> ## Documentation Index
> Fetch the complete documentation index at: https://dragonwingdocs.qualcomm.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Companion files: AI Hub native QNN YOLO depth

> Source files for the live YOLO depth tutorial using an AI Hub-compiled QNN model and a persistent native C++ QNN runner.

<div style={{ marginBottom: "2rem" }}>
  <div style={{ fontSize: "0.72rem", fontWeight: 700, color: "#31017D", letterSpacing: "1.5px", textTransform: "uppercase", marginBottom: "0.5rem" }}>
    Companion files
  </div>

  <p style={{ fontSize: "0.95rem", color: "#555", lineHeight: 1.7, margin: "0 0 0.75rem" }}>
    Copy these files into the paths shown in <a href="/tutorials/aihub-qnn-native-yolo-depth" style={{ color: "#31017D", fontWeight: 600, textDecoration: "none" }}>Live YOLO depth on the NPU with AI Hub and native QNN</a>.
  </p>
</div>

<hr style={{ border: "none", borderTop: "1px solid #eee", margin: "0 0 2rem" }} />

<Warning>
  These files are prototype tutorial code. Review error handling, memory ownership, tensor metadata discovery, and deployment policies before using them in production.
</Warning>

## `qnn_dlc_runner.cpp`

```cpp theme={null}
#include <QNN/QnnInterface.h>
#include <QNN/System/QnnSystemContext.h>
#include <QNN/System/QnnSystemInterface.h>
#include <QNN/QnnTypes.h>

#include <dlfcn.h>
#include <algorithm>
#include <chrono>
#include <cstdarg>
#include <cstring>
#include <fstream>
#include <iostream>
#include <numeric>
#include <stdexcept>
#include <string>
#include <vector>

using QnnInterfaceGetProvidersFn = Qnn_ErrorHandle_t (*)(const QnnInterface_t***, uint32_t*);
using QnnSystemInterfaceGetProvidersFn = Qnn_ErrorHandle_t (*)(const QnnSystemInterface_t***, uint32_t*);

#define CHECK_QNN(expr) do { \
  Qnn_ErrorHandle_t _e = (expr); \
  if (_e != QNN_SUCCESS) { \
    std::cerr << "QNN call failed: " #expr " -> 0x" << std::hex << (uint64_t)_e << std::dec << std::endl; \
    throw std::runtime_error("QNN error"); \
  } \
} while(0)

static std::vector<uint8_t> readFile(const std::string& path) {
  std::ifstream f(path, std::ios::binary);
  if (!f) throw std::runtime_error("failed to open " + path);
  f.seekg(0, std::ios::end);
  size_t n = (size_t)f.tellg();
  f.seekg(0);
  std::vector<uint8_t> b(n);
  f.read((char*)b.data(), n);
  if (!f) throw std::runtime_error("failed to read " + path);
  return b;
}

static void writeFile(const std::string& path, const void* data, size_t n) {
  std::ofstream f(path, std::ios::binary);
  if (!f) throw std::runtime_error("failed to open output " + path);
  f.write((const char*)data, n);
}

static size_t dtypeSize(Qnn_DataType_t dt) {
  switch (dt) {
    case QNN_DATATYPE_FLOAT_32: return 4;
    case QNN_DATATYPE_FLOAT_16: return 2;
    case QNN_DATATYPE_UINT_8: return 1;
    case QNN_DATATYPE_INT_8: return 1;
    case QNN_DATATYPE_UINT_16: return 2;
    case QNN_DATATYPE_INT_16: return 2;
    case QNN_DATATYPE_UINT_32: return 4;
    case QNN_DATATYPE_INT_32: return 4;
    default: throw std::runtime_error("unsupported dtype " + std::to_string((int)dt));
  }
}

static const Qnn_TensorV2_t& tv2(const Qnn_Tensor_t& t) {
  if (t.version != QNN_TENSOR_VERSION_2) throw std::runtime_error("expected tensor v2 metadata");
  return t.v2;
}

static uint64_t elemCount(const Qnn_TensorV2_t& t) {
  uint64_t n = 1;
  for (uint32_t i = 0; i < t.rank; ++i) n *= t.dimensions[i];
  return n;
