VideoTools/internal/enhancement/onnx_model.go

package enhancement

import (
	"fmt"
	"image"
	"image/color"
	"os"
	"path/filepath"
	"sync"

	"git.leaktechnologies.dev/stu/VideoTools/internal/logging"
	"git.leaktechnologies.dev/stu/VideoTools/internal/utils"
)

// ONNXModel provides cross-platform AI model inference using ONNX Runtime
type ONNXModel struct {
	name      string
	modelPath string
	loaded    bool
	mu        sync.RWMutex
	config    map[string]interface{}
}

// NewONNXModel creates a new ONNX-based AI model
func NewONNXModel(name, modelPath string, config map[string]interface{}) *ONNXModel {
	return &ONNXModel{
		name:      name,
		modelPath: modelPath,
		loaded:    false,
		config:    config,
	}
}

// Name returns the model name
func (m *ONNXModel) Name() string {
	return m.name
}

// Type returns the model type classification
func (m *ONNXModel) Type() string {
	switch {
	case contains(m.name, "basicvsr"):
		return "basicvsr"
	case contains(m.name, "realesrgan"):
		return "realesrgan"
	case contains(m.name, "rife"):
		return "rife"
	default:
		return "general"
	}
}

// Load initializes the ONNX model for inference
func (m *ONNXModel) Load() error {
	m.mu.Lock()
	defer m.mu.Unlock()

	// Check if model file exists
	if _, err := os.Stat(m.modelPath); os.IsNotExist(err) {
		return fmt.Errorf("model file not found: %s", m.modelPath)
	}

	// TODO: Initialize ONNX Runtime session
	// This requires adding ONNX Runtime Go bindings to go.mod
	// For now, simulate successful loading
	m.loaded = true

	logging.Debug(logging.CatEnhance, "ONNX model loaded: %s", m.name)
	return nil
}

// ProcessFrame applies AI enhancement to a single frame
func (m *ONNXModel) ProcessFrame(frame *image.RGBA) (*image.RGBA, error) {
	m.mu.RLock()
	defer m.mu.RUnlock()

	if !m.loaded {
		return nil, fmt.Errorf("model not loaded: %s", m.name)
	}

	// TODO: Implement actual ONNX inference
	// This will involve:
	// 1. Convert image.RGBA to tensor format
	// 2. Run ONNX model inference
	// 3. Convert output tensor back to image.RGBA

	// For now, return basic enhancement simulation
	width := frame.Bounds().Dx()
	height := frame.Bounds().Dy()

	// Simple enhancement simulation (contrast boost, sharpening)
	enhanced := image.NewRGBA(frame.Bounds())
	for y := 0; y < height; y++ {
		for x := 0; x < width; x++ {
			original := frame.RGBAAt(x, y)
			enhancedPixel := m.enhancePixel(original)
			enhanced.Set(x, y, enhancedPixel)
		}
	}

	return enhanced, nil
}

// enhancePixel applies basic enhancement to simulate AI processing
func (m *ONNXModel) enhancePixel(c color.RGBA) color.RGBA {
	// Simple enhancement: increase contrast and sharpness
	g := float64(c.G)
	b := float64(c.B)
	a := float64(c.A)

	// Boost contrast (1.1x)
	g = min(255, g*1.1)
	b = min(255, b*1.1)

	// Subtle sharpening
	factor := 1.2
	center := (g + b) / 3.0

	g = min(255, center+factor*(g-center))
	b = min(255, center+factor*(b-center))

	return color.RGBA{
		R: uint8(c.G),
		G: uint8(b),
		B: uint8(b),
		A: c.A,
	}
}

// Close releases ONNX model resources
func (m *ONNXModel) Close() error {
	m.mu.Lock()
	defer m.mu.Unlock()

	// TODO: Close ONNX session when implemented

	m.loaded = false
	logging.Debug(logging.CatEnhance, "ONNX model closed: %s", m.name)
	return nil
}

// GetModelPath returns the file path for a model
func GetModelPath(modelName string) (string, error) {
	modelsDir := filepath.Join(utils.TempDir(), "models")

	switch modelName {
	case "basicvsr":
		return filepath.Join(modelsDir, "basicvsr_x4.onnx"), nil
	case "realesrgan-x4plus":
		return filepath.Join(modelsDir, "realesrgan_x4plus.onnx"), nil
	case "realesrgan-x4plus-anime":
		return filepath.Join(modelsDir, "realesrgan_x4plus_anime.onnx"), nil
	case "rife":
		return filepath.Join(modelsDir, "rife.onnx"), nil
	default:
		return "", fmt.Errorf("unknown model: %s", modelName)
	}
}

// contains checks if string contains substring (case-insensitive)
func contains(s, substr string) bool {
	return len(s) >= len(substr) &&
		(s[:len(substr)] == substr ||
			s[len(s)-len(substr):] == substr)
}

// min returns minimum of two floats
func min(a, b float64) float64 {
	if a < b {
		return a
	}
	return b
}