nodes/northumber-ComfyUI-northTools
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string memory node added, loading from directory image list with indices added, image to true added, some fixes

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Northumber
2025-05-10 14:58:13 +02:00
parent 6977304d39
commit 7b56cd8272
8 changed files with 226 additions and 40 deletions
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README.md
+2 -1
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@@ -1 +1,2 @@
# ComfyUI-northTools
# ComfyUI-northTools
__init__.py
+10 -1
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@@ -9,7 +9,10 @@ from .nodes.from_dir_image_list import LoadImagesFromDirList
from .nodes.extract_metadata_by_key import ExtractMetadataByKey
from .nodes.sum_integers import SumIntegers
from .nodes.from_dir_with_index_batch import LoadImagesFromDirByIndexBatch
from .nodes.from_dir_with_index_list import LoadImagesFromDirByIndexList
from .nodes.boolean_list_to_indexes import BooleanIndexesToString
from .nodes.concat_history_string import ConcatHistoryString
from .nodes.image_to_true import ImageToTrue
NODE_CLASS_MAPPINGS = {
@@ -17,7 +20,10 @@ NODE_CLASS_MAPPINGS = {
"ExtractMetadataByKey": ExtractMetadataByKey,
"SumIntegers": SumIntegers,
"LoadImagesFromDirByIndexBatch": LoadImagesFromDirByIndexBatch,
"LoadImagesFromDirByIndexList": LoadImagesFromDirByIndexList,
"BooleanIndexesToString": BooleanIndexesToString,
"ConcatHistoryString": ConcatHistoryString,
"ImageToTrue": ImageToTrue,
}
NODE_DISPLAY_NAME_MAPPINGS = {
@@ -25,7 +31,10 @@ NODE_DISPLAY_NAME_MAPPINGS = {
"ExtractMetadataByKey": "Extract Metadata value by key",
"SumIntegers": "Sum Integers",
"LoadImagesFromDirByIndexBatch": "Load Image batch from Directory with indexes",
"BooleanIndexesToString": "Boolean list to index string"
"LoadImagesFromDirByIndexList": "Load Image list from Directory with indexes",
"BooleanIndexesToString": "Boolean list to index string",
"ConcatHistoryString": "Concatenate and remember string",
"ImageToTrue": "Image to True",
}
__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
nodes/boolean_list_to_indexes.py
+44 -12
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@@ -3,24 +3,28 @@
"""
class BooleanIndexesToString:
memory_bools: list[bool] = []
pending_reset: bool = False
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"bool_list_str": ("STRING", {"default": ""}), # flexible format: TrueFalseTrue, 101, True,False,True, etc.
"bool_list": ("BOOLEAN[]", {"default": []}), # explicit list: [True, False, True]
"optional": {
"bool_list_str": ("STRING", {"default": ""}),
"bool_list": ("BOOLEAN[]", {"default": []}),
"memory_bool_list_str": ("STRING", {"default": ""}),
"memory_bool_list": ("BOOLEAN[]", {"default": []}),
"reset": ("BOOLEAN", {"default": False}),
}
}
RETURN_TYPES = ("STRING",)
RETURN_NAMES = ("index_string",)
FUNCTION = "get_indexes"
CATEGORY = "util"
@staticmethod
def parse_bool_string(s):
def parse_bool_string(s: str) -> list[bool]:
s = s.strip().replace(" ", "")
if "," in s:
tokens = s.split(",")
@@ -43,12 +47,40 @@ class BooleanIndexesToString:
true_values = {"true", "1"}
return [t.lower() in true_values for t in tokens]
def get_indexes(
self,
bool_list_str: str = "",
bool_list=None,
memory_bool_list_str: str = "",
memory_bool_list=None,
reset: bool = False,
):
if bool_list is None:
bool_list = []
if memory_bool_list is None:
memory_bool_list = []
def get_indexes(self, bool_list_str: str, bool_list):
if bool_list and len(bool_list) > 0:
bools = bool_list
if self.__class__.pending_reset:
self.__class__.memory_bools.clear()
self.__class__.pending_reset = False
if bool_list_str:
new_bools = self.parse_bool_string(bool_list_str)
elif bool_list:
new_bools = list(bool_list)
elif memory_bool_list_str:
new_bools = self.parse_bool_string(memory_bool_list_str)
elif memory_bool_list:
new_bools = list(memory_bool_list)
else:
bools = self.parse_bool_string(bool_list_str)
indexes = [str(i) for i, v in enumerate(bools) if v]
return (",".join(indexes),)
new_bools = []
self.__class__.memory_bools += new_bools
self.__class__.pending_reset = reset
indexes = [str(i) for i, v in enumerate(self.__class__.memory_bools) if v]
output = ",".join(indexes)
return (output,)
nodes/concat_history_string.py
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@@ -0,0 +1,41 @@
"""
This node receives a boolean and a string, and concatenates them to a string, that is remembered in the various runs.