}

static Qnn_Tensor_t makeAppTensor(const Qnn_Tensor_t& meta, void* data, size_t bytes, Qnn_TensorType_t type) {
  Qnn_Tensor_t t = meta;
  if (t.version == QNN_TENSOR_VERSION_1) {
    t.v1.type = type;
    t.v1.memType = QNN_TENSORMEMTYPE_RAW;
    t.v1.clientBuf.data = data;
    t.v1.clientBuf.dataSize = bytes;
  } else if (t.version == QNN_TENSOR_VERSION_2) {
    t.v2.type = type;
    t.v2.memType = QNN_TENSORMEMTYPE_RAW;
    t.v2.clientBuf.data = data;
    t.v2.clientBuf.dataSize = bytes;
  } else {
    throw std::runtime_error("unsupported tensor version");
  }
  return t;
}

static void logCb(const char* fmt, QnnLog_Level_t level, uint64_t timestamp, va_list args) {
  (void)fmt; (void)level; (void)timestamp; (void)args;
}

int main(int argc, char** argv) {
  std::string dlc = "../aihub_compiled/yolo26n-depth-aihub-qcs8275-qnn-dlc.dlc";
  std::string input = "../aihub_dlc_test/input/images.raw";
  std::string output = "qnn_app_output.raw";
  int loops = 200;
  int warmup = 10;
  bool serverMode = false;
  for (int i = 1; i < argc; ++i) {
    std::string a = argv[i];
    if (a == "--dlc" && i + 1 < argc) dlc = argv[++i];
    else if (a == "--input" && i + 1 < argc) input = argv[++i];
    else if (a == "--output" && i + 1 < argc) output = argv[++i];
    else if (a == "--loops" && i + 1 < argc) loops = std::stoi(argv[++i]);
    else if (a == "--warmup" && i + 1 < argc) warmup = std::stoi(argv[++i]);
    else if (a == "--server") serverMode = true;
    else { std::cerr << "usage: " << argv[0] << " [--dlc file.dlc] [--input images.raw] [--output out.raw] [--loops N] [--warmup N] [--server]\n"; return 2; }
  }

  void* htpLib = dlopen("libQnnHtp.so", RTLD_NOW | RTLD_LOCAL);
  if (!htpLib) { std::cerr << "dlopen libQnnHtp.so failed: " << dlerror() << "\n"; return 1; }
  void* systemLib = dlopen("libQnnSystem.so", RTLD_NOW | RTLD_LOCAL);
  if (!systemLib) { std::cerr << "dlopen libQnnSystem.so failed: " << dlerror() << "\n"; return 1; }

  auto getProviders = (QnnInterfaceGetProvidersFn)dlsym(htpLib, "QnnInterface_getProviders");
  if (!getProviders) { std::cerr << "dlsym QnnInterface_getProviders failed\n"; return 1; }
  const QnnInterface_t** providers = nullptr;
  uint32_t numProviders = 0;
  CHECK_QNN(getProviders(&providers, &numProviders));
  if (numProviders == 0) throw std::runtime_error("no QNN providers");
  const auto& qnn = providers[0]->QNN_INTERFACE_VER_NAME;

  auto getSystemProviders = (QnnSystemInterfaceGetProvidersFn)dlsym(systemLib, "QnnSystemInterface_getProviders");
  if (!getSystemProviders) { std::cerr << "dlsym QnnSystemInterface_getProviders failed\n"; return 1; }
  const QnnSystemInterface_t** systemProviders = nullptr;
  uint32_t numSystemProviders = 0;
  CHECK_QNN(getSystemProviders(&systemProviders, &numSystemProviders));
  if (numSystemProviders == 0) throw std::runtime_error("no QNN System providers");
  const auto& sys = systemProviders[0]->QNN_SYSTEM_INTERFACE_VER_NAME;

  Qnn_LogHandle_t logger = nullptr;
  if (qnn.logCreate) qnn.logCreate(logCb, QNN_LOG_LEVEL_WARN, &logger);

  Qnn_BackendHandle_t backend = nullptr;
  CHECK_QNN(qnn.backendCreate(logger, nullptr, &backend));

  Qnn_DeviceHandle_t device = nullptr;
  if (qnn.deviceCreate) CHECK_QNN(qnn.deviceCreate(logger, nullptr, &device));

  auto bin = readFile(dlc);

  // Extract embedded HTP context binary from the DLC. qnn-net-run does this internally for --dlc_path.
