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import os
import hashlib
import numpy as np
import torch
from PIL import Image, ImageOps
import itertools
import folder_paths
from comfy.k_diffusion.utils import FolderOfImages
from comfy.utils import common_upscale, ProgressBar
from .logger import logger
from .utils import BIGMAX, calculate_file_hash, get_sorted_dir_files_from_directory, validate_path, strip_path
def is_changed_load_images(directory: str, image_load_cap: int = 0, skip_first_images: int = 0, select_every_nth: int = 1, **kwargs):
if not os.path.isdir(directory):
return False
dir_files = get_sorted_dir_files_from_directory(directory, skip_first_images, select_every_nth, FolderOfImages.IMG_EXTENSIONS)
if image_load_cap != 0:
dir_files = dir_files[:image_load_cap]
m = hashlib.sha256()
for filepath in dir_files:
m.update(calculate_file_hash(filepath).encode()) # strings must be encoded before hashing
return m.digest().hex()
def validate_load_images(directory: str):
if not os.path.isdir(directory):
return f"Directory '{directory}' cannot be found."
dir_files = os.listdir(directory)
if len(dir_files) == 0:
return f"No files in directory '{directory}'."
return True
def images_generator(directory: str, image_load_cap: int = 0, skip_first_images: int = 0, select_every_nth: int = 1, meta_batch=None, unique_id=None):
if not os.path.isdir(directory):
raise FileNotFoundError(f"Directory '{directory} cannot be found.")
dir_files = get_sorted_dir_files_from_directory(directory, skip_first_images, select_every_nth, FolderOfImages.IMG_EXTENSIONS)
if len(dir_files) == 0:
raise FileNotFoundError(f"No files in directory '{directory}'.")
if image_load_cap > 0:
dir_files = dir_files[:image_load_cap]
sizes = {}
has_alpha = False
for image_path in dir_files:
i = Image.open(image_path)
#exif_transpose can only ever rotate, but rotating can swap width/height
i = ImageOps.exif_transpose(i)
has_alpha |= 'A' in i.getbands()
count = sizes.get(i.size, 0)
sizes[i.size] = count +1
size = max(sizes.items(), key=lambda x: x[1])[0]
yield size[0], size[1], has_alpha
if meta_batch is not None:
yield min(image_load_cap, len(dir_files)) or len(dir_files)
iformat = "RGBA" if has_alpha else "RGB"
def load_image(file_path):
i = Image.open(file_path)
i = ImageOps.exif_transpose(i)
i = i.convert(iformat)
i = np.array(i, dtype=np.float32)
#This nonsense provides a nearly 50% speedup on my system
torch.from_numpy(i).div_(255)
if i.shape[0] != size[1] or i.shape[1] != size[0]:
i = torch.from_numpy(i).movedim(-1, 0).unsqueeze(0)
i = common_upscale(i, size[0], size[1], "lanczos", "center")
i = i.squeeze(0).movedim(0, -1).numpy()
if has_alpha:
i[:,:,-1] = 1 - i[:,:,-1]
return i
total_images = len(dir_files)
processed_images = 0
pbar = ProgressBar(total_images)
images = map(load_image, dir_files)
try:
prev_image = next(images)
while True:
next_image = next(images)
yield prev_image
processed_images += 1
pbar.update_absolute(processed_images, total_images)
prev_image = next_image
except StopIteration:
pass
if meta_batch is not None:
meta_batch.inputs.pop(unique_id)
meta_batch.has_closed_inputs = True
if prev_image is not None:
yield prev_image
def load_images(directory: str, image_load_cap: int = 0, skip_first_images: int = 0, select_every_nth: int = 1, meta_batch=None, unique_id=None):
if meta_batch is None or unique_id not in meta_batch.inputs:
gen = images_generator(directory, image_load_cap, skip_first_images, select_every_nth, meta_batch, unique_id)
(width, height, has_alpha) = next(gen)
if meta_batch is not None:
meta_batch.inputs[unique_id] = (gen, width, height, has_alpha)
meta_batch.total_frames = min(meta_batch.total_frames, next(gen))
else:
gen, width, height, has_alpha = meta_batch.inputs[unique_id]
if meta_batch is not None:
gen = itertools.islice(gen, meta_batch.frames_per_batch)
images = torch.from_numpy(np.fromiter(gen, np.dtype((np.float32, (height, width, 3 + has_alpha)))))
if has_alpha:
#tensors are not continuous. Rewrite will be required if this is an issue
masks = images[:,:,:,3]
images = images[:,:,:,:3]
else:
masks = torch.zeros((images.size(0), 64, 64), dtype=torch.float32, device="cpu")
if len(images) == 0:
raise FileNotFoundError(f"No images could be loaded from directory '{directory}'.")
