, y=transformed, t_x=translate_x, t_y=translate_y)
+
+ kernel = gauss_filter(transformed, blurred, BallStencil(5, ndim=2), 10).compile()
+ print(pystencils.show_code(kernel))
+ kernel(input_field=transformed, output_field=blurred)
+
+ pyconrad.show_everything()
+
+ while True:
+ sleep(100)
--
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