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// qcms
// Copyright (C) 2009 Mozilla Foundation
// Copyright (C) 1998-2007 Marti Maria
//
// Permission is hereby granted, free of charge, to any person obtaining
// a copy of this software and associated documentation files (the "Software"),
// to deal in the Software without restriction, including without limitation
// the rights to use, copy, modify, merge, publish, distribute, sublicense,
// and/or sell copies of the Software, and to permit persons to whom the Software
// is furnished to do so, subject to the following conditions:
//
// The above copyright notice and this permission notice shall be included in
// all copies or substantial portions of the Software.
//
// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO
// THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
// LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
// OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
// WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
use std::convert::TryInto;
use crate::{
iccread::{curveType, Profile},
s15Fixed16Number_to_float,
};
use crate::{matrix::Matrix, transform::PRECACHE_OUTPUT_MAX, transform::PRECACHE_OUTPUT_SIZE};
//XXX: could use a bettername
pub type uint16_fract_t = u16;
#[inline]
fn u8Fixed8Number_to_float(x: u16) -> f32 {
// 0x0000 = 0.
// 0x0100 = 1.
// 0xffff = 255 + 255/256
(x as i32 as f64 / 256.0f64) as f32
}
#[inline]
pub fn clamp_float(a: f32) -> f32 {
/* One would naturally write this function as the following:
if (a > 1.)
return 1.;
else if (a < 0)
return 0;
else
return a;
However, that version will let NaNs pass through which is undesirable
for most consumers.
*/
if a > 1. {
1.
} else if a >= 0. {
a
} else {
// a < 0 or a is NaN
0.
}
}
/* value must be a value between 0 and 1 */
//XXX: is the above a good restriction to have?
// the output range of this functions is 0..1
pub fn lut_interp_linear(mut input_value: f64, table: &[u16]) -> f32 {
input_value *= (table.len() - 1) as f64;
let upper: i32 = input_value.ceil() as i32;
let lower: i32 = input_value.floor() as i32;
let value: f32 = ((table[upper as usize] as f64) * (1. - (upper as f64 - input_value))
+ (table[lower as usize] as f64 * (upper as f64 - input_value)))
as f32;
/* scale the value */
value * (1.0 / 65535.0)
}
/* same as above but takes and returns a uint16_t value representing a range from 0..1 */
#[no_mangle]
pub fn lut_interp_linear16(input_value: u16, table: &[u16]) -> u16 {
/* Start scaling input_value to the length of the array: 65535*(length-1).
* We'll divide out the 65535 next */
let mut value: u32 = (input_value as i32 * (table.len() as i32 - 1)) as u32; /* equivalent to ceil(value/65535) */
let upper: u32 = (value + 65534) / 65535; /* equivalent to floor(value/65535) */
let lower: u32 = value / 65535;
/* interp is the distance from upper to value scaled to 0..65535 */
let interp: u32 = value % 65535; // 0..65535*65535
value = (table[upper as usize] as u32 * interp
+ table[lower as usize] as u32 * (65535 - interp))
/ 65535;
value as u16
}
/* same as above but takes an input_value from 0..PRECACHE_OUTPUT_MAX
* and returns a uint8_t value representing a range from 0..1 */
fn lut_interp_linear_precache_output(input_value: u32, table: &[u16]) -> u8 {
/* Start scaling input_value to the length of the array: PRECACHE_OUTPUT_MAX*(length-1).
* We'll divide out the PRECACHE_OUTPUT_MAX next */
let mut value: u32 = input_value * (table.len() - 1) as u32;
/* equivalent to ceil(value/PRECACHE_OUTPUT_MAX) */
let upper: u32 = (value + PRECACHE_OUTPUT_MAX as u32 - 1) / PRECACHE_OUTPUT_MAX as u32;
/* equivalent to floor(value/PRECACHE_OUTPUT_MAX) */
let lower: u32 = value / PRECACHE_OUTPUT_MAX as u32;
/* interp is the distance from upper to value scaled to 0..PRECACHE_OUTPUT_MAX */
let interp: u32 = value % PRECACHE_OUTPUT_MAX as u32;
/* the table values range from 0..65535 */
value = table[upper as usize] as u32 * interp
+ table[lower as usize] as u32 * (PRECACHE_OUTPUT_MAX as u32 - interp); // 0..(65535*PRECACHE_OUTPUT_MAX)
/* round and scale */
value += (PRECACHE_OUTPUT_MAX * 65535 / 255 / 2) as u32; // scale to 0..255
value /= (PRECACHE_OUTPUT_MAX * 65535 / 255) as u32;
value as u8
}
/* value must be a value between 0 and 1 */
//XXX: is the above a good restriction to have?