  // QnnContext_createFromBinary() expects the context record, not necessarily the whole DLC file.
  QnnSystemDlc_Handle_t dlcHandle = nullptr;
  QnnSystemDlc_RecordHandle_t* recordHandles = nullptr;
  uint32_t numRecordHandles = 0;
  QnnSystemDlc_RecordHandle_t contextRecord = nullptr;
  const uint8_t* contextData = bin.data();
  uint64_t contextSize = bin.size();

  if (sys.systemDlcCreateFromFile && sys.systemDlcGetRecordsByType && sys.systemDlcReadRecordDataMemoryMapped) {
    Qnn_ErrorHandle_t de = sys.systemDlcCreateFromFile(logger, dlc.c_str(), &dlcHandle);
    if (de == QNN_SUCCESS && dlcHandle) {
      de = sys.systemDlcGetRecordsByType(dlcHandle,
                                          QNN_SYSTEM_DLC_RECORD_PREFIX_HTP_CACHE_RECORD,
                                          1,
                                          &recordHandles,
                                          &numRecordHandles);
      if (de == QNN_SUCCESS && numRecordHandles > 0 && recordHandles && recordHandles[0]) {
        contextRecord = recordHandles[0];
        de = sys.systemDlcReadRecordDataMemoryMapped(contextRecord, &contextData, &contextSize);
        if (de == QNN_SUCCESS) {
          std::cerr << "extracted HTP context record from DLC: " << contextSize << " bytes\n";
        } else {
          std::cerr << "failed reading HTP context record; falling back to whole DLC\n";
          contextData = bin.data();
          contextSize = bin.size();
        }
      } else {
        std::cerr << "no HTP context record found in DLC; falling back to whole DLC\n";
      }
    } else {
      std::cerr << "systemDlcCreateFromFile failed; falling back to whole DLC\n";
    }
  }

  QnnSystemContext_Handle_t sysCtx = nullptr;
  const QnnSystemContext_BinaryInfo_t* info = nullptr;
  const char* graphName = nullptr;
  uint32_t numInputs = 0, numOutputs = 0;
  Qnn_Tensor_t* metaInputs = nullptr;
  Qnn_Tensor_t* metaOutputs = nullptr;

  Qnn_Tensor_t fallbackInput = QNN_TENSOR_INIT;
  Qnn_Tensor_t fallbackOutput = QNN_TENSOR_INIT;
  uint32_t inDims[4] = {1, 320, 320, 3};
  uint32_t outDims[4] = {1, 1, 320, 320};

  bool haveMetadata = false;
  if (sys.systemContextCreate && sys.systemContextGetMetaData) {
    if (sys.systemContextCreate(&sysCtx) == QNN_SUCCESS &&
        sys.systemContextGetMetaData(sysCtx, contextData, (Qnn_ContextBinarySize_t)contextSize, &info) == QNN_SUCCESS) {
      uint32_t numGraphs = 0;
      QnnSystemContext_GraphInfo_t* graphs = nullptr;
      if (info->version == QNN_SYSTEM_CONTEXT_BINARY_INFO_VERSION_1) {
        numGraphs = info->contextBinaryInfoV1.numGraphs;
        graphs = info->contextBinaryInfoV1.graphs;
      } else if (info->version == QNN_SYSTEM_CONTEXT_BINARY_INFO_VERSION_2) {
        numGraphs = info->contextBinaryInfoV2.numGraphs;
        graphs = info->contextBinaryInfoV2.graphs;
      } else if (info->version == QNN_SYSTEM_CONTEXT_BINARY_INFO_VERSION_3) {
        numGraphs = info->contextBinaryInfoV3.numGraphs;
