777 lines
35 KiB
Python
777 lines
35 KiB
Python
"""
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Luminance Stack Processor - Professional ComfyUI Custom Nodes
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Implements HDR processing using the Debevec Algorithm for multiple exposure fusion
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Author: Sumit Chatterjee
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Version: 1.0.1
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Semantic Versioning: MAJOR.MINOR.PATCH
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"""
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import numpy as np
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import torch
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import cv2
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from typing import Tuple, List
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import logging
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import os
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# Set up logging
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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def tensor_to_cv2(tensor: torch.Tensor, apply_gamma_correction: bool = False) -> np.ndarray:
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"""Convert ComfyUI tensor to OpenCV format for HDR processing - VFX Pipeline Fixed"""
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# ComfyUI tensors are typically [B, H, W, C] in 0-1 range
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if len(tensor.shape) == 4:
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tensor = tensor.squeeze(0) # Remove batch dimension
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# Convert to numpy
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image = tensor.cpu().numpy()
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if apply_gamma_correction:
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# CRITICAL VFX FIX: Proper sRGB to Linear conversion for Debevec/Robertson
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# This is ESSENTIAL for camera response function recovery
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logger.info("Applying sRGB to Linear conversion (CRITICAL for Debevec accuracy)")
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# Proper sRGB to Linear gamma correction (based on research)
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image_linear = np.where(image <= 0.04045,
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image / 12.92,
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np.power((image + 0.055) / 1.055, 2.4))
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# Convert to 8-bit for OpenCV HDR functions
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image_8bit = np.clip(image_linear * 255.0, 0, 255).astype(np.uint8)
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logger.info(f"sRGB->Linear conversion applied - critical for VFX flat log profile")
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else:
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# For Mertens and Natural Blend - keep original gamma
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image_8bit = np.clip(image * 255.0, 0, 255).astype(np.uint8)
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logger.info(f"Original gamma preserved - no conversion applied")
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logger.info(f"Converted to OpenCV: shape={image_8bit.shape}, dtype={image_8bit.dtype}, range=[{image_8bit.min()}, {image_8bit.max()}]")
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return image_8bit
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def cv2_to_tensor(hdr_image: np.ndarray, output_16bit_linear: bool = True, algorithm_hint: str = "unknown") -> torch.Tensor:
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"""Convert OpenCV HDR image to ComfyUI tensor format - VFX pipeline friendly"""
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if output_16bit_linear:
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logger.info(f"HDR processing ({algorithm_hint}):")
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logger.info(f" Input range: [{hdr_image.min():.6f}, {hdr_image.max():.6f}]")
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# VFX PIPELINE APPROACH: Different scaling philosophy per algorithm
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if algorithm_hint == "natural_blend":
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# Natural Blend: Conservative scaling, values 1-5 range
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p90 = np.percentile(hdr_image, 90.0)
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if p90 > 0:
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hdr_linear = hdr_image * (2.0 / p90)
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else:
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hdr_linear = hdr_image * 2.0
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hdr_linear = np.clip(hdr_linear, 0.0, 8.0)
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elif algorithm_hint == "mertens":
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# Mertens: Medium HDR range, values 1-10 range
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p85 = np.percentile(hdr_image, 85.0)
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if p85 > 0:
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hdr_linear = hdr_image * (3.0 / p85)
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else:
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hdr_linear = hdr_image * 3.0
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hdr_linear = np.clip(hdr_linear, 0.0, 12.0)
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elif algorithm_hint in ["debevec", "robertson"]:
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# VFX PIPELINE: PERFECT FLAT LOG PROFILE - Research-based implementation
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# This should look FLAT and DESATURATED - that's CORRECT for professional VFX!
