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*.tiff filter=lfs diff=lfs merge=lfs -text
*.jpeg filter=lfs diff=lfs merge=lfs -text

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build/*
build/
build

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# CLAUDE.md
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
## Project Overview
Chrome is a high-performance C++17 port of negfix8, a tool for processing scanned color negative film. It inverts and color-corrects 16-bit linear Rec.709 TIFF images from film scanners.
## Build Commands
```bash
# Configure and build
cmake -B build/ -S .
cd build && make
# The executable is built at build/chrome
```
**Dependencies:** OpenCV (core, imgproc, imgcodecs), OpenMP (optional, for parallelization)
## Testing
Manual testing only. Compare output against reference files in `test_data/`:
- `test_data/test_input.tiff` - Sample input scan
- `test_data/test_expected.tiff` - Reference output from original negfix8
- `test_data/current_output.tiff` - Current implementation output
```bash
./build/chrome -i test_data/test_input.tiff -o test_data/current_output.tiff
# Then visually compare or diff against test_data/test_expected.tiff
```
## Usage
```bash
./chrome -i input.tif [-o output.tif] [flags]
Flags:
-g <val> Set gamma (default 2.15)
-cs Enable contrast stretching
-sat Enable saturation boost (+20%)
-m Mirror image horizontally
-r <N> Binning (scale down by 1/N)
-c <file> Create profile and save to file (exits after)
-u <file> Use existing profile
```
## Architecture
Single-file implementation in `main.cpp` with this pipeline:
1. **Settings/Profile structs** - CLI options and image analysis data
2. **Image analysis** (`analyzeImage`) - Finds min/max per channel after shave+blur preprocessing
3. **Profile save/load** - Stores derived gamma corrections and offset values
4. **LUT generation** (`generateLUTs`) - Creates 65536-entry lookup tables for each channel using OpenMP
5. **Pixel processing** - Applies LUTs in parallel via OpenMP
6. **Post-processing** - Optional saturation boost, contrast stretch, mirror
7. **Output** - Writes LZW-compressed TIFF
The core algorithm inverts negative film by dividing film base values by pixel values, applies per-channel gamma correction to neutralize orange mask, then applies global gamma.

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cmake_minimum_required(VERSION 3.10)
project(chrome_cpp)
set(CMAKE_CXX_STANDARD 17)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O3 -march=native")
# FIX: Explicitly request ONLY the modules we need.
# This prevents the linker from looking for missing VTK/HDF5 libraries
# referenced by unused OpenCV modules.
find_package(OpenCV REQUIRED COMPONENTS core imgproc imgcodecs)
find_package(OpenMP)
if(OpenMP_CXX_FOUND)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}")
endif()
add_executable(chrome main.cpp)
# Link against the specific components found above
target_link_libraries(chrome ${OpenCV_LIBRARIES})
if(OpenMP_CXX_FOUND)
target_link_libraries(chrome OpenMP::OpenMP_CXX)
endif()

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# Chrome
A high-performance C++ port of [negfix8](https://github.com/chrishunt/negfix8) for processing scanned color negative film. Inverts and color-corrects 16-bit linear Rec.709 TIFF images from film scanners.
## Example
<table>
<tr>
<td align="center"><strong>Input (Scanned Negative)</strong></td>
<td align="center"></td>
<td align="center"><strong>Output (Processed)</strong></td>
</tr>
<tr>
<td><img src="test_data/test_input.jpeg" width="400" alt="Scanned negative"></td>
<td align="center">➜<br><code>chrome -cs -sat 50 -exp 1.2</code><br>➜</td>
<td><img src="test_data/current_output_adj.jpeg" width="400" alt="Processed output"></td>
</tr>
</table>
## Performance
| Tool | Time | Speedup |
|------|------|---------|
| negfix8 (original) | 18m 8s | 1x |
| **Chrome** | **~10s** | **~109x** |
*Benchmark: 9098×12160 16-bit TIFF (~332MB) on AMD Ryzen 9 7950X3D (32 threads, 64GB RAM)*
## Features
- LUT-based pixel processing with OpenMP parallelization
- Per-channel gamma correction to neutralize orange mask
- Profile save/load for consistent batch processing
- Optional saturation boost, contrast stretching, exposure adjustment, mirror
## Building
### Dependencies
- CMake 3.x+
- OpenCV (core, imgproc, imgcodecs)
- OpenMP (optional, for parallelization)
#### Ubuntu/Debian
```bash
sudo apt install cmake libopencv-dev
```
#### macOS (Homebrew)
```bash
brew install cmake opencv libomp
```
### Build
```bash
cmake -B build -S .
cmake --build build
```
The executable is built at `build/chrome`.