pub fn lut_interp_linear_float(mut value: f32, table: &[f32]) -> f32 {
value *= (table.len() - 1) as f32;
let upper: i32 = value.ceil() as i32;
let lower: i32 = value.floor() as i32;
//XXX: can we be more performant here?
value = (table[upper as usize] as f64 * (1.0f64 - (upper as f32 - value) as f64)
+ (table[lower as usize] * (upper as f32 - value)) as f64) as f32;
/* scale the value */
value
}
fn compute_curve_gamma_table_type1(gamma: u16) -> Box<[f32; 256]> {
let mut gamma_table = Vec::with_capacity(256);
let gamma_float: f32 = u8Fixed8Number_to_float(gamma);
for i in 0..256 {
// 0..1^(0..255 + 255/256) will always be between 0 and 1
gamma_table.push((i as f64 / 255.0f64).powf(gamma_float as f64) as f32);
}
gamma_table.into_boxed_slice().try_into().unwrap()
}
fn compute_curve_gamma_table_type2(table: &[u16]) -> Box<[f32; 256]> {
let mut gamma_table = Vec::with_capacity(256);
for i in 0..256 {
gamma_table.push(lut_interp_linear(i as f64 / 255.0f64, table));
}
gamma_table.into_boxed_slice().try_into().unwrap()
}
fn compute_curve_gamma_table_type_parametric(params: &[f32]) -> Box<[f32; 256]> {
let params = Param::new(params);
let mut gamma_table = Vec::with_capacity(256);
for i in 0..256 {
let X = i as f32 / 255.;
gamma_table.push(clamp_float(params.eval(X)));
}
gamma_table.into_boxed_slice().try_into().unwrap()
}
fn compute_curve_gamma_table_type0() -> Box<[f32; 256]> {
let mut gamma_table = Vec::with_capacity(256);
for i in 0..256 {
gamma_table.push((i as f64 / 255.0f64) as f32);
}
gamma_table.into_boxed_slice().try_into().unwrap()
}
pub(crate) fn build_input_gamma_table(TRC: Option<&curveType>) -> Option<Box<[f32; 256]>> {
let TRC = match TRC {
Some(TRC) => TRC,
None => return None,
};
Some(match TRC {
curveType::Parametric(params) => compute_curve_gamma_table_type_parametric(params),
curveType::Curve(data) => match data.len() {
0 => compute_curve_gamma_table_type0(),
1 => compute_curve_gamma_table_type1(data[0]),
_ => compute_curve_gamma_table_type2(data),
},
})
}
pub fn build_colorant_matrix(p: &Profile) -> Matrix {
let mut result: Matrix = Matrix { m: [[0.; 3]; 3] };
result.m[0][0] = s15Fixed16Number_to_float(p.redColorant.X);
result.m[0][1] = s15Fixed16Number_to_float(p.greenColorant.X);
result.m[0][2] = s15Fixed16Number_to_float(p.blueColorant.X);
result.m[1][0] = s15Fixed16Number_to_float(p.redColorant.Y);
result.m[1][1] = s15Fixed16Number_to_float(p.greenColorant.Y);
result.m[1][2] = s15Fixed16Number_to_float(p.blueColorant.Y);
result.m[2][0] = s15Fixed16Number_to_float(p.redColorant.Z);
result.m[2][1] = s15Fixed16Number_to_float(p.greenColorant.Z);
result.m[2][2] = s15Fixed16Number_to_float(p.blueColorant.Z);
result
}
/** Parametric representation of transfer function */
#[derive(Debug)]
struct Param {
g: f32,
a: f32,
b: f32,
c: f32,
d: f32,
e: f32,
f: f32,
}
impl Param {
#[allow(clippy::many_single_char_names)]
fn new(params: &[f32]) -> Param {
// convert from the variable number of parameters
// contained in profiles to a unified representation.