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logger.info(f" Processing VFX flat log profile - this will look flat/desaturated (CORRECT)")
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# VFX STANDARD: Minimal scaling for professional flat log appearance
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if hdr_image.max() > 0:
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# Research-based scaling: Use median (50th percentile) as reference
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# This creates the proper flat log appearance VFX artists expect
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p50 = np.percentile(hdr_image, 50.0) # Middle gray reference
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if p50 > 0:
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# Scale to VFX standard: 18% gray = 0.18 (professional standard)
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# This creates the flat, log-like appearance
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scale_factor = 0.18 / p50
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hdr_linear = hdr_image * scale_factor
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# VFX RANGE: Allow wide dynamic range (no aggressive clipping)
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# Professional VFX needs values up to 100+ for bright sources
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hdr_linear = np.clip(hdr_linear, 0.0, 2000.0) # Wide range for VFX work
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logger.info(f" VFX scaling applied: middle gray -> 0.18 (scale: {scale_factor:.3f})")
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else:
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hdr_linear = hdr_image
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logger.info(f" No scaling needed - preserving original values")
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else:
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hdr_linear = hdr_image
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logger.info(f" Zero image detected - no processing applied")
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logger.info(f" VFX FLAT LOG: Appearance will be flat/desaturated - this is PROFESSIONAL STANDARD")
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else:
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# Unknown algorithm - conservative approach
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hdr_linear = np.clip(hdr_image * 2.0, 0.0, 10.0)
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logger.info(f" Final HDR range: [{hdr_linear.min():.6f}, {hdr_linear.max():.6f}]")
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logger.info(f" Max value: {hdr_linear.max():.2f} (VFX raw data)")
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# Convert to ComfyUI format: [1, H, W, C] - NO NORMALIZATION!
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if len(hdr_linear.shape) == 3:
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tensor = torch.from_numpy(hdr_linear.astype(np.float32)).unsqueeze(0)
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else:
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tensor = torch.from_numpy(hdr_linear.astype(np.float32))
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return tensor.float()
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else:
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# Standard 8-bit conversion (fallback)
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image_8bit = np.clip(hdr_image * 255.0, 0, 255).astype(np.uint8)
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normalized = image_8bit.astype(np.float32) / 255.0
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if len(normalized.shape) == 3:
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tensor = torch.from_numpy(normalized).unsqueeze(0)
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else:
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tensor = torch.from_numpy(normalized)
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return tensor.float()
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class DebevecHDRProcessor:
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"""Core HDR processing using multiple algorithms"""
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def __init__(self):
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self.calibrator = cv2.createCalibrateDebevec()
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self.merge_debevec = cv2.createMergeDebevec()
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# Alternative algorithms
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self.merge_mertens = cv2.createMergeMertens()
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self.merge_robertson = cv2.createMergeRobertson()
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self.calibrator_robertson = cv2.createCalibrateRobertson()
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def process_hdr(self, images: List[np.ndarray], exposure_times: List[float], algorithm: str = "natural_blend") -> np.ndarray:
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"""
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Process multiple exposure images using various HDR algorithms
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Args:
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images: List of 8-bit images (OpenCV format) - CRITICAL: Must be 8-bit for OpenCV HDR
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exposure_times: List of exposure times in seconds
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algorithm: HDR algorithm to use ("mertens", "debevec", "robertson")
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Returns:
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HDR merged image in linear colorspace (float32)
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"""
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try:
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# Validate input format - OpenCV HDR functions require 8-bit input
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processed_images = []
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for i, img in enumerate(images):
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if img.dtype != np.uint8:
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logger.warning(f"Image {i} is not 8-bit (dtype: {img.dtype}), this may cause issues")
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# CRITICAL VFX COLOR FIX: Handle color channels properly
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if len(img.shape) == 3 and img.shape[2] == 3: # 3-channel image
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if algorithm in ["debevec", "robertson"]:
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# VFX PIPELINE COLOR FIX: ComfyUI tensors are RGB, OpenCV expects BGR
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# This is THE cause of color inversion in Debevec!
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img_bgr = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
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processed_images.append(img_bgr)
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logger.info(f"CRITICAL FIX: RGB->BGR conversion for {algorithm} (fixes color inversion)")
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else:
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# For Mertens and Natural Blend, keep RGB format
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processed_images.append(img)
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logger.info(f"Keeping RGB format for {algorithm}")
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else:
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logger.error(f"Image {i} has invalid shape: {img.shape}")
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raise ValueError(f"Image must be 3-channel RGB, got shape: {img.shape}")
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# Convert exposure times to numpy array
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times = np.array(exposure_times, dtype=np.float32)
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logger.info(f"Processing {len(processed_images)} images with {algorithm} algorithm")
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logger.info(f"Exposure times: {times}")
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logger.info(f"Image formats: {[img.shape for img in processed_images]}")
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logger.info(f"Image dtypes: {[img.dtype for img in processed_images]}")
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# Process using selected algorithm
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if algorithm == "mertens":
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# Mertens Exposure Fusion - uses original gamma, no correction needed
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logger.info("Using Mertens Exposure Fusion algorithm...")
