diff --git a/qim3d/viz/_data_exploration.py b/qim3d/viz/_data_exploration.py
index 84c0266e2ca9111572b63d3aa8d66dac783fc311..e8d3e0f4b4c72401db9dcb586e02c9406b27a92f 100644
--- a/qim3d/viz/_data_exploration.py
+++ b/qim3d/viz/_data_exploration.py
@@ -14,6 +14,16 @@ import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import skimage.measure
+from skimage.filters import (
+ threshold_otsu,
+ threshold_isodata,
+ threshold_li,
+ threshold_mean,
+ threshold_minimum,
+ threshold_triangle,
+ threshold_yen,
+)
+
from IPython.display import clear_output, display
import qim3d
@@ -26,7 +36,7 @@ def slices_grid(
slice_positions: Optional[Union[str, int, List[int]]] = None,
num_slices: int = 15,
max_columns: int = 5,
- color_map: str = 'magma',
+ color_map: str = "magma",
value_min: float = None,
value_max: float = None,
image_size: int = None,
@@ -36,7 +46,7 @@ def slices_grid(
display_positions: bool = True,
interpolation: Optional[str] = None,
color_bar: bool = False,
- color_bar_style: str = 'small',
+ color_bar_style: str = "small",
**matplotlib_imshow_kwargs,
) -> matplotlib.figure.Figure:
"""
@@ -90,18 +100,18 @@ def slices_grid(
# If we pass python None to the imshow function, it will set to
# default value 'antialiased'
if interpolation is None:
- interpolation = 'none'
+ interpolation = "none"
# Numpy array or Torch tensor input
if not isinstance(volume, (np.ndarray, da.core.Array)):
- raise ValueError('Data type not supported')
+ raise ValueError("Data type not supported")
if volume.ndim < 3:
raise ValueError(
- 'The provided object is not a volume as it has less than 3 dimensions.'
+ "The provided object is not a volume as it has less than 3 dimensions."
)
- color_bar_style_options = ['small', 'large']
+ color_bar_style_options = ["small", "large"]
if color_bar_style not in color_bar_style_options:
raise ValueError(
f"Value '{color_bar_style}' is not valid for colorbar style. Please select from {color_bar_style_options}."
@@ -119,11 +129,11 @@ def slices_grid(
# Here we deal with the case that the user wants to use the objects colormap directly
if (
type(color_map) == matplotlib.colors.LinearSegmentedColormap
- or color_map == 'segmentation'
+ or color_map == "segmentation"
):
num_labels = len(np.unique(volume))
- if color_map == 'segmentation':
+ if color_map == "segmentation":
color_map = qim3d.viz.colormaps.segmentation(num_labels)
# If value_min and value_max are not set like this, then in case the
# number of objects changes on new slice, objects might change
@@ -140,15 +150,15 @@ def slices_grid(
slice_idxs = np.linspace(0, n_total - 1, num_slices, dtype=int)
# Position is a string
elif isinstance(slice_positions, str) and slice_positions.lower() in [
- 'start',
- 'mid',
- 'end',
+ "start",
+ "mid",
+ "end",
]:
- if slice_positions.lower() == 'start':
+ if slice_positions.lower() == "start":
slice_idxs = _get_slice_range(0, num_slices, n_total)
- elif slice_positions.lower() == 'mid':
+ elif slice_positions.lower() == "mid":
slice_idxs = _get_slice_range(n_total // 2, num_slices, n_total)
- elif slice_positions.lower() == 'end':
+ elif slice_positions.lower() == "end":
slice_idxs = _get_slice_range(n_total - 1, num_slices, n_total)
# Position is an integer
elif isinstance(slice_positions, int):
@@ -229,25 +239,25 @@ def slices_grid(
ax.text(
0.0,
1.0,
- f'slice {slice_idxs[slice_idx]} ',
+ f"slice {slice_idxs[slice_idx]} ",
transform=ax.transAxes,
- color='white',
+ color="white",
fontsize=8,
- va='top',
- ha='left',
- bbox=dict(facecolor='#303030', linewidth=0, pad=0),
+ va="top",
+ ha="left",
+ bbox=dict(facecolor="#303030", linewidth=0, pad=0),
)
ax.text(
1.0,
0.0,
- f'axis {slice_axis} ',
+ f"axis {slice_axis} ",
transform=ax.transAxes,
- color='white',
+ color="white",
fontsize=8,
- va='bottom',
- ha='right',
- bbox=dict(facecolor='#303030', linewidth=0, pad=0),
+ va="bottom",
+ ha="right",
+ bbox=dict(facecolor="#303030", linewidth=0, pad=0),
)
except IndexError:
@@ -255,11 +265,11 @@ def slices_grid(
pass
# Hide the axis, so that we have a nice grid
- ax.axis('off')
+ ax.axis("off")
if color_bar:
with warnings.catch_warnings():
- warnings.simplefilter('ignore', category=UserWarning)
+ warnings.simplefilter("ignore", category=UserWarning)
fig.tight_layout()
norm = matplotlib.colors.Normalize(
