feat: add contrast/sharpen strength controls, disable binarization
Major improvements to preprocessing controls: Backend: - Add contrast_strength (0.5-3.0) and sharpen_strength (0.5-2.0) to PreprocessingConfig - Auto-detection now calculates optimal strength based on image quality metrics: - Lower contrast → Higher contrast_strength - Lower edge strength → Higher sharpen_strength - Disable binarization in auto mode (rarely beneficial) - CLAHE clipLimit now scales with contrast_strength - Sharpening uses unsharp mask with variable strength Frontend: - Add strength sliders for contrast and sharpen in manual mode - Sliders show current value and strength level (輕微/正常/強/最強) - Move binarize option to collapsible "進階選項" section - Updated i18n translations for strength labels 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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@@ -110,34 +110,61 @@ class LayoutPreprocessingService:
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"""
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Determine optimal preprocessing config based on image quality.
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Auto-detection calculates appropriate strength values:
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- Lower image contrast → Higher contrast_strength
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- Lower edge strength → Higher sharpen_strength
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- Binarization is disabled by default (rarely beneficial)
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Args:
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metrics: Image quality metrics from analyze_image_quality()
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Returns:
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PreprocessingConfig with recommended settings
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"""
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# Determine contrast enhancement
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# Determine contrast enhancement and strength
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if metrics.contrast < self.contrast_threshold:
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contrast = PreprocessingContrastEnum.CLAHE
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# Calculate strength based on how far below threshold
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# contrast=40 threshold, contrast=20 → strength=2.0, contrast=30 → strength=1.5
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contrast_ratio = (self.contrast_threshold - metrics.contrast) / self.contrast_threshold
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contrast_strength = min(1.0 + contrast_ratio * 2.0, 3.0) # Range: 1.0 to 3.0
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else:
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contrast = PreprocessingContrastEnum.NONE
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contrast_strength = 1.0
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# Determine sharpening
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sharpen = metrics.edge_strength < self.edge_threshold
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# Determine sharpening and strength
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if metrics.edge_strength < self.edge_threshold:
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sharpen = True
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# Calculate strength based on how far below threshold
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# edge=15 threshold, edge=5 → strength=1.67, edge=10 → strength=1.33
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edge_ratio = (self.edge_threshold - metrics.edge_strength) / self.edge_threshold
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sharpen_strength = min(1.0 + edge_ratio * 1.0, 2.0) # Range: 1.0 to 2.0
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else:
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sharpen = False
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sharpen_strength = 1.0
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# Determine binarization (only for very low contrast)
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binarize = metrics.contrast < self.binarize_threshold
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# Binarization is disabled by default - it rarely helps and often hurts
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# Only enable for extremely low contrast (< 15) which indicates a scan quality issue
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binarize = False # Disabled by default
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logger.debug(
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f"Auto config: contrast={contrast} strength={contrast_strength:.2f}, "
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f"sharpen={sharpen} strength={sharpen_strength:.2f}, binarize={binarize}"
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)
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return PreprocessingConfig(
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contrast=contrast,
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contrast_strength=round(contrast_strength, 2),
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sharpen=sharpen,
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sharpen_strength=round(sharpen_strength, 2),
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binarize=binarize
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)
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def apply_contrast_enhancement(
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self,
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image: np.ndarray,
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method: PreprocessingContrastEnum
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method: PreprocessingContrastEnum,
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strength: float = 1.0
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) -> np.ndarray:
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"""
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Apply contrast enhancement to image.
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@@ -145,6 +172,11 @@ class LayoutPreprocessingService:
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Args:
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image: Input image (BGR)
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method: Enhancement method (none, histogram, clahe)
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strength: Enhancement strength (0.5-3.0, default 1.0)
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- 0.5: Subtle enhancement
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- 1.0: Normal enhancement
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- 2.0: Strong enhancement
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- 3.0: Maximum enhancement
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Returns:
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Enhanced image (BGR)
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@@ -157,12 +189,17 @@ class LayoutPreprocessingService:
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l_channel, a_channel, b_channel = cv2.split(lab)
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if method == PreprocessingContrastEnum.HISTOGRAM:
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# Standard histogram equalization
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l_enhanced = cv2.equalizeHist(l_channel)
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# Standard histogram equalization (strength affects blending)
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l_equalized = cv2.equalizeHist(l_channel)
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# Blend original with equalized based on strength
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alpha = min(strength, 1.0) # Cap at 1.0 for histogram
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l_enhanced = cv2.addWeighted(l_equalized, alpha, l_channel, 1 - alpha, 0)
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elif method == PreprocessingContrastEnum.CLAHE:
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# Contrast Limited Adaptive Histogram Equalization
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# clipLimit controls contrast amplification: 2.0 is default, up to 6.0 for strong
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clip_limit = self.clahe_clip_limit * strength # 2.0 * 1.0 = 2.0, 2.0 * 2.0 = 4.0
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clahe = cv2.createCLAHE(
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clipLimit=self.clahe_clip_limit,
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clipLimit=clip_limit,
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tileGridSize=self.clahe_tile_grid_size
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)
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l_enhanced = clahe.apply(l_channel)
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@@ -175,18 +212,33 @@ class LayoutPreprocessingService:
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return enhanced_bgr
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def apply_sharpening(self, image: np.ndarray) -> np.ndarray:
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def apply_sharpening(self, image: np.ndarray, strength: float = 1.0) -> np.ndarray:
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"""
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Apply sharpening to enhance edges and faint lines.
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Apply sharpening to enhance edges and faint lines using unsharp mask.
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Args:
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image: Input image (BGR)
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strength: Sharpening strength (0.5-2.0, default 1.0)
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- 0.5: Subtle sharpening
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- 1.0: Normal sharpening
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- 1.5: Strong sharpening
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- 2.0: Maximum sharpening
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Returns:
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Sharpened image (BGR)
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"""
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# Apply unsharp mask style sharpening
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sharpened = cv2.filter2D(image, -1, self.sharpen_kernel)
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# Use unsharp mask technique for better control
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# 1. Create blurred version
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# 2. Subtract from original (scaled by strength)
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# 3. Add back to original
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# Gaussian blur with sigma based on strength
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sigma = 1.0
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blurred = cv2.GaussianBlur(image, (0, 0), sigma)
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# Unsharp mask: original + (original - blurred) * strength
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# This is equivalent to: original * (1 + strength) - blurred * strength
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sharpened = cv2.addWeighted(image, 1.0 + strength, blurred, -strength, 0)
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# Clip values to valid range
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sharpened = np.clip(sharpened, 0, 255).astype(np.uint8)
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@@ -277,15 +329,19 @@ class LayoutPreprocessingService:
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# Step 1: Contrast enhancement
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if config.contrast != PreprocessingContrastEnum.NONE:
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processed = self.apply_contrast_enhancement(processed, config.contrast)
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processed = self.apply_contrast_enhancement(
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processed,
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config.contrast,
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strength=config.contrast_strength
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)
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was_processed = True
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logger.debug(f"Applied contrast enhancement: {config.contrast}")
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logger.debug(f"Applied contrast enhancement: {config.contrast} (strength={config.contrast_strength})")
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# Step 2: Sharpening
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if config.sharpen:
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processed = self.apply_sharpening(processed)
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processed = self.apply_sharpening(processed, strength=config.sharpen_strength)
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was_processed = True
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logger.debug("Applied sharpening")
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logger.debug(f"Applied sharpening (strength={config.sharpen_strength})")
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# Step 3: Binarization (last step, overwrites color)
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if config.binarize:
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