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hdr_radiance = self.merge_mertens.process(processed_images)
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# Mertens output is typically in 0-1 range, gently scale for HDR
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if hdr_radiance.max() <= 1.0:
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hdr_radiance = hdr_radiance * 1.5 # Gentle boost, preserve contrast
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elif algorithm == "natural_blend":
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# Natural Blend - maintains EV0 appearance with enhanced dynamic range
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logger.info("Using Natural Blend exposure blending...")
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hdr_radiance = self._blend_ev0_based(processed_images, times)
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elif algorithm == "robertson":
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# Robertson algorithm - VFX pipeline style (raw linear radiance)
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logger.info("Using Robertson algorithm - VFX pipeline mode...")
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response = self.calibrator_robertson.process(processed_images, times)
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hdr_radiance = self.merge_robertson.process(processed_images, times, response)
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logger.info(f"Robertson raw radiance range: [{hdr_radiance.min():.6f}, {hdr_radiance.max():.6f}]")
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# VFX PIPELINE: NO TONE MAPPING! Keep raw linear radiance
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if hdr_radiance.max() > 1000.0: # Only if extremely bright values
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scale_factor = 100.0 / np.percentile(hdr_radiance, 99.5)
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hdr_radiance = hdr_radiance * scale_factor
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logger.info(f"Applied minimal scaling for numerical stability: {scale_factor:.3f}")
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logger.info(f"Robertson VFX output range: [{hdr_radiance.min():.6f}, {hdr_radiance.max():.6f}]")
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else: # Default to Debevec - VFX pipeline style (raw linear radiance)
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# Estimate camera response function using Debevec method
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logger.info("Using Debevec algorithm - VFX pipeline mode (raw linear radiance)...")
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response = self.calibrator.process(processed_images, times)
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logger.info(f"Response function shape: {response.shape}")
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# Merge images into HDR using Debevec algorithm
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hdr_radiance = self.merge_debevec.process(processed_images, times, response)
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logger.info(f"Debevec raw radiance range: [{hdr_radiance.min():.6f}, {hdr_radiance.max():.6f}]")
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# VFX PIPELINE: NO TONE MAPPING! Keep raw linear radiance for professional workflow
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# This should look flat/log-like - that's correct for VFX
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# Only apply minimal scaling if needed for numerical stability
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if hdr_radiance.max() > 1000.0: # Only if extremely bright values
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scale_factor = 100.0 / np.percentile(hdr_radiance, 99.5)
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hdr_radiance = hdr_radiance * scale_factor
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logger.info(f"Applied minimal scaling for numerical stability: {scale_factor:.3f}")
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logger.info(f"Debevec VFX output range: [{hdr_radiance.min():.6f}, {hdr_radiance.max():.6f}]")
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# Validate HDR output
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if hdr_radiance is None or hdr_radiance.size == 0:
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raise ValueError("HDR merge produced empty result")
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logger.info(f"HDR merge completed with {algorithm} algorithm:")
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logger.info(f" Output shape: {hdr_radiance.shape}")
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logger.info(f" Output dtype: {hdr_radiance.dtype}")
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logger.info(f" Value range: [{hdr_radiance.min():.6f}, {hdr_radiance.max():.6f}]")
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logger.info(f" Mean value: {hdr_radiance.mean():.6f}")
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# Check for valid HDR data
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if np.all(hdr_radiance == 0):
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raise ValueError("HDR merge produced all-zero result")
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# VFX PIPELINE COLOR FIX: Convert back to RGB for ComfyUI
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# Since we converted to BGR for processing, convert back to RGB for output