        graphs = info->contextBinaryInfoV3.graphs;
      }
      if (numGraphs >= 1) {
        auto& gmeta = graphs[0];
        if (gmeta.version == QNN_SYSTEM_CONTEXT_GRAPH_INFO_VERSION_1) {
          graphName = gmeta.graphInfoV1.graphName; numInputs = gmeta.graphInfoV1.numGraphInputs; metaInputs = gmeta.graphInfoV1.graphInputs; numOutputs = gmeta.graphInfoV1.numGraphOutputs; metaOutputs = gmeta.graphInfoV1.graphOutputs;
        } else if (gmeta.version == QNN_SYSTEM_CONTEXT_GRAPH_INFO_VERSION_2) {
          graphName = gmeta.graphInfoV2.graphName; numInputs = gmeta.graphInfoV2.numGraphInputs; metaInputs = gmeta.graphInfoV2.graphInputs; numOutputs = gmeta.graphInfoV2.numGraphOutputs; metaOutputs = gmeta.graphInfoV2.graphOutputs;
        } else if (gmeta.version == QNN_SYSTEM_CONTEXT_GRAPH_INFO_VERSION_3) {
          graphName = gmeta.graphInfoV3.graphName; numInputs = gmeta.graphInfoV3.numGraphInputs; metaInputs = gmeta.graphInfoV3.graphInputs; numOutputs = gmeta.graphInfoV3.numGraphOutputs; metaOutputs = gmeta.graphInfoV3.graphOutputs;
        }
        haveMetadata = (graphName && numInputs == 1 && numOutputs == 1 && metaInputs && metaOutputs);
      }
    }
  }

  if (!haveMetadata) {
    std::cerr << "QNN system metadata unavailable for DLC; using known graph/tensor metadata fallback\n";
    graphName = "graph_ymndtmzg";
    numInputs = 1;
    numOutputs = 1;
    fallbackInput.version = QNN_TENSOR_VERSION_2;
    fallbackInput.v2.id = 1;
    fallbackInput.v2.name = "images";
    fallbackInput.v2.type = QNN_TENSOR_TYPE_APP_WRITE;
    fallbackInput.v2.dataFormat = QNN_TENSOR_DATA_FORMAT_FLAT_BUFFER;
    fallbackInput.v2.dataType = QNN_DATATYPE_FLOAT_32;
    fallbackInput.v2.rank = 4;
    fallbackInput.v2.dimensions = inDims;
    fallbackInput.v2.memType = QNN_TENSORMEMTYPE_RAW;
    fallbackOutput.version = QNN_TENSOR_VERSION_2;
    fallbackOutput.v2.id = 842;
    fallbackOutput.v2.name = "output_0";
    fallbackOutput.v2.type = QNN_TENSOR_TYPE_APP_READ;
    fallbackOutput.v2.dataFormat = QNN_TENSOR_DATA_FORMAT_FLAT_BUFFER;
    fallbackOutput.v2.dataType = QNN_DATATYPE_FLOAT_32;
    fallbackOutput.v2.rank = 4;
    fallbackOutput.v2.dimensions = outDims;
    fallbackOutput.v2.memType = QNN_TENSORMEMTYPE_RAW;
    metaInputs = &fallbackInput;
    metaOutputs = &fallbackOutput;
  }

  std::cerr << "graph: " << graphName << " inputs=" << numInputs << " outputs=" << numOutputs << "\n";
  if (numInputs != 1 || numOutputs != 1) throw std::runtime_error("runner currently expects 1 input/1 output");

  const auto& inMeta = tv2(metaInputs[0]);
  const auto& outMeta = tv2(metaOutputs[0]);
  size_t inBytes = elemCount(inMeta) * dtypeSize(inMeta.dataType);
  size_t outBytes = elemCount(outMeta) * dtypeSize(outMeta.dataType);
  std::cerr << "input: " << inMeta.name << " bytes=" << inBytes << " dtype=0x" << std::hex << inMeta.dataType << std::dec << "\n";
  std::cerr << "output: " << outMeta.name << " bytes=" << outBytes << " dtype=0x" << std::hex << outMeta.dataType << std::dec << "\n";

