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部署#xff1a;
# 导入必要的库和模块
import os
import ujson # 超快的JS…训练K230 借助 AICube部署AI 视觉模型 YOLO等教程_嘉楠 ai cube多标签分类-CSDN博客K230模型训练ai cube报错生成部署文件异常_aicube部署模型显示生成部署文件异常-CSDN博客
部署
# 导入必要的库和模块
import os
import ujson # 超快的JSON解析库
import aicube # AI加速库
from media.sensor import * # 摄像头传感器相关
from media.display import * # 显示相关
from media.media import * # 媒体处理基础库
from time import * # 时间相关
import nncase_runtime as nn # 神经网络运行时库
import ulab.numpy as np # 嵌入式优化的numpy替代库
import time
import utime # 更精确的时间库
import image # 图像处理
import random
import gc # 垃圾回收
import utime# 显示模式配置LCD或HDMI
display_modelcd
if display_modelcd:DISPLAY_WIDTH ALIGN_UP(800, 16) # 对齐到16的倍数硬件要求DISPLAY_HEIGHT 480
else:DISPLAY_WIDTH ALIGN_UP(1920, 16)DISPLAY_HEIGHT 1080# RGB888输出分辨率配置
OUT_RGB888P_WIDTH ALIGN_UP(1080, 16)
OUT_RGB888P_HEIGH 720# 预定义的80种颜色ARGB格式用于不同类别的检测框显示
# 颜色盘
color_four [(255, 220, 20, 60), (255, 119, 11, 32), (255, 0, 0, 142), (255, 0, 0, 230),(255, 106, 0, 228), (255, 0, 60, 100), (255, 0, 80, 100), (255, 0, 0, 70),(255, 0, 0, 192), (255, 250, 170, 30), (255, 100, 170, 30), (255, 220, 220, 0),(255, 175, 116, 175), (255, 250, 0, 30), (255, 165, 42, 42), (255, 255, 77, 255),(255, 0, 226, 252), (255, 182, 182, 255), (255, 0, 82, 0), (255, 120, 166, 157),(255, 110, 76, 0), (255, 174, 57, 255), (255, 199, 100, 0), (255, 72, 0, 118),(255, 255, 179, 240), (255, 0, 125, 92), (255, 209, 0, 151), (255, 188, 208, 182),(255, 0, 220, 176), (255, 255, 99, 164), (255, 92, 0, 73), (255, 133, 129, 255),(255, 78, 180, 255), (255, 0, 228, 0), (255, 174, 255, 243), (255, 45, 89, 255),(255, 134, 134, 103), (255, 145, 148, 174), (255, 255, 208, 186),(255, 197, 226, 255), (255, 171, 134, 1), (255, 109, 63, 54), (255, 207, 138, 255),(255, 151, 0, 95), (255, 9, 80, 61), (255, 84, 105, 51), (255, 74, 65, 105),(255, 166, 196, 102), (255, 208, 195, 210), (255, 255, 109, 65), (255, 0, 143, 149),(255, 179, 0, 194), (255, 209, 99, 106), (255, 5, 121, 0), (255, 227, 255, 205),(255, 147, 186, 208), (255, 153, 69, 1), (255, 3, 95, 161), (255, 163, 255, 0),(255, 119, 0, 170), (255, 0, 182, 199), (255, 0, 165, 120), (255, 183, 130, 88),(255, 95, 32, 0), (255, 130, 114, 135), (255, 110, 129, 133), (255, 166, 74, 118),(255, 219, 142, 185), (255, 79, 210, 114), (255, 178, 90, 62), (255, 65, 70, 15),(255, 127, 167, 115), (255, 59, 105, 106), (255, 142, 108, 45), (255, 196, 172, 0),(255, 95, 54, 80), (255, 128, 76, 255), (255, 201, 57, 1), (255, 246, 0, 122),(255, 191, 162, 208)]# 配置文件路径
root_path/sdcard/mp_deployment_source/
config_pathroot_pathdeploy_config.json
deploy_conf{}
debug_mode1 # 调试模式开关# 性能分析工具类上下文管理器
class ScopedTiming:def __init__(self, info, enable_profileTrue):self.info infoself.enable_profile enable_profiledef __enter__(self):if self.enable_profile:self.start_time time.time_ns()return selfdef __exit__(self, exc_type, exc_value, traceback):if self.enable_profile:elapsed_time time.time_ns() - self.start_timeprint(f{self.info} took {elapsed_time / 1000000:.2f} ms)# 读取部署配置文件
def read_deploy_config(config_path):with open(config_path, r) as json_file:try:config ujson.load(json_file) # 使用ujson快速解析except ValueError as e:print(JSON 解析错误:, e)return config# 主检测函数