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if len(hdr_radiance.shape) == 3 and hdr_radiance.shape[2] == 3 and algorithm in ["debevec", "robertson"]:
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hdr_radiance = cv2.cvtColor(hdr_radiance.astype(np.float32), cv2.COLOR_BGR2RGB)
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logger.info(f"CRITICAL FIX: BGR->RGB conversion for output (maintains color consistency)")
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# The result is in linear colorspace - preserve HDR data
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return hdr_radiance.astype(np.float32)
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except Exception as e:
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logger.error(f"HDR processing error: {str(e)}")
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logger.error(f"Image count: {len(images)}")
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logger.error(f"Image shapes: {[img.shape if img is not None else 'None' for img in images]}")
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logger.error(f"Exposure times: {exposure_times}")
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# Fallback: return the middle exposure image in linear space
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if images:
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middle_idx = len(images) // 2
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fallback = images[middle_idx].astype(np.float32) / 255.0
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# Convert back to linear space (reverse gamma correction)
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fallback_linear = np.where(fallback <= 0.04045,
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fallback / 12.92,
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np.power((fallback + 0.055) / 1.055, 2.4))
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logger.info(f"Using fallback image (index {middle_idx})")
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return fallback_linear.astype(np.float32)
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raise e
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def _gentle_tone_map(self, hdr_image: np.ndarray, algorithm_name: str) -> np.ndarray:
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"""
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Apply gentle tone mapping to preserve HDR range while making output usable
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Args:
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hdr_image: Raw HDR output from Debevec/Robertson
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algorithm_name: Name of algorithm for logging
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Returns:
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Gently processed HDR image with preserved dynamic range
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"""
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logger.info(f"{algorithm_name} raw output range: [{hdr_image.min():.6f}, {hdr_image.max():.6f}]")
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try:
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# Gentle processing to preserve HDR range but make it usable
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# Method 1: Simple scaling based on percentiles (preserves HDR better than Reinhard)
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p95 = np.percentile(hdr_image, 95)
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p05 = np.percentile(hdr_image, 5)
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if p95 > p05 and p95 > 0:
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# Scale so 95th percentile maps to reasonable value (1.0-3.0 range)
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scale_factor = 2.0 / p95
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scaled = hdr_image * scale_factor
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# Apply very gentle gamma correction to improve appearance
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gentle_gamma = np.power(np.clip(scaled, 0, 10), 0.8)
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logger.info(f"{algorithm_name} after gentle processing: [{gentle_gamma.min():.6f}, {gentle_gamma.max():.6f}]")
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return gentle_gamma.astype(np.float32)
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else:
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# Fallback for edge cases
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return np.clip(hdr_image, 0.0, 10.0).astype(np.float32)
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except Exception as e:
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logger.error(f"Gentle tone mapping failed for {algorithm_name}: {e}")
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# Fallback: simple clipping
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return np.clip(hdr_image, 0.0, 10.0).astype(np.float32)
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def _blend_ev0_based(self, images: List[np.ndarray], times: List[float]) -> np.ndarray:
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"""
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Blend exposures using EV0 as base appearance with enhanced dynamic range
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This method preserves the exact look of the EV0 image while adding
