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import hashlib
import json
import os
import re
from pathlib import Path
import folder_paths
import numpy as np
import torch
from PIL import Image, ImageOps
from PIL.PngImagePlugin import PngInfo
from ..log import log
class MTB_LoadImageSequence:
"""Load an image sequence from a folder. The current frame is used to determine which image to load.
Usually used in conjunction with the `Primitive` node set to increment to load a sequence of images from a folder.
Use -1 to load all matching frames as a batch.
"""
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"path": ("STRING", {"default": "videos/####.png"}),
"current_frame": (
"INT",
{"default": 0, "min": -1, "max": 9999999},
),
},
"optional": {
"range": ("STRING", {"default": ""}),
},
}
CATEGORY = "mtb/IO"
FUNCTION = "load_image"
RETURN_TYPES = (
"IMAGE",
"MASK",
"INT",
"INT",
)
RETURN_NAMES = (
"image",
"mask",
"current_frame",
"total_frames",
)
def load_image(self, path=None, current_frame=0, range=""):
load_all = current_frame == -1
total_frames = 1
if range:
frames = self.get_frames_from_range(path, range)
imgs, masks = zip(*(img_from_path(frame) for frame in frames))
out_img = torch.cat(imgs, dim=0)
out_mask = torch.cat(masks, dim=0)
total_frames = len(imgs)
return (out_img, out_mask, -1, total_frames)
elif load_all:
log.debug(f"Loading all frames from {path}")
frames = resolve_all_frames(path)
log.debug(f"Found {len(frames)} frames")
imgs = []
masks = []
imgs, masks = zip(*(img_from_path(frame) for frame in frames))
out_img = torch.cat(imgs, dim=0)
out_mask = torch.cat(masks, dim=0)
total_frames = len(imgs)
return (out_img, out_mask, -1, total_frames)
log.debug(f"Loading image: {path}, {current_frame}")
resolved_path = resolve_path(path, current_frame)
image_path = folder_paths.get_annotated_filepath(resolved_path)
image, mask = img_from_path(image_path)
return (image, mask, current_frame, total_frames)
def get_frames_from_range(self, path, range_str):
try:
start, end = map(int, range_str.split("-"))
except ValueError:
raise ValueError(
f"Invalid range format: {range_str}. Expected format is 'start-end'."
)
frames = resolve_all_frames(path)
total_frames = len(frames)
if start < 0 or end >= total_frames:
raise ValueError(
f"Range {range_str} is out of bounds. Total frames available: {total_frames}"
)
if "#" in path:
frame_regex = re.escape(path).replace(r"\#", r"(\d+)")
frame_number_regex = re.compile(frame_regex)
matching_frames = []
for frame in frames:
match = frame_number_regex.search(frame)
if match:
frame_number = int(match.group(1))
if start <= frame_number <= end:
matching_frames.append(frame)
return matching_frames
else:
log.warning(
f"Wildcard pattern or directory will use indexes instead of frame numbers for : {path}"
)
selected_frames = frames[start : end + 1]
return selected_frames
@staticmethod
def IS_CHANGED(path="", current_frame=0, range=""):
print(f"Checking if changed: {path}, {current_frame}")
if range or current_frame == -1:
resolved_paths = resolve_all_frames(path)
timestamps = [
os.path.getmtime(folder_paths.get_annotated_filepath(p))
for p in resolved_paths
]
combined_hash = hashlib.sha256(
"".join(map(str, timestamps)).encode()
)
return combined_hash.hexdigest()
resolved_path = resolve_path(path, current_frame)
image_path = folder_paths.get_annotated_filepath(resolved_path)
if os.path.exists(image_path):
m = hashlib.sha256()
with open(image_path, "rb") as f:
m.update(f.read())
return m.digest().hex()
return "NONE"
# @staticmethod
# def VALIDATE_INPUTS(path="", current_frame=0):
# print(f"Validating inputs: {path}, {current_frame}")
# resolved_path = resolve_path(path, current_frame)
# if not folder_paths.exists_annotated_filepath(resolved_path):
# return f"Invalid image file: {resolved_path}"
# return True
import glob
def img_from_path(path):
img = Image.open(path)
img = ImageOps.exif_transpose(img)
