Feeding_control_system/vision/overflow_model/yiliao_main_rknn.py

import os
from pathlib import Path
import cv2
import numpy as np
import platform


# ---------------------------
# 类别映射
# ---------------------------
CLASS_NAMES = {
    0: "未堆料",
    1: "小堆料",
    2: "大堆料",
    3: "未浇筑满",
    4: "浇筑满"
}

# ---------------------------
# RKNN 全局实例（只加载一次）
# ---------------------------
_global_rknn = None
DEVICE_COMPATIBLE_NODE = '/proc/device-tree/compatible'


# =====================================================
#                   RKNN MODEL
# =====================================================
def init_rknn_model(model_path):
    from rknnlite.api import RKNNLite
    
    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


# ---------------------------
# 图像预处理（统一 640×640）
# ---------------------------
def preprocess(img, size=(640, 640)):
    img = cv2.resize(img, size)
    img = np.expand_dims(img, 0)
    return img


# ---------------------------
# 单次 RKNN 分类
# ---------------------------
def rknn_classify(img_resized, model_path):
    rknn = init_rknn_model(model_path)
    input_tensor = preprocess(img_resized)
    outs = rknn.inference([input_tensor])

    pred = outs[0].reshape(-1)
    class_id = int(np.argmax(pred))
    return class_id, pred.astype(float)


# =====================================================
#                    ROI 逻辑
# =====================================================
def load_rois(txt_path):
    rois = []
    if not os.path.exists(txt_path):
        print(f"❌ ROI 文件不存在: {txt_path}")
        return rois

    with open(txt_path) as f:
        for line in f:
            s = line.strip()
            if s:
                try:
                    x, y, w, h = map(int, s.split(','))
                    rois.append((x, y, w, h))
                except:
                    print("ROI 格式错误:", s)
    return rois


def crop_and_resize(img, rois, target_size=640):
    crops = []
    h_img, w_img = img.shape[:2]

    for idx, (x, y, w, h) in enumerate(rois):
        if x < 0 or y < 0 or x + w > w_img or y + h > h_img:
            continue
        roi = img[y:y + h, x:x + w]
        roi_resized = cv2.resize(roi, (target_size, target_size), interpolation=cv2.INTER_AREA)
        crops.append((roi_resized, idx))
    return crops


# =====================================================
#            class1/class2 加权分类增强
# =====================================================
def weighted_small_large(pred, threshold=0.4, w1=0.3, w2=0.7):
    p1 = float(pred[1])
    p2 = float(pred[2])
    total = p1 + p2

    score = (w1 * p1 + w2 * p2) / total if total > 0 else 0.0
    final_class = "大堆料" if score >= threshold else "小堆料"

    return final_class, score, p1, p2


# =====================================================
#             ⭐ 高复用：一行完成 ROI + 推理 ⭐
# =====================================================
def classify_frame_with_rois(model_path, frame, roi_file, threshold=0.4):
    """
    输入:
        - frame: BGR 图像 (numpy array)
        - model_path: RKNN 模型路径
        - roi_file: ROI 的 txt 文件
        - threshold: class1/class2 小/大堆料判断阈值

    输出:
        [
            { "roi": idx, "class": 类别, "score": 0.93, "p1": 0.22, "p2": 0.71 },
            ...
        ]
    """

    if frame is None or not isinstance(frame, np.ndarray):
        raise RuntimeError("❌ classify_frame_with_rois 传入的 frame 无效")

    rois = load_rois(roi_file)
    if not rois:
        raise RuntimeError("ROI 文件为空")

    crops = crop_and_resize(frame, rois)

    results = []

    for roi_img, idx in crops:
        class_id, pred = rknn_classify(roi_img, model_path)
        class_name = CLASS_NAMES.get(class_id, f"未知类别({class_id})")

        if class_id in [1, 2]:
            final_class, score, p1, p2 = weighted_small_large(pred, threshold)
        else:
            final_class = class_name
            score = float(pred[class_id])
            p1, p2 = float(pred[1]), float(pred[2])

        results.append({
            "roi": idx,
            "class": final_class,
            "score": round(score, 4),
            "p1": round(p1, 4),
            "p2": round(p2, 4)
        })

    return results


# =====================================================
#                   示例调用
# =====================================================
if __name__ == "__main__":
    model_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "yiliao_cls.rknn")
    roi_file = "./roi_coordinates/1_rois.txt"

    frame = cv2.imread("./test_image/2.jpg")

    outputs = classify_frame_with_rois(model_path, frame, roi_file)

    for res in outputs:
        print(res)