rknn替换，板子是3568的

ailai_pc/yolo_obb_dataset/11111.py

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+import os
+import cv2
+import numpy as np
+from rknnlite.api import RKNNLite
+
+# ====================== 配置 ======================
+MODEL_PATH = "yolo11.rknn"      # RKNN 模型路径
+IMG_PATH = "11.jpg"             # 待检测图片
+IMG_SIZE = (640, 640)           # 模型输入尺寸 (w,h)
+OBJ_THRESH = 0.001              # 目标置信度阈值
+NMS_THRESH = 0.45               # NMS 阈值
+CLASS_NAME = ["bag"]            # 单类别
+OUTPUT_DIR = "./result"
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+
+# ====================== 工具函数 ======================
+def letterbox_resize(image, size, bg_color=114):
+    target_w, target_h = size
+    h, w = image.shape[:2]
+    scale = min(target_w / w, target_h / h)
+    new_w, new_h = int(w * scale), int(h * scale)
+    resized = cv2.resize(image, (new_w, new_h))
+    canvas = np.full((target_h, target_w, 3), bg_color, dtype=np.uint8)
+    dx, dy = (target_w - new_w) // 2, (target_h - new_h) // 2
+    canvas[dy:dy + new_h, dx:dx + new_w] = resized
+    return canvas, scale, dx, dy
+
+def dfl_numpy(position):
+    """Distribution Focal Loss 解析，纯 NumPy 版本"""
+    n, c, h, w = position.shape
+    p_num = 4
+    mc = c // p_num
+    y = position.reshape(n, p_num, mc, h, w)
+    y = np.exp(y) / np.sum(np.exp(y), axis=2, keepdims=True)
+    acc = np.arange(mc).reshape(1,1,mc,1,1)
+    y = np.sum(y * acc, axis=2)
+    return y
+
+def box_process(position):
+    """解析网络输出的框坐标"""
+    grid_h, grid_w = position.shape[2:4]
+    col, row = np.meshgrid(np.arange(grid_w), np.arange(grid_h))
+    col = col.reshape(1,1,grid_h,grid_w)
+    row = row.reshape(1,1,grid_h,grid_w)
+    grid = np.concatenate((col,row), axis=1)
+    stride = np.array([IMG_SIZE[1] // grid_h, IMG_SIZE[0] // grid_w]).reshape(1,2,1,1)
+
+    position = dfl_numpy(position)
+    box_xy = grid + 0.5 - position[:,0:2,:,:]
+    box_xy2 = grid + 0.5 + position[:,2:4,:,:]
+    xyxy = np.concatenate((box_xy*stride, box_xy2*stride), axis=1)
+    return xyxy
+
+def filter_boxes(boxes, box_confidences, box_class_probs):
+    # sigmoid objectness
+    box_confidences = 1 / (1 + np.exp(-box_confidences))
+    # softmax class probs
+    box_class_probs = np.exp(box_class_probs)
+    box_class_probs /= np.sum(box_class_probs, axis=-1, keepdims=True)
+
+    box_confidences = box_confidences.reshape(-1)
+    class_max_score = np.max(box_class_probs, axis=-1)
+    classes = np.argmax(box_class_probs, axis=-1)
+    _pos = np.where(class_max_score * box_confidences >= OBJ_THRESH)
+    boxes = boxes[_pos]
+    classes = classes[_pos]
+    scores = (class_max_score * box_confidences)[_pos]
+    return boxes, classes, scores
+
+def post_process(outputs, scale, dx, dy):
+    boxes, classes_conf, scores = [], [], []
+    branch_num = 3
+    for i in range(branch_num):
+        boxes.append(box_process(outputs[i*3]))
+        classes_conf.append(outputs[i*3+1])
+        scores.append(outputs[i*3+2])  # 使用真实 class 输出
+
+    def sp_flatten(x):
+        ch = x.shape[1]
+        x = x.transpose(0,2,3,1)
+        return x.reshape(-1,ch)
+
+    boxes = np.concatenate([sp_flatten(b) for b in boxes])
+    classes_conf = np.concatenate([sp_flatten(c) for c in classes_conf])
+    scores = np.concatenate([sp_flatten(s) for s in scores])
+
+    boxes, classes, scores = filter_boxes(boxes, scores, classes_conf)
+
+    if boxes.shape[0] == 0:
+        return None, None, None
+
+    # 只保留置信度最高的框
+    max_idx = np.argmax(scores)
+    boxes = boxes[max_idx:max_idx+1]
+    classes = classes[max_idx:max_idx+1]
+    scores = scores[max_idx:max_idx+1]
+
+    # 映射回原图
+    boxes[:, [0,2]] -= dx
+    boxes[:, [1,3]] -= dy
+    boxes /= scale
+    boxes = boxes.clip(min=0)
+
+    return boxes, classes, scores
+
+def draw(image, boxes, scores, classes):
+    for box, score, cl in zip(boxes, scores, classes):
+        x1, y1, x2, y2 = [int(b) for b in box]
+        cv2.rectangle(image, (x1, y1), (x2, y2), (255,0,0), 2)
+        cv2.putText(image, f"{CLASS_NAME[cl]} {score:.3f}", (x1, y1-5),
+                    cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0,0,255), 2)
+
+# ====================== 主流程 ======================
+img = cv2.imread(IMG_PATH)
+if img is None:
+    raise ValueError(f"Image {IMG_PATH} not found!")
+
+img_resized, scale, dx, dy = letterbox_resize(img, IMG_SIZE)
+input_data = np.expand_dims(img_resized, 0)  # 4 维输入
+
+rknn = RKNNLite(verbose=False)
+rknn.load_rknn(MODEL_PATH)
+rknn.init_runtime()
+outputs = rknn.inference([input_data])
+rknn.release()
+
+print("Outputs len:", len(outputs))
+for i, out in enumerate(outputs):
+    print(f"outputs[{i}].shape = {out.shape}, min={out.min()}, max={out.max()}, mean={out.mean():.4f}")
+
+boxes, classes, scores = post_process(outputs, scale, dx, dy)
+if boxes is None:
+    print("Detected 0 boxes")
+else:
+    draw(img, boxes, scores, classes)
+    result_path = os.path.join(OUTPUT_DIR, os.path.basename(IMG_PATH))
+    cv2.imwrite(result_path, img)
+    print(f"Detection result saved to {result_path}")