DnCNN/python/axmodel_infer.py

#!/usr/bin/env python3
import os
import cv2
import numpy as np
import axengine as axe

# ============================================================
# 默认参数（可按需修改）
# ============================================================
AXMODEL_PATH = "dncnn_color_blind_416x416_sim.axmodel"
OUTPUT_DIR = "./"
# AX 量化模型输入格式: "float32" 或 "uint8"，取决于编译时的量化配置
INPUT_DTYPE = "uint8"           # 量化模型通常为 uint8；float 模型设为 "float32"
NOISE_SIGMA = 25               # 对原图施加的 AWGN 噪声强度（uint8 尺度，设为 0 则不加噪）

# ============================================================


def load_image(img_path):
    """读取图像，返回 BGR uint8 原始图"""
    img_bgr = cv2.imread(img_path, cv2.IMREAD_UNCHANGED)
    if img_bgr is None:
        raise FileNotFoundError(f"无法读取图像: {img_path}")
    if img_bgr.ndim == 2:
        img_bgr = cv2.cvtColor(img_bgr, cv2.COLOR_GRAY2BGR)
    return img_bgr


def add_awgn(img_bgr, sigma):
    """对 BGR uint8 图像施加 AWGN，返回加噪后的 BGR uint8 图像"""
    noise = np.random.randn(*img_bgr.shape).astype(np.float32) * sigma
    noisy = img_bgr.astype(np.float32) + noise
    noisy = np.clip(noisy, 0, 255).astype(np.uint8)
    return noisy


def preprocess(img_bgr, model_h, model_w):
    """
    预处理: BGR->RGB, resize to model size, 按 INPUT_DTYPE 做归一化或保持 uint8
    返回: 模型输入张量, 原始尺寸 (h, w)
    """
    orig_h, orig_w = img_bgr.shape[:2]
    img_rgb = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)
    img_resized = cv2.resize(img_rgb, (model_w, model_h), interpolation=cv2.INTER_LINEAR)

    if INPUT_DTYPE == "float32":
        img_float = img_resized.astype(np.float32) / 255.0
        tensor = np.transpose(img_float, (2, 0, 1))[np.newaxis, ...].astype(np.float32)
    else:
        tensor = np.transpose(img_resized, (2, 0, 1))[np.newaxis, ...].astype(np.uint8)
    return tensor, (orig_h, orig_w)


def postprocess(tensor, orig_h, orig_w):
    """
    后处理: squeeze batch, CHW->HWC, clip, uint8, resize 回原始尺寸
    返回: BGR uint8 图像
    """
    if tensor.dtype == np.uint8:
        arr = np.squeeze(tensor, axis=0)
        arr = np.transpose(arr, (1, 2, 0))
        arr_uint = arr
    else:
        arr = np.clip(np.squeeze(tensor, axis=0), 0.0, 1.0)
        arr = np.transpose(arr, (1, 2, 0))
        arr_uint = (arr * 255.0).round().astype(np.uint8)
    arr_uint = cv2.resize(arr_uint, (orig_w, orig_h), interpolation=cv2.INTER_LINEAR)
    result_bgr = cv2.cvtColor(arr_uint, cv2.COLOR_RGB2BGR)
    return result_bgr


def make_concat(original_bgr, noisy_bgr, result_bgr,
                label_left="Original", label_mid="Noisy", label_right="DnCNN AXModel"):
    """左中右拼接原图、加噪图与结果图，并添加顶部标注"""
    imgs = [original_bgr, noisy_bgr, result_bgr]
    labels = [label_left, label_mid, label_right]
    hs = [im.shape[0] for im in imgs]
    h = max(hs)

    resized = []
    for im in imgs:
        hh, ww = im.shape[:2]
        if hh != h:
            im = cv2.resize(im, (int(ww * h / hh), h), interpolation=cv2.INTER_LINEAR)
        resized.append(im)

    ws = [im.shape[1] for im in resized]
    total_w = sum(ws)
    label_h = max(30, h // 25)
    canvas = np.full((h + label_h, total_w, 3), 255, dtype=np.uint8)

    x = 0
    for idx, im in enumerate(resized):
        canvas[label_h:, x:x + ws[idx]] = im
        x += ws[idx]

    font = cv2.FONT_HERSHEY_SIMPLEX
    font_scale = label_h / 30.0
    thickness = max(1, int(font_scale))
    color = (0, 0, 0)

    x = 0
    for idx, (label, w) in enumerate(zip(labels, ws)):
        (tw, th), _ = cv2.getTextSize(label, font, font_scale, thickness)
        cv2.putText(canvas, label, (x + w // 2 - tw // 2, label_h - (label_h - th) // 2),
                    font, font_scale, color, thickness, cv2.LINE_AA)
        x += w

    return canvas


def main():
    img_path = './3096.png'
    os.makedirs(OUTPUT_DIR, exist_ok=True)

    # ---- 加载 AX 模型 ----
    session = axe.InferenceSession(AXMODEL_PATH, providers=["AxEngineExecutionProvider"])
    input_name = session.get_inputs()[0].name
    _, _, model_h, model_w = session.get_inputs()[0].shape

    # ---- 读取原图 ----
    img_bgr = load_image(img_path)

    # ---- 加噪 ----
    if NOISE_SIGMA > 0:
        noisy_bgr = add_awgn(img_bgr, NOISE_SIGMA)
    else:
        noisy_bgr = img_bgr

    # ---- 预处理（对加噪图） ----
    tensor, (orig_h, orig_w) = preprocess(noisy_bgr, model_h, model_w)

    # ---- AX 推理 ----
    result = session.run(None, {input_name: tensor})[0]

    # ---- 后处理 & 还原原始尺寸 ----
    result_bgr = postprocess(result, orig_h, orig_w)

    # ---- 三图拼接输出 ----
    label_mid = f"Noisy (sigma={NOISE_SIGMA})" if NOISE_SIGMA > 0 else "Input"
    concat = make_concat(img_bgr, noisy_bgr, result_bgr, label_mid=label_mid)

    name = os.path.splitext(os.path.basename(img_path))[0]
    out_path = os.path.join(OUTPUT_DIR, "axmodel_res.png")
    cv2.imwrite(out_path, concat)
    print(f"输出已保存: {out_path}")


if __name__ == "__main__":
    main()