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added documentation to mri scan
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‎examples/mri_testscan.py‎

Lines changed: 157 additions & 81 deletions
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"""
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MRI affine transformation demo using Heat.
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This example loads a sample MRI volume, extracts a middle slice,
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applies various affine transformations (rotation, scaling,
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translation, shear), and visualizes the results.
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MRI sample data © 2018 Adam Wolf, used under the MIT License.
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See heat/datasets/mri_sample_LICENSE.txt for details.
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"""
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import numpy as np
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import matplotlib.pyplot as plt
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import nibabel as nib
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import heat as ht
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from heat.ndimage.affine import affine_transform
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"""
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MRI sample data © 2018 Adam Wolf, used under MIT License.
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See heat/datasets/mri_sample_LICENSE.txt for details.
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"""
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# ============================================================
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# Load MRI volume
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# ============================================================
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def load_mri(path):
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"""
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Load an MRI volume from a NIfTI file.
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Parameters
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----------
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path : str
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Path to the .nii or .nii.gz MRI file.
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Returns
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-------
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numpy.ndarray
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MRI volume as a float32 array of shape (D, H, W),
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normalized to the range [0, 255].
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"""
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nii = nib.load(path)
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vol = nii.get_fdata().astype(np.float32) # (D,H,W)
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vol = nii.get_fdata().astype(np.float32)
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vol = vol / np.max(vol) * 255
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return vol
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# ============================================================
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# Slice helper
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# ============================================================
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def middle_slice(volume):
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"""
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Extract the middle axial slice from a 3D volume.
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Parameters
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----------
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volume : numpy.ndarray
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3D array of shape (D, H, W).
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Returns
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-------
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numpy.ndarray
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2D slice of shape (H, W).
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"""
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mid = volume.shape[0] // 2
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return volume[mid, :, :]
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# ============================================================
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# Convert numpy MRI slice → Heat array
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# ============================================================
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3370
def to_heat_slice(slice2d):
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"""
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Convert a 2D NumPy array into a Heat array.
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Parameters
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----------
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slice2d : numpy.ndarray
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2D image slice.
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Returns
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-------
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ht.DNDarray
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Heat array representation of the slice.
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"""
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return ht.array(slice2d, dtype=ht.float32)
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# ============================================================
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# Plot grid
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# ============================================================
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def show_results(titles, images, save_path=None):
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"""
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Display a grid of images with titles.
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Parameters
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----------
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titles : list of str
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Titles for each subplot.
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images : list of numpy.ndarray
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Images to display.
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save_path : str, optional
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If given, the figure is saved to this path.
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"""
41104
n = len(images)
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cols = 3
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rows = (n + cols - 1) // cols
@@ -62,107 +125,121 @@ def show_results(titles, images, save_path=None):
62125
# ============================================================
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# MAIN
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# ============================================================
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65129
def main():
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"""
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Run the MRI affine transformation demo.
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This function:
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1. Loads a sample MRI volume
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2. Extracts the middle slice
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3. Applies multiple affine transformations using Heat
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4. Visualizes and saves the results
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"""
66139
# -------- LOAD MRI --------
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vol = load_mri("/Users/marka.k/1900_Image_transformations/heat/heat/datasets/flair.nii.gz")
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vol = load_mri(
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"/Users/marka.k/1900_Image_transformations/heat/heat/datasets/flair.nii.gz"
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)
68143
print("Loaded MRI:", vol.shape)
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70-
orig = middle_slice(vol) # (H,W)
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orig = middle_slice(vol)
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x = to_heat_slice(orig)
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H, W = orig.shape
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# ========================================================
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# Define transforms (all center-aware)
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# Define transforms (center-aware)
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# ========================================================
78153

