ELF-RV1126B YOLOv8官方例程目标检测部署验证

python3 -m venv venv

source venv/bin/activate

pip install rknn-toolkit-lite2==2.3.2 -i https://pypi.tuna.tsinghua.edu.cn/simple pip install opencv-python -i https://pypi.tuna.tsinghua.edu.cn/simple

v4l2-ctl --list-formats-ext -d /dev/video52

#抑制 Qt字体警告

os.environ["QT_LOGGING_RULES"] = "qt.qpa.fonts.warning=false"

import cv2

import time

from rknnpool.rknnpool_ld import rknnPoolExecutor

from func.func_yolov8_optimize import myFunc

# ---------------性能采集工具函数 ---------------

def get_npu_load():

"""读取 RV1126B NPU负载百分比，返回字符串如 '45%'或 'N/A'"""

paths = [

"/sys/kernel/debug/rknpu/load",

"/sys/class/devfreq/ffbc0000.npu/load",

]

for p in paths:

try:

with open(p, "r") as f:

text = f.read().strip()

#取第一行

first_line = text.split("n")[0].strip()

#格式可能是 "NPU load: 4%"或 "45%"或 "45 / 100"

if "%" in first_line:

#提取百分比数字，如 "NPU load: 4%" -> "4%"

import re

m = re.search(r'(d+)s*%', first_line)

if m:

return f"{m.group(1)}%"

return first_line

parts = first_line.replace("/", " ").split()

if len(parts) >= 2:

return f"{int(int(parts[0]) * 100 / int(parts[1]))}%"

return first_line

except (FileNotFoundError, PermissionError, ValueError):

continue

return "N/A"

def get_cpu_usage(prev_stat):

"""

通过 /proc/stat两次采样差值计算 CPU使用率。

prev_stat:上次采样的 (idle, total)元组，首次传 None。

返回 (cpu_percent_str, current_stat)

"""

try:

with open("/proc/stat", "r") as f:

line = f.readline() #第一行 "cpu ..."

parts = [int(x) for x in line.split()[1:]]

idle = parts[3]

total = sum(parts)

if prev_stat is None:

return ("--", (idle, total))

d_idle = idle - prev_stat[0]

d_total = total - prev_stat[1]

if d_total == 0:

return ("0.0%", (idle, total))

usage = (1.0 - d_idle / d_total) * 100

return (f"{usage:.1f}%", (idle, total))

except (FileNotFoundError, PermissionError, ValueError):

return ("N/A", prev_stat)

def draw_osd(frame, fps_str, cpu_str, npu_str):

"""在帧左上角绘制半透明黑底 +性能信息"""

lines = [

f"FPS : {fps_str}",

f"CPU : {cpu_str}",

f"NPU : {npu_str}",

]

font = cv2.FONT_HERSHEY_SIMPLEX

scale, thickness = 0.7, 2

y0, dy = 25, 30

#半透明背景

overlay = frame.copy()

cv2.rectangle(overlay, (5, 5), (220, y0 + dy * len(lines) - 10), (0, 0, 0), -1)

cv2.addWeighted(overlay, 0.5, frame, 0.5, 0, frame)

for i, text in enumerate(lines):

cv2.putText(frame, text, (10, y0 + i * dy), font, scale, (0, 255, 0), thickness)

return frame

# --------------- X11显示检测 ---------------

#修复 sudo下 X11授权丢失问题:

# sudo会切换到 root，但 root没有原用户的 .Xauthority令牌

sudo_user = os.environ.get("SUDO_USER")

if sudo_user:

#从原用户的 home目录继承 .Xauthority

sudo_home = os.path.expanduser(f"~{sudo_user}")

xauth_file = os.path.join(sudo_home, ".Xauthority")

if os.path.exists(xauth_file) and "XAUTHORITY" not in os.environ:

os.environ["XAUTHORITY"] = xauth_file

print(f"已设置 XAUTHORITY={xauth_file} (来自用户 {sudo_user})")

if "DISPLAY" not in os.environ:

print("警告:未检测到 DISPLAY环境变量")

print("请确保 MobaXterm已开启 X11转发 (ssh -X / -Y)")

print("尝试设置 DISPLAY=localhost:10.0继续...")

