2.0

ailai_pc/detect.py

@@ -0,0 +1,134 @@
+import cv2
+import numpy as np
+from rknnlite.api import RKNNLite
+
+MODEL_PATH = "detect.rknn"
+CLASS_NAMES = ["bag"]  # 单类
+
+
+class Yolo11Detector:
+    def __init__(self, model_path):
+        self.rknn = RKNNLite(verbose=False)
+
+        # 加载 RKNN 模型
+        ret = self.rknn.load_rknn(model_path)
+        assert ret == 0, "❌ Load RKNN model failed"
+
+        # 初始化运行时（使用 NPU 核心0）
+        ret = self.rknn.init_runtime(core_mask=RKNNLite.NPU_CORE_0)
+        assert ret == 0, "❌ Init runtime failed"
+
+        # 模型输入大小
+        self.input_size = 640
+        # YOLO anchors（根据你训练的模型）
+        self.anchors = {
+            8: [[10, 13], [16, 30], [33, 23]],
+            16: [[30, 61], [62, 45], [59, 119]],
+            32: [[116, 90], [156, 198], [373, 326]]
+        }
+
+    def preprocess(self, img):
+        """高性能预处理：缩放+RGB"""
+        h, w = img.shape[:2]
+        scale = min(self.input_size / w, self.input_size / h)
+        new_w, new_h = int(w * scale), int(h * scale)
+        img_resized = cv2.resize(img, (new_w, new_h))
+        canvas = np.full((self.input_size, self.input_size, 3), 114, dtype=np.uint8)
+        dw, dh = (self.input_size - new_w) // 2, (self.input_size - new_h) // 2
+        canvas[dh:dh + new_h, dw:dw + new_w, :] = img_resized
+        img_rgb = cv2.cvtColor(canvas, cv2.COLOR_BGR2RGB)
+        return np.expand_dims(img_rgb, 0).astype(np.uint8), scale, dw, dh
+
+    def postprocess(self, outputs, scale, dw, dh, conf_thresh=0.25, iou_thresh=0.45):
+        """解析 YOLO 输出"""
+        # 注意：根据 RKNN 输出节点选择
+        preds = outputs[0].reshape(-1, outputs[0].shape[1])  # 假设输出 [1, N, C]
+        boxes, scores, class_ids = [], [], []
+
+        for p in preds:
+            conf = p[4]
+            if conf < conf_thresh:
+                continue
+            cls_conf = p[5]  # 单类模型
+            score = conf * cls_conf
+            if score < conf_thresh:
+                continue
+            cx, cy, w, h = p[:4]
+            x1 = (cx - w / 2 - dw) / scale
+            y1 = (cy - h / 2 - dh) / scale
+            x2 = (cx + w / 2 - dw) / scale
+            y2 = (cy + h / 2 - dh) / scale
+            boxes.append([x1, y1, x2, y2])
+            scores.append(score)
+            class_ids.append(0)  # 单类
+
+        if len(boxes) == 0:
+            return []
+
+        boxes = np.array(boxes)
+        scores = np.array(scores)
+        class_ids = np.array(class_ids)
+
+        # 简单 NMS
+        idxs = np.argsort(scores)[::-1]
+        keep = []
+        while len(idxs) > 0:
+            i = idxs[0]
+            keep.append(i)
+            if len(idxs) == 1:
+                break
+            x1, y1, x2, y2 = boxes[i]
+            xx1 = np.maximum(x1, boxes[idxs[1:], 0])
+            yy1 = np.maximum(y1, boxes[idxs[1:], 1])
+            xx2 = np.minimum(x2, boxes[idxs[1:], 2])
+            yy2 = np.minimum(y2, boxes[idxs[1:], 3])
+            inter = np.maximum(0, xx2 - xx1) * np.maximum(0, yy2 - yy1)
+            area_i = (x2 - x1) * (y2 - y1)
+            area_j = (boxes[idxs[1:], 2] - boxes[idxs[1:], 0]) * (boxes[idxs[1:], 3] - boxes[idxs[1:], 1])
+            iou = inter / (area_i + area_j - inter + 1e-6)
+            idxs = idxs[1:][iou < iou_thresh]
+
+        results = []
+        for i in keep:
+            results.append({
+                "box": boxes[i],
+                "score": scores[i],
+                "class_id": class_ids[i]
+            })
+        return results
+
+    def detect(self, img):
+        img_data, scale, dw, dh = self.preprocess(img)
+        outputs = self.rknn.inference([img_data])
+        results = self.postprocess(outputs, scale, dw, dh)
+        return results
+
+    def release(self):
+        self.rknn.release()
+
+
+if __name__ == "__main__":
+    detector = Yolo11Detector(MODEL_PATH)
+    cap = cv2.VideoCapture(0)  # 可以换成图片路径
+
+    while True:
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        results = detector.detect(frame)
+
+        for r in results:
+            x1, y1, x2, y2 = map(int, r["box"])
+            cls_id = r["class_id"]
+            score = r["score"]
+            cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
+            cv2.putText(frame, f"{CLASS_NAMES[cls_id]} {score:.2f}", (x1, y1 - 10),
+                        cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
+
+        cv2.imshow("YOLOv11 Detection", frame)
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            break
+
+    detector.release()
+    cap.release()