@@ -267,15 +277,15 @@ def slices_grid(
)
mappable = matplotlib.cm.ScalarMappable(norm=norm, cmap=color_map)
- if color_bar_style == 'small':
+ if color_bar_style == "small":
# Figure coordinates of top-right axis
tr_pos = np.atleast_1d(axs[0])[-1].get_position()
# The width is divided by ncols to make it the same relative size to the images
color_bar_ax = fig.add_axes(
[tr_pos.x1 + 0.05 / ncols, tr_pos.y0, 0.05 / ncols, tr_pos.height]
)
- fig.colorbar(mappable=mappable, cax=color_bar_ax, orientation='vertical')
- elif color_bar_style == 'large':
+ fig.colorbar(mappable=mappable, cax=color_bar_ax, orientation="vertical")
+ elif color_bar_style == "large":
# Figure coordinates of bottom- and top-right axis
br_pos = np.atleast_1d(axs[-1])[-1].get_position()
tr_pos = np.atleast_1d(axs[0])[-1].get_position()
@@ -288,7 +298,7 @@ def slices_grid(
(tr_pos.y1 - br_pos.y0) - 0.0015,
]
)
- fig.colorbar(mappable=mappable, cax=color_bar_ax, orientation='vertical')
+ fig.colorbar(mappable=mappable, cax=color_bar_ax, orientation="vertical")
if display_figure:
plt.show()
@@ -319,7 +329,7 @@ def _get_slice_range(position: int, num_slices: int, n_total: int) -> np.ndarray
def slicer(
volume: np.ndarray,
slice_axis: int = 0,
- color_map: str = 'magma',
+ color_map: str = "magma",
value_min: float = None,
value_max: float = None,
image_height: int = 3,
@@ -363,14 +373,14 @@ def slicer(
image_height = image_size
image_width = image_size
- color_bar_options = [None, 'slices', 'volume']
+ color_bar_options = [None, "slices", "volume"]
if color_bar not in color_bar_options:
raise ValueError(
f"Unrecognized value '{color_bar}' for parameter color_bar. "
- f'Expected one of {color_bar_options}.'
+ f"Expected one of {color_bar_options}."
)
show_color_bar = color_bar is not None
- if color_bar == 'slices':
+ if color_bar == "slices":
# Precompute the minimum and maximum along each slice for faster widget sliding.
non_slice_axes = tuple(i for i in range(volume.ndim) if i != slice_axis)
slice_mins = np.min(volume, axis=non_slice_axes)
@@ -378,7 +388,7 @@ def slicer(
# Create the interactive widget
def _slicer(slice_positions):
- if color_bar == 'slices':
+ if color_bar == "slices":
dynamic_min = slice_mins[slice_positions]
dynamic_max = slice_maxs[slice_positions]
else:
@@ -407,18 +417,18 @@ def slicer(
value=volume.shape[slice_axis] // 2,
min=0,
max=volume.shape[slice_axis] - 1,
- description='Slice',
+ description="Slice",
continuous_update=True,
)
slicer_obj = widgets.interactive(_slicer, slice_positions=position_slider)
- slicer_obj.layout = widgets.Layout(align_items='flex-start')
+ slicer_obj.layout = widgets.Layout(align_items="flex-start")
return slicer_obj
def slicer_orthogonal(
volume: np.ndarray,
- color_map: str = 'magma',
+ color_map: str = "magma",
value_min: float = None,
value_max: float = None,
image_height: int = 3,
@@ -474,9 +484,9 @@ def slicer_orthogonal(
y_slicer = get_slicer_for_axis(slice_axis=1)
x_slicer = get_slicer_for_axis(slice_axis=2)
- z_slicer.children[0].description = 'Z'
- y_slicer.children[0].description = 'Y'
- x_slicer.children[0].description = 'X'
+ z_slicer.children[0].description = "Z"
+ y_slicer.children[0].description = "Y"
+ x_slicer.children[0].description = "X"
return widgets.HBox([z_slicer, y_slicer, x_slicer])
@@ -484,7 +494,7 @@ def slicer_orthogonal(
def fade_mask(
volume: np.ndarray,
axis: int = 0,
- color_map: str = 'magma',
+ color_map: str = "magma",
value_min: float = None,
value_max: float = None,
) -> widgets.interactive:
@@ -534,8 +544,8 @@ def fade_mask(
axes[0].imshow(
slice_img, cmap=color_map, vmin=new_value_min, vmax=new_value_max
)
- axes[0].set_title('Original')
- axes[0].axis('off')
+ axes[0].set_title("Original")
+ axes[0].axis("off")
mask = qim3d.operations.fade_mask(
np.ones_like(volume),
@@ -546,8 +556,8 @@ def fade_mask(
invert=invert,
)
axes[1].imshow(mask[position, :, :], cmap=color_map)
- axes[1].set_title('Mask')
- axes[1].axis('off')
+ axes[1].set_title("Mask")
+ axes[1].axis("off")
masked_volume = qim3d.operations.fade_mask(
volume,
@@ -573,22 +583,22 @@ def fade_mask(
axes[2].imshow(
slice_img, cmap=color_map, vmin=new_value_min, vmax=new_value_max
)
- axes[2].set_title('Masked')
- axes[2].axis('off')
+ axes[2].set_title("Masked")
+ axes[2].axis("off")
return fig
shape_dropdown = widgets.Dropdown(