  auto inputBuf = readFile(input);
  if (inputBuf.size() != inBytes) throw std::runtime_error("input size mismatch: got " + std::to_string(inputBuf.size()) + " expected " + std::to_string(inBytes));
  std::vector<uint8_t> outputBuf(outBytes);

  Qnn_ContextHandle_t context = nullptr;
  CHECK_QNN(qnn.contextCreateFromBinary(backend, device, nullptr, contextData, (Qnn_ContextBinarySize_t)contextSize, &context, nullptr));
  Qnn_GraphHandle_t graph = nullptr;
  CHECK_QNN(qnn.graphRetrieve(context, graphName, &graph));

  Qnn_Tensor_t inTensor = makeAppTensor(metaInputs[0], inputBuf.data(), inputBuf.size(), QNN_TENSOR_TYPE_APP_WRITE);
  Qnn_Tensor_t outTensor = makeAppTensor(metaOutputs[0], outputBuf.data(), outputBuf.size(), QNN_TENSOR_TYPE_APP_READ);

  for (int i = 0; i < warmup; ++i) {
    CHECK_QNN(qnn.graphExecute(graph, &inTensor, 1, &outTensor, 1, nullptr, nullptr));
  }

  if (serverMode) {
    // Persistent mode. Read commands from stdin:
    //   RUN <input.raw> <output.raw>
    // Reply on stdout:
    //   OK <ms> <output_bytes>
    // or:
    //   ERR <message>
    std::cout << "READY " << inBytes << " " << outBytes << std::endl;
    std::string cmd, inPath, outPath;
    while (std::cin >> cmd) {
      if (cmd == "QUIT" || cmd == "EXIT") break;
      if (cmd != "RUN") {
        std::cout << "ERR expected RUN" << std::endl;
        continue;
      }
      if (!(std::cin >> inPath >> outPath)) {
        std::cout << "ERR missing paths" << std::endl;
        break;
      }
      try {
        auto frameBuf = readFile(inPath);
        if (frameBuf.size() != inBytes) {
          std::cout << "ERR input_size got=" << frameBuf.size() << " expected=" << inBytes << std::endl;
          continue;
        }
        std::memcpy(inputBuf.data(), frameBuf.data(), inBytes);
        auto t0 = std::chrono::steady_clock::now();
        CHECK_QNN(qnn.graphExecute(graph, &inTensor, 1, &outTensor, 1, nullptr, nullptr));
        auto t1 = std::chrono::steady_clock::now();
        double oneMs = std::chrono::duration<double, std::milli>(t1 - t0).count();
        writeFile(outPath, outputBuf.data(), outputBuf.size());
        std::cout << "OK " << oneMs << " " << outputBuf.size() << std::endl;
      } catch (const std::exception& e) {
        std::cout << "ERR " << e.what() << std::endl;
      }
    }
  } else {
    std::vector<double> ms;
    ms.reserve(loops);
    for (int i = 0; i < loops; ++i) {
      auto t0 = std::chrono::steady_clock::now();
      CHECK_QNN(qnn.graphExecute(graph, &inTensor, 1, &outTensor, 1, nullptr, nullptr));
      auto t1 = std::chrono::steady_clock::now();
      ms.push_back(std::chrono::duration<double, std::milli>(t1 - t0).count());
    }
    writeFile(output, outputBuf.data(), outputBuf.size());

    double sum = std::accumulate(ms.begin(), ms.end(), 0.0);
    double avg = sum / ms.size();
    std::sort(ms.begin(), ms.end());
    double p50 = ms[ms.size()/2];
    double p90 = ms[(size_t)(ms.size()*0.90)];