ailai_pc/detet_pc.py

@@ -0,0 +1,72 @@
+# detect_pt.py
+import cv2
+import torch
+from ultralytics import YOLO
+
+# ======================
+# 配置参数
+# ======================
+MODEL_PATH = 'best.pt'           # 你的训练模型路径（yolov8n.pt 或你自己训练的）
+#IMG_PATH = '/home/hx/开发/ailai_image_obb/ailai_pc/train/192.168.0.234_01_202510141514352.jpg'            # 测试图像路径
+IMG_PATH = '1.jpg'
+OUTPUT_PATH = '/home/hx/开发/ailai_image_obb/ailai_pc/output_pt.jpg'    # 可视化结果保存路径
+CONF_THRESH = 0.5                # 置信度阈值
+CLASS_NAMES = ['bag']          # 你的类别名列表（按训练时顺序）
+
+# 是否显示窗口（适合有 GUI 的 PC）
+SHOW_IMAGE = True
+
+# ======================
+# 主函数
+# ======================
+def main():
+    # 检查 CUDA
+    device = 'cuda' if torch.cuda.is_available() else 'cpu'
+    print(f"✅ 使用设备: {device}")
+
+    # 加载模型
+    print("➡️  加载 YOLO 模型...")
+    model = YOLO(MODEL_PATH)  # 自动加载架构和权重
+    model.to(device)
+
+    # 推理
+    print("➡️  开始推理...")
+    results = model(IMG_PATH, imgsz=640, conf=CONF_THRESH, device=device)
+
+    # 获取第一张图的结果
+    r = results[0]
+
+    # 获取原始图像（BGR）
+    img = cv2.imread(IMG_PATH)
+    if img is None:
+        raise FileNotFoundError(f"无法读取图像: {IMG_PATH}")
+
+    print("\n📋 检测结果:")
+    for box in r.boxes:
+        # 获取数据
+        xyxy = box.xyxy[0].cpu().numpy()  # [x1, y1, x2, y2]
+        conf = box.conf.cpu().numpy()[0]  # 置信度
+        cls_id = int(box.cls.cpu().numpy()[0])  # 类别 ID
+        cls_name = CLASS_NAMES[cls_id]         # 类别名
+
+        x1, y1, x2, y2 = map(int, xyxy)
+        print(f"  类别: {cls_name}, 置信度: {conf:.3f}, 框: [{x1}, {y1}, {x2}, {y2}]")
+
+        # 画框
+        cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2)
+        # 画标签
+        label = f"{cls_name} {conf:.2f}"
+        cv2.putText(img, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
+
+    # 保存结果
+    cv2.imwrite(OUTPUT_PATH, img)
+    print(f"\n🖼️  可视化结果已保存: {OUTPUT_PATH}")
+
+    # 显示（可选）
+    if SHOW_IMAGE:
+        cv2.imshow("YOLOv8 Detection", img)
+        cv2.waitKey(0)
+        cv2.destroyAllWindows()
+
+if __name__ == '__main__':
+    main()