Jet Huang
64eacbf48d
New nodes (50 registered): - Depth Anything V2 (ONNX Runtime, small model Apache-2.0, pinned revision + SHA256) - Depth Lens Blur (disc/gaussian bokeh, focus_mask, built-in depth estimation) - SAM One-Click Mask (points, Face Detect bbox_json or mask hint; vit_b/mobile_sam/vit_l/vit_h) - Palette Extract (k-means++ in LAB, strip + HEX JSON + quantized image) - Dust & Spot Removal (compact spots in calm areas, Gaussian fill) Existing nodes, new optional inputs with unchanged defaults: - Color Matcher / Color Matcher Region: method = stats | histogram - ColorLab: exposure_mode = display | linear Model nodes get keep_model_loaded to free VRAM after a run. onnxruntime is optional (Depth Anything only).
67 lines
2.1 KiB
Python
67 lines
2.1 KiB
Python
from __future__ import annotations
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import importlib.util
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import json
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import sys
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from pathlib import Path
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import pytest
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import torch
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_PACK_ROOT = Path(__file__).resolve().parent.parent
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_PACK_MODULE_NAME = "comfyui_jh_pixelpro_under_test_palette"
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def _load_pack():
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if _PACK_MODULE_NAME in sys.modules:
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return sys.modules[_PACK_MODULE_NAME]
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spec = importlib.util.spec_from_file_location(
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_PACK_MODULE_NAME,
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_PACK_ROOT / "__init__.py",
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submodule_search_locations=[str(_PACK_ROOT)],
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)
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module = importlib.util.module_from_spec(spec)
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sys.modules[_PACK_MODULE_NAME] = module
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spec.loader.exec_module(module)
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return module
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@pytest.fixture(scope="module")
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def node_cls():
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return _load_pack().NODE_CLASS_MAPPINGS["JHPixelProPaletteExtract"]
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def test_node_registered() -> None:
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pack = _load_pack()
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assert pack.NODE_DISPLAY_NAME_MAPPINGS["JHPixelProPaletteExtract"] == "Color: Palette Extract"
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def test_node_metadata(node_cls) -> None:
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assert node_cls.CATEGORY == "ComfyUI-JH-PixelPro/color"
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assert node_cls.RETURN_TYPES == ("IMAGE", "STRING", "IMAGE")
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assert node_cls.RETURN_NAMES == ("palette", "palette_json", "quantized")
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spec = node_cls.INPUT_TYPES()
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colors_cfg = spec["required"]["colors"][1]
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assert (colors_cfg["default"], colors_cfg["min"], colors_cfg["max"]) == (6, 2, 16)
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assert spec["optional"]["mask"][0] == "MASK"
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for _, cfg in (*spec["required"].values(), *spec["optional"].values()):
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assert "tooltip" in cfg
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def test_node_runs_and_returns_json(node_cls) -> None:
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img = torch.zeros(1, 32, 32, 3)
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img[:, :, :16] = torch.tensor([1.0, 0.0, 0.0])
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img[:, :, 16:] = torch.tensor([0.0, 0.0, 1.0])
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strip, text, quantized = node_cls().extract(img, 2)
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data = json.loads(text)
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assert {c["hex"] for c in data} == {"#ff0000", "#0000ff"}
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assert strip.shape == (1, 192, 1024, 3)
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assert quantized.shape == img.shape
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def test_node_batch_json_is_nested(node_cls) -> None:
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img = torch.rand(2, 16, 16, 3)
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_, text, _ = node_cls().extract(img, 3)
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data = json.loads(text)
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assert len(data) == 2 and len(data[0]) == 3
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