- options=['spherical', 'cylindrical'],
- value='spherical', # default value
- description='Geometry',
+ options=["spherical", "cylindrical"],
+ value="spherical", # default value
+ description="Geometry",
)
position_slider = widgets.IntSlider(
value=volume.shape[0] // 2,
min=0,
max=volume.shape[0] - 1,
- description='Slice',
+ description="Slice",
continuous_update=False,
)
decay_rate_slider = widgets.FloatSlider(
@@ -596,7 +606,7 @@ def fade_mask(
min=1,
max=50,
step=1.0,
- description='Decay Rate',
+ description="Decay Rate",
continuous_update=False,
)
ratio_slider = widgets.FloatSlider(
@@ -604,14 +614,14 @@ def fade_mask(
min=0.1,
max=1,
step=0.01,
- description='Ratio',
+ description="Ratio",
continuous_update=False,
)
# Create the Checkbox widget
invert_checkbox = widgets.Checkbox(
value=False,
- description='Invert', # default value
+ description="Invert", # default value
)
slicer_obj = widgets.interactive(
@@ -622,7 +632,7 @@ def fade_mask(
geometry=shape_dropdown,
invert=invert_checkbox,
)
- slicer_obj.layout = widgets.Layout(align_items='flex-start')
+ slicer_obj.layout = widgets.Layout(align_items="flex-start")
return slicer_obj
@@ -654,15 +664,15 @@ def chunks(zarr_path: str, **kwargs) -> widgets.interactive:
"""
# Load the Zarr dataset
- zarr_data = zarr.open(zarr_path, mode='r')
+ zarr_data = zarr.open(zarr_path, mode="r")
# Save arguments for later use
# visualization_method = visualization_method
# preserved_kwargs = kwargs
# Create label to display the chunk coordinates
- widget_title = widgets.HTML('<h2>Chunk Explorer</h2>')
- chunk_info_label = widgets.HTML(value='Chunk info will be displayed here')
+ widget_title = widgets.HTML("<h2>Chunk Explorer</h2>")
+ chunk_info_label = widgets.HTML(value="Chunk info will be displayed here")
def load_and_visualize(
scale, z_coord, y_coord, x_coord, visualization_method, **kwargs
@@ -696,13 +706,13 @@ def chunks(zarr_path: str, **kwargs) -> widgets.interactive:
# Update the chunk info label with the chunk coordinates
info_string = (
- f'<b>shape:</b> {chunk_shape}\n'
- + f'<b>coordinates:</b> ({z_coord}, {y_coord}, {x_coord})\n'
- + f'<b>ranges: </b>Z({z_start}-{z_stop}) Y({y_start}-{y_stop}) X({x_start}-{x_stop})\n'
- + f'<b>dtype:</b> {chunk.dtype}\n'
- + f'<b>min value:</b> {np.min(chunk)}\n'
- + f'<b>max value:</b> {np.max(chunk)}\n'
- + f'<b>mean value:</b> {np.mean(chunk)}\n'
+ f"<b>shape:</b> {chunk_shape}\n"
+ + f"<b>coordinates:</b> ({z_coord}, {y_coord}, {x_coord})\n"
+ + f"<b>ranges: </b>Z({z_start}-{z_stop}) Y({y_start}-{y_stop}) X({x_start}-{x_stop})\n"
+ + f"<b>dtype:</b> {chunk.dtype}\n"
+ + f"<b>min value:</b> {np.min(chunk)}\n"
+ + f"<b>max value:</b> {np.max(chunk)}\n"
+ + f"<b>mean value:</b> {np.mean(chunk)}\n"
)
chunk_info_label.value = f"""
@@ -716,22 +726,22 @@ def chunks(zarr_path: str, **kwargs) -> widgets.interactive:
"""
# Prepare chunk visualization based on the selected method
- if visualization_method == 'slicer': # return a widget
+ if visualization_method == "slicer": # return a widget
viz_widget = qim3d.viz.slicer(chunk, **kwargs)
- elif visualization_method == 'slices': # return a plt.Figure
+ elif visualization_method == "slices": # return a plt.Figure
viz_widget = widgets.Output()
with viz_widget:
viz_widget.clear_output(wait=True)
fig = qim3d.viz.slices_grid(chunk, **kwargs)
display(fig)
- elif visualization_method == 'volume':
+ elif visualization_method == "volume":
viz_widget = widgets.Output()
with viz_widget:
viz_widget.clear_output(wait=True)
out = qim3d.viz.volumetric(chunk, show=False, **kwargs)
display(out)
else:
- log.info(f'Invalid visualization method: {visualization_method}')
+ log.info(f"Invalid visualization method: {visualization_method}")
return viz_widget
@@ -740,16 +750,16 @@ def chunks(zarr_path: str, **kwargs) -> widgets.interactive:
return [(s + chunk_size[i] - 1) // chunk_size[i] for i, s in enumerate(shape)]
scale_options = {
- f'{i} {zarr_data[i].shape}': i for i in range(len(zarr_data))
+ f"{i} {zarr_data[i].shape}": i for i in range(len(zarr_data))
} # len(zarr_data) gives number of scales
- description_width = '128px'
+ description_width = "128px"
# Create dropdown for scale
scale_dropdown = widgets.Dropdown(
options=scale_options,
value=0, # Default to first scale
- description='OME-Zarr scale',