os.environ["DISPLAY"] = "localhost:10.0"

# ---------------初始化 ---------------

cap = cv2.VideoCapture("/dev/video52")

modelPath = "./rknnModel/best.rknn"

TPEs = 8

os.makedirs("output", exist_ok=True)

if not cap.isOpened():

print("错误:无法打开摄像头 /dev/video52")

os.system("ls -l /dev/video*")

exit(-1)

print(f"摄像头已打开,分辨率: {int(cap.get(3))}x{int(cap.get(4))}")

pool = rknnPoolExecutor(

rknnModel=modelPath,

TPEs=TPEs,

func=myFunc

)

#预填充推理流水线

if cap.isOpened():

for i in range(TPEs + 1):

ret, frame = cap.read()

if not ret:

print(f"错误:第 {i}帧读取失败")

cap.release()

pool.release()

exit(-1)

pool.put(frame)

# --------------- X11窗口 (带降级) ---------------

WINDOW_NAME = "YOLOv8 - RV1126B"

use_x11 = False

try:

cv2.namedWindow(WINDOW_NAME, cv2.WINDOW_NORMAL)

cv2.resizeWindow(WINDOW_NAME, 640, 480)

use_x11 = True

print("X11显示窗口已创建")

except cv2.error as e:

print(f"警告: X11窗口创建失败 ({e})")

print("将以无头模式运行 (仅保存图片到 output/)")

print("提示:请尝试以下命令解决 X11问题:")

print(f" 1.用普通用户运行: python3 {os.path.basename(__file__)}")

print(f" (先执行: sudo chmod 666 /dev/video52)")

print(f" 2.或传递 X11授权: sudo -E XAUTHORITY=$HOME/.Xauthority ./venv/bin/python3 {os.path.basename(__file__)}")

print(f" 3.或允许 root访问 X11: xhost +local:root")

# ---------------推理主循环 ---------------

# X11转发带宽有限，跳帧显示以避免拖慢推理帧率

#每 X11_SKIP帧才做一次 imshow，其余帧只推理不显示

X11_SKIP = 10

print("开始推理循环,按 'q'或 Ctrl+C停止...")

frames, loopTime, initTime = 0, time.time(), time.time()

fps_display = "..."

#预采样 CPU一次，使首次输出不是 "--"

cpu_display, cpu_stat = get_cpu_usage(None)

npu_display = get_npu_load()

last_good_frame = None

try:

while cap.isOpened():

ret, frame = cap.read()

if not ret:

print(f"第 {frames + 1}帧读取失败,退出")

break

pool.put(frame)

frame, flag = pool.get()

if not flag:

print(f"第 {frames + 1}帧推理失败,退出")

break

frames += 1

last_good_frame = frame

#每30帧更新性能数据

if frames % 30 == 0:

elapsed = time.time() - loopTime

fps_val = 30 / elapsed if elapsed > 0 else 0

fps_display = f"{fps_val:.1f}"

npu_display = get_npu_load()

cpu_display, cpu_stat = get_cpu_usage(cpu_stat)

loopTime = time.time()

#保存图片

save_path = f"output/result_{frames}.jpg"

cv2.imwrite(save_path, frame)

print(f"FPS: {fps_display} CPU: {cpu_display} NPU: {npu_display} | 已保存: {save_path}")

# OSD叠加

draw_osd(frame, fps_display, cpu_display, npu_display)

#通过 X11显示 (跳帧以减少 X11转发开销)

if use_x11 and frames % X11_SKIP == 0:

cv2.imshow(WINDOW_NAME, frame)

if cv2.waitKey(1) & 0xFF == ord('q'):

print("按 'q'退出")

break

except KeyboardInterrupt:

print("n手动停止")

finally:

if last_good_frame is not None:

cv2.imwrite("output/result_latest.jpg", last_good_frame)

print(f"最后一帧已保存: output/result_latest.jpg")

if frames > 0:

total_time = time.time() - initTime

print(f"总帧数: {frames} 总平均帧率: {frames / total_time:.2f}")

else:

print("未处理任何帧")

cap.release()

pool.release()

if use_x11:

cv2.destroyAllWindows()

可以设置跳帧X11_SKIP =15（X11转发是瓶颈）