    double p99 = ms[std::min(ms.size()-1, (size_t)(ms.size()*0.99))];
    std::cout << "loops=" << loops << " warmup=" << warmup << "\n";
    std::cout << "avg_ms=" << avg << " p50_ms=" << p50 << " p90_ms=" << p90 << " p99_ms=" << p99 << " fps=" << (1000.0/avg) << "\n";
    std::cout << "wrote=" << output << " bytes=" << outBytes << "\n";
  }

  if (qnn.contextFree) qnn.contextFree(context, nullptr);
  if (qnn.deviceFree && device) qnn.deviceFree(device);
  if (qnn.backendFree) qnn.backendFree(backend);
  if (qnn.logFree && logger) qnn.logFree(logger);
  if (sysCtx && sys.systemContextFree) sys.systemContextFree(sysCtx);
  if (contextRecord && sys.systemDlcFreeRecord) sys.systemDlcFreeRecord(contextRecord);
  if (dlcHandle && sys.systemDlcFree) sys.systemDlcFree(dlcHandle);
  dlclose(systemLib);
  dlclose(htpLib);
  return 0;
}

```

## `Makefile`

```makefile theme={null}
CXX ?= g++
CXXFLAGS := -std=c++17 -O3 -Wall -Wextra -I/usr/include -I/usr/include/QNN
LDFLAGS := -ldl
all: qnn_dlc_runner
qnn_dlc_runner: qnn_dlc_runner.cpp
	$(CXX) $(CXXFLAGS) $< -o $@ $(LDFLAGS)
clean:
	rm -f qnn_dlc_runner

```

## `live_aihub_qnn_native.py`

```python theme={null}
#!/usr/bin/env python3
"""Live USB camera depth viewer using the persistent AI Hub QNN/NPU native app."""

import argparse
import subprocess
import sys
import tempfile
import time
from pathlib import Path

import cv2
import numpy as np
from ultralytics.utils.plotting import colorize_depth

ROOT = Path(__file__).resolve().parent
APP = ROOT / "qnn_app" / "qnn_dlc_runner"
CONTEXT = ROOT / "qnn_context" / "yolo26n-depth-aihub-qcs8275-context.bin.bin"
SIZE = 320


def cam_id(value):
    try:
        return int(value)
    except ValueError:
        return value


def letterbox_bgr(frame, size=SIZE):
    h, w = frame.shape[:2]
    scale = min(size / h, size / w)
    nh, nw = int(round(h * scale)), int(round(w * scale))
    resized = cv2.resize(frame, (nw, nh), interpolation=cv2.INTER_LINEAR)
    canvas = np.full((size, size, 3), 114, dtype=np.uint8)
    top = (size - nh) // 2
    left = (size - nw) // 2
    canvas[top : top + nh, left : left + nw] = resized
    rgb = cv2.cvtColor(canvas, cv2.COLOR_BGR2RGB)
    x = (rgb.astype(np.float32) / 255.0)[None, ...]
    meta = (top, left, nh, nw, h, w)
    return x, meta


def unletterbox_depth(depth_320, meta):
    top, left, nh, nw, h, w = meta
    crop = depth_320[top : top + nh, left : left + nw]
    return cv2.resize(crop, (w, h), interpolation=cv2.INTER_LINEAR)


def read_ready(proc, timeout=20.0):
    deadline = time.time() + timeout
    lines = []
    while time.time() < deadline:
        line = proc.stdout.readline()
        if not line:
            if proc.poll() is not None:
                raise RuntimeError("native QNN app exited early:\n" + "".join(lines))
            time.sleep(0.01)
            continue
        lines.append(line)
        if line.startswith("READY "):
            print("Native QNN app ready:", line.strip())
            return line.strip()