## Usage
```bash
./build/chrome -i input.tif [-o output.tif] [flags]
```
### Flags
| Flag | Description |
|------|-------------|
| `-g <val>` | Set gamma (default 2.15) |
| `-cs` | Enable contrast stretching |
| `-exp <N>` | Exposure adjustment in stops (-10 to +10) |
| `-sat [N]` | Saturation boost, N=0-100 (default 20 if no value) |
| `-m` | Mirror image horizontally |
| `-r <N>` | Binning/downscale by factor N |
| `-c <file>` | Create profile from image and save (exits after) |
| `-u <file>` | Use existing profile for processing |
### Examples
```bash
# Basic conversion
./build/chrome -i scan.tiff -o output.tiff
# With adjustments (contrast stretch, 50% saturation boost, +1.2 stops exposure)
./build/chrome -i scan.tiff -o output.tiff -cs -sat 50 -exp 1.2
# Create a profile for batch processing
./build/chrome -i scan.tiff -c myfilm.profile
# Apply saved profile to multiple images
./build/chrome -i scan2.tiff -o output2.tiff -u myfilm.profile
```
## Testing
Test files are located in `test_data/`:
| File | Description |
|------|-------------|
| `test_input.tiff` | Sample 16-bit scanned negative |
| `test_expected.tiff` | Reference output from original negfix8 |
| `current_output.tiff` | Chrome output (default settings) |
| `current_output_adj.tiff` | Chrome output with `-cs -sat 50 -exp 1.2` |
```bash
# Basic conversion
./build/chrome -i test_data/test_input.tiff -o test_data/current_output.tiff
# With adjustments (as shown in example above)
./build/chrome -i test_data/test_input.tiff -o test_data/current_output_adj.tiff -cs -sat 50 -exp 1.2
```
## Credits
- Original [negfix8](https://github.com/chrishunt/negfix8) by Chris Hunt
- Original negfix algorithm created by JaZ99
## License
Copyright 2025 DWS (Dubey Web Services, DWS LLC)
Licensed under the Apache License, Version 2.0. See [LICENSE](LICENSE) for details.

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/**
* negfix_cpp - High performance C++ port of negfix8
* Expects Linear Rec 709 16-bit TIFF Input.
*
* Usage:
* ./negfix -i input.tif -o output.tif [flags]
*/
#include <iostream>
#include <vector>
#include <string>
#include <cmath>
#include <algorithm>
#include <filesystem>
#include <fstream>
#include <opencv2/opencv.hpp>
#include <omp.h>
// Default settings from original script
struct Settings {
double gamma = 2.15;
bool contrastStretch = false;
bool mirror = false;
int binning = 1;
int saturationBoost = 0; // 0 = disabled, 1-100 = boost percentage
double exposureStops = 0.0; // 0 = disabled, range -10 to +10
std::string profileLoadPath = "";
std::string profileSavePath = "";
std::string inputPath = "";
std::string outputPath = "";
};
struct Profile {
double minR = 0, minG = 0, minB = 0;
double maxR = 0, maxG = 0, maxB = 0;
double gammaG = 0, gammaB = 0;
double offset = 0; // Calculated offset for black point
bool valid = false;
};
// Helper to parse arguments
bool parseArgs(int argc, char** argv, Settings& settings) {
for (int i = 1; i < argc; ++i) {
std::string arg = argv[i];
if (arg == "-i" && i + 1 < argc) settings.inputPath = argv[++i];
else if (arg == "-o" && i + 1 < argc) settings.outputPath = argv[++i];
else if (arg == "-g" && i + 1 < argc) settings.gamma = std::stod(argv[++i]);
else if (arg == "-cs") settings.contrastStretch = true;
else if (arg == "-m") settings.mirror = true;
else if (arg == "-sat") {
// Check if next argument is a number (optional value)
if (i + 1 < argc) {
try {
int val = std::stoi(argv[i + 1]);
if (val >= 0 && val <= 100) {