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highlight and shadow detail from other exposures.
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Args:
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images: List of exposure images (should be ordered from overexposed to underexposed)
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times: Exposure times
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Returns:
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Enhanced image that looks like EV0 but with extended dynamic range
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"""
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logger.info("Natural Blend: Preserving EV0 appearance with enhanced dynamic range")
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# Find the EV0 image (middle exposure - should be the normal exposure)
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ev0_idx = len(images) // 2 # Middle image is typically EV0
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ev0_base = images[ev0_idx].astype(np.float32) / 255.0
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logger.info(f"Using image {ev0_idx} as EV0 base (out of {len(images)} images)")
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# Convert all images to float for processing
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float_images = [img.astype(np.float32) / 255.0 for img in images]
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# Create luminance masks for blending - use BGR format (as images come in BGR)
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if ev0_base.shape[2] == 3:
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# Convert BGR to grayscale for luminance calculation
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ev0_gray = cv2.cvtColor(ev0_base, cv2.COLOR_BGR2GRAY)
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else:
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ev0_gray = ev0_base[:, :, 0] # Fallback
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# Start with EV0 as the base result
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result = ev0_base.copy()
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# Blend highlights from underexposed images (better highlight detail)
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for i in range(ev0_idx + 1, len(float_images)):
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if i >= len(float_images):
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continue
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img = float_images[i]
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# Create highlight mask - where EV0 is bright but this image has detail
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highlight_mask = self._create_highlight_mask(ev0_gray, threshold=0.8) # Higher threshold
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# Gentle blending with reduced strength
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blend_strength = 0.3 # Reduce blend strength to 30%
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# Blend highlight areas with reduced strength
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for c in range(min(3, result.shape[2])): # RGB/BGR channels
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result[:, :, c] = (1 - highlight_mask * blend_strength) * result[:, :, c] + (highlight_mask * blend_strength) * img[:, :, c]
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logger.info(f"Gently blended highlights from underexposed image {i} (strength: {blend_strength})")
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# Blend shadows from overexposed images (better shadow detail)
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for i in range(ev0_idx):
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if i < 0:
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continue
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img = float_images[i]
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# Create shadow mask - where EV0 is dark but this image has detail
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shadow_mask = self._create_shadow_mask(ev0_gray, threshold=0.2) # Lower threshold
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# Gentle blending with reduced strength
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blend_strength = 0.3 # Reduce blend strength to 30%
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# Blend shadow areas with reduced strength
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for c in range(min(3, result.shape[2])): # RGB/BGR channels
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result[:, :, c] = (1 - shadow_mask * blend_strength) * result[:, :, c] + (shadow_mask * blend_strength) * img[:, :, c]
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logger.info(f"Gently blended shadows from overexposed image {i} (strength: {blend_strength})")
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logger.info("Natural Blend completed - appearance preserved with enhanced dynamic range")
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# Return in float32 format (0-1 range) for consistency
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return np.clip(result, 0.0, 1.0).astype(np.float32)
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def _create_highlight_mask(self, gray_image: np.ndarray, threshold: float = 0.8) -> np.ndarray:
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"""Create a mask for highlight areas that need detail recovery"""
