# camera_match
`camera_match` is a Python library that provides basic models to match camera colour responses. Using `camera_match`, you can take two cameras with different colour profiles and build a colour pipeline that minimises the difference between them.
Currently, `camera_match` implements the following models:
- Linear Colour Correction Matrix
- Root Polynomial Matrix
- Steve Yedlin's Tetrahedral Matrix
- (Experimental) EMoR Response Curves
- RGB Curve Interpolation
- Radial Basis Functions
## Installation
(Recommended) Install the full package with the optional RBF library:
```bash
pip install camera_match[RBF]
```
If you don't need to create LUT's using RBF, you can install the base library:
```bash
pip install camera_match
```
## Examples
### Creating a 3x3 Matrix
A simple matrix that can be used with Resolve's Colour Mixer or any RGB matrix. Can only capture linear changes in colour.
```python
import numpy as np
from camera_match import LinearMatrix
# Import samples of a colour chart for your source camera:
bmpcc_data = np.array([
[0.0460915677249, 0.0414372496307, 0.0392063446343],
[0.0711114183068, 0.0562727414072, 0.0510282665491],
[0.0467581525445, 0.0492189191282, 0.0505541190505]
# ...Additional colour samples
])
# Import corresponding colour patches for your target camera:
film_data = np.array([
[0.0537128634751, 0.0549002364278, 0.0521950721741],
[0.0779063776135, 0.0621158666909, 0.0541097335517],
[0.051306720823, 0.0570512823761, 0.0635398775339]
# ...Additional colour samples
])
# Create a new LinearMatrix:
matrix = LinearMatrix()
# Find the optimum values to match the two cameras:
matrix.solve(bmpcc_data, film_data)
# Plot the result:
matrix.plot()
# Print the matrix:
print(matrix.matrix)
```
### Creating a LUT using RBF
Radial Basis Functions (RBF) allows you to create a LUT that smoothly maps your dataset in 3D. This means you can capture complex colour responses that linear matricies can't capture.
```python
import numpy as np
from camera_match import RBF
# Import samples of a colour chart for your source camera:
bmpcc_data = np.array([
[0.0460915677249, 0.0414372496307, 0.0392063446343],
[0.0711114183068, 0.0562727414072, 0.0510282665491],
[0.0467581525445, 0.0492189191282, 0.0505541190505]
# ...Additional colour samples
])
# Import corresponding colour patches for your target camera:
film_data = np.array([
[0.0537128634751, 0.0549002364278, 0.0521950721741],
[0.0779063776135, 0.0621158666909, 0.0541097335517],
[0.051306720823, 0.0570512823761, 0.0635398775339]
# ...Additional colour samples
])
# Create a new RBF node:
rbf = RBF()
# Find the optimum values to match the two cameras:
rbf.solve(bmpcc_data, film_data)
# Plot the result:
rbf.plot()
# Export as a LUT:
rbf.export_LUT(path="LUT.cube")
```