        # Keep setup messages visible; useful if something fails.
        print("[qnn]", line.rstrip())
    raise TimeoutError("Timed out waiting for native QNN app READY. Last output:\n" + "".join(lines[-30:]))


def main():
    ap = argparse.ArgumentParser(description="Live AI Hub QNN/NPU native depth camera")
    ap.add_argument("--camera", default=0, type=cam_id, help="Camera index/path, e.g. 0 or /dev/video0")
    ap.add_argument("--width", default=640, type=int)
    ap.add_argument("--height", default=480, type=int)
    ap.add_argument("--display-width", default=1280, type=int, help="Display width; 0 keeps native stacked width")
    ap.add_argument("--display-height", default=0, type=int, help="Display height; 0 preserves aspect")
    ap.add_argument("--font-scale", default=1.0, type=float)
    ap.add_argument("--metric", action="store_true", help="Use metric color mode")
    ap.add_argument("--vmax", default=20.0, type=float, help="Max meters for --metric mode")
    ap.add_argument("--save-dir", default="aihub_qnn_native_captures")
    ap.add_argument("--app", default=str(APP))
    ap.add_argument("--context", default=str(CONTEXT))
    args = ap.parse_args()

    app = Path(args.app)
    context = Path(args.context)
    if not app.exists():
        raise SystemExit(f"Missing native app: {app}\nBuild it with: cd {ROOT/qnn_app} && make")
    if not context.exists():
        raise SystemExit(f"Missing QNN context: {context}")

    cv2.setNumThreads(1)

    tmp = tempfile.TemporaryDirectory(prefix="qnn_live_")
    tmpdir = Path(tmp.name)
    in_raw = tmpdir / "input.raw"
    out_raw = tmpdir / "output.raw"

    # The native runner validates the initial --input path even in --server mode,
    # so create a correctly-sized dummy NHWC float32 input before launching it.
    np.zeros((1, SIZE, SIZE, 3), dtype=np.float32).tofile(in_raw)

    cmd = [str(app), "--dlc", str(context), "--input", str(in_raw), "--output", str(out_raw), "--warmup", "10", "--loops", "1", "--server"]
    print("Starting native QNN app:", " ".join(cmd))
    # stderr is merged so we can show setup logs and still wait for READY.
    proc = subprocess.Popen(cmd, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True, bufsize=1)
    try:
        read_ready(proc)

        cap = cv2.VideoCapture(args.camera, cv2.CAP_V4L2)
        if not cap.isOpened():
            raise SystemExit(f"Could not open camera {args.camera!r}. Try --camera /dev/video0 or check: ls /dev/video*")
        cap.set(cv2.CAP_PROP_FRAME_WIDTH, args.width)
        cap.set(cv2.CAP_PROP_FRAME_HEIGHT, args.height)
        cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)

        save_dir = Path(args.save_dir)
        save_dir.mkdir(exist_ok=True)

        print("Live AI Hub QNN native depth started")
        print("Controls: q/Esc quit, s save current RGB/depth/overlay")
        last_depth = None
        last_color = None
        last_overlay = None
        last_frame = None
        frame_idx = 0
        fps_smooth = None

        while True:
            ok, frame = cap.read()
            if not ok:
                print("Camera read failed")
                break
            last_frame = frame.copy()

            x, meta = letterbox_bgr(frame, SIZE)
            x.tofile(in_raw)

            t0 = time.perf_counter()
            proc.stdin.write(f"RUN {in_raw} {out_raw}\n")
            proc.stdin.flush()
            line = proc.stdout.readline().strip()
            while line and not (line.startswith("OK ") or line.startswith("ERR ")):
