385 lines
16 KiB
Python
385 lines
16 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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# 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) -> np.ndarray:
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"""Convert ComfyUI tensor to OpenCV format for HDR processing"""
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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 and scale to 8-bit (input images are already 8-bit)
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image = tensor.cpu().numpy()
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# Apply gamma correction (sRGB to linear) before HDR processing
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# This is crucial for proper camera response function recovery
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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 back to 8-bit for OpenCV HDR functions (since inputs are 8-bit)
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image_8bit = np.clip(image_linear * 255.0, 0, 255).astype(np.uint8)
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logger.info(f"Converted tensor to CV2: 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) -> torch.Tensor:
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"""Convert OpenCV HDR image to ComfyUI tensor format"""
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if output_16bit_linear:
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# Convert HDR linear data to 16-bit linear format
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# Scale to 16-bit range while preserving linear characteristics
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# Find a reasonable scaling factor based on HDR content
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# Use 95th percentile to avoid extreme outliers affecting scaling
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scale_reference = np.percentile(hdr_image, 95.0)
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if scale_reference > 0:
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# Scale so that the 95th percentile maps to ~80% of 16-bit range
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# This preserves highlights while utilizing most of the 16-bit range
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scale_factor = (0.8 * 65535.0) / scale_reference
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scaled_linear = hdr_image * scale_factor
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else:
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scaled_linear = hdr_image * 65535.0
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# Clamp to 16-bit range but preserve linear values above 1.0
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hdr_16bit_linear = np.clip(scaled_linear, 0.0, 65535.0)
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# Convert to ComfyUI tensor format (0-1 range) while preserving 16-bit precision
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normalized_16bit = hdr_16bit_linear / 65535.0
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logger.info(f"HDR 16-bit linear output:")
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logger.info(f" Original range: [{hdr_image.min():.6f}, {hdr_image.max():.6f}]")
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logger.info(f" 16-bit range: [{hdr_16bit_linear.min():.1f}, {hdr_16bit_linear.max():.1f}]")
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logger.info(f" Final range: [{normalized_16bit.min():.6f}, {normalized_16bit.max():.6f}]")
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logger.info(f" Scale factor: {scale_factor:.2f}")
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else:
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# Legacy floating point output (not recommended for 16-bit workflow)
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max_val = np.percentile(hdr_image, 99.9)
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if max_val > 0:
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normalized_16bit = hdr_image / max_val
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else:
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normalized_16bit = hdr_image
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normalized_16bit = np.clip(normalized_16bit, 0.0, 10.0)
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# Add batch dimension and convert to tensor
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tensor = torch.from_numpy(normalized_16bit.astype(np.float32)).unsqueeze(0)
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return tensor
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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 = "mertens") -> 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 FIX: Convert BGR to RGB for proper color handling
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# OpenCV uses BGR by default, but HDR processing expects RGB
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if len(img.shape) == 3 and img.shape[2] == 3: # RGB
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img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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processed_images.append(img_rgb)
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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 - often produces better results
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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, scale appropriately
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if hdr_radiance.max() <= 1.0:
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hdr_radiance = hdr_radiance * 2.0 # Boost for better dynamic range
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elif algorithm == "robertson":
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# Robertson algorithm - alternative to Debevec
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logger.info("Using Robertson algorithm...")
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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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else: # Default to Debevec
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# Estimate camera response function using Debevec method
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logger.info("Using Debevec algorithm...")
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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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# 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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# CRITICAL FIX: Convert back from RGB to BGR for consistency
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# This ensures the output color channels are in the expected order
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if len(hdr_radiance.shape) == 3 and hdr_radiance.shape[2] == 3:
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hdr_radiance = cv2.cvtColor(hdr_radiance, cv2.COLOR_RGB2BGR)
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# The result is already in linear colorspace (scene radiance values)
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# Don't apply tone mapping - preserve the HDR data as-is
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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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class LuminanceStackProcessor3Stops:
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"""
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Professional ComfyUI Custom Node for 3-stop HDR processing
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Processes EV+2, EV+0, EV-2 exposures using Debevec Algorithm
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"""
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"ev_plus_2": ("IMAGE",),
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"ev_0": ("IMAGE",),
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"ev_minus_2": ("IMAGE",),
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},
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"optional": {
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"exposure_step": ("FLOAT", {
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"default": 2.0,
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"min": 0.5,
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"max": 5.0,
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"step": 0.1,
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"display": "number"
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}),
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"hdr_algorithm": (["mertens", "debevec", "robertson"], {
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"default": "mertens"
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}),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("hdr_image",)
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FUNCTION = "process_3_stop_hdr"
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CATEGORY = "image/luminance"
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def __init__(self):
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self.processor = DebevecHDRProcessor()
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def process_3_stop_hdr(self, ev_plus_2, ev_0, ev_minus_2, exposure_step=2.0, hdr_algorithm="mertens"):
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"""
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Process 3-stop HDR merge
