zjsh_code_jicheng/charge_3cls/charge_cls_rknn.py

import os
import cv2
import numpy as np
from rknnlite.api import RKNNLite
from collections import deque

class StableClassJudge:
    """
    连续三帧稳定判决器：
    - class0 / class1 连续 3 帧 -> 输出
    - class2 -> 清空计数，重新统计
    """

    def __init__(self, stable_frames=3, ignore_class=2):
        self.stable_frames = stable_frames
        self.ignore_class = ignore_class
        self.buffer = deque(maxlen=stable_frames)

    def reset(self):
        self.buffer.clear()

    def update(self, class_id):
        """
        输入单帧分类结果
        返回：
          - None：尚未稳定
          - class_id：稳定输出结果
        """

        # 遇到 class2，直接清空重新计数
        if class_id == self.ignore_class:
            self.reset()
            return None

        self.buffer.append(class_id)

        # 缓冲未满
        if len(self.buffer) < self.stable_frames:
            return None

        # 三帧完全一致
        if len(set(self.buffer)) == 1:
            stable_class = self.buffer[0]
            self.reset()   # 输出一次后重新计数（防止重复触发）
            return stable_class

        return None

# ---------------------------
# 三分类映射
# ---------------------------
CLASS_NAMES = {
    0: "插好",
    1: "未插好",
    2: "有遮挡"
}

# ---------------------------
# RKNN 全局实例（只加载一次）
# ---------------------------
_global_rknn = None


def init_rknn_model(model_path):
    global _global_rknn
    if _global_rknn is not None:
        return _global_rknn

    rknn = RKNNLite(verbose=False)
    ret = rknn.load_rknn(model_path)
    if ret != 0:
        raise RuntimeError(f"Load RKNN failed: {ret}")

    ret = rknn.init_runtime(core_mask=RKNNLite.NPU_CORE_0)
    if ret != 0:
        raise RuntimeError(f"Init runtime failed: {ret}")

    _global_rknn = rknn
    print(f"[INFO] RKNN 模型加载成功：{model_path}")
    return rknn


# ---------------------------
# 预处理
# ---------------------------
def letterbox(image, new_size=640, color=(114, 114, 114)):
    h, w = image.shape[:2]
    scale = min(new_size / h, new_size / w)
    nh, nw = int(h * scale), int(w * scale)
    resized = cv2.resize(image, (nw, nh))
    new_img = np.full((new_size, new_size, 3), color, dtype=np.uint8)
    top = (new_size - nh) // 2
    left = (new_size - nw) // 2
    new_img[top:top + nh, left:left + nw] = resized
    return new_img


def resize_stretch(image, size=640):
    return cv2.resize(image, (size, size))


def preprocess_image_for_rknn(
        img,
        size=640,
        resize_mode="stretch",
        to_rgb=True,
        normalize=False,
        layout="NHWC"
):
    if resize_mode == "letterbox":
        img_box = letterbox(img, new_size=size)
    else:
        img_box = resize_stretch(img, size=size)

    if to_rgb:
        img_box = cv2.cvtColor(img_box, cv2.COLOR_BGR2RGB)

    img_f = img_box.astype(np.float32)

    if normalize:
        img_f /= 255.0

    if layout == "NHWC":
        out = np.expand_dims(img_f, axis=0)
    else:
        out = np.expand_dims(np.transpose(img_f, (2, 0, 1)), axis=0)

    return np.ascontiguousarray(out)


# ---------------------------
# 单次 RKNN 推理（三分类）
# ---------------------------
def rknn_classify_preprocessed(input_tensor, model_path):
    rknn = init_rknn_model(model_path)

    outs = rknn.inference([input_tensor])
    logits = outs[0].reshape(-1).astype(np.float32)  # shape = (3,)

    # softmax
    exp = np.exp(logits - np.max(logits))
    probs = exp / np.sum(exp)

    class_id = int(np.argmax(probs))
    return class_id, probs


# ---------------------------
# 单张图片推理（三分类）- 已移除 ROI 逻辑
# ---------------------------
def classify_single_image(
        frame,
        model_path,
        size=640,
        resize_mode="stretch",
        to_rgb=True,
        normalize=False,
        layout="NHWC"
):
    if frame is None:
        raise FileNotFoundError("❌ 输入帧为空")

    # 直接使用整图，不再裁剪
    input_tensor = preprocess_image_for_rknn(
        frame,
        size=size,
        resize_mode=resize_mode,
        to_rgb=to_rgb,
        normalize=normalize,
        layout=layout
    )

    class_id, probs = rknn_classify_preprocessed(input_tensor, model_path)
    class_name = CLASS_NAMES.get(class_id, f"未知类别 ({class_id})")

    return {
        "class_id": class_id,
        "class": class_name,
        "score": round(float(probs[class_id]), 4),
        "raw": probs.tolist()
    }


# ---------------------------
# 示例调用
# ---------------------------
if __name__ == "__main__":
    model_path = "charge_cls.rknn"
    # roi_file 已移除
    image_path = "class2.png"

    frame = cv2.imread(image_path)
    if frame is None:
        raise FileNotFoundError(f"❌ 无法读取图片：{image_path}")

    # 调用
    result = classify_single_image(frame, model_path)
    print("[RESULT]", result)