ocr detect

src-tauri/src/lib.rs

@@ -95,6 +95,7 @@ use ab_glyph::{FontRef, PxScale};
 use tauri::{AppHandle, Manager};
 
 mod lama;
+mod ocr;
 
 // Embed the font to ensure it's always available without path issues
 const FONT_DATA: &[u8] = include_bytes!("../assets/fonts/Roboto-Regular.ttf");
@@ -453,52 +454,63 @@ struct Rect {
 }
 
 #[tauri::command]
-async fn detect_watermark(path: String) -> Result<DetectionResult, String> {
+async fn detect_watermark(app: AppHandle, path: String) -> Result<DetectionResult, String> {
-    let img = image::open(&path).map_err(|e| e.to_string())?;
+    let img = image::open(&path).map_err(|e| e.to_string())?.to_rgba8();
-    let (width, height) = img.dimensions();
-    let gray = img.to_luma8();
     
-    // "Stroke Detection" Algorithm
+    // 1. Try OCR Detection
-    // Distinguishes "Text" (Thin White Strokes) from "Solid White Areas" (Walls, Sky)
+    let ocr_model_path = app.path().resource_dir()
-    // Logic: A white text pixel must be "sandwiched" by dark pixels within a short distance.
+        .map_err(|e| e.to_string())?
+        .join("resources")
+        .join("en_PP-OCRv3_det_infer.onnx");
+
+    if ocr_model_path.exists() {
+        println!("Using OCR model for detection");
+        match ocr::run_ocr_detection(&ocr_model_path, &img) {
+            Ok(boxes) => {
+                let rects = boxes.into_iter().map(|b| Rect {
+                    x: b.x,
+                    y: b.y,
+                    width: b.width,
+                    height: b.height,
+                }).collect();
+                return Ok(DetectionResult { rects });
+            }
+            Err(e) => {
+                eprintln!("OCR Detection failed: {}", e);
+                // Fallthrough to legacy
+            }
+        }
+    } else {
+        eprintln!("OCR model not found at {:?}", ocr_model_path);
+    }
+
+    // 2. Legacy Detection (Fallback)
+    println!("Falling back to legacy detection");
+    let (width, height) = img.dimensions();
+    let gray = image::DynamicImage::ImageRgba8(img.clone()).to_luma8();
     
     let cell_size = 10;
     let grid_w = (width + cell_size - 1) / cell_size;
     let grid_h = (height + cell_size - 1) / cell_size;
     let mut grid = vec![false; (grid_w * grid_h) as usize];
 
-    // Focus Areas: Top 15%, Bottom 25%
     let top_limit = (height as f64 * 0.15) as u32;
     let bottom_start = (height as f64 * 0.75) as u32;
-    
+    let max_stroke_width = 15; 
-    let max_stroke_width = 15; // Max pixels for a text stroke thickness
+    let contrast_threshold = 40; 
-    let contrast_threshold = 40; // How much darker the background must be
+    let brightness_threshold = 200; 
-    let brightness_threshold = 200; // Text must be at least this white
 
     for y in 1..height-1 {
         if y > top_limit && y < bottom_start { continue; }
 
         for x in 1..width-1 {
             let p = gray.get_pixel(x, y)[0];
-            
-            // 1. Must be Bright
             if p < brightness_threshold { continue; }
 
-            // 2. Stroke Check
-            // We check for "Vertical Stroke" (Dark - Bright - Dark vertically)
-            // OR "Horizontal Stroke" (Dark - Bright - Dark horizontally)
-            
             let mut is_stroke = false;
-
-            // Check Horizontal Stroke (Vertical boundaries? No, Vertical Stroke has Left/Right boundaries? 
-            // Terminology: "Vertical Stroke" is like 'I'. It has Left/Right boundaries.
-            // "Horizontal Stroke" is like '-', It has Up/Down boundaries.
-            
-            // Let's check Left/Right boundaries (Vertical Stroke)
             let mut left_bound = false;
             let mut right_bound = false;
             
-            // Search Left
             for k in 1..=max_stroke_width {
                 if x < k { break; }
                 let neighbor = gray.get_pixel(x - k, y)[0];
@@ -507,7 +519,6 @@ async fn detect_watermark(path: String) -> Result<DetectionResult, String> {
                     break;
                 }
             }
-            // Search Right
             if left_bound {
                 for k in 1..=max_stroke_width {
                     if x + k >= width { break; }
@@ -522,11 +533,8 @@ async fn detect_watermark(path: String) -> Result<DetectionResult, String> {
             if left_bound && right_bound {
                 is_stroke = true;
             } else {
-                // Check Up/Down boundaries (Horizontal Stroke)
                 let mut up_bound = false;
                 let mut down_bound = false;
-                
-                // Search Up
                 for k in 1..=max_stroke_width {
                     if y < k { break; }
                     let neighbor = gray.get_pixel(x, y - k)[0];
@@ -535,7 +543,6 @@ async fn detect_watermark(path: String) -> Result<DetectionResult, String> {
                         break;
                     }
                 }
-                // Search Down
                 if up_bound {
                     for k in 1..=max_stroke_width {
                         if y + k >= height { break; }
@@ -546,10 +553,7 @@ async fn detect_watermark(path: String) -> Result<DetectionResult, String> {
                         }
                     }
                 }
-
+                if up_bound && down_bound { is_stroke = true; }
-                if up_bound && down_bound {
-                    is_stroke = true;
-                }
             }
 