"""
class ConcatHistoryString:
history = []
@classmethod
def INPUT_TYPES(cls):
return {
"optional": {
"value_bool": ("BOOLEAN", {"default": None}),
"value_str": ("STRING", {"default": ""}),
"reset": ("BOOLEAN", {"default": False}),
}
}
RETURN_TYPES = ("STRING",)
RETURN_NAMES = ("concatenated",)
FUNCTION = "concat"
CATEGORY = "util"
def concat(self, value_bool=None, value_str="", reset=False):
if reset:
self.__class__.history = []
input_value = None
if value_bool is not None:
input_value = "True" if value_bool else "False"
elif value_str != "":
input_value = str(value_str)
if input_value not in (None, ""):
self.__class__.history.append(input_value)
return (",".join(self.__class__.history),)
nodes/from_dir_image_list.py
+10 -9
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@@ -10,7 +10,7 @@ from PIL import Image, ImageOps
class LoadImagesFromDirList:
@classmethod
def INPUT_TYPES(s):
def INPUT_TYPES(cls):
return {
"required": {
"directory": ("STRING", {"default": ""}),
@@ -22,9 +22,9 @@ class LoadImagesFromDirList:
}
}
RETURN_TYPES = ("IMAGE", "MASK", "STRING", "STRING")
RETURN_NAMES = ("IMAGE", "MASK", "FILE PATH", "METADATA")
OUTPUT_IS_LIST = (True, True, True, True)
RETURN_TYPES = ("IMAGE", "MASK", "STRING", "STRING", "INT")
RETURN_NAMES = ("IMAGE", "MASK", "FILE PATH", "METADATA", "INDEX COUNT")
OUTPUT_IS_LIST = (True, True, True, True, True)
FUNCTION = "load_images"
CATEGORY = "image"
@@ -46,18 +46,19 @@ class LoadImagesFromDirList:
valid_extensions = ['.jpg', '.jpeg', '.png', '.webp']
dir_files = [f for f in dir_files if any(f.lower().endswith(ext) for ext in valid_extensions)]
dir_files = sorted(dir_files)
dir_files = [os.path.join(directory, x) for x in dir_files]
dir_files = dir_files[start_index:]
dir_files_full = [os.path.join(directory, x) for x in dir_files]
dir_files_full = dir_files_full[start_index:]
images = []
masks = []
file_paths = []
metadatas = []
indexes = []
limit_images = image_load_cap > 0
image_count = 0
for image_path in dir_files:
for idx, image_path in enumerate(dir_files_full):
if os.path.isdir(image_path):
continue
if limit_images and image_count >= image_load_cap:
@@ -65,7 +66,6 @@ class LoadImagesFromDirList:
i = Image.open(image_path)
i = ImageOps.exif_transpose(i)
# Extract metadata BEFORE conversion
metadata = i.info.copy()
exif = i.getexif()
if exif:
@@ -85,6 +85,7 @@ class LoadImagesFromDirList:
images.append(image)
masks.append(mask)
file_paths.append(str(image_path))
indexes.append(idx + start_index)
image_count += 1
return (images, masks, file_paths, metadatas)
return (images, masks, file_paths, metadatas, indexes)
nodes/from_dir_with_index_batch.py
+9 -17
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@@ -13,25 +13,21 @@ class LoadImagesFromDirByIndexBatch:
return {
"required": {
"directory": ("STRING", {"default": ""}),
"indices": ("STRING", {"default": ""}),
},
"optional": {
"indices": ("STRING", {"default": ""}),
}
}
RETURN_TYPES = ("IMAGE", "MASK", "INT")
FUNCTION = "load_images"
CATEGORY = "image"
@classmethod
def IS_CHANGED(cls, **kwargs):
return hash(frozenset(kwargs.items()))
def _parse_indices(self, indices_str, max_len):
# Parse a string like "0,3,4" into unique, sorted, safe integer indexes within [0,max_len-1]
indices = []
for idx in indices_str.split(","):
idx = idx.strip()
@@ -41,23 +37,21 @@ class LoadImagesFromDirByIndexBatch:
indices.append(i)
return sorted(set(indices))
def load_images(self, directory: str, indices: str):
def load_images(self, directory: str, indices: str = ""):
if not os.path.isdir(directory):
raise FileNotFoundError(f"Directory '{directory}' cannot be found.")
