Qualcomm SNPE（Neural Processing SDK）集成到 OpenWRT + QCS6490 的完整配置后续

基于前面的内容本期继续接着开发过程来说，下面给出 三套完整可直接使用的源码，全部基于 OpenWRT + QCS6490 + SNPE 环境：

1. 自动化模型转换脚本（主机端，ONNX → DLC + 量化 + 校验）
2. LuCI 推理控制面板（Web 上传模型、选择运行时、实时推理）
3. OTA 模型更新服务（设备端自动下载、校验、替换、通知）

---

1. 自动化模型转换脚本（主机端）

文件：convert_model.sh
路径：~/snpe-tools/convert_model.sh
功能：一键 ONNX → DLC → 量化 → 校验 → 打包 OTA 包

#!/bin/bash
# convert_model.sh
# 用法: ./convert_model.sh yolov8n.onnx yolov8n

set -e

MODEL_ONNX="$1"
MODEL_NAME="$2"
INPUT_SIZE="${3:-640}"  # 默认 640x640
QUANTIZE="${4:-true}"   # 是否量化
SNPE_ROOT="$HOME/snpe-sdk"
OUTPUT_DIR="./ota_models"

# 1. 检查环境
if [ ! -d "$SNPE_ROOT" ]; then
  echo "SNPE_ROOT not found: $SNPE_ROOT"
  exit 1
fi

source "$SNPE_ROOT/bin/envsetup.sh"

mkdir -p "$OUTPUT_DIR"

DLC_FILE="${OUTPUT_DIR}/${MODEL_NAME}.dlc"
DLC_QUANT="${OUTPUT_DIR}/${MODEL_NAME}_quant.dlc"
INPUT_LIST="${OUTPUT_DIR}/input_list.txt"
OTA_PKG="${OUTPUT_DIR}/${MODEL_NAME}_ota.tar.gz"

# 2. 生成输入列表（随机图像）
echo "Generating input list..."
mkdir -p images
for i in {
   
   1..5}; do
  convert -size ${INPUT_SIZE}x${INPUT_SIZE} xc:gray images/calib_$i.jpg
  echo "images/calib_$i.jpg" >> "$INPUT_LIST"
done

# 3. ONNX → DLC
echo "Converting ONNX to DLC..."
snpe-onnx-to-dlc \
  --input_model "$MODEL_ONNX" \
  --output_model "$DLC_FILE" \
  --input_dim input_0 1,3,${INPUT_SIZE},${INPUT_SIZE}

# 4. 量化（可选）
if [ "$QUANTIZE" = "true" ]; then
  echo "Quantizing to INT8..."
  snpe-dlc-quantize \
    --input_dlc "$DLC_FILE" \
    --input_list "$INPUT_LIST" \
    --output_dlc "$DLC_QUANT" \
    --use_enhanced_quantizer \
    --use_adjusted_weights_quantizer
  FINAL_DLC="$DLC_QUANT"
else
  FINAL_DLC="$DLC_FILE"
fi

# 5. 校验模型
echo "Validating model..."
snpe-dlc-info --input_dlc "$FINAL_DLC" | head -20

# 6. 打包 OTA 包（含校验码）
echo "Packaging OTA update..."
cat > "${OUTPUT_DIR}/model.json" << EOF
{
  "name": "$MODEL_NAME",
  "version": "$(date +%Y%m%d%H%M)",
  "size": $(stat -c%s "$FINAL_D cellular"),
  "sha256": "$(sha256sum "$FINAL_DLC" | cut -d' ' -f1)",
  "input_size": ${INPUT_SIZE},
  "quantized": $QUANTIZE
}
EOF

tar -czf "$OTA_PKG" -C "$OUTPUT_DIR" \
  "$(basename $FINAL_DLC)" model.json

echo "OTA package ready: $OTA_PKG"
echo "Upload to: http://your-server.com/ota/${MODEL_NAME}_ota.tar.gz"

使用示例：

./convert_model.sh yolov8n.onnx yolov8n 640 true

---

2. LuCI 推理控制面板（Web 界面）

路径：package/luci-app-snpe/

目录结构

luci-app-snpe/
├── root/
│   └── usr/share/luci/menu.d/snpe.json
├── htdocs/
│   └── luci-static/resources/view/snpe/
│       ├── dashboard.htm
│       └── inference.js
└── src/
    └── Makefile

---

src/Makefile

include $(TOPDIR)/rules.mk

PKG_NAME:=luci-app-snpe
PKG_VERSION:=1.0
PKG_RELEASE:=1

include $(INCLUDE_DIR)/package.mk

define Package/luci-app-snpe
  SECTION:=luci
  CATEGORY:=LuCI
  SUBMENU:=3. Applications
  TITLE:=SNPE AI Inference Panel
  DEPENDS:=+snpe +python3 +python3-opencv +luci-base
endef

define Build/Compile
endef

define Package/luci-app-snpe/install
	$(INSTALL_DIR) $(1)/usr/share/luci/menu.d
	$(CP) ./root/usr/share/luci/menu.d/snpe.json $(1)/usr/share/luci/menu.d/

	$(INSTALL_DIR) $(1)/usr/share/luci/static/resources/view/snpe
	$(CP) ./htdocs/luci-static/resources/view/snpe/* $(1)/usr/share/luci/static/resources/view/snpe/
endef

$(eval $(call BuildPackage,luci-app-snpe))

---

root/usr/share/luci/menu.d/snpe.json

{
   
   
  "admin/ai": {
   
   
    "snpe": {
   
   
      "title": "SNPE Inference",
      "order": 10,
      "action": {
   
   
        "type": "view",
        "path": "snpe/dashboard"
      }
    }
  }
}

---

htdocs/luci-static/resources/view/snpe/dashboard.htm

<%+header%>
<h2>SNPE AI Inference Panel</h2>

<div class="cbi-section">
  <div class="cbi-section-node">
    <label class="cbi-value-title">Model</label>
    <div class="cbi-value-field">
      <select id="model_select" class="cbi-input-select"></select>
      <button onclick="uploadModel()">Upload New</button>
      <input type="file" id="model_file" style="display:none" accept=".dlc">
    </div>
  </div>

  <div class="cbi-section-node">
    <label>Runtime</label>
    <select id="runtime">
      <option value="DSP">DSP (Hexagon)</option>
      <option value="HTP">HTP (NPU)</option>
    </select>
  </div>

  <div class="cbi-section-node