- style={'description_width': description_width, 'text_align': 'left'},
+ description="OME-Zarr scale",
+ style={"description_width": description_width, "text_align": "left"},
)
# Initialize the options for x, y, and z based on the first scale by default
@@ -760,44 +770,44 @@ def chunks(zarr_path: str, **kwargs) -> widgets.interactive:
z_dropdown = widgets.Dropdown(
options=list(range(num_chunks[0])),
value=0,
- description='First dimension (Z)',
- style={'description_width': description_width, 'text_align': 'left'},
+ description="First dimension (Z)",
+ style={"description_width": description_width, "text_align": "left"},
)
y_dropdown = widgets.Dropdown(
options=list(range(num_chunks[1])),
value=0,
- description='Second dimension (Y)',
- style={'description_width': description_width, 'text_align': 'left'},
+ description="Second dimension (Y)",
+ style={"description_width": description_width, "text_align": "left"},
)
x_dropdown = widgets.Dropdown(
options=list(range(num_chunks[2])),
value=0,
- description='Third dimension (X)',
- style={'description_width': description_width, 'text_align': 'left'},
+ description="Third dimension (X)",
+ style={"description_width": description_width, "text_align": "left"},
)
method_dropdown = widgets.Dropdown(
- options=['slicer', 'slices', 'volume'],
- value='slicer',
- description='Visualization',
- style={'description_width': description_width, 'text_align': 'left'},
+ options=["slicer", "slices", "volume"],
+ value="slicer",
+ description="Visualization",
+ style={"description_width": description_width, "text_align": "left"},
)
# Funtion to temporarily disable observers
def disable_observers():
- x_dropdown.unobserve(update_visualization, names='value')
- y_dropdown.unobserve(update_visualization, names='value')
- z_dropdown.unobserve(update_visualization, names='value')
- method_dropdown.unobserve(update_visualization, names='value')
+ x_dropdown.unobserve(update_visualization, names="value")
+ y_dropdown.unobserve(update_visualization, names="value")
+ z_dropdown.unobserve(update_visualization, names="value")
+ method_dropdown.unobserve(update_visualization, names="value")
# Funtion to enable observers
def enable_observers():
- x_dropdown.observe(update_visualization, names='value')
- y_dropdown.observe(update_visualization, names='value')
- z_dropdown.observe(update_visualization, names='value')
- method_dropdown.observe(update_visualization, names='value')
+ x_dropdown.observe(update_visualization, names="value")
+ y_dropdown.observe(update_visualization, names="value")
+ z_dropdown.observe(update_visualization, names="value")
+ method_dropdown.observe(update_visualization, names="value")
# Function to update the x, y, z dropdowns when the scale changes and reset the coordinates to 0
def update_coordinate_dropdowns(scale):
@@ -850,7 +860,7 @@ def chunks(zarr_path: str, **kwargs) -> widgets.interactive:
# Attach an observer to scale dropdown to update x, y, z dropdowns when the scale changes
scale_dropdown.observe(
- lambda change: update_coordinate_dropdowns(scale_dropdown.value), names='value'
+ lambda change: update_coordinate_dropdowns(scale_dropdown.value), names="value"
)
enable_observers()
@@ -878,95 +888,87 @@ def chunks(zarr_path: str, **kwargs) -> widgets.interactive:
def histogram(
volume: np.ndarray,
- bins: Union[int, str] = 'auto',
- slice_idx: Union[int, str] = None,
+ bins: Union[int, str] = "auto",
+ slice_idx: Union[int, str, None] = None,
+ vertical_line: int = None,
axis: int = 0,
kde: bool = True,
log_scale: bool = False,
despine: bool = True,
show_title: bool = True,
- color: str = 'qim3d',
- edgecolor: str | None = None,
- figsize: tuple[float, float] = (8, 4.5),
- element: str = 'step',
+ color: str = "qim3d",
+ edgecolor: Optional[str] = None,
+ figsize: Tuple[float, float] = (8, 4.5),
+ element: str = "step",
return_fig: bool = False,
show: bool = True,
- **sns_kwargs,
-) -> None | matplotlib.figure.Figure:
+ ax: Optional[plt.Axes] = None,
+ **sns_kwargs: Union[str, float, int, bool]
+) -> Optional[Union[plt.Figure, plt.Axes]]:
"""
Plots a histogram of voxel intensities from a 3D volume, with options to show a specific slice or the entire volume.
-
+
Utilizes [seaborn.histplot](https://seaborn.pydata.org/generated/seaborn.histplot.html) for visualization.