return images, masks, images.size(0)
class LoadImagesFromDirectoryUpload:
@classmethod
def INPUT_TYPES(s):
input_dir = folder_paths.get_input_directory()
directories = []
for item in os.listdir(input_dir):
if not os.path.isfile(os.path.join(input_dir, item)) and item != "clipspace":
directories.append(item)
return {
"required": {
"directory": (directories,),
},
"optional": {
"image_load_cap": ("INT", {"default": 0, "min": 0, "max": BIGMAX, "step": 1}),
"skip_first_images": ("INT", {"default": 0, "min": 0, "max": BIGMAX, "step": 1}),
"select_every_nth": ("INT", {"default": 1, "min": 1, "max": BIGMAX, "step": 1}),
"meta_batch": ("VHS_BatchManager",),
},
"hidden": {
"unique_id": "UNIQUE_ID"
},
}
RETURN_TYPES = ("IMAGE", "MASK", "INT")
RETURN_NAMES = ("IMAGE", "MASK", "frame_count")
FUNCTION = "load_images"
CATEGORY = "Video Helper Suite 🎥🅥🅗🅢"
def load_images(self, directory: str, **kwargs):
directory = folder_paths.get_annotated_filepath(strip_path(directory))
return load_images(directory, **kwargs)
@classmethod
def IS_CHANGED(s, directory: str, **kwargs):
directory = folder_paths.get_annotated_filepath(strip_path(directory))
return is_changed_load_images(directory, **kwargs)
@classmethod
def VALIDATE_INPUTS(s, directory: str, **kwargs):
directory = folder_paths.get_annotated_filepath(strip_path(directory))
return validate_load_images(directory)
class LoadImagesFromDirectoryPath:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"directory": ("STRING", {"placeholder": "X://path/to/images", "vhs_path_extensions": []}),
},
"optional": {
"image_load_cap": ("INT", {"default": 0, "min": 0, "max": BIGMAX, "step": 1}),
"skip_first_images": ("INT", {"default": 0, "min": 0, "max": BIGMAX, "step": 1}),
"select_every_nth": ("INT", {"default": 1, "min": 1, "max": BIGMAX, "step": 1}),
"meta_batch": ("VHS_BatchManager",),
},
"hidden": {
"unique_id": "UNIQUE_ID"
},
}
RETURN_TYPES = ("IMAGE", "MASK", "INT")
RETURN_NAMES = ("IMAGE", "MASK", "frame_count")
FUNCTION = "load_images"
CATEGORY = "Video Helper Suite 🎥🅥🅗🅢"
def load_images(self, directory: str, **kwargs):
directory = strip_path(directory)
if directory is None or validate_load_images(directory) != True:
raise Exception("directory is not valid: " + directory)
return load_images(directory, **kwargs)
@classmethod
def IS_CHANGED(s, directory: str, **kwargs):
if directory is None:
return "input"
return is_changed_load_images(directory, **kwargs)
@classmethod
def VALIDATE_INPUTS(s, directory: str, **kwargs):
if directory is None:
return True
return validate_load_images(strip_path(directory))
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