def detection():print(det_infer start)# 加载部署配置deploy_confread_deploy_config(config_path)# 解析配置参数kmodel_namedeploy_conf[kmodel_path] # 模型文件路径labelsdeploy_conf[categories] # 类别标签confidence_threshold deploy_conf[confidence_threshold] # 置信度阈值nms_threshold deploy_conf[nms_threshold] # NMS阈值img_sizedeploy_conf[img_size] # 模型输入尺寸num_classesdeploy_conf[num_classes] # 类别数量nms_option deploy_conf[nms_option] # NMS选项model_type deploy_conf[model_type] # 模型类型AnchorBaseDet等# 锚点配置如果是基于锚点的检测模型if model_type AnchorBaseDet:anchors deploy_conf[anchors][0] deploy_conf[anchors][1] deploy_conf[anchors][2]# 图像预处理参数计算kmodel_frame_size img_size # 模型输入尺寸frame_size [OUT_RGB888P_WIDTH,OUT_RGB888P_HEIGH] # 实际输入尺寸strides [8,16,32] # 特征图步长# 计算保持长宽比的缩放参数ori_w OUT_RGB888P_WIDTHori_h OUT_RGB888P_HEIGHwidth, height kmodel_frame_sizeratiow width / ori_wratioh height / ori_hratio min(ratiow, ratioh) # 选择较小比例保持长宽比new_w int(ratio * ori_w)new_h int(ratio * ori_h)# 计算填充参数居中填充dw (width - new_w) / 2dh (height - new_h) / 2top int(round(dh - 0.1))bottom int(round(dh 0.1))left int(round(dw - 0.1))right int(round(dw - 0.1))# 初始化神经网络推理组件kpu nn.kpu() # 创建KPU神经网络加速器实例ai2d nn.ai2d() # 创建AI2D图像预处理加速器实例# 加载kmodel模型文件kpu.load_kmodel(root_pathkmodel_name)# 配置AI2D预处理参数ai2d.set_dtype(nn.ai2d_format.NCHW_FMT, nn.ai2d_format.NCHW_FMT, np.uint8, np.uint8)ai2d.set_pad_param(True, [0,0,0,0,top,bottom,left,right], 0, [114,114,114]) # 填充灰色ai2d.set_resize_param(True, nn.interp_method.tf_bilinear, nn.interp_mode.half_pixel)# 构建AI2D预处理流水线ai2d_builder ai2d.build([1,3,OUT_RGB888P_HEIGH,OUT_RGB888P_WIDTH], [1,3,height,width])# 初始化摄像头传感器sensor Sensor(id 1)sensor.reset()# 摄像头参数配置sensor.set_hmirror(False) # 水平镜像关闭sensor.set_vflip(False) # 垂直翻转关闭# 配置显示通道YUV420格式sensor.set_framesize(DISPLAY_WIDTH, DISPLAY_HEIGHT)sensor.set_pixformat(PIXEL_FORMAT_YUV_SEMIPLANAR_420)# 配置AI处理通道RGB888格式sensor.set_framesize(OUT_RGB888P_WIDTH, OUT_RGB888P_HEIGH, chnCAM_CHN_ID_2)sensor.set_pixformat(PIXEL_FORMAT_RGB_888_PLANAR, chnCAM_CHN_ID_2)# 绑定显示层sensor_bind_info sensor.bind_info(x0, y0, chnCAM_CHN_ID_0)Display.bind_layer(**sensor_bind_info, layerDisplay.LAYER_VIDEO1)# 初始化显示设备if display_modelcd:Display.init(Display.ST7701, to_ideTrue) # 7寸LCD屏else:Display.init(Display.LT9611, to_ideTrue) # HDMI输出# 创建OSD层用于绘制检测结果osd_img image.Image(DISPLAY_WIDTH, DISPLAY_HEIGHT, image.ARGB8888)try:MediaManager.init() # 初始化媒体系统sensor.run() # 启动摄像头# 初始化AI2D输入输出张量ai2d_input_tensor Noneai2d_output_tensor nn.from_numpy(np.ones((1,3,width,height), dtypenp.uint8))# 主循环while True:with ScopedTiming(total, debug_mode 0):# 捕获图像rgb888p_img sensor.snapshot(chnCAM_CHN_ID_2)if rgb888p_img.format() image.RGBP888:# 转换为numpy格式ai2d_input rgb888p_img.to_numpy_ref()ai2d_input_tensor nn.from_numpy(ai2d_input)# 执行预处理缩放填充ai2d_builder.run(ai2d_input_tensor, ai2d_output_tensor)# 模型推理kpu.set_input_tensor(0, ai2d_output_tensor)kpu.run()# 获取输出results []for i in range(kpu.outputs_size()):out_data kpu.get_output_tensor(i)result out_data.to_numpy().flatten()results.append(result)del out_data # 及时释放内存gc.collect() # 垃圾回收# 后处理if model_type AnchorBaseDet:det_boxes aicube.anchorbasedet_post_process(...)elif model_type GFLDet:det_boxes aicube.gfldet_post_process(...)else:det_boxes aicube.anchorfreedet_post_process(...)# 清空OSD层并绘制结果osd_img.clear()if det_boxes:for box in det_boxes:# 坐标转换到显示尺寸x1, y1, x2, y2 box[2], box[3], box[4], box[5]w (x2 - x1) * DISPLAY_WIDTH / OUT_RGB888P_WIDTHh (y2 - y1) * DISPLAY_HEIGHT / OUT_RGB888P_HEIGH# 绘制矩形框osd_img.draw_rectangle(int(x1 * DISPLAY_WIDTH / OUT_RGB888P_WIDTH),int(y1 * DISPLAY_HEIGHT / OUT_RGB888P_HEIGH),int(w), int(h),colorcolor_four[box[0]][1:] # 根据类别选择颜色)# 显示OSD层Display.show_image(osd_img, 0, 0, Display.LAYER_OSD3)gc.collect()rgb888p_img None # 释放图像引用except Exception as e:print(f运行异常: {e})finally:# 资源清理os.exitpoint(os.EXITPOINT_ENABLE_SLEEP)del ai2d_input_tensordel ai2d_output_tensorsensor.stop() # 停止摄像头Display.deinit() # 关闭显示MediaManager.deinit()# 释放媒体资源gc.collect()time.sleep(1)nn.shrink_memory_pool() # 释放神经网络内存print(det_infer end)return 0if __name____main__:detection()