Args:
volume (np.ndarray): A 3D NumPy array representing the volume to be visualized.
- bins (int or str, optional): Number of histogram bins or a binning strategy (e.g., "auto"). Default is "auto".
+ bins (Union[int, str], optional): Number of histogram bins or a binning strategy (e.g., "auto"). Default is "auto".
axis (int, optional): Axis along which to take a slice. Default is 0.
- slice_idx (int or str or None, optional): Specifies the slice to visualize. If an integer, it represents the slice index along the selected axis.
+ slice_idx (Union[int, str], optional): Specifies the slice to visualize. If an integer, it represents the slice index along the selected axis.
If "middle", the function uses the middle slice. If None, the entire volume is visualized. Default is None.
+ vertical_line (int, optional): Intensity value for a vertical line to be drawn on the histogram. Default is None.
kde (bool, optional): Whether to overlay a kernel density estimate. Default is True.
log_scale (bool, optional): Whether to use a logarithmic scale on the y-axis. Default is False.
despine (bool, optional): If True, removes the top and right spines from the plot for cleaner appearance. Default is True.
show_title (bool, optional): If True, displays a title with slice information. Default is True.
color (str, optional): Color for the histogram bars. If "qim3d", defaults to the qim3d color. Default is "qim3d".
edgecolor (str, optional): Color for the edges of the histogram bars. Default is None.
- figsize (tuple of floats, optional): Size of the figure (width, height). Default is (8, 4.5).
+ figsize (tuple, optional): Size of the figure (width, height). Default is (8, 4.5).
element (str, optional): Type of histogram to draw ('bars', 'step', or 'poly'). Default is "step".
return_fig (bool, optional): If True, returns the figure object instead of showing it directly. Default is False.
show (bool, optional): If True, displays the plot. If False, suppresses display. Default is True.
- **sns_kwargs (Any): Additional keyword arguments for `seaborn.histplot`.
+ ax (matplotlib.axes.Axes, optional): Axes object where the histogram will be plotted. Default is None.
+ **sns_kwargs: Additional keyword arguments for `seaborn.histplot`.
Returns:
- fig (Optional[matplotlib.figure.Figure]): If `return_fig` is True, returns the generated figure object. Otherwise, returns None.
+ Optional[matplotlib.figure.Figure or matplotlib.axes.Axes]:
+ If `return_fig` is True, returns the generated figure object.
+ If `return_fig` is False and `ax` is provided, returns the `Axes` object.
+ Otherwise, returns None.
Raises:
ValueError: If `axis` is not a valid axis index (0, 1, or 2).
ValueError: If `slice_idx` is an integer and is out of range for the specified axis.
-
- Example:
- ```python
- import qim3d
-
- vol = qim3d.examples.bone_128x128x128
- qim3d.viz.histogram(vol)
- ```
- 
-
- ```python
- import qim3d
-
- vol = qim3d.examples.bone_128x128x128
- qim3d.viz.histogram(vol, bins=32, slice_idx="middle", axis=1, kde=False, log_scale=True)
- ```
- 
-
"""
-
if not (0 <= axis < volume.ndim):
- raise ValueError(f'Axis must be an integer between 0 and {volume.ndim - 1}.')
+ raise ValueError(f"Axis must be an integer between 0 and {volume.ndim - 1}.")
- if slice_idx == 'middle':
+ if slice_idx == "middle":
slice_idx = volume.shape[axis] // 2
- if slice_idx:
+ if slice_idx is not None:
if 0 <= slice_idx < volume.shape[axis]:
img_slice = np.take(volume, indices=slice_idx, axis=axis)
data = img_slice.ravel()
- title = f'Intensity histogram of slice #{slice_idx} {img_slice.shape} along axis {axis}'
+ title = f"Intensity histogram of slice #{slice_idx} {img_slice.shape} along axis {axis}"
else:
raise ValueError(
- f'Slice index out of range. Must be between 0 and {volume.shape[axis] - 1}.'
+ f"Slice index out of range. Must be between 0 and {volume.shape[axis] - 1}."