image = img.convert("RGB")
image = np.array(image).astype(np.float32) / 255.0
image = torch.from_numpy(image)[None,]
if "A" in img.getbands():
mask = np.array(img.getchannel("A")).astype(np.float32) / 255.0
mask = 1.0 - torch.from_numpy(mask)
else:
mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
return (
image,
mask,
)
def resolve_all_frames(path: str):
frames: list[str] = []
if "#" not in path:
pth = Path(path)
if pth.is_dir():
for f in pth.iterdir():
if f.suffix in [".jpg", ".png"]:
frames.append(f.as_posix())
elif "*" in path:
frames = glob.glob(path)
else:
raise ValueError(
"The path doesn't contain a # or a * or is not a directory"
)
frames.sort()
return frames
pattern = path
folder_path, file_pattern = os.path.split(pattern)
log.debug(f"Resolving all frames in {folder_path}")
hash_count = file_pattern.count("#")
frame_pattern = re.sub(r"#+", "*", file_pattern)
log.debug(f"Found pattern: {frame_pattern}")
matching_files = glob.glob(os.path.join(folder_path, frame_pattern))
log.debug(f"Found {len(matching_files)} matching files")
frame_regex = re.escape(file_pattern).replace(r"\#", r"(\d+)")
frame_number_regex = re.compile(frame_regex)
for file in matching_files:
match = frame_number_regex.search(file)
if match:
frame_number = match.group(1)
log.debug(f"Found frame number: {frame_number}")
# resolved_file = pattern.replace("*" * frame_number.count("#"), frame_number)
frames.append(file)
frames.sort() # Sort frames alphabetically
return frames
def resolve_path(path, frame):
hashes = path.count("#")
padded_number = str(frame).zfill(hashes)
return re.sub("#+", padded_number, path)
class MTB_SaveImageSequence:
"""Save an image sequence to a folder. The current frame is used to determine which image to save.
This is merely a wrapper around the `save_images` function with formatting for the output folder and filename.
"""
def __init__(self):
self.output_dir = folder_paths.get_output_directory()
self.type = "output"
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"images": ("IMAGE",),
"filename_prefix": ("STRING", {"default": "Sequence"}),
"current_frame": (
"INT",
{"default": 0, "min": 0, "max": 9999999},
),
},
"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
}
RETURN_TYPES = ()
FUNCTION = "save_images"
OUTPUT_NODE = True
CATEGORY = "mtb/IO"
def save_images(
self,
images,
filename_prefix="Sequence",
current_frame=0,
prompt=None,
extra_pnginfo=None,
):
# full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0])
# results = list()
# for image in images:
# i = 255. * image.cpu().numpy()
# img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
# metadata = PngInfo()
# if prompt is not None:
# metadata.add_text("prompt", json.dumps(prompt))
# if extra_pnginfo is not None:
# for x in extra_pnginfo:
# metadata.add_text(x, json.dumps(extra_pnginfo[x]))
# file = f"{filename}_{counter:05}_.png"
# img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=4)
# results.append({
# "filename": file,
# "subfolder": subfolder,
# "type": self.type
# })
# counter += 1
if len(images) > 1:
raise ValueError("Can only save one image at a time")
resolved_path = Path(self.output_dir) / filename_prefix
resolved_path.mkdir(parents=True, exist_ok=True)
resolved_img = (
resolved_path / f"{filename_prefix}_{current_frame:05}.png"
)
output_image = images[0].cpu().numpy()
img = Image.fromarray(
np.clip(output_image * 255.0, 0, 255).astype(np.uint8)
)
metadata = PngInfo()
if prompt is not None:
metadata.add_text("prompt", json.dumps(prompt))
if extra_pnginfo is not None:
for x in extra_pnginfo:
metadata.add_text(x, json.dumps(extra_pnginfo[x]))
img.save(resolved_img, pnginfo=metadata, compress_level=4)
return {
"ui": {
"images": [
{
"filename": resolved_img.name,
"subfolder": resolved_path.name,
"type": self.type,
}
]
}
}
__nodes__ = [
MTB_LoadImageSequence,
MTB_SaveImageSequence,
]
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