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hyperparameter_search_image_comp.py
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hyperparameter_search_image_comp.py
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#!/usr/bin/env python3
import numpy as np
import h5py
from pathlib import Path
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import utils.plot as pplot
from utils.datasets import load_and_preprocess_images
from utils.training.run_configs import get_test_samples_managers
# -----------------------------------------------------------------------------
# Settings
# -----------------------------------------------------------------------------
datasets_dir = Path('./data/datasets/test')
dset_path = datasets_dir.joinpath('20200304-ge9ld-random-phantom-test-set.hdf5')
group_key = 'images'
# Sample index to display (index displayed in Fig. S4: 125)
sample_index = 125
# Trained model managers
trained_models_dir = Path('./data/trained-models')
model_managers = get_test_samples_managers(trained_models_dir)
# Data ranges
vmin, vmax = -62, 36
# -----------------------------------------------------------------------------
# Load ellipsoidal inclusion definitions
# -----------------------------------------------------------------------------
incl_keys = 'amp', 'ang', 'pos', 'semiaxes',
incl_dict = {}
with h5py.File(dset_path, 'r') as h5r:
for k in incl_keys:
dset = h5r['inclusions/' + k]
incl_dict[k] = dset[sample_index]
# -----------------------------------------------------------------------------
# Load images: inputs and targets
# -----------------------------------------------------------------------------
bm_dict = {}
image_keys = 'lq', 'hq', 'uq'
input_signal = 'iq'
output_signal = 'bm'
dset_path = dset_path.resolve(strict=True)
# Load images
for k in image_keys:
# Build dataset key
dset_name = '/'.join([group_key, k])
print(f"Loading '{dset_name}' from '{dset_path}'")
# Load images as B-mode (display only)
images, image_axes = load_and_preprocess_images(
path=dset_path,
name=dset_name,
input_signal=input_signal,
input_factor='0db',
output_signal=output_signal,
samples_slicer=sample_index,
)
# Store
bm_dict[k] = np.copy(images)
# -----------------------------------------------------------------------------
# Load images: predictions
# -----------------------------------------------------------------------------
predictions_dir = datasets_dir
pred_suffix = 'predictions'
pred_path = predictions_dir.joinpath(dset_path.stem + '-' + pred_suffix)
pred_path = pred_path.with_suffix(dset_path.suffix)
pred_path = pred_path.resolve(strict=True)
# Load images
pred_dict = {}
dset_path = pred_path
for k, mgr in model_managers.items():
# Build dataset key
dset_name = '/'.join([group_key, k])
print(f"Loading '{dset_name}' from '{dset_path}'")
# Load images as B-mode (display only)
input_signal = mgr.run_config.mapping_config.output_signal
images, image_axes = load_and_preprocess_images(
path=dset_path,
name=dset_name,
input_signal=input_signal,
output_signal=output_signal,
samples_slicer=sample_index
)
# Store
bm_dict[k] = images
# -----------------------------------------------------------------------------
# Figures setups
# -----------------------------------------------------------------------------
# Extract figure properties
keys = (
'lq', 'hq', 'uq',
'bm-uq-mae',
'env-uq-mslae',
'iq-hq-mslae',
'iq-uq-mslae'
)
bm_seq = [bm_dict[k] for k in keys]
xaxis, zaxis = image_axes
# Axes labels and titles
label_seq = (
"LQ (CNN Input)", "HQ", "UQ",
"UQ + B-mode + MAE",
"UQ + Envelope + MSLAE",
"HQ + IQ + MSLAE",
"UQ + IQ + MSLAE",
)
y_label = "Axial Dimension (mm)"
x_label = "Lateral Dimension (mm)"
pht_title = "Phantom Geometry"
# Axes ticks
y_ticks = [0.01, 0.02, 0.03, 0.04, 0.05, 0.06]
x_ticks = [-0.02, -0.01, 0, 0.01, 0.02]
# Figure kwargs
figsize = 12.8, 12.8 / 1.5
fig_kwargs = {'figsize': figsize, 'constrained_layout': True}
# Limits and extent
xaxis, zaxis = image_axes
xmin, xmax = xaxis[0], xaxis[-1]
zmin, zmax = zaxis[0], zaxis[-1]
dx = (xmax - xmin) / (xaxis.size - 1)
dz = (zmax - zmin) / (zaxis.size - 1)
extent = [xmin - dx / 2, xmax + dx / 2, zmax + dz / 2, zmin - dz / 2]
# Imshow settings
db_range = vmax - vmin
cmap = 'gray'
cm_kwargs = {'cmap': cmap, 'vmin': vmin, 'vmax': vmax}
im_kwargs = {**cm_kwargs, 'extent': extent}
# -----------------------------------------------------------------------------
# Figure: sample B-mode images
# -----------------------------------------------------------------------------
fig, axes = plt.subplots(
nrows=2, ncols=4, sharex='all', sharey='all', **fig_kwargs)
# Axes pointers
ax_pg: plt.Axes = axes[0, 0]
ax_seq = axes.ravel()[1:]
# Phantom geometry
ax_pg.set_title("Phantom Geometry")
ax_pg.set_xlim(xmin=xmin, xmax=xmax) # not needed with share all
ax_pg.set_ylim(ymin=zmax, ymax=zmin) # not needed with share all
ax_pg.set_aspect(1)
# Background
bckg_botleft = xmin, zmin
echogen = 0 # filled with 0 dB
color = np.clip((echogen - vmin) / (vmax - vmin), a_min=0, a_max=1)
bckg = mpatches.Rectangle(
# xy=bckg_botleft, width=xmax - xmin, height=zmax - zmin, color="0"
xy=bckg_botleft, width=xmax - xmin, height=zmax - zmin, color=str(color)
)
ax_pg.add_artist(bckg)
# Inclusions
incl_zipper = zip(
incl_dict['amp'], incl_dict['ang'],
incl_dict['pos'].T, incl_dict['semiaxes'].T
)
for amp, ang, pos, semiaxes in incl_zipper:
# Color
echogen = 20 * np.log10(amp)
color = np.clip((echogen - vmin) / (vmax - vmin), a_min=0, a_max=1)
color = str(color)
# Ellipse
el_width, el_height = 2 * semiaxes[0], 2 * semiaxes[1]
el_center = pos[0], pos[2]
# ang_deg = np.rad2deg(ang)
ang_deg = -np.rad2deg(ang) # Note: were inverted
ellipse = mpatches.Ellipse(
xy=el_center, width=el_width, height=el_height, angle=ang_deg,
color=color, linewidth=0
)
ax_pg.add_artist(ellipse)
# B-mode images
for ax, bm, lbl in zip(ax_seq, bm_seq, label_seq):
bm: np.ndarray
ax: plt.Axes
ax.imshow(bm.T, **im_kwargs)
ax.set_title(lbl)
# Ticks formatting and fine tuning
for ax in axes.ravel():
pplot.format_axes(axes=ax, scale=1e3, decimals=0)
ax.set_yticks(y_ticks)
ax.set_xticks(x_ticks)
# Axes labels
for ax in axes[:, 0]:
ax.set_ylabel(y_label)
for ax in axes[-1]:
ax.set_xlabel(x_label)
# Show
plt.show()