# normal_grain_merge
This implements a combined version of the blend modes normal and grain merge.
Grain merge is performed on *s* and *t* with the result normal-merged with *b*.
Subscripts indicate channels, with alpha (α) channels broadcast to three channels.
$$
(((\mathrm{t_{rgb}} + \mathrm{s_{rgb}} - 0.5) * \mathrm{t_\alpha} + \mathrm{t_{rgb}} * (1 - \mathrm{t_\alpha})) * (1 - 0.3) + \mathrm{s_{rgb}} * 0.3) * \mathrm{t_\alpha} + \mathrm{b_{rgb}} * (1 - \mathrm{t_\alpha})
$$
## Usage
```py
import numpy as np
from normal_grain_merge import normal_grain_merge, KernelKind
# Example arrays
base = np.zeros((100, 100, 3), dtype=np.uint8)
texture = np.zeros((100, 100, 3), dtype=np.uint8)
skin = np.zeros((100, 100, 4), dtype=np.uint8)
im_alpha = np.zeros((100, 100), dtype=np.uint8)
result_scalar = normal_grain_merge(base, texture, skin, im_alpha, KernelKind.KERNEL_SCALAR.value)
print(result_scalar.shape, result_scalar.dtype)
```
There are three kernels implemented in this module as defined in `KernelKind`.
- `KERNEL_AUTO`: Automatically chooses the kernel, preferring AVX2
- `KERNEL_SCALAR`: Portable scalar implementation.
- `KERNEL_SSE42`: SSE4.2 intrinsics kernel. Likely better on AMD CPUs.
- `KERNEL_AVX2`: AVX2 intrinsics kernel. Likely better on Intel CPUs.
### Parameters
All input matrices should have the same height and width.
#### `base`
RGB or RGBA, dropping the alpha channel if it exists.
The base image for application.
#### `texture`
RGB or RGBA, applying the alpha if it exists.
This is the texture to be applied.
#### `skin`
RGBA, the segmented portion of base to texture.
The "skin" of the object the texture is to be applied to.
#### `im_alpha`
The alpha of parameter `skin`.
This is mostly a holdover from the Python implementation to deal with NumPy.
#### `kernel`
One of `KernelKind`.
## Performance
The entire reason for me writing this was NumPy being slow when this operation is in the hot path.
So, I decided to write a SIMD version that does the type casting outside NumPy with only the intermediate values being in FP32.
How much of a speedup is this? All numbers are from a Ryzen 7 4800H running Windows 11 and Python 3.12.4.
| Method/Kernel | Average Iteration Time |
|-------------------|------------------------|
| C scalar kernel | 0.019565s |
| C SSE4.2 kernel | 0.013705s |
| C AVX2 kernel | 0.016842s |
| NumPy version | 0.228098s |
| Old NumPy version | 0.350554s |
| Method Comparison | Speedup |
|--------------------|----------|
| NumPy -> scalar | 91.4227% |
| NumPy -> SSE4.2 | 93.9915% |
| NumPy -> AVX2 | 92.6165% |
| Old np -> SSE4.2 | 96.0904% |
| C scalar -> SSE4.2 | 29.9487% |
| C scalar -> AVX2 | 13.9183% |
Raw data
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"description": "# normal_grain_merge\n\nThis implements a combined version of the blend modes normal and grain merge.\nGrain merge is performed on *s* and *t* with the result normal-merged with *b*.\nSubscripts indicate channels, with alpha (\u03b1) channels broadcast to three channels.\n\n$$\n(((\\mathrm{t_{rgb}} + \\mathrm{s_{rgb}} - 0.5) * \\mathrm{t_\\alpha} + \\mathrm{t_{rgb}} * (1 - \\mathrm{t_\\alpha})) * (1 - 0.3) + \\mathrm{s_{rgb}} * 0.3) * \\mathrm{t_\\alpha} + \\mathrm{b_{rgb}} * (1 - \\mathrm{t_\\alpha})\n$$\n\n## Usage\n```py\nimport numpy as np\nfrom normal_grain_merge import normal_grain_merge, KernelKind\n\n\n# Example arrays\nbase = np.zeros((100, 100, 3), dtype=np.uint8)\ntexture = np.zeros((100, 100, 3), dtype=np.uint8)\nskin = np.zeros((100, 100, 4), dtype=np.uint8)\nim_alpha = np.zeros((100, 100), dtype=np.uint8)\n\nresult_scalar = normal_grain_merge(base, texture, skin, im_alpha, KernelKind.KERNEL_SCALAR.value)\nprint(result_scalar.shape, result_scalar.dtype)\n```\n\nThere are three kernels implemented in this module as defined in `KernelKind`.\n\n- `KERNEL_AUTO`: Automatically chooses the kernel, preferring AVX2\n- `KERNEL_SCALAR`: Portable scalar implementation.\n- `KERNEL_SSE42`: SSE4.2 intrinsics kernel. Likely better on AMD CPUs.\n- `KERNEL_AVX2`: AVX2 intrinsics kernel. Likely better on Intel CPUs.\n\n### Parameters\n\nAll input matrices should have the same height and width.\n\n#### `base`\n\nRGB or RGBA, dropping the alpha channel if it exists.\nThe base image for application.\n\n#### `texture`\n\nRGB or RGBA, applying the alpha if it exists.\nThis is the texture to be applied.\n\n#### `skin`\n\nRGBA, the segmented portion of base to texture.\nThe \"skin\" of the object the texture is to be applied to.\n\n#### `im_alpha`\n\nThe alpha of parameter `skin`. \nThis is mostly a holdover from the Python implementation to deal with NumPy.\n\n#### `kernel`\n\nOne of `KernelKind`.\n\n## Performance\n\nThe entire reason for me writing this was NumPy being slow when this operation is in the hot path.\nSo, I decided to write a SIMD version that does the type casting outside NumPy with only the intermediate values being in FP32.\n\nHow much of a speedup is this? All numbers are from a Ryzen 7 4800H running Windows 11 and Python 3.12.4.\n\n| Method/Kernel | Average Iteration Time |\n|-------------------|------------------------|\n| C scalar kernel | 0.019565s |\n| C SSE4.2 kernel | 0.013705s |\n| C AVX2 kernel | 0.016842s |\n| NumPy version | 0.228098s |\n| Old NumPy version | 0.350554s |\n\n| Method Comparison | Speedup |\n|--------------------|----------|\n| NumPy -> scalar | 91.4227% |\n| NumPy -> SSE4.2 | 93.9915% |\n| NumPy -> AVX2 | 92.6165% |\n| Old np -> SSE4.2 | 96.0904% |\n| C scalar -> SSE4.2 | 29.9487% |\n| C scalar -> AVX2 | 13.9183% |\n",
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