nodes/sumitchatterjee13-Luminance-Stack-Processor
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Multiple architectural upgrades Enhanced Algorithms

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sumitchatterjee13
2025-09-21 01:41:25 +10:00
parent bc227d7cd3
commit 16e772fd1d
3 changed files with 78 additions and 20 deletions
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CHANGELOG.md
+4 -1
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@@ -16,10 +16,13 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
### Fixed
- **CRITICAL: Fixed color inversion issues** - Added proper BGR↔RGB conversion for correct color handling
- **Fixed brightness problems** - Improved scaling and algorithm selection
- **Fixed brightness problems** - Improved scaling and algorithm selection
- **Added Mertens Exposure Fusion algorithm** - Now default, produces better results like Adobe Lightroom
- **Added Robertson algorithm** - Alternative HDR method for different use cases
- **Enhanced algorithm selection** - Users can choose between Mertens (default), Debevec, and Robertson
- **CRITICAL: Fixed Debevec algorithm issues** - Added Reinhard tone mapping to fix brightness/color inversion
- **Improved Debevec output** - Now produces natural results similar to Mertens
- **Enhanced per-algorithm scaling** - Different scaling strategies for different algorithms
## [1.0.1] - 2025-01-20
README.md
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@@ -117,8 +117,9 @@ The nodes implement **multiple HDR algorithms** with **Mertens Exposure Fusion**
#### **Debevec Algorithm (Classic HDR)**
- **Industry standard**: Original HDR reconstruction method from 1997
- **True scene radiance**: Recovers actual physical light values
- **Research accurate**: Mathematically precise but may require tone mapping for display
- **True scene radiance**: Recovers actual physical light values with automatic tone mapping
- **Natural output**: Now includes Reinhard tone mapping for proper brightness and colors
- **Research accurate**: Mathematically precise with practical usability improvements
#### **Robertson Algorithm**
- **Alternative approach**: Different camera response function estimation method
@@ -161,14 +162,15 @@ The nodes implement **multiple HDR algorithms** with **Mertens Exposure Fusion**
- Restart ComfyUI completely
2. **Color inversion or wrong colors**:
- Try switching to **Mertens algorithm** (default) - usually fixes color issues
- Mertens produces more natural colors similar to Adobe Lightroom
- Debevec/Robertson may need additional tone mapping for proper color appearance
- **Mertens algorithm** (default) - produces natural colors, no issues expected
- **Debevec algorithm** - now includes automatic Reinhard tone mapping for proper colors
- **Robertson algorithm** - also includes tone mapping for natural appearance
3. **Image too bright or too dark**:
- **Mertens algorithm** (default) produces best brightness levels
- **All algorithms** now produce proper brightness levels automatically
- **Mertens** (default) - natural brightness similar to Adobe Lightroom
- **Debevec/Robertson** - include automatic Reinhard tone mapping for proper brightness
- Output should look like enhanced EV0 with extended dynamic range
- For Debevec/Robertson, you may need tone mapping in post-processing
4. **Poor HDR results**:
- Ensure input images are properly exposed (not all over/under)
luminance_stack_processor.py
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@@ -41,21 +41,26 @@ def tensor_to_cv2(tensor: torch.Tensor) -> np.ndarray:
return image_8bit
def cv2_to_tensor(hdr_image: np.ndarray, output_16bit_linear: bool = True) -> torch.Tensor:
def cv2_to_tensor(hdr_image: np.ndarray, output_16bit_linear: bool = True, algorithm_hint: str = "unknown") -> torch.Tensor:
"""Convert OpenCV HDR image to ComfyUI tensor format"""
if output_16bit_linear:
# Convert HDR linear data to 16-bit linear format
# Scale to 16-bit range while preserving linear characteristics
# Find a reasonable scaling factor based on HDR content
# Use 95th percentile to avoid extreme outliers affecting scaling
scale_reference = np.percentile(hdr_image, 95.0)
# Different scaling strategies based on algorithm
if algorithm_hint == "mertens":
# Mertens typically produces 0-2 range, use 90th percentile
scale_reference = np.percentile(hdr_image, 90.0)
target_scale = 0.8 # 80% of 16-bit range
else:
# Debevec/Robertson can have very wide ranges, use 95th percentile
scale_reference = np.percentile(hdr_image, 95.0)
target_scale = 0.7 # 70% of 16-bit range for more headroom
if scale_reference > 0:
# Scale so that the 95th percentile maps to ~80% of 16-bit range
# This preserves highlights while utilizing most of the 16-bit range
scale_factor = (0.8 * 65535.0) / scale_reference
# Scale so that the percentile maps to target % of 16-bit range
scale_factor = (target_scale * 65535.0) / scale_reference
scaled_linear = hdr_image * scale_factor
else:
scaled_linear = hdr_image * 65535.0
@@ -66,8 +71,9 @@ def cv2_to_tensor(hdr_image: np.ndarray, output_16bit_linear: bool = True) -> to
# Convert to ComfyUI tensor format (0-1 range) while preserving 16-bit precision
normalized_16bit = hdr_16bit_linear / 65535.0
logger.info(f"HDR 16-bit linear output:")
logger.info(f"HDR 16-bit linear output ({algorithm_hint}):")
logger.info(f" Original range: [{hdr_image.min():.6f}, {hdr_image.max():.6f}]")
logger.info(f" Scale reference ({90 if algorithm_hint == 'mertens' else 95}th percentile): {scale_reference:.6f}")
logger.info(f" 16-bit range: [{hdr_16bit_linear.min():.1f}, {hdr_16bit_linear.max():.1f}]")
logger.info(f" Final range: [{normalized_16bit.min():.6f}, {normalized_16bit.max():.6f}]")
logger.info(f" Scale factor: {scale_factor:.2f}")
@@ -148,6 +154,8 @@ class DebevecHDRProcessor:
logger.info("Using Robertson algorithm...")