             if is_stroke {
@@ -560,7 +564,6 @@ async fn detect_watermark(path: String) -> Result<DetectionResult, String> {
         }
     }
 
-    // Connected Components on Grid (Simple merging)
     let mut rects = Vec::new();
     let mut visited = vec![false; grid.len()];
 
@@ -568,7 +571,6 @@ async fn detect_watermark(path: String) -> Result<DetectionResult, String> {
         for gx in 0..grid_w {
             let idx = (gy * grid_w + gx) as usize;
             if grid[idx] && !visited[idx] {
-                // Start a new component
                 let mut min_gx = gx;
                 let mut max_gx = gx;
                 let mut min_gy = gy;
@@ -583,7 +585,6 @@ async fn detect_watermark(path: String) -> Result<DetectionResult, String> {
                     if cy < min_gy { min_gy = cy; }
                     if cy > max_gy { max_gy = cy; }
 
-                    // Neighbors
                     let neighbors = [
                         (cx.wrapping_sub(1), cy), (cx + 1, cy),
                         (cx, cy.wrapping_sub(1)), (cx, cy + 1)
@@ -600,8 +601,6 @@ async fn detect_watermark(path: String) -> Result<DetectionResult, String> {
                     }
                 }
 
-                // Convert grid rect to normalized image rect
-                // Add padding (1 cell)
                 let px = (min_gx * cell_size) as f64;
                 let py = (min_gy * cell_size) as f64;
                 let pw = ((max_gx - min_gx + 1) * cell_size) as f64;

