I couldn't find it unexpectedly, so it is written in HSV Color Space-Wikipedia. Implemented based on the expression.

If you just want to convert RGB and HSV, you can use other libraries (PIL, OpenCV, etc.), but you can't differentiate with that, so you can't incorporate it inside the neural network. So I wrote a function to convert RGB and HSV to each other using only PyTorch functions.

` color_convert.py`

```
import torch
def rgb2hsv(input, epsilon=1e-10):
assert(input.shape[1] == 3)
r, g, b = input[:, 0], input[:, 1], input[:, 2]
max_rgb, argmax_rgb = input.max(1)
min_rgb, argmin_rgb = input.min(1)
max_min = max_rgb - min_rgb + epsilon
h1 = 60.0 * (g - r) / max_min + 60.0
h2 = 60.0 * (b - g) / max_min + 180.0
h3 = 60.0 * (r - b) / max_min + 300.0
h = torch.stack((h2, h3, h1), dim=0).gather(dim=0, index=argmin_rgb.unsqueeze(0)).squeeze(0)
s = max_min / (max_rgb + epsilon)
v = max_rgb
return torch.stack((h, s, v), dim=1)
def hsv2rgb(input):
assert(input.shape[1] == 3)
h, s, v = input[:, 0], input[:, 1], input[:, 2]
h_ = (h - torch.floor(h / 360) * 360) / 60
c = s * v
x = c * (1 - torch.abs(torch.fmod(h_, 2) - 1))
zero = torch.zeros_like(c)
y = torch.stack((
torch.stack((c, x, zero), dim=1),
torch.stack((x, c, zero), dim=1),
torch.stack((zero, c, x), dim=1),
torch.stack((zero, x, c), dim=1),
torch.stack((x, zero, c), dim=1),
torch.stack((c, zero, x), dim=1),
), dim=0)
index = torch.repeat_interleave(torch.floor(h_).unsqueeze(1), 3, dim=1).unsqueeze(0).to(torch.long)
rgb = (y.gather(dim=0, index=index) + (v - c)).squeeze(0)
return rgb
```

Both `rgb2hsv`

and` hsv2rgb`

take images in a mini-batch of images (NCHW format) as input.
The range of H (hue) is from 0 to 360 (the outside of the range loops).
The range of RGB and SV is 0 to 1.

Recommended Posts