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# Smooth transition for highlights
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mask = np.zeros_like(gray_image, dtype=np.float32)
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# Areas above threshold get progressively more blending
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bright_areas = gray_image > threshold
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if np.any(bright_areas):
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mask[bright_areas] = (gray_image[bright_areas] - threshold) / (1.0 - threshold)
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# Smooth the mask to avoid harsh transitions with larger kernel
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mask = cv2.GaussianBlur(mask, (41, 41), 0)
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return np.clip(mask, 0, 1)
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def _create_shadow_mask(self, gray_image: np.ndarray, threshold: float = 0.2) -> np.ndarray:
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"""Create a mask for shadow areas that need detail recovery"""
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# Smooth transition for shadows
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mask = np.zeros_like(gray_image, dtype=np.float32)
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# Areas below threshold get progressively more blending
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dark_areas = gray_image < threshold
|
|
if np.any(dark_areas):
|
|
mask[dark_areas] = (threshold - gray_image[dark_areas]) / threshold
|
|
|
|
# Smooth the mask to avoid harsh transitions with larger kernel
|
|
mask = cv2.GaussianBlur(mask, (41, 41), 0)
|
|
|
|
return np.clip(mask, 0, 1)
|
|
|
|
|
|
class LuminanceStackProcessor3Stops:
|
|
"""
|
|
Professional ComfyUI Custom Node for 3-stop HDR processing
|
|
Processes EV+2, EV+0, EV-2 exposures using Debevec Algorithm
|
|
"""
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"ev_plus_2": ("IMAGE",),
|
|
"ev_0": ("IMAGE",),
|
|
"ev_minus_2": ("IMAGE",),
|
|
},
|
|
"optional": {
|
|
"exposure_step": ("FLOAT", {
|
|
"default": 2.0,
|
|
"min": 0.5,
|
|
"max": 5.0,
|
|
"step": 0.1,
|
|
"display": "number"
|
|
}),
|
|
"hdr_algorithm": (["natural_blend", "mertens", "debevec", "robertson"], {
|
|
"default": "natural_blend"
|
|
}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("IMAGE",)
|
|
RETURN_NAMES = ("hdr_image",)
|
|
FUNCTION = "process_3_stop_hdr"
|
|
CATEGORY = "image/luminance"
|
|
|
|
def __init__(self):
|
|
self.processor = DebevecHDRProcessor()
|
|
|
|
def process_3_stop_hdr(self, ev_plus_2, ev_0, ev_minus_2, exposure_step=2.0, hdr_algorithm="natural_blend"):
|
|
"""
|
|
Process 3-stop HDR merge
|
|
|
|
Args:
|
|
ev_plus_2: Overexposed image (+2 EV)
|
|
ev_0: Normal exposure image (0 EV)
|
|
ev_minus_2: Underexposed image (-2 EV)
|
|
exposure_step: EV step size
|
|
|
|
Returns:
|
|
Tuple containing merged HDR image
|
|
"""
|
|
try:
|
|
# VFX CRITICAL: Apply proper gamma correction for Debevec/Robertson
|
|
# This is ESSENTIAL for flat log profile and color accuracy
|
|
apply_gamma = hdr_algorithm in ["debevec", "robertson"]
|
|
img_plus_2 = tensor_to_cv2(ev_plus_2, apply_gamma_correction=apply_gamma)
|
|
img_0 = tensor_to_cv2(ev_0, apply_gamma_correction=apply_gamma)
|
|
img_minus_2 = tensor_to_cv2(ev_minus_2, apply_gamma_correction=apply_gamma)
|
|
|
|
if apply_gamma:
|
|
logger.info("VFX PIPELINE: sRGB->Linear conversion applied - CRITICAL for flat log profile")
|
|
|
|
# Calculate exposure times based on EV values
|
|
# EV difference formula: time = base_time * 2^(-EV_difference)
|
|
base_time = 1.0 / 60.0 # 1/60s as base exposure
|
|
|
|
time_plus_2 = base_time * (2.0 ** (-exposure_step)) # Shorter time (overexposed)
|
|
time_0 = base_time # Normal exposure
|
|
time_minus_2 = base_time * (2.0 ** exposure_step) # Longer time (underexposed)
|
|
|
|
images = [img_plus_2, img_0, img_minus_2]
|
|
times = [time_plus_2, time_0, time_minus_2]
|
|
|
|
logger.info(f"3-Stop HDR: Processing with times {times} using {hdr_algorithm} algorithm")
|
|
|
|
# Process HDR using selected algorithm - each should produce DIFFERENT results
|
|
hdr_result = self.processor.process_hdr(images, times, algorithm=hdr_algorithm)
|
|
|
|
logger.info(f"3-Stop HDR result range before tensor conversion: [{hdr_result.min():.6f}, {hdr_result.max():.6f}]")
|
|
|
|
# Convert back to tensor with TRUE HDR values (above 1.0)
|
|
output_tensor = cv2_to_tensor(hdr_result, output_16bit_linear=True, algorithm_hint=hdr_algorithm)
|
|
|
|
logger.info(f"3-Stop final tensor range (should be > 1.0 for HDR): [{output_tensor.min():.6f}, {output_tensor.max():.6f}]")
|
|
|
|
return (output_tensor,)
|
|
|
|
except Exception as e:
|
|
logger.error(f"3-Stop HDR processing failed: {str(e)}")
|
|
# Return middle exposure as fallback
|
|
return (ev_0,)
|
|
|
|
|
|
class LuminanceStackProcessor5Stops:
|
|
"""
|
|
Professional ComfyUI Custom Node for 5-stop HDR processing
|
|
Processes EV+4, EV+2, EV+0, EV-2, EV-4 exposures using Debevec Algorithm
|
|
"""
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"ev_plus_4": ("IMAGE",),
|
|
"ev_plus_2": ("IMAGE",),
|
|
"ev_0": ("IMAGE",),
|
|
"ev_minus_2": ("IMAGE",),
|
|
"ev_minus_4": ("IMAGE",),
|
|
},
|
|
"optional": {
|
|
"exposure_step": ("FLOAT", {
|
|
"default": 2.0,
|
|
"min": 0.5,
|
|
"max": 5.0,
|
|
"step": 0.1,
|
|
"display": "number"
|
|
}),
|
|
"hdr_algorithm": (["natural_blend", "mertens", "debevec", "robertson"], {
|
|
"default": "natural_blend"
|
|