src-tauri/src/ocr.rs

@@ -0,0 +1,168 @@
+use image::RgbaImage;
+use ort::session::{Session, builder::GraphOptimizationLevel};
+use ort::value::Value;
+use std::path::Path;
+
+#[derive(Clone, Debug)]
+pub struct DetectedBox {
+    pub x: f64,
+    pub y: f64,
+    pub width: f64,
+    pub height: f64,
+}
+
+pub fn run_ocr_detection(
+    model_path: &Path,
+    input_image: &RgbaImage,
+) -> Result<Vec<DetectedBox>, String> {
+    // 1. Load Model
+    let mut session = Session::builder()
+        .map_err(|e| format!("Failed to create session: {}", e))?
+        .with_optimization_level(GraphOptimizationLevel::Level3)
+        .map_err(|e| format!("Failed to set opt: {}", e))?
+        .with_intra_threads(4)
+        .map_err(|e| format!("Failed to set threads: {}", e))?
+        .commit_from_file(model_path)
+        .map_err(|e| format!("Failed to load OCR model: {}", e))?;
+
+    // 2. Preprocess
+    // DBNet expects standard normalization: (img - mean) / std
+    // Mean: [0.485, 0.456, 0.406], Std: [0.229, 0.224, 0.225]
+    // And usually resized to multiple of 32. Limit max size for speed.
+    let max_side = 1600; // Increase resolution limit
+    let (orig_w, orig_h) = input_image.dimensions();
+    
+    let mut resize_w = orig_w;
+    let mut resize_h = orig_h;
+    
+    // Resize logic: Limit max side, preserve aspect, ensure divisible by 32
+    if resize_w > max_side || resize_h > max_side {
+        let ratio = max_side as f64 / (orig_w.max(orig_h) as f64);
+        resize_w = (orig_w as f64 * ratio) as u32;
+        resize_h = (orig_h as f64 * ratio) as u32;
+    }
+    
+    // Align to 32
+    resize_w = (resize_w + 31) / 32 * 32;
+    resize_h = (resize_h + 31) / 32 * 32;
+    
+    // Minimum size
+    resize_w = resize_w.max(32);
+    resize_h = resize_h.max(32);
+
+    let resized = image::imageops::resize(input_image, resize_w, resize_h, image::imageops::FilterType::Triangle);
+    
+    let channel_stride = (resize_w * resize_h) as usize;
+    let mut input_data = Vec::with_capacity(1 * 3 * channel_stride);
+    let mut r_plane = Vec::with_capacity(channel_stride);
+    let mut g_plane = Vec::with_capacity(channel_stride);
+    let mut b_plane = Vec::with_capacity(channel_stride);
+
+    let mean = [0.485, 0.456, 0.406];
+    let std = [0.229, 0.224, 0.225];
+
+    for (_x, _y, pixel) in resized.enumerate_pixels() {
+        let r = pixel[0] as f32 / 255.0;
+        let g = pixel[1] as f32 / 255.0;
+        let b = pixel[2] as f32 / 255.0;
+        
+        r_plane.push((r - mean[0]) / std[0]);
+        g_plane.push((g - mean[1]) / std[1]);
+        b_plane.push((b - mean[2]) / std[2]);
+    }
+    input_data.extend(r_plane);
+    input_data.extend(g_plane);
+    input_data.extend(b_plane);
+
+    // 3. Inference
+    let input_shape = vec![1, 3, resize_h as i64, resize_w as i64];
+    let input_value = Value::from_array((input_shape, input_data))
+        .map_err(|e| format!("Failed to create input tensor: {}", e))?;
+
+    let inputs = ort::inputs![input_value]; // For PP-OCR, usually just one input "x"
+    let outputs = session.run(inputs).map_err(|e| format!("Inference failed: {}", e))?;
+    
+    let output_tensor = outputs.values().next().ok_or("No output")?;
+    let (shape, data) = output_tensor.try_extract_tensor::<f32>()
+        .map_err(|e| format!("Failed to extract output: {}", e))?;
+
+    // 4. Post-process
+    // Output shape is [1, 1, H, W] probability map
+    if shape.len() < 4 {
+        return Err("Unexpected output shape".to_string());
+    }
+    
+    let map_w = shape[3] as u32;
+    let map_h = shape[2] as u32;
+    
+    // Create binary map (threshold 0.3)
+    let threshold = 0.2; // Lower threshold to catch more text
+    let mut binary_map = vec![false; (map_w * map_h) as usize];
+    
+    for i in 0..binary_map.len() {
+        if data[i] > threshold {
+            binary_map[i] = true;
+        }
+    }
+
+    // Find Connected Components (Simple Bounding Box finding)
+    let mut visited = vec![false; binary_map.len()];
+    let mut boxes = Vec::new();
+    
+    for y in 0..map_h {
+        for x in 0..map_w {
+            let idx = (y * map_w + x) as usize;
+            if binary_map[idx] && !visited[idx] {
+                // Flood fill
+                let mut stack = vec![(x, y)];
+                visited[idx] = true;
+                
+                let mut min_x = x;
+                let mut max_x = x;
+                let mut min_y = y;
+                let mut max_y = y;
+                let mut pixel_count = 0;
+                
+                while let Some((cx, cy)) = stack.pop() {
+                    pixel_count += 1;
+                    if cx < min_x { min_x = cx; }
+                    if cx > max_x { max_x = cx; }
+                    if cy < min_y { min_y = cy; }
+                    if cy > max_y { max_y = cy; }
+                    
+                    let neighbors = [
+                        (cx.wrapping_sub(1), cy), (cx + 1, cy),
+                        (cx, cy.wrapping_sub(1)), (cx, cy + 1)
+                    ];
+                    
+                    for (nx, ny) in neighbors {
+                        if nx < map_w && ny < map_h {
+                            let nidx = (ny * map_w + nx) as usize;
+                            if binary_map[nidx] && !visited[nidx] {
+                                visited[nidx] = true;
+                                stack.push((nx, ny));
+                            }
+                        }
+                    }
+                }
+                
+                // Filter small noise
+                if pixel_count < 10 { continue; }
+                
+                // Scale back to original
+                let scale_x = orig_w as f64 / resize_w as f64;
+                let scale_y = orig_h as f64 / resize_h as f64;
+                
+                // Removed brightness check to allow detection of any text detected by DBNet
+                boxes.push(DetectedBox {
+                    x: min_x as f64 * scale_x / orig_w as f64,
+                    y: min_y as f64 * scale_y / orig_h as f64,
+                    width: (max_x - min_x + 1) as f64 * scale_x / orig_w as f64,
+                    height: (max_y - min_y + 1) as f64 * scale_y / orig_h as f64,
+                });
+            }
+        }
+    }
+
+    Ok(boxes)
+}

src-tauri/tauri.conf.json

@@ -28,7 +28,7 @@
   "bundle": {
     "active": true,
     "targets": "all",
-    "resources": ["resources/lama_fp32.onnx"],
+    "resources": ["resources/lama_fp32.onnx", "resources/en_PP-OCRv3_det_infer.onnx"],
     "icon": [
       "icons/32x32.png",
       "icons/128x128.png",