settings.saturationBoost = val;
++i; // consume the value argument
} else {
settings.saturationBoost = 20; // default if out of range
}
} catch (...) {
// Next arg is not a number, use default
settings.saturationBoost = 20;
}
} else {
settings.saturationBoost = 20; // default
}
}
else if (arg == "-exp" && i + 1 < argc) {
double val = std::stod(argv[++i]);
if (val < -10.0 || val > 10.0) {
std::cerr << "Error: -exp value must be between -10 and +10 stops\n";
exit(1);
}
settings.exposureStops = val;
}
else if (arg == "-r" && i + 1 < argc) settings.binning = std::stoi(argv[++i]);
else if (arg == "-c" && i + 1 < argc) settings.profileSavePath = argv[++i];
else if (arg == "-u" && i + 1 < argc) settings.profileLoadPath = argv[++i];
else if (arg == "-h" || arg == "--help") return false;
}
return !settings.inputPath.empty();
}
void printUsage() {
std::cout << "Usage: negfix_cpp -i input.tif [-o output.tif] [flags]\n"
<< "Flags:\n"
<< " -g <val> Set gamma (default 2.15)\n"
<< " -cs Enable contrast stretching\n"
<< " -exp <N> Exposure adjustment in stops (-10 to +10)\n"
<< " -sat [N] Saturation boost, N=0-100 (default 20)\n"
<< " -m Mirror image horizontally\n"
<< " -r <N> Binning (scale down by 1/N)\n"
<< " -c <file> Create profile and save to file (exits after)\n"
<< " -u <file> Use existing profile\n";
}
// Analyze image to find Min/Max (Film Base and Highlights)
// Simulates: -shave 10x10 -blur 3x3 then finding min/max
Profile analyzeImage(const cv::Mat& img) {
Profile prof;
// Create a copy for analysis to avoid modifying original yet
cv::Mat work;
img.copyTo(work);
// 1. Shave 10 pixels
int shave = 10;
if (work.cols > 20 && work.rows > 20) {
cv::Rect roi(shave, shave, work.cols - (shave*2), work.rows - (shave*2));
work = work(roi);
}
// 2. Blur 3x3
cv::blur(work, work, cv::Size(3, 3));
// 3. Split channels
std::vector<cv::Mat> channels(3);
cv::split(work, channels);
// 4. Find Min/Max
// Note: OpenCV loads 16bit TIFF as CV_16U.
// In original script:
// MINIMA (Darkest in file) corresponds to Scene HIGHLIGHTS (Densest negative)
// MAXIMA (Brightest in file) corresponds to Scene SHADOWS (Clear film base)
double minVal, maxVal;
// Blue (channel 0 in OpenCV)
cv::minMaxLoc(channels[0], &minVal, &maxVal);
prof.minB = minVal; prof.maxB = maxVal;
// Green (channel 1)
cv::minMaxLoc(channels[1], &minVal, &maxVal);
prof.minG = minVal; prof.maxG = maxVal;
// Red (channel 2)
cv::minMaxLoc(channels[2], &minVal, &maxVal);
prof.minR = minVal; prof.maxR = maxVal;
// Validate
if (prof.minR == 0 || prof.maxR == 0) {
std::cerr << "Error: Image stats invalid (0 values found). scan might be empty.\n";
exit(1);
}
prof.valid = true;
return prof;
}
void saveProfile(const std::string& path, const Profile& p, double userGamma) {
std::ofstream out(path);
// Format mimics original script: minR minG minB offset gammaG gammaB gammaGlobal
// Calculate the derived values to store
// Replicating original math:
// Offset calculation from script:
// ((minR / maxR)^(1/gamma)) * QuantumRange * 0.95
double quantum = 65535.0;
double offset = std::pow(p.minR / p.maxR, 1.0/userGamma) * quantum * 0.95;
// Gammas
double gG = std::log(p.maxG / p.minG) / std::log(p.maxR / p.minR);
double gB = std::log(p.maxB / p.minB) / std::log(p.maxR / p.minR);
out << p.minR << " " << p.minG << " " << p.minB << " "
<< offset << " " << gG << " " << gB << " " << userGamma << "\n";
out.close();
std::cout << "Profile saved to " << path << "\n";
}
Profile loadProfile(const std::string& path) {
Profile p;