let g: f32 = params[0];
match params[1..] {
[] => Param {
g,
a: 1.,
b: 0.,
c: 1.,
d: 0.,
e: 0.,
f: 0.,
},
[a, b] => Param {
g,
a,
b,
c: 0.,
d: -b / a,
e: 0.,
f: 0.,
},
[a, b, c] => Param {
g,
a,
b,
c: 0.,
d: -b / a,
e: c,
f: c,
},
[a, b, c, d] => Param {
g,
a,
b,
c,
d,
e: 0.,
f: 0.,
},
[a, b, c, d, e, f] => Param {
g,
a,
b,
c,
d,
e,
f,
},
_ => panic!(),
}
}
fn eval(&self, x: f32) -> f32 {
if x < self.d {
self.c * x + self.f
} else {
(self.a * x + self.b).powf(self.g) + self.e
}
}
#[allow(clippy::many_single_char_names)]
fn invert(&self) -> Option<Param> {
// First check if the function is continuous at the cross-over point d.
let d1 = (self.a * self.d + self.b).powf(self.g) + self.e;
let d2 = self.c * self.d + self.f;
if (d1 - d2).abs() > 0.1 {
return None;
}
let d = d1;
// y = (a * x + b)^g + e
// y - e = (a * x + b)^g
// (y - e)^(1/g) = a*x + b
// (y - e)^(1/g) - b = a*x
// (y - e)^(1/g)/a - b/a = x
// ((y - e)/a^g)^(1/g) - b/a = x
// ((1/(a^g)) * y - e/(a^g))^(1/g) - b/a = x
let a = 1. / self.a.powf(self.g);
let b = -self.e / self.a.powf(self.g);
let g = 1. / self.g;
let e = -self.b / self.a;
// y = c * x + f
// y - f = c * x
// y/c - f/c = x
let (c, f);
if d <= 0. {
c = 1.;
f = 0.;
} else {
c = 1. / self.c;
f = -self.f / self.c;
}
// if self.d > 0. and self.c == 0 as is likely with type 1 and 2 parametric function
// then c and f will not be finite.
if !(g.is_finite()
&& a.is_finite()
&& b.is_finite()
&& c.is_finite()
&& d.is_finite()
&& e.is_finite()
&& f.is_finite())
{
return None;
}
Some(Param {
g,
a,
b,
c,
d,
e,
f,
})
}
}
#[test]
fn param_invert() {
let p3 = Param::new(&[2.4, 0.948, 0.052, 0.077, 0.04]);
p3.invert().unwrap();
let g2_2 = Param::new(&[2.2]);
g2_2.invert().unwrap();
let g2_2 = Param::new(&[2.2, 0.9, 0.052]);
g2_2.invert().unwrap();
let g2_2 = dbg!(Param::new(&[2.2, 0.9, -0.52]));
g2_2.invert().unwrap();
let g2_2 = dbg!(Param::new(&[2.2, 0.9, -0.52, 0.1]));
assert!(g2_2.invert().is_none());
}
/* The following code is copied nearly directly from lcms.
* I think it could be much better. For example, Argyll seems to have better code in
* icmTable_lookup_bwd and icmTable_setup_bwd. However, for now this is a quick way
* to a working solution and allows for easy comparing with lcms. */
#[no_mangle]
#[allow(clippy::many_single_char_names)]
pub fn lut_inverse_interp16(Value: u16, LutTable: &[u16]) -> uint16_fract_t {
let mut l: i32 = 1; // 'int' Give spacing for negative values
let mut r: i32 = 0x10000;
let mut x: i32 = 0;
let mut res: i32;
let length = LutTable.len() as i32;
let mut NumZeroes: i32 = 0;
while LutTable[NumZeroes as usize] as i32 == 0 && NumZeroes < length - 1 {
NumZeroes += 1
}
// There are no zeros at the beginning and we are trying to find a zero, so
// return anything. It seems zero would be the less destructive choice
/* I'm not sure that this makes sense, but oh well... */
if NumZeroes == 0 && Value as i32 == 0 {
return 0u16;
}
let mut NumPoles: i32 = 0;
while LutTable[(length - 1 - NumPoles) as usize] as i32 == 0xffff && NumPoles < length - 1 {
NumPoles += 1
}
// Does the curve belong to this case?
