"""

flim - Bean's Filmic Transform

Input Color Space: Linear BT.709 I-D65

Output Color Space: sRGB 2.2

Repo:

https://github.com/bean-mhm/flim

"""

import numpy as np

import colour

import joblib

from utils import *

vt_name = 'flim'

vt_version = '0.4.0'

# Transform a 3D LUT

def apply_transform(table: np.ndarray, compress_lg2_min, compress_lg2_max, parallel):

if len(table.shape) != 4:

raise Exception('table must have 4 dimensions (3 for xyz, 1 for the color channels)')

if table.shape[3] != 3:

raise Exception('the fourth axis must have a size of 3 (RGB)')

# Decompress: Map Range

table = colour.algebra.linear_conversion(

table,

np.array([0.0, 1.0]),

np.array([compress_lg2_min, compress_lg2_max])

)

# Decompress: Exponent

colour.algebra.set_spow_enable(True)

table = np.power(2.0, table)

# Decompress: Black Point

offset = (2.0**compress_lg2_min)

table -= offset

# Eliminate negative values (useless)

table = np.maximum(table, 0.0)

# Pre-Exposure

pre_exposure = 1.0

table *= (2**pre_exposure)

# Apply element-wise transform (calls transform_rgb)

if parallel:

print('Starting parallel element-wise transform...')

num_points = table.shape[0] * table.shape[1] * table.shape[2]

stride_y = table.shape[0]

stride_z = table.shape[0] * table.shape[1]

results = joblib.Parallel(n_jobs=8)(

joblib.delayed(run_parallel)(table, (i % stride_y, (i % stride_z) // stride_y, i // stride_z)) for i in range(num_points)

)

# Arrange the results

print('Arranging the results...')

for z in range(table.shape[2]):

for y in range(table.shape[1]):

for x in range(table.shape[0]):

index = x + (y * stride_y) + (z * stride_z)

table[x, y, z] = results[index]

else:

print('Starting serial element-wise transform...')

for z in range(table.shape[2]):

for y in range(table.shape[1]):

print(f'at [0, {y}, {z}]')

for x in range(table.shape[0]):

table[x, y, z] = transform_rgb(table[x, y, z])

# OETF (Gamma 2.2)

table = colour.algebra.spow(table, 1.0 / 2.2)

return table

def run_parallel(table, indices):

result = transform_rgb(table[indices])

print(f'{indices} done')

return result

# Transform a single RGB triplet

# This function should only be called by apply_transform.

def transform_rgb(inp):

# Gamut Extension Matrix (Linear BT.709)

extend_mat = flim_gamut_extension_mat(red_scale = 1.05, green_scale = 1.12, blue_scale = 1.045, red_rot = 0.5, green_rot = 3.0, blue_rot = 0.0)

extend_mat_inv = np.linalg.inv(extend_mat)

# Convert to extended gamut

inp = np.matmul(extend_mat, inp)

# Develop Negative

inp = flim_rgb_develop(inp, exposure = 5.5, blue_sens = 1.0, green_sens = 1.0, red_sens = 1.0, max_density = 10.0)

# Develop Print

inp = flim_rgb_develop(inp, exposure = 5.5, blue_sens = 1.0, green_sens = 1.0, red_sens = 1.0, max_density = 14.2)

# Convert from extended gamut

inp = np.matmul(extend_mat_inv, inp)

# Eliminate negative values

inp = np.maximum(inp, 0.0)

# Highlight Cap

inp = inp / 0.883169

# Black Point

inp = rgb_uniform_offset(inp, black_point = 0.84, white_point = 0.0)

# Clamp

inp = np.clip(inp, 0, 1)

# Midtone Saturation

mono = rgb_avg(inp)

mix = map_range_clamp(mono, 0.05, 0.5, 0.0, 1.0) if mono <= 0.5 else map_range_clamp(mono, 0.5, 0.95, 1.0, 0.0)

inp = lerp(inp, blender_hue_sat(inp, 0.5, 1.02, 1.0), mix)

# Clip and return

return np.clip(inp, 0, 1)