std::ifstream in(path);
if (!in.is_open()) {
std::cerr << "Could not open profile: " << path << "\n";
exit(1);
}
double dummyGamma;
in >> p.minR >> p.minG >> p.minB >> p.offset >> p.gammaG >> p.gammaB >> dummyGamma;
p.valid = true;
return p;
}
// Generate LUTs for blazing fast application
void generateLUTs(std::vector<uint16_t>& lutR, std::vector<uint16_t>& lutG, std::vector<uint16_t>& lutB,
const Profile& p, double gamma) {
lutR.resize(65536);
lutG.resize(65536);
lutB.resize(65536);
// Calculate Gamma corrections if we analyzed image fresh
// If loaded from profile, these are already in p.gammaG/B
double gG, gB, offset;
if (p.offset == 0 && p.gammaG == 0) {
// Just analyzed, calculate derived values
gG = std::log(p.maxG / p.minG) / std::log(p.maxR / p.minR);
gB = std::log(p.maxB / p.minB) / std::log(p.maxR / p.minR);
offset = std::pow(p.minR / p.maxR, 1.0/gamma) * 65535.0 * 0.95;
} else {
// Loaded from file
gG = p.gammaG;
gB = p.gammaB;
offset = p.offset;
}
double invGamma = 1.0 / gamma;
// OpenMP loop for LUT generation
#pragma omp parallel for
for (int i = 0; i < 65536; ++i) {
double u = (double)i;
if (u < 1.0) u = 1.0; // avoid div by zero
// 1. Inversion Logic: Base / Input
// Original Script: R_out = MinR / Input
double r_norm = p.minR / u;
double g_norm = p.minG / u;
double b_norm = p.minB / u;
// 2. Channel Gamma Alignment
// R is reference (gamma 1 relative to itself)
// G and B: ImageMagick's -gamma X applies pow(value, 1/X)
// So we need pow(g_norm, 1/gG) not pow(g_norm, gG)
double r_aligned = r_norm;
double g_aligned = std::pow(g_norm, 1.0/gG);
double b_aligned = std::pow(b_norm, 1.0/gB);
// 3. Global Gamma Application
double r_final = std::pow(r_aligned, invGamma);
double g_final = std::pow(g_aligned, invGamma);
double b_final = std::pow(b_aligned, invGamma);
// 4. Scale to QuantumRange (assume internal math was normalized to 0..1?
// No, min/u results in small numbers if u is large.
// Wait, if u = minR, r_norm = 1.0. If u = maxR, r_norm = small.
// The power functions preserve the 0..1 range mostly.
// We need to scale up to 65535 BEFORE subtraction?
// Let's trace unit:
// MinR is ~300. u is ~300. Ratio = 1.
// MinR is ~300. u is ~60000. Ratio = 0.005.
// So we are in 0..1 range effectively.
r_final *= 65535.0;
g_final *= 65535.0;
b_final *= 65535.0;
// 5. Subtract Offset
r_final -= offset;
g_final -= offset;
b_final -= offset;
// Clamp
lutR[i] = cv::saturate_cast<uint16_t>(r_final);
lutG[i] = cv::saturate_cast<uint16_t>(g_final);
lutB[i] = cv::saturate_cast<uint16_t>(b_final);
}
}
int main(int argc, char** argv) {
Settings settings;
if (!parseArgs(argc, argv, settings)) {
printUsage();
return 0;
}
// Load Image
std::cout << "Loading " << settings.inputPath << "..." << std::endl;
cv::Mat img = cv::imread(settings.inputPath, cv::IMREAD_UNCHANGED);
if (img.empty()) {
std::cerr << "Failed to load image.\n";
return 1;
}
if (img.depth() != CV_16U) {
std::cerr << "Error: Input must be 16-bit.\n";
return 1;
}
// Determine Profile
Profile prof;
if (!settings.profileLoadPath.empty()) {
prof = loadProfile(settings.profileLoadPath);
std::cout << "Loaded profile " << settings.profileLoadPath << "\n";
} else {
std::cout << "Analyzing image profile..." << std::endl;
prof = analyzeImage(img);
if (!settings.profileSavePath.empty()) {
saveProfile(settings.profileSavePath, prof, settings.gamma);
return 0; // Exit after creating profile as per script logic behavior
}
}
// Binning (Resize)
if (settings.binning > 1) {