)
else:
data = volume.ravel()
- title = f'Intensity histogram for whole volume {volume.shape}'
+ title = f"Intensity histogram for whole volume {volume.shape}"
- fig, ax = plt.subplots(figsize=figsize)
+ # Use provided Axes or create new figure
+ if ax is None:
+ fig, ax = plt.subplots(figsize=figsize)
+ else:
+ fig = None
if log_scale:
- plt.yscale('log')
+ ax.set_yscale("log")
- if color == 'qim3d':
+ if color == "qim3d":
color = qim3d.viz.colormaps.qim(1.0)
sns.histplot(
@@ -976,35 +978,46 @@ def histogram(
color=color,
element=element,
edgecolor=edgecolor,
+ ax=ax, # Plot directly on the specified Axes
**sns_kwargs,
)
+ if vertical_line is not None:
+ ax.axvline(
+ x=vertical_line,
+ color='red',
+ linestyle="--",
+ linewidth=2,
+
+ )
+
if despine:
sns.despine(
fig=None,
- ax=None,
+ ax=ax,
top=True,
right=True,
left=False,
bottom=False,
- offset={'left': 0, 'bottom': 18},
+ offset={"left": 0, "bottom": 18},
trim=True,
)
- plt.xlabel('Voxel Intensity')
- plt.ylabel('Frequency')
+ ax.set_xlabel("Voxel Intensity")
+ ax.set_ylabel("Frequency")
if show_title:
- plt.title(title, fontsize=10)
+ ax.set_title(title, fontsize=10)
# Handle show and return
- if show:
+ if show and fig is not None:
plt.show()
- else:
- plt.close(fig)
if return_fig:
return fig
+ elif ax is not None:
+ return ax
+
class _LineProfile:
@@ -1045,29 +1058,29 @@ class _LineProfile:
self.y_widget.value = self.y_max // 2
def initialize_widgets(self):
- layout = widgets.Layout(width='300px', height='auto')
+ layout = widgets.Layout(width="300px", height="auto")
self.x_widget = widgets.IntSlider(
- min=self.pad, step=1, description='', layout=layout
+ min=self.pad, step=1, description="", layout=layout
)
self.y_widget = widgets.IntSlider(
- min=self.pad, step=1, description='', layout=layout
+ min=self.pad, step=1, description="", layout=layout
)
self.angle_widget = widgets.IntSlider(
- min=0, max=360, step=1, value=0, description='', layout=layout
+ min=0, max=360, step=1, value=0, description="", layout=layout
)
self.line_fraction_widget = widgets.FloatRangeSlider(
- min=0, max=1, step=0.01, value=[0, 1], description='', layout=layout
+ min=0, max=1, step=0.01, value=[0, 1], description="", layout=layout
)
self.slice_axis_widget = widgets.Dropdown(
- options=[0, 1, 2], value=self.slice_axis, description='Slice axis'
+ options=[0, 1, 2], value=self.slice_axis, description="Slice axis"
)
- self.slice_axis_widget.layout.width = '250px'
+ self.slice_axis_widget.layout.width = "250px"
self.slice_index_widget = widgets.IntSlider(
- min=0, step=1, description='Slice index', layout=layout
+ min=0, step=1, description="Slice index", layout=layout
)
- self.slice_index_widget.layout.width = '400px'
+ self.slice_index_widget.layout.width = "400px"
def calculate_line_endpoints(self, x, y, angle):
"""
@@ -1108,7 +1121,7 @@ class _LineProfile:
image = np.take(self.volume, slice_index, slice_axis)
angle = np.radians(angle_deg)
src, dst = (
- np.array(point, dtype='float32')
+ np.array(point, dtype="float32")
for point in self.calculate_line_endpoints(x, y, angle)
)
@@ -1136,12 +1149,12 @@ class _LineProfile:
colors = self.cmap(norm(np.arange(num_segments - 1)))
lc = matplotlib.collections.LineCollection(segments, colors=colors, linewidth=2)
- ax[0].imshow(image, cmap='gray')
+ ax[0].imshow(image, cmap="gray")
ax[0].add_collection(lc)
# pivot point
- ax[0].plot(y, x, marker='s', linestyle='', color='cyan', markersize=4)
- ax[0].set_xlabel(f'axis {np.delete(np.arange(3), self.slice_axis)[1]}')
- ax[0].set_ylabel(f'axis {np.delete(np.arange(3), self.slice_axis)[0]}')
+ ax[0].plot(y, x, marker="s", linestyle="", color="cyan", markersize=4)
+ ax[0].set_xlabel(f"axis {np.delete(np.arange(3), self.slice_axis)[1]}")
+ ax[0].set_ylabel(f"axis {np.delete(np.arange(3), self.slice_axis)[0]}")
# Profile intensity plot
norm = plt.Normalize(0, vmax=len(y_pline) - 1)
@@ -1154,7 +1167,7 @@ class _LineProfile:
ax[1].add_collection(lc)
ax[1].autoscale()
- ax[1].set_xlabel('Distance along line')
+ ax[1].set_xlabel("Distance along line")
ax[1].grid(True)
plt.tight_layout()
plt.show()
@@ -1162,7 +1175,7 @@ class _LineProfile:
def build_interactive(self):
# Group widgets into two columns
title_style = (
- 'text-align:center; font-size:16px; font-weight:bold; margin-bottom:5px;'
+ "text-align:center; font-size:16px; font-weight:bold; margin-bottom:5px;"
)
title_column1 = widgets.HTML(
f"<div style='{title_style}'>Line parameterization</div>"
@@ -1172,11 +1185,11 @@ class _LineProfile:
)