79-
cx, cy = W/2, H/2
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# Helper to shift center for rotation/scale
82154
def recenter(M):
83-
"""
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Input: 2x3 affine matrix.
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Output: 2x3 affine matrix recentered around the image center.
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"""
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cx, cy = W/2, H/2
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# Convert 2×3 → 3×3 homogeneous
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M3 = np.array([
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[M[0,0], M[0,1], M[0,2]],
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[M[1,0], M[1,1], M[1,2]],
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[0, 0, 1 ]
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], dtype=np.float32)
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# Center shift matrices
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T1 = np.array([
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[1, 0, -cx],
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[0, 1, -cy],
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[0, 0, 1 ]
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], np.float32)
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T2 = np.array([
105-
[1, 0, cx],
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[0, 1, cy],
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[0, 0, 1 ]
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], np.float32)
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# Recenter: T2 * M * T1
111-
M_centered = T2 @ M3 @ T1
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# Return as 2×3
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return np.array([
115-
[M_centered[0,0], M_centered[0,1], M_centered[0,2]],
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[M_centered[1,0], M_centered[1,1], M_centered[1,2]],
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], dtype=np.float32)
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"""
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Recenter a 2D affine matrix around the image center.
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Parameters
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----------
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M : numpy.ndarray
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Affine matrix of shape (2, 3).
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Returns
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-------
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numpy.ndarray
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Recentered affine matrix of shape (2, 3).
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"""
168+
cx, cy = W / 2, H / 2
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M3 = np.array(
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[
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[M[0, 0], M[0, 1], M[0, 2]],
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[M[1, 0], M[1, 1], M[1, 2]],
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[0, 0, 1],
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],
176+
dtype=np.float32,
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)
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179+
T1 = np.array(
180+
[[1, 0, -cx], [0, 1, -cy], [0, 0, 1]],
181+
dtype=np.float32,
182+
)
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184+
T2 = np.array(
185+
[[1, 0, cx], [0, 1, cy], [0, 0, 1]],
186+
dtype=np.float32,
187+
)
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189+
M_centered = T2 @ M3 @ T1
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191+
return np.array(
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[
193+
[M_centered[0, 0], M_centered[0, 1], M_centered[0, 2]],
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[M_centered[1, 0], M_centered[1, 1], M_centered[1, 2]],
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],
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dtype=np.float32,
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)
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119199
# ROTATION
120200
angle = np.radians(20)
121-
M_rot = np.array([
122-
[np.cos(angle), -np.sin(angle), 0],
123-
[np.sin(angle), np.cos(angle), 0]
124-
], np.float32)
125-
M_rot = recenter(M_rot)
201+
M_rot = recenter(
202+
np.array(
203+
[[np.cos(angle), -np.sin(angle), 0],
204+
[np.sin(angle), np.cos(angle), 0]],
205+
dtype=np.float32,
206+
)
207+
)
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127209
# SCALE
128210
s = 1.2
129-
M_scale = np.array([
130-
[s, 0, 0],
131-
[0, s, 0]
132-
], np.float32)
133-
M_scale = recenter(M_scale)
134-
135-
# TRANSLATE
136-
M_trans = np.array([
137-
[1, 0, 20],
138-
[0, 1, -20]
139-
], np.float32)
211+
M_scale = recenter(
212+
np.array([[s, 0, 0], [0, s, 0]], dtype=np.float32)
213+
)
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# TRANSLATION
216+
M_trans = np.array([[1, 0, 20], [0, 1, -20]], dtype=np.float32)
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141218
# SHEAR
142219
sh = 0.3
143-
M_shear = np.array([
144-
[1, sh, 0],
145-
[0, 1, 0]
146-
], np.float32)
147-
M_shear = recenter(M_shear)
220+
M_shear = recenter(
221+
np.array([[1, sh, 0], [0, 1, 0]], dtype=np.float32)
222+
)
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149-
# 3D ROTATION ABOUT Z AXIS APPLIED TO 2D SLICE (equivalent)
224+
# ROTATION (Z-axis equivalent)
150225
angle = np.radians(35)
151-
M_rotZ = np.array([
152-
[np.cos(angle), -np.sin(angle), 0],
153-
[np.sin(angle), np.cos(angle), 0]
154-
], np.float32)
155-
M_rotZ = recenter(M_rotZ)
226+
M_rotZ = recenter(
227+
np.array(
228+
[[np.cos(angle), -np.sin(angle), 0],
229+
[np.sin(angle), np.cos(angle), 0]],
230+
dtype=np.float32,
231+
)
232+
)
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157234
# ========================================================
158235
# Run transformations
159236
# ========================================================
160237

161-
out_rot = affine_transform(x, M_rot, order=1).numpy()
162-
out_scale = affine_transform(x, M_scale, order=1).numpy()
163-
out_trans = affine_transform(x, M_trans, order=1).numpy()
164-
out_shear = affine_transform(x, M_shear, order=1).numpy()
165-
out_rotZ = affine_transform(x, M_rotZ, order=1).numpy()
238+
out_rot = affine_transform(x, M_rot, order=1).numpy()
239+
out_scale = affine_transform(x, M_scale, order=1).numpy()
240+
out_trans = affine_transform(x, M_trans, order=1).numpy()
241+
out_shear = affine_transform(x, M_shear, order=1).numpy()
242+
out_rotZ = affine_transform(x, M_rotZ, order=1).numpy()
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167244
# ========================================================
168245
# Show + save
@@ -172,15 +249,14 @@ def recenter(M):
172249
"Original (middle slice)",
173250
"Rotate 20°",
174251
"Scale ×1.2",
175-
"Translate (+20, -20)",
252+
"Translate (+20, −20)",
176253
"Shear (0.3)",
177-
"3D Rotation (Z-axis 35°)",
254+
"Rotation (Z-axis 35°)",
178255
]
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180257
imgs = [orig, out_rot, out_scale, out_trans, out_shear, out_rotZ]
181258

182259
save_path = "mri_affine_demo.png"
183-
184260
show_results(titles, imgs, save_path=save_path)
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186262
print(f"\nSaved result figure → {save_path}")

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