Implement Compass Image Loader node with dynamic directory discovery and autocomplete

This commit is contained in:
2026-04-22 09:18:52 -07:00
parent 56bee4b747
commit 0db6ca8277
3 changed files with 282 additions and 0 deletions

9
__init__.py Normal file
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from .compass_image_loader import CompassImageLoader
NODE_CLASS_MAPPINGS = {
"CompassImageLoader": CompassImageLoader,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"CompassImageLoader": "Compass Image Loader",
}

157
compass_image_loader.py Normal file
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from PIL import Image
import folder_paths
import os
import torch
import numpy as np
VALID_DIRECTIONS = {"n", "ne", "e", "se", "s", "sw", "w", "nw"}
VALID_MODALITIES = {"image", "depth", "openpose"}
def _discover_directories():
base_dir = folder_paths.get_input_directory()
if not os.path.exists(base_dir):
return []
candidates = set()
for root, subdirs, _ in os.walk(base_dir, followlinks=True):
current_path = root[os.path.dirname(root) + 1:]
sub_dirs_lower = {s.lower() for s in subdirs}
if VALID_DIRECTIONS & sub_dirs_lower or VALID_MODALITIES & sub_dirs_lower:
candidates.add(current_path)
return sorted(candidates)
class CompassImageLoader:
CATEGORY = "image/loaders"
@classmethod
def INPUT_TYPES(cls):
directories = _discover_directories()
return {
"required": {
"directory": (directories if directories else ["(none found)"],),
"direction": (["", "n", "ne", "e", "se", "s", "sw", "w", "nw"],),
"modality": (["image", "depth", "openpose"],),
"frame": ("STRING", {"default": ""}),
"width": ("INT", {"default": 0, "min": 0, "max": 16384, "step": 1}),
"height": ("INT", {"default": 0, "min": 0, "max": 16384, "step": 1}),
},
}
RETURN_TYPES = ("IMAGE", "STRING", "INT", "INT", "INT")
RETURN_NAMES = ("IMAGE", "path", "width", "height", "frame_count")
FUNCTION = "load_images"
def load_images(self, directory, direction, modality, frame=None, width=0, height=0):
base_dir = folder_paths.get_input_directory()
if direction and direction.strip():
target_dir = os.path.join(base_dir, directory, direction, modality)
else:
target_dir = os.path.join(base_dir, directory, modality)
if not os.path.isdir(target_dir):
raise RuntimeError(f"Compass directory not found: {target_dir}")
supported_extensions = {"png", "jpg", "jpeg", "webp", "bmp", "gif", "tiff"}
files = [
f for f in sorted(os.listdir(target_dir))
if os.path.isfile(os.path.join(target_dir, f)) and f.split(".")[-1].lower() in supported_extensions
]
if not files:
raise RuntimeError(f"No images found in: {target_dir}")
if frame is None or str(frame).strip() == "":
selected_files = files
output_path = target_dir
else:
try:
index = int(str(frame).strip())
except (ValueError, TypeError):
raise RuntimeError(f"Invalid frame number: '{frame}'. Must be an integer.")
if index < 0 or index >= len(files):
raise RuntimeError(
f"Frame index {index} out of bounds. Found {len(files)} images in {target_dir}."
)
selected_files = [files[index]]
output_path = os.path.join(target_dir, files[index])
tensors = []
final_w, final_h = 0, 0
for filename in selected_files:
filepath = os.path.join(target_dir, filename)
image = Image.open(filepath).convert("RGB")
orig_w, orig_h = image.size
if width == 0 and height == 0:
pass
elif width > 0 and height == 0:
fw = width
fh = int(orig_h * (width / orig_w))
image = image.resize((fw, fh), Image.Resampling.LANCZOS)
elif height > 0 and width == 0:
fh = height
fw = int(orig_w * (height / orig_h))
image = image.resize((fw, fh), Image.Resampling.LANCZOS)
else:
scale = max(width / orig_w, height / orig_h)
new_w = int(orig_w * scale)
new_h = int(orig_h * scale)
image = image.resize((new_w, new_h), Image.Resampling.LANCZOS)
left = (new_w - width) // 2
top = (new_h - height) // 2
right = left + width
bottom = top + height
image = image.crop((left, top, right, bottom))
final_w, final_h = image.size[0], image.size[1]
np_image = np.array(image).astype(np.float32) / 255.0
tensor = torch.from_numpy(np_image)[None,]
tensors.append(tensor)
if len(tensors) == 1:
image_batch = tensors[0]
else:
image_batch = torch.cat(tensors, dim=0)
return (image_batch, output_path, final_w, final_h, len(selected_files))
@classmethod
def IS_CHANGED(cls, directory, direction, modality, frame=None, width=0, height=0):
base_dir = folder_paths.get_input_directory()
if direction and direction.strip():
target_dir = os.path.join(base_dir, directory, direction, modality)
else:
target_dir = os.path.join(base_dir, directory, modality)
import hashlib
m = hashlib.sha256()
m.update(f"{directory}:{direction}:{modality}:{frame}:{width}:{height}".encode("utf-8"))
if not os.path.isdir(target_dir):
return ""
supported_extensions = {"png", "jpg", "jpeg", "webp", "bmp", "gif", "tiff"}
files = [
f for f in sorted(os.listdir(target_dir))
if os.path.isfile(os.path.join(target_dir, f)) and f.split(".")[-1].lower() in supported_extensions
]
if frame is None or str(frame).strip() == "":
m.update(":".join(files).encode("utf-8"))
else:
try:
index = int(str(frame).strip())
filepath = os.path.join(target_dir, files[index])
with open(filepath, "rb") as f:
m.update(f.read(65536))
except (ValueError, IndexErro