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Args:
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ev_plus_2: Overexposed image (+2 EV)
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ev_0: Normal exposure image (0 EV)
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ev_minus_2: Underexposed image (-2 EV)
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exposure_step: EV step size
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Returns:
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Tuple containing merged HDR image
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"""
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try:
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# Convert tensors to OpenCV format
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img_plus_2 = tensor_to_cv2(ev_plus_2)
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img_0 = tensor_to_cv2(ev_0)
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img_minus_2 = tensor_to_cv2(ev_minus_2)
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# Calculate exposure times based on EV values
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# EV difference formula: time = base_time * 2^(-EV_difference)
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base_time = 1.0 / 60.0 # 1/60s as base exposure
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time_plus_2 = base_time * (2.0 ** (-exposure_step)) # Shorter time (overexposed)
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time_0 = base_time # Normal exposure
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time_minus_2 = base_time * (2.0 ** exposure_step) # Longer time (underexposed)
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images = [img_plus_2, img_0, img_minus_2]
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times = [time_plus_2, time_0, time_minus_2]
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logger.info(f"3-Stop HDR: Processing with times {times} using {hdr_algorithm} algorithm")
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# Process HDR using selected algorithm (default: Mertens for better results)
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hdr_result = self.processor.process_hdr(images, times, algorithm=hdr_algorithm)
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# Convert back to tensor - output 16-bit linear data
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output_tensor = cv2_to_tensor(hdr_result, output_16bit_linear=True)
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return (output_tensor,)
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except Exception as e:
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logger.error(f"3-Stop HDR processing failed: {str(e)}")
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# Return middle exposure as fallback
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return (ev_0,)
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class LuminanceStackProcessor5Stops:
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"""
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Professional ComfyUI Custom Node for 5-stop HDR processing
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Processes EV+4, EV+2, EV+0, EV-2, EV-4 exposures using Debevec Algorithm
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"""
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"ev_plus_4": ("IMAGE",),
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"ev_plus_2": ("IMAGE",),
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"ev_0": ("IMAGE",),
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"ev_minus_2": ("IMAGE",),
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"ev_minus_4": ("IMAGE",),
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},
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"optional": {
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"exposure_step": ("FLOAT", {
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"default": 2.0,
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"min": 0.5,
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"max": 5.0,
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"step": 0.1,
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"display": "number"
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}),
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"hdr_algorithm": (["mertens", "debevec", "robertson"], {
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"default": "mertens"
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}),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("hdr_image",)
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FUNCTION = "process_5_stop_hdr"
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CATEGORY = "image/luminance"
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def __init__(self):
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self.processor = DebevecHDRProcessor()
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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="mertens"):
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"""
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Process 5-stop HDR merge
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Args:
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ev_plus_4: Most overexposed image (+4 EV)
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ev_plus_2: Overexposed image (+2 EV)
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ev_0: Normal exposure image (0 EV)
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ev_minus_2: Underexposed image (-2 EV)
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ev_minus_4: Most underexposed image (-4 EV)
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exposure_step: EV step size
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Returns:
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Tuple containing merged HDR image
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"""
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try:
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# Convert tensors to OpenCV format
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img_plus_4 = tensor_to_cv2(ev_plus_4)
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img_plus_2 = tensor_to_cv2(ev_plus_2)
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img_0 = tensor_to_cv2(ev_0)
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img_minus_2 = tensor_to_cv2(ev_minus_2)
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img_minus_4 = tensor_to_cv2(ev_minus_4)
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# Calculate exposure times based on EV values
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base_time = 1.0 / 60.0 # 1/60s as base exposure
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time_plus_4 = base_time * (2.0 ** (-2 * exposure_step))
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time_plus_2 = base_time * (2.0 ** (-exposure_step))
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time_0 = base_time
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time_minus_2 = base_time * (2.0 ** exposure_step)
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time_minus_4 = base_time * (2.0 ** (2 * exposure_step))
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images = [img_plus_4, img_plus_2, img_0, img_minus_2, img_minus_4]
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times = [time_plus_4, time_plus_2, time_0, time_minus_2, time_minus_4]
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logger.info(f"5-Stop HDR: Processing with times {times} using {hdr_algorithm} algorithm")
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# Process HDR using selected algorithm (default: Mertens for better results)
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hdr_result = self.processor.process_hdr(images, times, algorithm=hdr_algorithm)
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# Convert back to tensor - output 16-bit linear data
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output_tensor = cv2_to_tensor(hdr_result, output_16bit_linear=True)
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return (output_tensor,)
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except Exception as e:
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logger.error(f"5-Stop HDR processing failed: {str(e)}")
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# Return middle exposure as fallback
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return (ev_0,)
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# Node class mappings for ComfyUI
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NODE_CLASS_MAPPINGS = {
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"LuminanceStackProcessor3Stops": LuminanceStackProcessor3Stops,
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"LuminanceStackProcessor5Stops": LuminanceStackProcessor5Stops,
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}
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# Display names for nodes in ComfyUI interface
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NODE_DISPLAY_NAME_MAPPINGS = {
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"LuminanceStackProcessor3Stops": "Luminance Stack Processor (3 Stops)",
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"LuminanceStackProcessor5Stops": "Luminance Stack Processor (5 Stops)",
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}
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