response = self.calibrator_robertson.process(processed_images, times)
hdr_radiance = self.merge_robertson.process(processed_images, times, response)
# Apply same tone mapping as Debevec
hdr_radiance = self._tone_map_debevec_output(hdr_radiance, "Robertson")
else: # Default to Debevec
# Estimate camera response function using Debevec method
@@ -157,6 +165,8 @@ class DebevecHDRProcessor:
# Merge images into HDR using Debevec algorithm
hdr_radiance = self.merge_debevec.process(processed_images, times, response)
# Apply tone mapping to fix brightness and make output similar to Mertens
hdr_radiance = self._tone_map_debevec_output(hdr_radiance, "Debevec")
# Validate HDR output
if hdr_radiance is None or hdr_radiance.size == 0:
@@ -177,8 +187,7 @@ class DebevecHDRProcessor:
if len(hdr_radiance.shape) == 3 and hdr_radiance.shape[2] == 3:
hdr_radiance = cv2.cvtColor(hdr_radiance, cv2.COLOR_RGB2BGR)
# The result is already in linear colorspace (scene radiance values)
# Don't apply tone mapping - preserve the HDR data as-is
# The result is in linear colorspace - preserve HDR data
return hdr_radiance.astype(np.float32)
except Exception as e:
@@ -199,6 +208,50 @@ class DebevecHDRProcessor:
return fallback_linear.astype(np.float32)
raise e
def _tone_map_debevec_output(self, hdr_image: np.ndarray, algorithm_name: str) -> np.ndarray:
"""
Apply tone mapping to Debevec/Robertson output to fix brightness and color issues
Args:
hdr_image: Raw HDR output from Debevec/Robertson
algorithm_name: Name of algorithm for logging
Returns:
Tone-mapped HDR image with proper brightness and color balance
"""
logger.info(f"{algorithm_name} raw output range: [{hdr_image.min():.6f}, {hdr_image.max():.6f}]")
# Debevec often produces extremely bright values - apply gamma correction and scaling
try:
# Method 1: Reinhard tone mapping for natural results
# This is similar to what Lightroom/Photoshop do for HDR
reinhard = cv2.createTonemapReinhard(gamma=1.8, intensity=-1.0, light_adapt=0.8, color_adapt=0.0)
tone_mapped = reinhard.process(hdr_image.astype(np.float32))
logger.info(f"{algorithm_name} after Reinhard tone mapping: [{tone_mapped.min():.6f}, {tone_mapped.max():.6f}]")
# Ensure reasonable dynamic range
if tone_mapped.max() > 0:
# Scale to have similar range as Mertens (0-2 range typical)
max_val = np.percentile(tone_mapped, 98) # Use 98th percentile
if max_val > 0:
tone_mapped = tone_mapped * (2.0 / max_val)
# Clamp extreme values while preserving HDR range
tone_mapped = np.clip(tone_mapped, 0.0, 10.0)
logger.info(f"{algorithm_name} final tone mapped range: [{tone_mapped.min():.6f}, {tone_mapped.max():.6f}]")
return tone_mapped.astype(np.float32)
except Exception as e:
logger.error(f"Tone mapping failed for {algorithm_name}: {e}")
# Fallback: simple normalization
if hdr_image.max() > 0:
normalized = hdr_image / np.percentile(hdr_image, 95)
return np.clip(normalized, 0.0, 5.0).astype(np.float32)
return hdr_image.astype(np.float32)
class LuminanceStackProcessor3Stops:
"""
@@ -272,7 +325,7 @@ class LuminanceStackProcessor3Stops:
hdr_result = self.processor.process_hdr(images, times, algorithm=hdr_algorithm)
# Convert back to tensor - output 16-bit linear data
output_tensor = cv2_to_tensor(hdr_result, output_16bit_linear=True)
output_tensor = cv2_to_tensor(hdr_result, output_16bit_linear=True, algorithm_hint=hdr_algorithm)
return (output_tensor,)
@@ -361,7 +414,7 @@ class LuminanceStackProcessor5Stops:
hdr_result = self.processor.process_hdr(images, times, algorithm=hdr_algorithm)
# Convert back to tensor - output 16-bit linear data
output_tensor = cv2_to_tensor(hdr_result, output_16bit_linear=True)
output_tensor = cv2_to_tensor(hdr_result, output_16bit_linear=True, algorithm_hint=hdr_algorithm)
return (output_tensor,)