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js/compass_image_loader.js Normal file
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import { app } from "../../../scripts/app.js";
app.registerExtension({
name: "CompassImageLoader",
async beforeRegisterNodeDef(nodeType, nodeData) {
if (nodeData.name !== "CompassImageLoader") return;
const origOnNodeCreated = nodeType.prototype.onNodeCreated;
nodeType.prototype.onNodeCreated = function () {
if (origOnNodeCreated) origOnNodeCreated.apply(this, []);
const directoryWidget = this.widgets.find(
(w) => w.name === "directory"
);
if (!directoryWidget) return;
directoryWidget._all_values = [...(directoryWidget.options.values || [])];
try {
if (typeof this.addDOMWidget === "function") {
const inputEl = document.createElement("input");
inputEl.type = "text";
inputEl.value = directoryWidget.value;
inputEl.addEventListener("change", () => {
const filter = inputEl.value.toLowerCase();
const filtered = directoryWidget._all_values.filter((v) =>
v.toLowerCase().includes(filter)
);
directoryWidget.options.values = filtered;
directoryWidget.value = filtered[0] || "";
this.onResize?.();
});
const dropdownEl = document.createElement("div");
dropdownEl.style.overflowY = "auto";
dropdownEl.style.maxHeight = "200px";
const listEl = document.createElement("ul");
listEl.style.padding = "4px";
listEl.style.margin = "0";
listEl.style.background = "#333";
inputEl.addEventListener("focus", () => {
listEl.innerHTML = "";
directoryWidget._all_values.forEach((v) => {
const li = document.createElement("li");
li.textContent = v;
li.style.padding = "4px 8px";
li.style.cursor = "pointer";
li.addEventListener("click", () => {
inputEl.value = v;
directoryWidget.value = v;
listEl.innerHTML = "";
});
listEl.appendChild(li);
});
});
inputEl.addEventListener("blur", (e) => {
setTimeout(() => {
if (!listEl.contains(e.relatedTarget)) {
listEl.innerHTML = "";
}
}, 0);
});
const textWidget = this.addDOMWidget(
"directory_filter",
"Directory Filter",
inputEl,
{},
{ widget: directoryWidget }
);
textWidget.computeSize = () => [1, 40];
}
} catch (e) {
console.warn("[CompassImageLoader] Widget setup failed:", e);
}
};
},
async loadedGraphNode(node) {
if (node.type !== "CompassImageLoader") return;
const directoryWidget = node.widgets?.find((w) => w.name === "directory");
if (!directoryWidget) return;
setTimeout(() => {
try {
app.api.addWebSocketMessageEventListener("fresh-node-defs", (event) => {
const defs = event.detail || {};
const compassDef = defs["CompassImageLoader"];
if (!compassDef) return;
const dirs = compassDef.input?.required?.directory;
if (!dirs || !Array.isArray(dirs)) return;
directoryWidget._all_values = [...dirs];
directoryWidget.options.values = [...dirs];
if (!directoryWidget._all_values.includes(directoryWidget.value)) {
directoryWidget.value = directoryWidget._all_values[0] || "";
}
});
} catch (e) {
console.warn("[CompassImageLoader] Refresh listener failed:", e);
}
}, 100);
},
async getCustomWidgets() {
return null;
}
});