# Make label widgets instead of descriptions which have different lengths.
- label_layout = widgets.Layout(width='120px')
- label_x = widgets.Label('Vertical position', layout=label_layout)
- label_y = widgets.Label('Horizontal position', layout=label_layout)
- label_angle = widgets.Label('Angle (°)', layout=label_layout)
- label_fraction = widgets.Label('Fraction range', layout=label_layout)
+ label_layout = widgets.Layout(width="120px")
+ label_x = widgets.Label("Vertical position", layout=label_layout)
+ label_y = widgets.Label("Horizontal position", layout=label_layout)
+ label_angle = widgets.Label("Angle (°)", layout=label_layout)
+ label_fraction = widgets.Label("Fraction range", layout=label_layout)
row_x = widgets.HBox([label_x, self.x_widget])
row_y = widgets.HBox([label_y, self.y_widget])
@@ -1194,12 +1207,12 @@ class _LineProfile:
interactive_plot = widgets.interactive_output(
self.update,
{
- 'slice_axis': self.slice_axis_widget,
- 'slice_index': self.slice_index_widget,
- 'x': self.x_widget,
- 'y': self.y_widget,
- 'angle_deg': self.angle_widget,
- 'fraction_range': self.line_fraction_widget,
+ "slice_axis": self.slice_axis_widget,
+ "slice_index": self.slice_index_widget,
+ "x": self.x_widget,
+ "y": self.y_widget,
+ "angle_deg": self.angle_widget,
+ "fraction_range": self.line_fraction_widget,
},
)
@@ -1209,9 +1222,9 @@ class _LineProfile:
def line_profile(
volume: np.ndarray,
slice_axis: int = 0,
- slice_index: int | str = 'middle',
- vertical_position: int | str = 'middle',
- horizontal_position: int | str = 'middle',
+ slice_index: int | str = "middle",
+ vertical_position: int | str = "middle",
+ horizontal_position: int | str = "middle",
angle: int = 0,
fraction_range: Tuple[float, float] = (0.00, 1.00),
) -> widgets.interactive:
@@ -1246,16 +1259,16 @@ def line_profile(
if isinstance(pos, int):
if not pos_range[0] <= pos < pos_range[1]:
raise ValueError(
- f'Value for {name} must be inside [{pos_range[0]}, {pos_range[1]}]'
+ f"Value for {name} must be inside [{pos_range[0]}, {pos_range[1]}]"
)
return pos
elif isinstance(pos, str):
pos = pos.lower()
- if pos == 'start':
+ if pos == "start":
return pos_range[0]
- elif pos == 'middle':
+ elif pos == "middle":
return pos_range[0] + (pos_range[1] - pos_range[0]) // 2
- elif pos == 'end':
+ elif pos == "end":
return pos_range[1]
else:
raise ValueError(
@@ -1263,27 +1276,27 @@ def line_profile(
"Must be 'start', 'middle', or 'end'."
)
else:
- raise TypeError('Axis position must be of type int or str.')
+ raise TypeError("Axis position must be of type int or str.")
if not isinstance(volume, (np.ndarray, da.core.Array)):
- raise ValueError('Data type for volume not supported.')
+ raise ValueError("Data type for volume not supported.")
if volume.ndim != 3:
- raise ValueError('Volume must be 3D.')
+ raise ValueError("Volume must be 3D.")
dims = volume.shape
- slice_index = parse_position(slice_index, (0, dims[slice_axis] - 1), 'slice_index')
+ slice_index = parse_position(slice_index, (0, dims[slice_axis] - 1), "slice_index")
# the omission of the ends for the pivot point is due to border issues.
vertical_position = parse_position(
- vertical_position, (1, np.delete(dims, slice_axis)[0] - 2), 'vertical_position'
+ vertical_position, (1, np.delete(dims, slice_axis)[0] - 2), "vertical_position"
)
horizontal_position = parse_position(
horizontal_position,
(1, np.delete(dims, slice_axis)[1] - 2),
- 'horizontal_position',
+ "horizontal_position",
)
if not isinstance(angle, int | float):
- raise ValueError('Invalid type for angle.')
+ raise ValueError("Invalid type for angle.")
angle = round(angle) % 360
if not (
@@ -1291,7 +1304,7 @@ def line_profile(
and 0.0 <= fraction_range[1] <= 1.0
and fraction_range[0] <= fraction_range[1]
):
- raise ValueError('Invalid values for fraction_range.')
+ raise ValueError("Invalid values for fraction_range.")