                # Forward any unexpected QNN log lines.
                print("[qnn]", line)
                line = proc.stdout.readline().strip()
            if line.startswith("ERR "):
                raise RuntimeError(line)
            if not line.startswith("OK "):
                raise RuntimeError("native QNN app produced no response")
            qnn_ms = float(line.split()[1])
            total_ms = (time.perf_counter() - t0) * 1000.0
            inst_fps = 1000.0 / total_ms if total_ms > 0 else 0.0
            fps_smooth = inst_fps if fps_smooth is None else (0.9 * fps_smooth + 0.1 * inst_fps)

            d320 = np.fromfile(out_raw, dtype=np.float32).reshape(1, 1, SIZE, SIZE)[0, 0]
            depth = unletterbox_depth(d320, meta).astype(np.float32)
            last_depth = depth
            if args.metric:
                color = colorize_depth(depth, vmin=0.0, vmax=args.vmax, cmap="inferno", mode="metric")
            else:
                color = colorize_depth(depth, cmap="spectral", mode="disparity")
            overlay = cv2.addWeighted(frame, 0.45, color, 0.55, 0)
            last_color = color
            last_overlay = overlay

            view = np.hstack([frame, color, overlay])
            valid = depth[np.isfinite(depth) & (depth > 0)]
            if valid.size:
                txt = f"QNN {qnn_ms:.1f} ms | total {total_ms:.1f} ms | {fps_smooth:.1f} FPS | depth {valid.min():.1f}/{valid.mean():.1f}/{valid.max():.1f} m | q quit | s save"
            else:
                txt = f"QNN {qnn_ms:.1f} ms | total {total_ms:.1f} ms | {fps_smooth:.1f} FPS | q quit | s save"
            fs = args.font_scale
            thick_bg = max(3, int(round(4 * fs)))
            thick_fg = max(1, int(round(1.5 * fs)))
            y_top = int(38 * fs)
            y_bottom = view.shape[0] - int(18 * fs)
            for text, pos in [(txt, (15, y_top)), ("RGB | DEPTH | OVERLAY", (15, y_bottom))]:
                cv2.putText(view, text, pos, cv2.FONT_HERSHEY_SIMPLEX, fs, (0, 0, 0), thick_bg, cv2.LINE_AA)
                cv2.putText(view, text, pos, cv2.FONT_HERSHEY_SIMPLEX, fs, (255, 255, 255), thick_fg, cv2.LINE_AA)

            show = view
            if args.display_width or args.display_height:
                h, w = view.shape[:2]
                if args.display_width and args.display_height:
                    dw, dh = args.display_width, args.display_height
                elif args.display_width:
                    dw = args.display_width
                    dh = max(1, int(h * dw / w))
                else:
                    dh = args.display_height
                    dw = max(1, int(w * dh / h))
                show = cv2.resize(view, (dw, dh), interpolation=cv2.INTER_AREA if dw < w else cv2.INTER_LINEAR)

            cv2.namedWindow("AI Hub QNN Native Live Depth", cv2.WINDOW_NORMAL)
            cv2.imshow("AI Hub QNN Native Live Depth", show)
            key = cv2.waitKey(1) & 0xFF
            if key in (ord("q"), 27):
                break
            if key == ord("s") and last_depth is not None:
                stamp = time.strftime("%Y%m%d-%H%M%S")
                cv2.imwrite(str(save_dir / f"{stamp}_rgb.jpg"), last_frame)
                cv2.imwrite(str(save_dir / f"{stamp}_depth_color.png"), last_color)
                cv2.imwrite(str(save_dir / f"{stamp}_overlay.png"), last_overlay)
                np.save(save_dir / f"{stamp}_depth.npy", last_depth)
                print("Saved", save_dir.resolve(), stamp)

            frame_idx += 1

        cap.release()
        cv2.destroyAllWindows()
    finally:
        try:
            if proc.stdin:
                proc.stdin.write("QUIT\n")
                proc.stdin.flush()
        except Exception:
            pass
        try:
            proc.terminate()
            proc.wait(timeout=2)
        except Exception:
            proc.kill()
        tmp.cleanup()


if __name__ == "__main__":
    main()

```

## `live_aihub_qnn_native.sh`

```bash theme={null}
#!/usr/bin/env bash
set -euo pipefail
cd "$(dirname "$0")"
source ~/yolo-depth-venv/bin/activate
python ./live_aihub_qnn_native.py "$@"

```