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experimental_results_figure.py
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experimental_results_figure.py
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#!/usr/bin/env python3
import numpy as np
from pathlib import Path
import matplotlib.pyplot as plt
import utils.plot as pplot
from utils.datasets import load_and_preprocess_images
from utils.training.run_configs import get_experimental_test_managers
from utils.metrics.phantoms.cirsmodel054gshypo2 import CIRSModel054GSHypo2
# -----------------------------------------------------------------------------
# Settings
# -----------------------------------------------------------------------------
datasets_dir = Path('./data/datasets/test')
group_key = 'images'
# Trained model managers
trained_models_dir = Path('./data/trained-models')
model_managers = get_experimental_test_managers(trained_models_dir)
# Configurations
config_pht = {
'dset_path': datasets_dir.joinpath(
'20200527-ge9ld-experimental-test-set-cirs054gs-hypo2.hdf5'
),
'vmin': -42, 'vmax': 36,
'zlim': (15e-3, 50e-3),
'samples_slicer': slice(0, 1)
}
config_caro = {
'dset_path': datasets_dir.joinpath(
'20200527-ge9ld-experimental-test-set-carotid-long.hdf5'
),
'vmin': -14, 'vmax': 36,
'zlim': (5e-3, 40e-3),
'samples_slicer': slice(45, 46)
}
configs = {'phantom': config_pht, 'carotid': config_caro}
# Results dict (to store data required for figure)
results_dict = {k: {} for k in configs.keys()}
# -----------------------------------------------------------------------------
# Load images: inputs and targets
# -----------------------------------------------------------------------------
image_keys = 'lq', 'hq'
input_signal = 'iq'
output_signal = 'bm'
for cfg, res in zip(configs.values(), results_dict.values()):
# dset_path = dset_path.resolve(strict=True)
dset_path = cfg['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 envelope to compute metrics
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=cfg['samples_slicer'],
)
# Crop
xaxis, zaxis = image_axes
zmin_crop, zmax_crop = cfg['zlim']
zmin_ind = np.where(zaxis < zmin_crop)[0][-1]
zmax_ind = np.where(zaxis > zmax_crop)[0][0]
slice_crop_z = slice(zmin_ind, zmax_ind + 1)
slice_crop = Ellipsis, slice_crop_z
zaxis = zaxis[slice_crop_z]
image_axes = xaxis, zaxis
images = images[slice_crop]
# Store
fig_dict = {
'bmode': images[0],
'bmode_axes': image_axes,
'vmin': cfg['vmin'],
'vmax': cfg['vmax'],
}
res[k] = {'figures': fig_dict}
# -----------------------------------------------------------------------------
# Load images: predictions
# -----------------------------------------------------------------------------
for cfg, res in zip(configs.values(), results_dict.values()):
dset_path = cfg['dset_path'].resolve(strict=True)
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}'")
# Convert prediction signal (CNN output) to envelope
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=cfg['samples_slicer']
)
# Crop
xaxis, zaxis = image_axes
zmin_crop, zmax_crop = cfg['zlim']
zmin_ind = np.where(zaxis < zmin_crop)[0][-1]
zmax_ind = np.where(zaxis > zmax_crop)[0][0]
slice_crop_z = slice(zmin_ind, zmax_ind + 1)
slice_crop = Ellipsis, slice_crop_z
zaxis = zaxis[slice_crop_z]
image_axes = xaxis, zaxis
images = images[slice_crop]
# Store
fig_dict = {
'bmode': images[0],
'bmode_axes': image_axes,
'vmin': cfg['vmin'],
'vmax': cfg['vmax'],
}
res[k] = {'figures': fig_dict}
# -----------------------------------------------------------------------------
# Metrics phantom
# -----------------------------------------------------------------------------
phantom = CIRSModel054GSHypo2()
# -----------------------------------------------------------------------------
# Figure: sample B-mode images
# -----------------------------------------------------------------------------
keys = 'lq', 'mslae-16', 'hq'
# Axes labels and titles
label_seq = ["LQ (CNN Input)"] + [k.upper() for k in keys[1:]]
y_label = "Axial Dimension (mm)"
x_label = "Lateral Dimension (mm)"
pht_title = "Phantom Geometry"
# Axes ticks
x_ticks = [-0.02, -0.01, 0, 0.01, 0.02]
# Figure kwargs
figsize = 12.8, 7.66
fig_kwargs = {'figsize': figsize, 'constrained_layout': True}
# -----------------------------------------------------------------------------
# Figure: sample B-mode images
# -----------------------------------------------------------------------------
# Create figure and image grid
fig, axes = plt.subplots(
nrows=2, ncols=3, sharex='row', sharey='row', **fig_kwargs)
# --------
# In vitro phantom
# --------
# Axes pointers
ax_seq = axes[0]
res_dict = results_dict['phantom']
bm_seq = [res_dict[k]['figures']['bmode'] for k in keys]
xaxis, zaxis = res_dict['lq']['figures']['bmode_axes']
# Limits and extent
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]
# Axes ticks
y_ticks = [0.02, 0.03, 0.04, 0.05]
# Settings
vmin = res_dict['lq']['figures']['vmin']
vmax = res_dict['lq']['figures']['vmax']
db_range = vmax - vmin
cmap = 'gray'
cm_kwargs = {'cmap': cmap, 'vmin': vmin, 'vmax': vmax}
im_kwargs = {**cm_kwargs, 'extent': extent}
# 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)
# Metrics ROIs
phantom.draw_metric_rois(ax=ax)
phantom.draw_metric_labels(ax=ax)
# Ticks
for ax in ax_seq:
ax.set_yticks(y_ticks)
ax.set_xticks(x_ticks)
# --------
# Carotid
# --------
# Axes pointers
ax_seq = axes[1]
res_dict = results_dict['carotid']
bm_seq = [res_dict[k]['figures']['bmode'] for k in keys]
xaxis, zaxis = res_dict['lq']['figures']['bmode_axes']
# Limits and extent
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]
# Axes ticks
y_ticks = [0.01, 0.02, 0.03, 0.04]
# Settings
vmin = res_dict['lq']['figures']['vmin']
vmax = res_dict['lq']['figures']['vmax']
db_range = vmax - vmin
cmap = 'gray'
cm_kwargs = {'cmap': cmap, 'vmin': vmin, 'vmax': vmax}
im_kwargs = {**cm_kwargs, 'extent': extent}
# 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
for ax in ax_seq:
ax.set_yticks(y_ticks)
ax.set_xticks(x_ticks)
# --------
# General stuff
# --------
# Ticks formatting and fine tuning
for ax in axes.ravel():
pplot.format_axes(axes=ax, scale=1e3, decimals=0)
# 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()