lp = _LineProfile(
volume,
@@ -1307,7 +1320,7 @@ def line_profile(
def threshold(
volume: np.ndarray,
- cmap_image: str = 'viridis',
+ cmap_image: str = 'magma',
vmin: float = None,
vmax: float = None,
) -> widgets.VBox:
@@ -1363,19 +1376,19 @@ def threshold(
# Centralized state dictionary to track current parameters
state = {
- 'position': volume.shape[0] // 2,
- 'threshold': int((volume.min() + volume.max()) / 2),
- 'method': 'Manual',
+ "position": volume.shape[0] // 2,
+ "threshold": int((volume.min() + volume.max()) / 2),
+ "method": "Manual",
}
threshold_methods = {
- 'Otsu': threshold_otsu,
- 'Isodata': threshold_isodata,
- 'Li': threshold_li,
- 'Mean': threshold_mean,
- 'Minimum': threshold_minimum,
- 'Triangle': threshold_triangle,
- 'Yen': threshold_yen,
+ "Otsu": threshold_otsu,
+ "Isodata": threshold_isodata,
+ "Li": threshold_li,
+ "Mean": threshold_mean,
+ "Minimum": threshold_minimum,
+ "Triangle": threshold_triangle,
+ "Yen": threshold_yen,
}
# Create an output widget to display the plot
@@ -1384,24 +1397,24 @@ def threshold(
# Function to update the state and trigger visualization
def update_state(change):
# Update state based on widget values
- state['position'] = position_slider.value
- state['method'] = method_dropdown.value
+ state["position"] = position_slider.value
+ state["method"] = method_dropdown.value
- if state['method'] == 'Manual':
- state['threshold'] = threshold_slider.value
+ if state["method"] == "Manual":
+ state["threshold"] = threshold_slider.value
threshold_slider.disabled = False
else:
- threshold_func = threshold_methods.get(state['method'])
+ threshold_func = threshold_methods.get(state["method"])
if threshold_func:
- slice_img = volume[state['position'], :, :]
+ slice_img = volume[state["position"], :, :]
computed_threshold = threshold_func(slice_img)
- state['threshold'] = computed_threshold
+ state["threshold"] = computed_threshold
# Programmatically update the slider without triggering callbacks
threshold_slider.unobserve_all()
threshold_slider.value = computed_threshold
threshold_slider.disabled = True
- threshold_slider.observe(update_state, names='value')
+ threshold_slider.observe(update_state, names="value")
else:
raise ValueError(f"Unsupported thresholding method: {state['method']}")
@@ -1410,7 +1423,7 @@ def threshold(
# Visualization function
def update_visualization():
- slice_img = volume[state['position'], :, :]
+ slice_img = volume[state["position"], :, :]
with output:
output.clear_output(wait=True) # Clear previous plot
fig, axes = plt.subplots(1, 4, figsize=(25, 5))
@@ -1427,15 +1440,15 @@ def threshold(
else vmax
)
axes[0].imshow(slice_img, cmap=cmap_image, vmin=new_vmin, vmax=new_vmax)
- axes[0].set_title('Original')
- axes[0].axis('off')
+ axes[0].set_title("Original")
+ axes[0].axis("off")
# Histogram
histogram(
volume=volume,
bins=32,
- slice_idx=state['position'],
- vertical_line=state['threshold'],
+ slice_idx=state["position"],
+ vertical_line=state["threshold"],
axis=1,
kde=False,
ax=axes[1],
@@ -1444,65 +1457,65 @@ def threshold(
axes[1].set_title(f"Histogram with Threshold = {int(state['threshold'])}")
# Binary mask
- mask = slice_img > state['threshold']
- axes[2].imshow(mask, cmap='gray')
- axes[2].set_title('Binary mask')
- axes[2].axis('off')
+ mask = slice_img > state["threshold"]
+ axes[2].imshow(mask, cmap="gray")
+ axes[2].set_title("Binary mask")
+ axes[2].axis("off")
# Overlay
mask_rgb = np.zeros((mask.shape[0], mask.shape[1], 3), dtype=np.uint8)
mask_rgb[:, :, 0] = mask
- masked_volume = qim3d.processing.operations.overlay_rgb_images(
+ masked_volume = qim3d.operations.overlay_rgb_images(
background=slice_img,
foreground=mask_rgb,
)
axes[3].imshow(masked_volume, vmin=new_vmin, vmax=new_vmax)
- axes[3].set_title('Overlay')
- axes[3].axis('off')
+ axes[3].set_title("Overlay")
+ axes[3].axis("off")
plt.show()
# Widgets
position_slider = widgets.IntSlider(
- value=state['position'],
+ value=state["position"],
min=0,
max=volume.shape[0] - 1,
- description='Slice',
+ description="Slice",
)
threshold_slider = widgets.IntSlider(
- value=state['threshold'],
+ value=state["threshold"],
min=volume.min(),
max=volume.max(),
- description='Threshold',
+ description="Threshold",
)
method_dropdown = widgets.Dropdown(
options=[
- 'Manual',
- 'Otsu',
- 'Isodata',
- 'Li',
- 'Mean',
- 'Minimum',
- 'Triangle',
- 'Yen',
+ "Manual",
+ "Otsu",
+ "Isodata",
+ "Li",
+ "Mean",
+ "Minimum",
+ "Triangle",
+ "Yen",
],
- value=state['method'],
- description='Method',
+ value=state["method"],
+ description="Method",
)
# Attach the state update function to widgets
- position_slider.observe(update_state, names='value')
- threshold_slider.observe(update_state, names='value')
- method_dropdown.observe(update_state, names='value')
+ position_slider.observe(update_state, names="value")
+ threshold_slider.observe(update_state, names="value")
+ method_dropdown.observe(update_state, names="value")
# Layout
controls_left = widgets.VBox([position_slider, threshold_slider])
controls_right = widgets.VBox([method_dropdown])
controls_layout = widgets.HBox(
[controls_left, controls_right],
- layout=widgets.Layout(justify_content='flex-start'),
+ layout=widgets.Layout(justify_content="flex-start"),
)
interactive_ui = widgets.VBox([controls_layout, output])
update_visualization()