AI_RDKS100P_Robot/test/det_test.py

import numpy as np
from hbm_runtime import HB_HBMRuntime
import os
import cv2

hb_dtype_map = {
    "U8": np.uint8,
    "S8": np.int8,
    "F32": np.float32,
    "F16": np.float16,
    "U16": np.uint16,
    "S16": np.int16,
    "S32": np.int32,
    "U32": np.uint32,
    "BOOL8": np.bool_,
}

MODEL_PATH = "../models/cowCheck/det/det_640x640.hbm"

# 1️⃣ 加载模型
model = HB_HBMRuntime(MODEL_PATH)
model_name = model.model_names[0]

print("Loaded model:", model_name)

# 2️⃣ 查看输入信息
input_names = model.input_names[model_name]
input_shapes = model.input_shapes[model_name]
input_dtypes = model.input_dtypes[model_name]

print("Input info:")
for name in input_names:
    print("  Name:", name)
    print("  Shape:", input_shapes[name])
    print("  Dtype:", input_dtypes[name])

# 3️⃣ 使用真实图片测试
IMAGE_DIR = "/home/sunrise/opt/dev/project/tools/images_20260316_142840"

image_files = [f for f in os.listdir(IMAGE_DIR) if f.lower().endswith(".jpg")]
image_files.sort()

print(f"\nFound {len(image_files)} images.")

for img_name in image_files:
    img_path = os.path.join(IMAGE_DIR, img_name)
    img = cv2.imread(img_path)

    if img is None:
        print(f"Skip invalid image: {img_name}")
        continue

    # ===== YOLOv8 Letterbox 预处理（保持比例）=====
    h0, w0 = img.shape[:2]
    target_size = 640

    r = min(target_size / h0, target_size / w0)
    new_w, new_h = int(w0 * r), int(h0 * r)

    resized = cv2.resize(img, (new_w, new_h))

    pad_w = target_size - new_w
    pad_h = target_size - new_h

    top = pad_h // 2
    bottom = pad_h - top
    left = pad_w // 2
    right = pad_w - left

    img_letterbox = cv2.copyMakeBorder(
        resized, top, bottom, left, right,
        cv2.BORDER_CONSTANT, value=(114, 114, 114)
    )

    img_display = img_letterbox.copy()
    img_input_float = img_letterbox.astype(np.float32) / 255.0

    input_tensors = {}

    for name in input_names:
        shape = input_shapes[name]
        dtype = hb_dtype_map.get(input_dtypes[name].name, np.float32)

        # 默认假设输入是 NCHW F32
        if len(shape) == 4:
            # HWC -> CHW
            img_input = np.transpose(img_input_float, (2, 0, 1))
            img_input = np.expand_dims(img_input, axis=0)
            img_input = img_input.astype(dtype)
            input_tensors[name] = img_input
        else:
            # 其他情况 fallback
            input_tensors[name] = np.zeros(shape, dtype=dtype)

    results = model.run(input_tensors)

    print(f"\nImage: {img_name}")
    for output_name, output_data in results[model_name].items():
        print("  Output:", output_name, "shape:", output_data.shape)

        # ===== YOLOv8 解码 =====
        # (1, 7, 8400) -> (8400, 7)
        preds = output_data[0].transpose(1, 0)

        boxes = preds[:, 0:4]      # xywh (already decoded in export)
        cls_scores = preds[:, 4:]  # class logits

        # YOLOv8 uses sigmoid (NOT softmax)
        cls_probs = 1.0 / (1.0 + np.exp(-cls_scores))

        cls_ids = np.argmax(cls_probs, axis=1)
        scores = np.max(cls_probs, axis=1)

        # 提高置信度阈值（避免全屏框）
        CONF_THRESH = 0.6

        # 过滤低置信度
        keep_mask = scores > CONF_THRESH
        boxes = boxes[keep_mask]
        scores = scores[keep_mask]
        cls_ids = cls_ids[keep_mask]

        if len(boxes) == 0:
            cv2.imshow("det_result", img_display)
            if cv2.waitKey(1) & 0xFF == ord('q'):
                break
            continue

        # 转换为 xyxy
        xyxy_boxes = []
        for b in boxes:
            cx, cy, w, h = b
            x1 = cx - w / 2
            y1 = cy - h / 2
            x2 = cx + w / 2
            y2 = cy + h / 2
            xyxy_boxes.append([x1, y1, x2 - x1, y2 - y1])  # cv2 NMSBoxes uses x,y,w,h

        # OpenCV NMS
        indices = cv2.dnn.NMSBoxes(
            xyxy_boxes,
            scores.tolist(),
            CONF_THRESH,
            0.45
        )

        if len(indices) > 0:
            indices = indices.flatten()
        else:
            indices = []

        for i in indices:

            cx, cy, w, h = boxes[i]

            x1 = int(cx - w / 2)
            y1 = int(cy - h / 2)
            x2 = int(cx + w / 2)
            y2 = int(cy + h / 2)

            cv2.rectangle(img_display, (x1, y1), (x2, y2), (0, 255, 0), 2)
            cv2.putText(
                img_display,
                f"{cls_ids[i]}:{scores[i]:.2f}",
                (x1, y1 - 5),
                cv2.FONT_HERSHEY_SIMPLEX,
                0.5,
                (0, 255, 0),
                1
            )

        # ===== 视频流方式显示 =====
        cv2.imshow("det_result", img_display)

        # 1ms 刷新，按 q 退出
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

cv2.destroyAllWindows()