diff --git a/qim3d/utils/__init__.py b/qim3d/utils/__init__.py
index 5b4866e28b024633a564f662517f78cb9d80bf59..3d9e2150f8e949b4a00e31a2e88e340100be382c 100644
--- a/qim3d/utils/__init__.py
+++ b/qim3d/utils/__init__.py
@@ -2,7 +2,7 @@
from . import doi, internal_tools
from .augmentations import Augmentation
from .data import Dataset, prepare_dataloaders, prepare_datasets
-from .img import overlay_rgb_images
+from .img import generate_volume, overlay_rgb_images
from .models import inference, model_summary, train_model
-from .system import Memory
from .preview import image_preview
+from .system import Memory
diff --git a/qim3d/utils/img.py b/qim3d/utils/img.py
index 347e4cc2ecbc1176ca4fc663884f7febbfc93598..c75aa93c20ca05be328cb779640e935041af8c48 100644
--- a/qim3d/utils/img.py
+++ b/qim3d/utils/img.py
@@ -1,4 +1,7 @@
import numpy as np
+import scipy.ndimage
+from noise import pnoise3
+
def overlay_rgb_images(background, foreground, alpha=0.5):
"""Overlay a RGB foreground onto an RGB background using alpha blending.
@@ -35,7 +38,9 @@ def overlay_rgb_images(background, foreground, alpha=0.5):
# Normalize if we have something
if np.max(foreground_max_projection) > 0:
- foreground_max_projection = foreground_max_projection / np.max(foreground_max_projection)
+ foreground_max_projection = foreground_max_projection / np.max(
+ foreground_max_projection
+ )
composite = background * (1 - alpha) + foreground * alpha
composite = np.clip(composite, 0, 255).astype("uint8")
@@ -44,3 +49,88 @@ def overlay_rgb_images(background, foreground, alpha=0.5):
composite = composite + (background * (1 - alpha)) * (1 - foreground_max_projection)
return composite.astype("uint8")
+
+
+def generate_volume(
+ base_shape=(128, 128, 128),
+ final_shape=(128, 128, 128),
+ noise_scale=0.05,
+ order=1,
+ gamma=1.0,
+ max_value=255,
+ threshold=0.5,
+ dtype="uint8",
+):
+ """
+ Generate a 3D volume with Perlin noise, spherical gradient, and optional scaling and gamma correction.
+
+ Args:
+ base_shape (tuple, optional): Shape of the initial volume to generate. Defaults to (128, 128, 128).
+ final_shape (tuple, optional): Desired shape of the final volume. Defaults to (128, 128, 128).
+ noise_scale (float, optional): Scale factor for Perlin noise. Defaults to 0.05.
+ order (int, optional): Order of the spline interpolation used in resizing. Defaults to 1.
+ gamma (float, optional): Gamma correction factor. Defaults to 1.0.
+ max_value (int, optional): Maximum value for the volume intensity. Defaults to 255.
+ threshold (float, optional): Threshold value for clipping low intensity values. Defaults to 0.5.
+ dtype (str, optional): Desired data type of the output volume. Defaults to "uint8".
+
+ Returns:
+ numpy.ndarray: Generated 3D volume with specified parameters.
+
+ Raises:
+ ValueError: If `final_shape` is not a tuple or does not have three elements.
+ ValueError: If `dtype` is not a valid numpy number type.
+
+ Example:
+ import qim3d
+ vol = qim3d.utils.generate_volume()
+ qim3d.viz.slices(vol, vmin=0, vmax=255)
+ """
+
+ if not isinstance(final_shape, tuple) or len(final_shape) != 3:
+ raise ValueError("Size must be a tuple")
+ if not np.issubdtype(dtype, np.number):
+ raise ValueError("Invalid data type")
+
+ # Define the dimensions of the shape for generating Perlin noise
+
+ # Initialize the 3D array for the shape
+ volume = np.empty((base_shape[0], base_shape[1], base_shape[2]), dtype=np.float32)
+
+ # Fill the 3D array with values from the Perlin noise function
+ for i in range(base_shape[0]):
+ for j in range(base_shape[1]):
+ for k in range(base_shape[2]):
+ # Calculate the distance from the center of the shape
+ dist = np.sqrt(
+ (i - base_shape[0] / 2) ** 2
+ + (j - base_shape[1] / 2) ** 2
+ + (k - base_shape[2] / 2) ** 2
+ ) / np.sqrt(3 * ((base_shape[0] / 2) ** 2))
+ # Generate Perlin noise and adjust the values based on the distance from the center
+ # This creates a spherical shape with noise
+ volume[i][j][k] = (1 + pnoise3(i * noise_scale, j * noise_scale, k * noise_scale)) * (
+ 1 - dist
+ )
+
+ # Normalize
+ volume = (volume - np.min(volume)) / (np.max(volume) - np.min(volume))
+
+ # Gamma correction
+ volume = np.power(volume, gamma)
+
+ # Scale the volume to the maximum value
+ volume = volume * max_value
+
+ # clip the low values of the volume to create a coherent volume
+ volume[volume < threshold * max_value] = 0
+
+ # Clip high values
+ volume[volume > max_value] = max_value
+
+ # Scale up the volume of volume to size
+ volume = scipy.ndimage.zoom(
+ volume, np.array(final_shape) / np.array(base_shape), order=order
+ )
+
+ return volume.astype(dtype)
diff --git a/requirements.txt b/requirements.txt
index b7508e720f85eb6623bcefa9cd20bae3ad451acf..c22d3ed74bbf8c91aa0e336395bb2060ed552993 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -23,4 +23,5 @@ dask>=2023.6.0,
k3d>=2.16.1
olefile>=0.46
psutil>=5.9.0
-structure-tensor>=0.2.1
\ No newline at end of file
+structure-tensor>=0.2.1
+noise>=1.2.2
\ No newline at end of file
diff --git a/setup.py b/setup.py
index 34fa87fbd4c4f7d71f7c8406b604136300740c04..6cd31a6e6a912ac5d95faa6bf439fea5042892e9 100644
--- a/setup.py
+++ b/setup.py
@@ -62,6 +62,7 @@ setup(
"k3d>=2.16.1",
"olefile>=0.46",
"psutil>=5.9.0",
- "structure-tensor>=0.2.1"
+ "structure-tensor>=0.2.1",
+ "noise>=1.2.2"
],
)