if NumZeroes > 1 || NumPoles > 1 {
let a_0: i32;
let b_0: i32;
// Identify if value fall downto 0 or FFFF zone
if Value as i32 == 0 {
return 0u16;
}
// if (Value == 0xFFFF) return 0xFFFF;
// else restrict to valid zone
if NumZeroes > 1 {
a_0 = (NumZeroes - 1) * 0xffff / (length - 1);
l = a_0 - 1
}
if NumPoles > 1 {
b_0 = (length - 1 - NumPoles) * 0xffff / (length - 1);
r = b_0 + 1
}
}
if r <= l {
// If this happens LutTable is not invertible
return 0u16;
}
// Seems not a degenerated case... apply binary search
while r > l {
x = (l + r) / 2;
res = lut_interp_linear16((x - 1) as uint16_fract_t, LutTable) as i32;
if res == Value as i32 {
// Found exact match.
return (x - 1) as uint16_fract_t;
}
if res > Value as i32 {
r = x - 1
} else {
l = x + 1
}
}
// Not found, should we interpolate?
// Get surrounding nodes
debug_assert!(x >= 1);
let val2: f64 = (length - 1) as f64 * ((x - 1) as f64 / 65535.0f64);
let cell0: i32 = val2.floor() as i32;
let cell1: i32 = val2.ceil() as i32;
if cell0 == cell1 {
return x as uint16_fract_t;
}
let y0: f64 = LutTable[cell0 as usize] as f64;
let x0: f64 = 65535.0f64 * cell0 as f64 / (length - 1) as f64;
let y1: f64 = LutTable[cell1 as usize] as f64;
let x1: f64 = 65535.0f64 * cell1 as f64 / (length - 1) as f64;
let a: f64 = (y1 - y0) / (x1 - x0);
let b: f64 = y0 - a * x0;
if a.abs() < 0.01f64 {
return x as uint16_fract_t;
}
let f: f64 = (Value as i32 as f64 - b) / a;
if f < 0.0f64 {
return 0u16;
}
if f >= 65535.0f64 {
return 0xffffu16;
}
(f + 0.5f64).floor() as uint16_fract_t
}
/*
The number of entries needed to invert a lookup table should not
necessarily be the same as the original number of entries. This is
especially true of lookup tables that have a small number of entries.
For example:
Using a table like:
{0, 3104, 14263, 34802, 65535}
invert_lut will produce an inverse of:
{3, 34459, 47529, 56801, 65535}
which has an maximum error of about 9855 (pixel difference of ~38.346)
For now, we punt the decision of output size to the caller. */
fn invert_lut(table: &[u16], out_length: usize) -> Vec<u16> {
/* for now we invert the lut by creating a lut of size out_length
* and attempting to lookup a value for each entry using lut_inverse_interp16 */
let mut output = Vec::with_capacity(out_length);
for i in 0..out_length {
let x: f64 = i as f64 * 65535.0f64 / (out_length - 1) as f64;
let input: uint16_fract_t = (x + 0.5f64).floor() as uint16_fract_t;
output.push(lut_inverse_interp16(input, table));
}
output
}
#[allow(clippy::needless_range_loop)]
fn compute_precache_pow(output: &mut [u8; PRECACHE_OUTPUT_SIZE], gamma: f32) {
for v in 0..PRECACHE_OUTPUT_SIZE {
//XXX: don't do integer/float conversion... and round?
output[v] = (255. * (v as f32 / PRECACHE_OUTPUT_MAX as f32).powf(gamma)) as u8;
}
}
#[allow(clippy::needless_range_loop)]
pub fn compute_precache_lut(output: &mut [u8; PRECACHE_OUTPUT_SIZE], table: &[u16]) {
for v in 0..PRECACHE_OUTPUT_SIZE {
output[v] = lut_interp_linear_precache_output(v as u32, table);
}
}
#[allow(clippy::needless_range_loop)]
pub fn compute_precache_linear(output: &mut [u8; PRECACHE_OUTPUT_SIZE]) {
for v in 0..PRECACHE_OUTPUT_SIZE {
//XXX: round?
output[v] = (v / (PRECACHE_OUTPUT_SIZE / 256)) as u8;
}
}
pub(crate) fn compute_precache(trc: &curveType, output: &mut [u8; PRECACHE_OUTPUT_SIZE]) {
match trc {
curveType::Parametric(params) => {
let mut gamma_table_uint: [u16; 256] = [0; 256];
let mut inverted_size: usize = 256;
let gamma_table = compute_curve_gamma_table_type_parametric(params);
let mut i: u16 = 0u16;
while (i as i32) < 256 {
gamma_table_uint[i as usize] = (gamma_table[i as usize] * 65535f32) as u16;
i += 1
}
//XXX: the choice of a minimum of 256 here is not backed by any theory,
// measurement or data, however it is what lcms uses.