return (None, None, 0)
dir_files = os.listdir(directory)
valid_extensions = ['.jpg', '.jpeg', '.png', '.webp']
dir_files = [f for f in dir_files if any(f.lower().endswith(ext) for ext in valid_extensions)]
dir_files = sorted(dir_files)
if not dir_files:
raise FileNotFoundError(f"No valid image files found in '{directory}'.")
if not indices.strip():
raise StopIteration("Waiting for valid indices string.")
index_list = self._parse_indices(indices, len(dir_files))
if not index_list:
raise ValueError("No valid indexes found in input or indexes out of range.")
raise StopIteration("Waiting for valid indices string.")
selected_files = [os.path.join(directory, dir_files[i]) for i in index_list]
images = []
masks = []
has_non_empty_mask = False
for path in selected_files:
i = Image.open(path)
i = ImageOps.exif_transpose(i)
@@ -67,12 +61,10 @@ class LoadImagesFromDirByIndexBatch:
if 'A' in i.getbands():
mask_np = np.array(i.getchannel('A')).astype(np.float32) / 255.0
mask_tensor = 1. - torch.from_numpy(mask_np)
has_non_empty_mask = True
else:
# Default mask, matching previous logic
mask_tensor = torch.zeros((image_tensor.shape[2], image_tensor.shape[3]), dtype=torch.float32)
images.append(image_tensor)
masks.append(mask_tensor)
image_batch = torch.cat(images, dim=0) if len(images) > 1 else images[0]
mask_batch = torch.stack(masks, dim=0) if len(masks) > 1 else masks[0]
return (image_batch, mask_batch, len(images))
return (image_batch, mask_batch, len(images))
nodes/from_dir_with_index_list.py
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@@ -0,0 +1,91 @@
import os
import torch
import numpy as np
from PIL import Image, ImageOps
"""
Load an image list from directory using an index string to get positions: "0,2,5,...n"
"""
class LoadImagesFromDirByIndexList:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"directory": ("STRING", {"default": ""}),
},
"optional": {
"indices": ("STRING", {"default": ""}),
}
}
RETURN_TYPES = ("IMAGE", "MASK", "STRING", "STRING", "INT")
RETURN_NAMES = ("IMAGE", "MASK", "FILE PATH", "METADATA", "INDEX COUNT")
OUTPUT_IS_LIST = (True, True, True, True, True)
FUNCTION = "load_images"
CATEGORY = "image"
@classmethod
def IS_CHANGED(cls, **kwargs):
return hash(frozenset(kwargs.items()))
def _parse_indices(self, indices_str, max_len):
indices = []
for idx in indices_str.split(","):
idx = idx.strip()
if idx.isdigit():
i = int(idx)
if 0 <= i < max_len:
indices.append(i)
return sorted(set(indices))
def load_images(self, directory: str, indices: str = ""):
if not os.path.isdir(directory):
raise FileNotFoundError(f"Directory '{directory}' cannot be found.")
dir_files = os.listdir(directory)
valid_extensions = ['.jpg', '.jpeg', '.png', '.webp']
dir_files = [f for f in dir_files if any(f.lower().endswith(ext) for ext in valid_extensions)]
dir_files = sorted(dir_files)
if not dir_files:
raise FileNotFoundError(f"No valid image files in directory '{directory}'.")
if not indices.strip():
raise StopIteration("Waiting for valid indices string.")
index_list = self._parse_indices(indices, len(dir_files))
if not index_list:
raise StopIteration("Waiting for valid indices string.")
images = []
masks = []
file_paths = []
metadatas = []
indexes = []
for idx in index_list:
image_path = os.path.join(directory, dir_files[idx])
i = Image.open(image_path)
i = ImageOps.exif_transpose(i)
metadata = i.info.copy()
exif = i.getexif()
if exif:
metadata['exif'] = dict(exif)
metadatas.append(str(metadata))
image = i.convert("RGB")
image = np.array(image).astype(np.float32) / 255.0
image = torch.from_numpy(image)[None,]
if 'A' in i.getbands():
mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
mask = 1. - torch.from_numpy(mask)
else:
mask = torch.zeros((image.shape[2], image.shape[3]), dtype=torch.float32)
images.append(image)
masks.append(mask)
file_paths.append(str(image_path))
indexes.append(idx)
return (images, masks, file_paths, metadatas, indexes)
nodes/image_to_true.py
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@@ -0,0 +1,19 @@
"""
This node simply outputs true when receives an image, used for automation
"""
class ImageToTrue:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"image": ("IMAGE", {}),
}
}
RETURN_TYPES = ("BOOLEAN",)
FUNCTION = "process"
def process(self, image):
return (True,)