213 lines
6.6 KiB
Python
Executable File
213 lines
6.6 KiB
Python
Executable File
# -*- coding: utf-8 -*-
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"""
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木条检测模块（基于RKNNLite + ROI + 单类别NMS）
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功能：
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- 检测ROI区域内木条数量
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- 输出实际木条数量
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"""
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import os
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from typing import Optional
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import cv2
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import numpy as np
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from rknnlite.api import RKNNLite
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# =====================================================
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# 常量配置
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# =====================================================
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RKNN_MODEL_PATH: str = "wood_detect.rknn"
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ROI: tuple[int, int, int, int] = (1, 1, 1000, 1000) # ROI 坐标(x1, y1, x2, y2)
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IMG_SIZE: tuple[int, int] = (640, 640) # 模型输入大小
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OBJ_THRESH: float = 0.25 # 置信度阈值
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NMS_THRESH: float = 0.45 # NMS阈值
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CLASS_NAME: list[str] = ["bag"] # 单类别名称，后续改成wood
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# =====================================================
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# 私有工具函数
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# =====================================================
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def _softmax(x: np.ndarray, axis: int = -1) -> np.ndarray:
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"""计算softmax概率"""
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x = x - np.max(x, axis=axis, keepdims=True)
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exp_x = np.exp(x)
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return exp_x / np.sum(exp_x, axis=axis, keepdims=True)
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def _letterbox_resize(image: np.ndarray,
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size: tuple[int, int],
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bg_color: int = 114) -> tuple[np.ndarray, float, int, int]:
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"""保持长宽比缩放并填充到目标尺寸"""
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target_w, target_h = size
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h, w = image.shape[:2]
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scale = min(target_w / w, target_h / h)
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new_w, new_h = int(w * scale), int(h * scale)
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resized = cv2.resize(image, (new_w, new_h))
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canvas = np.full((target_h, target_w, 3), bg_color, dtype=np.uint8)
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dx = (target_w - new_w) // 2
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dy = (target_h - new_h) // 2
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canvas[dy:dy + new_h, dx:dx + new_w] = resized
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return canvas, scale, dx, dy
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def _nms(boxes: np.ndarray, scores: np.ndarray, thresh: float) -> list[int]:
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"""非极大值抑制"""
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x1, y1, x2, y2 = boxes.T
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areas = (x2 - x1) * (y2 - y1)
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order = scores.argsort()[::-1]
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keep: list[int] = []
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while order.size > 0:
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i = order[0]
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keep.append(i)
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xx1 = np.maximum(x1[i], x1[order[1:]])
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yy1 = np.maximum(y1[i], y1[order[1:]])
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xx2 = np.minimum(x2[i], x2[order[1:]])
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yy2 = np.minimum(y2[i], y2[order[1:]])
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inter = np.maximum(0, xx2 - xx1) * np.maximum(0, yy2 - yy1)
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iou = inter / (areas[i] + areas[order[1:]] - inter)
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order = order[1:][iou <= thresh]
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return keep
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def _post_process(outputs: list[np.ndarray],
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scale: float,
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dx: int,
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dy: int) -> Optional[np.ndarray]:
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"""RKNN模型输出后处理：解码坐标 + NMS，返回有效boxes"""
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boxes_list, scores_list = [], []
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strides = [8, 16, 32]
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for i, stride in enumerate(strides):
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reg = outputs[i * 3 + 0][0]
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cls = outputs[i * 3 + 1][0]
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obj = outputs[i * 3 + 2][0]
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num_classes, H, W = cls.shape
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reg_max = reg.shape[0] // 4
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grid_y, grid_x = np.meshgrid(np.arange(H), np.arange(W), indexing="ij")
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grid_x = grid_x.astype(np.float32).ravel()
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grid_y = grid_y.astype(np.float32).ravel()
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cls_flat = cls.reshape(num_classes, -1).T
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obj_flat = obj.ravel()
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max_cls_score = np.max(cls_flat, axis=1)
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scores = max_cls_score * obj_flat
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valid_mask = scores >= OBJ_THRESH
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if not np.any(valid_mask):
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continue
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valid_idx = np.where(valid_mask)[0]
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scores_v = scores[valid_idx]
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gx = grid_x[valid_idx]
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gy = grid_y[valid_idx]
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reg_valid = reg.reshape(4, reg_max, -1)[:, :, valid_idx]
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reg_softmax = _softmax(reg_valid, axis=1)
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acc = np.arange(reg_max, dtype=np.float32).reshape(1, -1, 1)
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distance = np.sum(reg_softmax * acc, axis=1)
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cx = (gx + 0.5) * stride
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cy = (gy + 0.5) * stride
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l, t, r, b = distance[0], distance[1], distance[2], distance[3]
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x1 = cx - l * stride
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y1 = cy - t * stride
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x2 = cx + r * stride
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y2 = cy + b * stride
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boxes = np.stack([x1, y1, x2, y2], axis=1)
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boxes[:, [0, 2]] = (boxes[:, [0, 2]] - dx) / scale
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boxes[:, [1, 3]] = (boxes[:, [1, 3]] - dy) / scale
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boxes_list.append(boxes)
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scores_list.append(scores_v)
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if not boxes_list:
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return None
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boxes_all = np.concatenate(boxes_list, axis=0)
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scores_all = np.concatenate(scores_list, axis=0)
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keep_idx = _nms(boxes_all, scores_all, NMS_THRESH)
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return boxes_all[keep_idx]
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# =====================================================
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# RKNN模型全局初始化
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# =====================================================
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_global_rknn = RKNNLite()
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_global_rknn.load_rknn(RKNN_MODEL_PATH)
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_global_rknn.init_runtime()
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# =====================================================
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# 木条检测类
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# =====================================================
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class WoodDetectorRKNN:
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"""基于RKNN的木条检测器"""
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def __init__(self):
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"""初始化木条检测器"""
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self.rknn = _global_rknn
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def detect(self, img_np: np.ndarray) -> int:
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"""
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检测木条数量
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Args:
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img_np (np.ndarray): 原始BGR图像
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Returns:
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int: 检测到的木条数量
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"""
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# ROI裁剪
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h, w = img_np.shape[:2]
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x1, y1, x2, y2 = ROI
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x1 = max(0, min(x1, w - 1))
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x2 = max(0, min(x2, w))
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y1 = max(0, min(y1, h - 1))
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y2 = max(0, min(y2, h))
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roi_img = img_np[y1:y2, x1:x2]
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# letterbox resize
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resized_img, scale, dx, dy = _letterbox_resize(roi_img, IMG_SIZE)
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# 推理
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input_tensor = np.expand_dims(resized_img.astype(np.float32), axis=0)
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outputs = self.rknn.inference([input_tensor])
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boxes = _post_process(outputs, scale, dx, dy)
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if boxes is None:
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return 0
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return len(boxes)
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# =====================================================
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# 对外统一接口
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# =====================================================
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_detector = WoodDetectorRKNN()
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def detect_wood(img_np: np.ndarray) -> int:
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"""
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对外木条检测接口（返回木条数量）
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Args:
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img_np (np.ndarray): 原始BGR图像
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Returns:
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int: 检测到的木条数量
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"""
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return _detector.detect(img_np)
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# =====================================================
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# 主程序入口
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# =====================================================
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if __name__ == "__main__":
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img_path = "1.jpg"
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if not os.path.exists(img_path):
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raise FileNotFoundError(f"图片不存在: {img_path}")
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img = cv2.imread(img_path)
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if img is None:
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raise ValueError("图像加载失败")
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total_count = detect_wood(img)
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print(f"检测到木条数量: {total_count}")
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