mirror of
https://github.com/hanzoai/anomalib.git
synced 2026-10-30 18:48:11 +00:00
zeekay
dda5b2a367
The previous commit was meant to remove .github/workflows and removed every tracked path instead — 892 files, the whole repo. This puts back the 874 that were never in scope; the eighteen workflow files stay gone, for the reasons that commit gives. Content is byte-identical to 9d2aec9 outside .github/workflows.
42 lines
1.1 KiB
Python
42 lines
1.1 KiB
Python
# Copyright (C) 2024 Intel Corporation
|
|
# SPDX-License-Identifier: Apache-2.0
|
|
|
|
"""Getting Started with Anomalib Inference using the Python API.
|
|
|
|
This example shows how to perform inference on a trained model
|
|
using the Anomalib Python API.
|
|
"""
|
|
|
|
# 1. Import required modules
|
|
from pathlib import Path
|
|
|
|
from anomalib.data import PredictDataset
|
|
from anomalib.engine import Engine
|
|
from anomalib.models import EfficientAd
|
|
|
|
# 2. Initialize the model and load weights
|
|
model = EfficientAd()
|
|
engine = Engine()
|
|
|
|
# 3. Prepare test data
|
|
# You can use a single image or a folder of images
|
|
dataset = PredictDataset(
|
|
path=Path("path/to/test/images"),
|
|
image_size=(256, 256),
|
|
)
|
|
|
|
# 4. Get predictions
|
|
predictions = engine.predict(
|
|
model=model,
|
|
dataset=dataset,
|
|
ckpt_path="path/to/model.ckpt",
|
|
)
|
|
|
|
# 5. Access the results
|
|
if predictions is not None:
|
|
for prediction in predictions:
|
|
image_path = prediction.image_path
|
|
anomaly_map = prediction.anomaly_map # Pixel-level anomaly heatmap
|
|
pred_label = prediction.pred_label # Image-level label (0: normal, 1: anomalous)
|
|
pred_score = prediction.pred_score # Image-level anomaly score
|