cv::resize(img, img, cv::Size(img.cols/settings.binning, img.rows/settings.binning), 0, 0, cv::INTER_AREA);
}
// Generate LUTs
std::cout << "Generating Lookup Tables..." << std::endl;
std::vector<uint16_t> lutR, lutG, lutB;
generateLUTs(lutR, lutG, lutB, prof, settings.gamma);
// Apply LUTs
std::cout << "Processing pixels..." << std::endl;
// If mono, process differently, but assuming Color input for Negfix logic
int rows = img.rows;
int cols = img.cols;
if (img.channels() == 3) {
// Parallel Loop over rows
#pragma omp parallel for
for (int r = 0; r < rows; ++r) {
uint16_t* ptr = img.ptr<uint16_t>(r);
for (int c = 0; c < cols; ++c) {
// OpenCV is BGR by default
uint16_t b = ptr[3*c + 0];
uint16_t g = ptr[3*c + 1];
uint16_t r_val = ptr[3*c + 2];
ptr[3*c + 0] = lutB[b];
ptr[3*c + 1] = lutG[g];
ptr[3*c + 2] = lutR[r_val];
}
}
} else {
// Monochrome case
#pragma omp parallel for
for (int r = 0; r < rows; ++r) {
uint16_t* ptr = img.ptr<uint16_t>(r);
for (int c = 0; c < cols; ++c) {
ptr[c] = lutG[ptr[c]]; // Use Green channel logic for B&W usually
}
}
}
// Optional: Exposure adjustment (applied before saturation)
if (settings.exposureStops != 0.0) {
std::cout << "Applying Exposure Adjustment (" << std::showpos << settings.exposureStops
<< std::noshowpos << " stops)..." << std::endl;
// multiplier = 2^stops: +1 stop = 2x, -1 stop = 0.5x
double multiplier = std::pow(2.0, settings.exposureStops);
#pragma omp parallel for
for (int r = 0; r < rows; ++r) {
uint16_t* ptr = img.ptr<uint16_t>(r);
for (int c = 0; c < cols * img.channels(); ++c) {
ptr[c] = cv::saturate_cast<uint16_t>(ptr[c] * multiplier);
}
}
}
// Optional: Saturation boost using Rec.709 luminance method
if (settings.saturationBoost > 0) {
std::cout << "Applying Saturation Boost (" << settings.saturationBoost << "%)..." << std::endl;
// sat_factor: 1.0 = no change, 1.2 = 20% boost, 2.0 = 100% boost
double sat_factor = 1.0 + (settings.saturationBoost / 100.0);
// Rec.709 luma coefficients (matches input colorspace)
const double luma_r = 0.2126;
const double luma_g = 0.7152;
const double luma_b = 0.0722;
#pragma omp parallel for
for (int r = 0; r < rows; ++r) {
uint16_t* ptr = img.ptr<uint16_t>(r);
for (int c = 0; c < cols; ++c) {
// OpenCV is BGR
double b = ptr[3*c + 0];
double g = ptr[3*c + 1];
double r_val = ptr[3*c + 2];
// Calculate Rec.709 luminance
double luma = luma_r * r_val + luma_g * g + luma_b * b;
// Apply saturation: new = luma + sat_factor * (original - luma)
double new_r = luma + sat_factor * (r_val - luma);
double new_g = luma + sat_factor * (g - luma);
double new_b = luma + sat_factor * (b - luma);
ptr[3*c + 0] = cv::saturate_cast<uint16_t>(new_b);
ptr[3*c + 1] = cv::saturate_cast<uint16_t>(new_g);
ptr[3*c + 2] = cv::saturate_cast<uint16_t>(new_r);
}
}
}
// Optional: Contrast Stretch
if (settings.contrastStretch) {
std::cout << "Contrast Stretching..." << std::endl;
// Simple MinMax normalization
cv::normalize(img, img, 0, 65535, cv::NORM_MINMAX);
}
// Optional: Mirror
if (settings.mirror) {
cv::flip(img, img, 1);
}
// Save
std::string out = settings.outputPath.empty() ? "output.tif" : settings.outputPath;
std::cout << "Saving to " << out << "..." << std::endl;
// Set compression params for TIFF
std::vector<int> params;
params.push_back(cv::IMWRITE_TIFF_COMPRESSION);
params.push_back(5); // LZW (equivalent to Zip roughly in OpenCV terms, usually 5 or 1)
cv::imwrite(out, img, params);
std::cout << "Done." << std::endl;
return 0;
}

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