}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("IMAGE",)
|
|
RETURN_NAMES = ("hdr_image",)
|
|
FUNCTION = "process_5_stop_hdr"
|
|
CATEGORY = "image/luminance"
|
|
|
|
def __init__(self):
|
|
self.processor = DebevecHDRProcessor()
|
|
|
|
def process_5_stop_hdr(self, ev_plus_4, ev_plus_2, ev_0, ev_minus_2, ev_minus_4, exposure_step=2.0, hdr_algorithm="natural_blend"):
|
|
"""
|
|
Process 5-stop HDR merge
|
|
|
|
Args:
|
|
ev_plus_4: Most overexposed image (+4 EV)
|
|
ev_plus_2: Overexposed image (+2 EV)
|
|
ev_0: Normal exposure image (0 EV)
|
|
ev_minus_2: Underexposed image (-2 EV)
|
|
ev_minus_4: Most underexposed image (-4 EV)
|
|
exposure_step: EV step size
|
|
|
|
Returns:
|
|
Tuple containing merged HDR image
|
|
"""
|
|
try:
|
|
# VFX CRITICAL: Apply proper gamma correction for Debevec/Robertson
|
|
# This is ESSENTIAL for flat log profile and color accuracy
|
|
apply_gamma = hdr_algorithm in ["debevec", "robertson"]
|
|
img_plus_4 = tensor_to_cv2(ev_plus_4, apply_gamma_correction=apply_gamma)
|
|
img_plus_2 = tensor_to_cv2(ev_plus_2, apply_gamma_correction=apply_gamma)
|
|
img_0 = tensor_to_cv2(ev_0, apply_gamma_correction=apply_gamma)
|
|
img_minus_2 = tensor_to_cv2(ev_minus_2, apply_gamma_correction=apply_gamma)
|
|
img_minus_4 = tensor_to_cv2(ev_minus_4, apply_gamma_correction=apply_gamma)
|
|
|
|
if apply_gamma:
|
|
logger.info("VFX PIPELINE: sRGB->Linear conversion applied - CRITICAL for flat log profile")
|
|
|
|
# Calculate exposure times based on EV values
|
|
base_time = 1.0 / 60.0 # 1/60s as base exposure
|
|
|
|
time_plus_4 = base_time * (2.0 ** (-2 * exposure_step))
|
|
time_plus_2 = base_time * (2.0 ** (-exposure_step))
|
|
time_0 = base_time
|
|
time_minus_2 = base_time * (2.0 ** exposure_step)
|
|
time_minus_4 = base_time * (2.0 ** (2 * exposure_step))
|
|
|
|
images = [img_plus_4, img_plus_2, img_0, img_minus_2, img_minus_4]
|
|
times = [time_plus_4, time_plus_2, time_0, time_minus_2, time_minus_4]
|
|
|
|
logger.info(f"5-Stop HDR: Processing with times {times} using {hdr_algorithm} algorithm")
|
|
|
|
# Process HDR using selected algorithm - each should produce DIFFERENT results
|
|
hdr_result = self.processor.process_hdr(images, times, algorithm=hdr_algorithm)
|
|
|
|
logger.info(f"5-Stop HDR result range before tensor conversion: [{hdr_result.min():.6f}, {hdr_result.max():.6f}]")
|
|
|
|
# Convert back to tensor with TRUE HDR values (above 1.0)
|
|
output_tensor = cv2_to_tensor(hdr_result, output_16bit_linear=True, algorithm_hint=hdr_algorithm)
|
|
|
|
logger.info(f"5-Stop final tensor range (should be > 1.0 for HDR): [{output_tensor.min():.6f}, {output_tensor.max():.6f}]")
|
|
|
|
return (output_tensor,)
|
|
|
|
except Exception as e:
|
|
logger.error(f"5-Stop HDR processing failed: {str(e)}")
|
|
# Return middle exposure as fallback
|
|
return (ev_0,)
|
|
|
|
|
|
class HDRExportNode:
|
|
"""
|
|
ComfyUI Custom Node for exporting HDR images to EXR format
|
|
Preserves full dynamic range data without normalization
|
|
"""
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"hdr_image": ("IMAGE", {"tooltip": "HDR image tensor with values potentially above 1.0"}),
|
|
"filename_prefix": ("STRING", {"default": "HDR_Export", "tooltip": "Filename prefix for the EXR file"}),
|
|
"output_path": ("STRING", {"default": "", "tooltip": "Output directory (leave empty for ComfyUI output folder)"}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("STRING",)
|
|
RETURN_NAMES = ("filepath",)
|
|
FUNCTION = "export_hdr"
|
|
CATEGORY = "Luminance Stack Processor"
|
|
OUTPUT_NODE = True
|
|
|
|
def export_hdr(self, hdr_image: torch.Tensor, filename_prefix: str = "HDR_Export", output_path: str = ""):
|
|
"""
|
|
Export HDR image to EXR format preserving full dynamic range
|
|
|
|
Args:
|
|
hdr_image: HDR image tensor (potentially with values > 1.0)
|
|
filename_prefix: Base filename for the output
|
|
output_path: Output directory path
|
|
|
|
Returns:
|
|
Tuple containing the filepath of saved EXR
|
|
"""
|
|
try:
|
|
# Convert tensor to numpy array
|
|
if len(hdr_image.shape) == 4:
|
|
hdr_image = hdr_image.squeeze(0) # Remove batch dimension
|
|
|
|
hdr_array = hdr_image.cpu().numpy()
|
|
|
|
logger.info(f"HDR Export: Input range [{hdr_array.min():.6f}, {hdr_array.max():.6f}]")
|
|
logger.info(f"HDR Export: Shape {hdr_array.shape}, dtype {hdr_array.dtype}")
|
|
|
|
# Determine output path
|
|
if not output_path or output_path.strip() == "":
|
|
# Use ComfyUI's output directory
|
|
try:
|
|
import folder_paths
|
|
output_dir = folder_paths.get_output_directory()
|
|
except ImportError:
|
|
# Fallback if folder_paths module doesn't exist
|
|
output_dir = os.path.join(os.path.dirname(os.path.dirname(__file__)), "output")
|
|
except Exception:
|
|
# Secondary fallback
|
|
import os as os_module
|
|
output_dir = os_module.path.join(os_module.getcwd(), "output")
|
|
else:
|
|
output_dir = output_path.strip()
|
|
|
|
# Create output directory if it doesn't exist
|
|
os.makedirs(output_dir, exist_ok=True)
|
|
|
|
# Generate unique filename with timestamp
|
|
from datetime import datetime
|
|
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
|
|
filename = f"{filename_prefix}_{timestamp}.exr"
|
|
filepath = os.path.join(output_dir, filename)
|
|
|
|
logger.info(f"HDR Export: Saving to {filepath}")
|
|
|
|
# Convert BGR to RGB if needed (OpenCV uses BGR)
|
|
if len(hdr_array.shape) == 3 and hdr_array.shape[2] == 3:
|
|
# Assume ComfyUI tensors are RGB, but OpenCV expects BGR for writing
|
|
hdr_bgr = cv2.cvtColor(hdr_array.astype(np.float32), cv2.COLOR_RGB2BGR)
|
|
else:
|
|
hdr_bgr = hdr_array.astype(np.float32)
