FFA-Net/python/axmodel_infer.py

#!/usr/bin/env python3
"""Single-image FFA-Net axmodel inference."""

import argparse
import sys
from pathlib import Path

import numpy as np
import axengine as axe
from PIL import Image


FILE = Path(__file__).resolve()
NET_DIR = FILE.parent
ROOT_DIR = NET_DIR.parent
sys.path.insert(0, str(NET_DIR))


def parse_args():
    parser = argparse.ArgumentParser(description="FFA-Net axmodel single-image inference.")
    parser.add_argument("--axmodel", default='./FFANet.axmodel', help="axmodel model path.")
    parser.add_argument("--input", default='outdoor_natural/nh(4).jpg', help="Path to input hazy image.")
    parser.add_argument("--output", default="axmodel_result.png", help="Output image path.")
    parser.add_argument("--height", type=int, default=512, help="axmodel input height.")
    parser.add_argument("--width", type=int, default=512, help="axmodel input width.")
    return parser.parse_args()

# MEAN = np.array([0.64, 0.6, 0.58], dtype=np.float32).reshape(3, 1, 1)
# STD = np.array([0.14, 0.15, 0.152], dtype=np.float32).reshape(3, 1, 1)

def preprocess(image_path, height, width):
    image = Image.open(image_path).convert("RGB")
    image = image.resize((width, height), Image.BICUBIC)
    arr = np.asarray(image).astype(np.float32)
    arr = arr.transpose(2, 0, 1)
    # arr = (arr - MEAN) / STD          # 训练同款归一化
    return arr[None, ...].astype(np.uint8)


def postprocess(output):
    arr = np.squeeze(output, axis=0).transpose(1, 2, 0)
    arr = np.clip(arr, 0.0, 1.0)
    return Image.fromarray((arr * 255.0 + 0.5).astype(np.uint8))


def main():
    args = parse_args()

    inp = preprocess(args.input, args.height, args.width)
    session = axe.InferenceSession(args.axmodel, providers=["AxEngineExecutionProvider"])
    input_name = session.get_inputs()[0].name
    out = session.run(None, {input_name: inp})[0]

    output_path = Path(args.output)
    output_path.parent.mkdir(parents=True, exist_ok=True)
    result = postprocess(out)
    result.save(str(output_path))
    print(f"Saved: {output_path}")


if __name__ == "__main__":
    main()