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"""
@author: louisblankemeier
"""
import os
import numpy as np
from scipy.ndimage import zoom
from comp2comp.visualization.detectron_visualizer import Visualizer
from comp2comp.visualization.linear_planar_reformation import (
linear_planar_reformation,
)
def method_visualizer(
sagittal_image,
axial_image,
axial_slice,
sagittal_slice,
center_sagittal,
radius_sagittal,
center_axial,
radius_axial,
output_dir,
anatomy,
):
if not os.path.exists(output_dir):
os.makedirs(output_dir)
axial_image = np.clip(axial_image, -300, 1800)
axial_image = normalize_img(axial_image) * 255.0
sagittal_image = np.clip(sagittal_image, -300, 1800)
sagittal_image = normalize_img(sagittal_image) * 255.0
sagittal_image = sagittal_image.reshape(
(sagittal_image.shape[0], sagittal_image.shape[1], 1)
)
img_rgb = np.tile(sagittal_image, (1, 1, 3))
vis = Visualizer(img_rgb)
vis.draw_circle(
circle_coord=center_sagittal, color=[0, 1, 0], radius=radius_sagittal
)
vis.draw_binary_mask(sagittal_slice)
vis_obj = vis.get_output()
vis_obj.save(os.path.join(output_dir, f"{anatomy}_sagittal_method.png"))
axial_image = axial_image.reshape((axial_image.shape[0], axial_image.shape[1], 1))
img_rgb = np.tile(axial_image, (1, 1, 3))
vis = Visualizer(img_rgb)
vis.draw_circle(circle_coord=center_axial, color=[0, 1, 0], radius=radius_axial)
vis.draw_binary_mask(axial_slice)
vis_obj = vis.get_output()
vis_obj.save(os.path.join(output_dir, f"{anatomy}_axial_method.png"))
def hip_roi_visualizer(
medical_volume,
roi,
centroid,
hu,
output_dir,
anatomy,
):
zooms = medical_volume.header.get_zooms()
zoom_factor = zooms[2] / zooms[1]
sagittal_image = medical_volume.get_fdata()[centroid[0], :, :]
sagittal_roi = roi[centroid[0], :, :]
sagittal_image = zoom(sagittal_image, (1, zoom_factor), order=1).round()
sagittal_roi = zoom(sagittal_roi, (1, zoom_factor), order=3).round()
sagittal_image = np.flip(sagittal_image.T)
sagittal_roi = np.flip(sagittal_roi.T)
axial_image = medical_volume.get_fdata()[:, :, round(centroid[2])]
axial_roi = roi[:, :, round(centroid[2])]
axial_image = np.flip(axial_image.T)
axial_roi = np.flip(axial_roi.T)
_ROI_COLOR = np.array([1.000, 0.340, 0.200])
sagittal_image = np.clip(sagittal_image, -300, 1800)
sagittal_image = normalize_img(sagittal_image) * 255.0
sagittal_image = sagittal_image.reshape(
(sagittal_image.shape[0], sagittal_image.shape[1], 1)
)
img_rgb = np.tile(sagittal_image, (1, 1, 3))
vis = Visualizer(img_rgb)
vis.draw_binary_mask(
sagittal_roi,
color=_ROI_COLOR,
edge_color=_ROI_COLOR,
alpha=0.0,
area_threshold=0,
)
vis.draw_text(
text=f"Mean HU: {round(hu)}",
position=(412, 10),
color=_ROI_COLOR,
font_size=9,
horizontal_alignment="left",
)
vis_obj = vis.get_output()
vis_obj.save(os.path.join(output_dir, f"{anatomy}_hip_roi_sagittal.png"))
"""
axial_image = np.clip(axial_image, -300, 1800)
axial_image = normalize_img(axial_image) * 255.0
axial_image = axial_image.reshape((axial_image.shape[0], axial_image.shape[1], 1))
img_rgb = np.tile(axial_image, (1, 1, 3))
vis = Visualizer(img_rgb)
vis.draw_binary_mask(
axial_roi, color=_ROI_COLOR, edge_color=_ROI_COLOR, alpha=0.0, area_threshold=0
)
vis.draw_text(
text=f"Mean HU: {round(hu)}",
position=(412, 10),
color=_ROI_COLOR,
font_size=9,
horizontal_alignment="left",
)
vis_obj = vis.get_output()
vis_obj.save(os.path.join(output_dir, f"{anatomy}_hip_roi_axial.png"))
"""
def hip_report_visualizer(medical_volume, roi, centroids, output_dir, anatomy, labels):
_ROI_COLOR = np.array([1.000, 0.340, 0.200])
image, mask = linear_planar_reformation(
medical_volume, roi, centroids, dimension="axial"
)
# add 3rd dim to image
image = np.flip(image.T)
mask = np.flip(mask.T)
mask[mask > 1] = 1
# mask = np.expand_dims(mask, axis=2)
image = np.expand_dims(image, axis=2)
image = np.clip(image, -300, 1800)
image = normalize_img(image) * 255.0
img_rgb = np.tile(image, (1, 1, 3))
vis = Visualizer(img_rgb)
vis.draw_binary_mask(
mask, color=_ROI_COLOR, edge_color=_ROI_COLOR, alpha=0.0, area_threshold=0
)
pos_idx = 0
for key, value in labels.items():
vis.draw_text(
text=f"{key}: {value}",
position=(310, 10 + pos_idx * 17),
color=_ROI_COLOR,
font_size=9,
horizontal_alignment="left",
)
pos_idx += 1
vis_obj = vis.get_output()
vis_obj.save(os.path.join(output_dir, f"{anatomy}_report_axial.png"))
def normalize_img(img: np.ndarray) -> np.ndarray:
"""Normalize the image.
Args:
img (np.ndarray): Input image.
Returns:
np.ndarray: Normalized image.
"""
return (img - img.min()) / (img.max() - img.min())
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