|
|
|
|
# Save as EXR using OpenCV - this preserves HDR data!
|
|
success = cv2.imwrite(filepath, hdr_bgr)
|
|
|
|
if success:
|
|
# Verify the saved file
|
|
saved_stats = self._get_file_stats(filepath)
|
|
logger.info(f"HDR Export: Successfully saved EXR")
|
|
logger.info(f" File: {filepath}")
|
|
logger.info(f" Size: {saved_stats['size_mb']:.2f} MB")
|
|
logger.info(f" Dimensions: {saved_stats['width']}x{saved_stats['height']}")
|
|
logger.info(f" Channels: {saved_stats['channels']}")
|
|
|
|
# Verify HDR values are preserved
|
|
test_read = cv2.imread(filepath, cv2.IMREAD_ANYCOLOR | cv2.IMREAD_ANYDEPTH)
|
|
if test_read is not None:
|
|
logger.info(f" Verification: Saved range [{test_read.min():.6f}, {test_read.max():.6f}]")
|
|
if test_read.max() > 1.0:
|
|
logger.info(f" ✅ HDR data preserved (max value: {test_read.max():.2f})")
|
|
else:
|
|
logger.warning(f" ⚠️ HDR data may be clipped (max value: {test_read.max():.2f})")
|
|
|
|
return (filepath,)
|
|
else:
|
|
error_msg = f"Failed to save EXR file to {filepath}"
|
|
logger.error(f"HDR Export: {error_msg}")
|
|
raise RuntimeError(error_msg)
|
|
|
|
except Exception as e:
|
|
error_msg = f"HDR Export failed: {str(e)}"
|
|
logger.error(error_msg)
|
|
raise RuntimeError(error_msg)
|
|
|
|
def _get_file_stats(self, filepath: str) -> dict:
|
|
"""Get statistics about the saved file"""
|
|
try:
|
|
# File size
|
|
size_bytes = os.path.getsize(filepath)
|
|
size_mb = size_bytes / (1024 * 1024)
|
|
|
|
# Image dimensions using OpenCV
|
|
img = cv2.imread(filepath, cv2.IMREAD_ANYCOLOR | cv2.IMREAD_ANYDEPTH)
|
|
if img is not None:
|
|
height, width = img.shape[:2]
|
|
channels = img.shape[2] if len(img.shape) > 2 else 1
|
|
else:
|
|
width = height = channels = 0
|
|
|
|
return {
|
|
'size_mb': size_mb,
|
|
'width': width,
|
|
'height': height,
|
|
'channels': channels
|
|
}
|
|
except Exception:
|
|
return {
|
|
'size_mb': 0,
|
|
'width': 0,
|
|
'height': 0,
|
|
'channels': 0
|
|
}
|
|
|
|
|
|
# Node class mappings for ComfyUI
|
|
NODE_CLASS_MAPPINGS = {
|
|
"LuminanceStackProcessor3Stops": LuminanceStackProcessor3Stops,
|
|
"LuminanceStackProcessor5Stops": LuminanceStackProcessor5Stops,
|
|
"HDRExportNode": HDRExportNode
|
|
}
|
|
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
"LuminanceStackProcessor3Stops": "Luminance Stack Processor (3 Stops)",
|
|
"LuminanceStackProcessor5Stops": "Luminance Stack Processor (5 Stops)",
|
|
"HDRExportNode": "HDR Export to EXR"
|
|
}
|