// the maximum number we would need is 65535 because that's the
// accuracy used for computing the pre cache table
if inverted_size < 256 {
inverted_size = 256
}
let inverted = invert_lut(&gamma_table_uint, inverted_size);
compute_precache_lut(output, &inverted);
}
curveType::Curve(data) => {
match data.len() {
0 => compute_precache_linear(output),
1 => compute_precache_pow(output, 1. / u8Fixed8Number_to_float(data[0])),
_ => {
let mut inverted_size = data.len();
//XXX: the choice of a minimum of 256 here is not backed by any theory,
// measurement or data, however it is what lcms uses.
// the maximum number we would need is 65535 because that's the
// accuracy used for computing the pre cache table
if inverted_size < 256 {
inverted_size = 256
} //XXX turn this conversion into a function
let inverted = invert_lut(data, inverted_size);
compute_precache_lut(output, &inverted);
}
}
}
}
}
fn build_linear_table(length: usize) -> Vec<u16> {
let mut output = Vec::with_capacity(length);
for i in 0..length {
let x: f64 = i as f64 * 65535.0f64 / (length - 1) as f64;
let input: uint16_fract_t = (x + 0.5f64).floor() as uint16_fract_t;
output.push(input);
}
output
}
fn build_pow_table(gamma: f32, length: usize) -> Vec<u16> {
let mut output = Vec::with_capacity(length);
for i in 0..length {
let mut x: f64 = i as f64 / (length - 1) as f64;
x = x.powf(gamma as f64);
let result: uint16_fract_t = (x * 65535.0f64 + 0.5f64).floor() as uint16_fract_t;
output.push(result);
}
output
}
fn to_lut(params: &Param, len: usize) -> Vec<u16> {
let mut output = Vec::with_capacity(len);
for i in 0..len {
let X = i as f32 / (len-1) as f32;
output.push((params.eval(X) * 65535.) as u16);
}
output
}
pub(crate) fn build_lut_for_linear_from_tf(trc: &curveType,
lut_len: Option<usize>) -> Vec<u16> {
match trc {
curveType::Parametric(params) => {
let lut_len = lut_len.unwrap_or(256);
let params = Param::new(params);
to_lut(¶ms, lut_len)
},
curveType::Curve(data) => {
let autogen_lut_len = lut_len.unwrap_or(4096);
match data.len() {
0 => build_linear_table(autogen_lut_len),
1 => {
let gamma = u8Fixed8Number_to_float(data[0]);
build_pow_table(gamma, autogen_lut_len)
}
_ => {
let lut_len = lut_len.unwrap_or(data.len());
assert_eq!(lut_len, data.len());
data.clone() // I feel bad about this.
}
}
},
}
}
pub(crate) fn build_lut_for_tf_from_linear(trc: &curveType) -> Option<Vec<u16>> {
match trc {
curveType::Parametric(params) => {
let lut_len = 256;
let params = Param::new(params);
if let Some(inv_params) = params.invert() {
return Some(to_lut(&inv_params, lut_len));
}
// else return None instead of fallthrough to generic lut inversion.
return None;
},
curveType::Curve(data) => {
let autogen_lut_len = 4096;
match data.len() {
0 => {
return Some(build_linear_table(autogen_lut_len));
},
1 => {
let gamma = 1. / u8Fixed8Number_to_float(data[0]);
return Some(build_pow_table(gamma, autogen_lut_len));
},
_ => {},
}
},
}
let linear_from_tf = build_lut_for_linear_from_tf(trc, None);
//XXX: the choice of a minimum of 256 here is not backed by any theory,
// measurement or data, however it is what lcms uses.
let inverted_lut_len = std::cmp::max(linear_from_tf.len(), 256);
Some(invert_lut(&linear_from_tf, inverted_lut_len))
}
pub(crate) fn build_output_lut(trc: &curveType) -> Option<Vec<u16>> {
build_lut_for_tf_from_linear(trc)
}