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Enums

Auto-generated documentation for musicalgestures._enums module.

Enumeration types for MGT-python parameter validation.

Using StrEnum so that enum members compare equal to their string values, maintaining full backward compatibility with code that passes plain strings. All enumerations support case-insensitive construction:

>>> BlurType("average") == BlurType.AVERAGE
True
>>> BlurType("AVERAGE") == BlurType.AVERAGE
True

## BlurType

[[find in source code]](https://github.com/fourMs/MGT-python/blob/master/musicalgestures/_enums.py#L56)

```python
class BlurType(_MgEnum):

Spatial blur applied before the frame-difference computation.

Attributes

NONE: No blurring is applied. AVERAGE: A 10 × 10 pixel box-blur is applied.

CropMode

[find in source code]

class CropMode(_MgEnum):

Video cropping strategy.

Attributes

NONE: No cropping. MANUAL: Opens an interactive window; the user draws a rectangle. AUTO: Automatically detects the area of significant motion.

DataFormat

[find in source code]

class DataFormat(_MgEnum):

Output data file format.

Attributes

CSV: Comma-separated values. TSV: Tab-separated values. TXT: Plain text (space-separated).

Note

JSON and HDF5 are reserved for future use and are not yet implemented by any processing function. Passing them will silently fall back to CSV. Use 'csv', 'tsv', or 'txt'.

FilterType

[find in source code]

class FilterType(_MgEnum):

Pixel-value filter applied to the frame-difference stream.

Attributes

REGULAR: Values below threshold are set to 0; values above are kept as-is. BINARY: Values below threshold → 0; values above threshold → 255. BLOB: Individual pixels are removed with an erosion filter.

PoseDevice

[find in source code]

class PoseDevice(_MgEnum):

Compute backend for pose estimation inference.

Attributes

CPU: Run on CPU. GPU: Run on GPU (CUDA / OpenCL).

PoseModel

[find in source code]

class PoseModel(_MgEnum):

Pose estimation skeleton model.

Attributes

BODY_25: OpenPose BODY_25 dataset (25 keypoints). COCO: COCO dataset (18 keypoints). MPI: MPII dataset (15 keypoints). MEDIAPIPE: Google MediaPipe Pose (33 landmarks).