Files

126 lines
4.7 KiB
Python

import os
os.environ["OPENCV_IO_ENABLE_OPENEXR"] = "1"
import cv2 as cv
import numpy as np
import torch
import requests
import json
def linear_to_srgb(np_array):
"""Converts a linear RGB numpy array to sRGB."""
less = np_array <= 0.0031308
np_array[less] = np_array[less] * 12.92
np_array[~less] = np.power(np_array[~less], 1/2.4) * 1.055 - 0.055
return np_array
class HttpExrSequenceInput:
"""
Node to load a sequence of EXR images from a list of URLs provided as a JSON string.
"""
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"urls_json": ("STRING", {"multiline": True, "default": "[]"}),
"tonemap": (["linear", "sRGB", "Reinhard"], {"default": "sRGB"}),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
},
"optional": {
"default_image": ("IMAGE",),
"default_mask": ("MASK",),
},
}
RETURN_TYPES = ("IMAGE", "MASK")
RETURN_NAMES = ("image", "mask",)
FUNCTION = "run"
CATEGORY = "🔗ComfyDeploy/EXR"
def load_exr_from_data(self, exr_data):
try:
nparr = np.frombuffer(exr_data, np.uint8)
image = cv.imdecode(nparr, cv.IMREAD_UNCHANGED)
if image is None:
raise ValueError("Failed to decode EXR data.")
return image.astype(np.float32)
except Exception as e:
print(f"Error decoding EXR data: {e}")
return None
def run(self, urls_json, tonemap, seed, default_image=None, default_mask=None):
try:
urls = json.loads(urls_json)
if not isinstance(urls, list) or not all(isinstance(u, str) for u in urls):
raise ValueError("urls_json must be a JSON array of URL strings.")
except (json.JSONDecodeError, ValueError) as e:
print(f"Error parsing urls_json: {e}. Using default image if available.")
urls = []
if not urls:
if default_image is not None and default_mask is not None:
return (default_image, default_mask)
print("Warning: No valid URLs and no default image. Returning a black image.")
blank_image = torch.zeros((1, 64, 64, 3), dtype=torch.float32)
blank_mask = torch.zeros((1, 64, 64), dtype=torch.float32)
return (blank_image, blank_mask)
rgb_frames = []
mask_frames = []
for url in urls:
image = None
try:
print(f"Fetching EXR from URL: {url}")
response = requests.get(url)
response.raise_for_status()
image = self.load_exr_from_data(response.content)
except requests.exceptions.RequestException as e:
print(f"Error fetching EXR from URL {url}: {e}")
if image is None:
print(f"Warning: Could not decode EXR from {url}. Skipping frame.")
continue
if len(image.shape) == 2: # Grayscale
image = np.repeat(image[..., np.newaxis], 3, axis=2)
rgb = np.flip(image[:, :, :3], 2).copy() # BGR to RGB
if tonemap == "sRGB":
rgb = linear_to_srgb(rgb)
rgb = np.clip(rgb, 0, 1)
elif tonemap == "Reinhard":
rgb = np.clip(rgb, 0, None)
rgb = rgb / (rgb + 1)
rgb = linear_to_srgb(rgb)
rgb = np.clip(rgb, 0, 1)
rgb_frames.append(torch.from_numpy(rgb))
if image.shape[2] > 3:
mask = np.clip(image[:, :, 3], 0, 1)
else:
mask = np.ones_like(rgb[:, :, 0])
mask_frames.append(torch.from_numpy(mask))
if not rgb_frames:
print("Could not load any frames. Returning default image if available.")
if default_image is not None and default_mask is not None:
return (default_image, default_mask)
print("Warning: Failed to load any frames and no default image. Returning a black image.")
blank_image = torch.zeros((1, 64, 64, 3), dtype=torch.float32)
blank_mask = torch.zeros((1, 64, 64), dtype=torch.float32)
return (blank_image, blank_mask)
print(f"Loaded {len(rgb_frames)} frames successfully.")
return (torch.stack(rgb_frames, 0), torch.stack(mask_frames, 0))
NODE_CLASS_MAPPINGS = {
"HttpExrSequenceInput": HttpExrSequenceInput
}
NODE_DISPLAY_NAME_MAPPINGS = {
"HttpExrSequenceInput": "HTTP EXR Sequence Input (ComfyDeploy)"
}