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IMAGE-PROCESSING

uniform_filter

Multidimensional uniform filter.

Example of usage

Detect, enhance or quantify structures in a synthetic quality-inspection grayscale image.

Usage / limitations

+Use when: Transform and analyse images.

−Limitations: Check input requirements, mathematical assumptions and version-specific behaviour before using.

Mathematical formula

See mathematical definition for uniform_filter; this example verifies numerical properties.
Sample dataShow 
{
  "variable": "image_data",
  "domain": "Industrial visual quality inspection",
  "feature_names": [
    "pixel_row",
    "pixel_column",
    "intensity_level"
  ],
  "pixels": [
    [
      0,
      0,
      0,
      0,
      0,
      0,
      0
    ],
    [
      0,
      0,
      0,
      0,
      0,
      0,
      0
    ],
    [
      0,
      0,
      2,
      3,
      2,
      0,
      0
    ],
    [
      0,
      0,
      3,
      9,
      3,
      0,
      0
    ],
    [
      0,
      0,
      2,
      3,
      2,
      0,
      0
    ],
    [
      0,
      0,
      0,
      0,
      0,
      0,
      0
    ],
    [
      0,
      0,
      0,
      0,
      0,
      0,
      0
    ]
  ],
  "pixel_unit": "relative grayscale intensity (0–9)"
}

Code example (Python)

# DataLens.ai example — uniform_filter for visual quality inspection.
# Input image_data is provided by Copy input + code + test.
import numpy as np
from scipy import ndimage as ndi
# Construct a small grayscale inspection image with a bright object.
image = np.asarray(image_data['pixels'], dtype=float)
mask = image > 0
# Apply the selected image processing algorithm.
result = ndi.uniform_filter(image, size=3)
# Expected output: verify the defined numerical and shape properties.
assert result.shape == image.shape and np.isclose(result.sum(), image.sum(), atol=1e-6)
print("Expected output verified")