重现步骤
from libs.PipeLine import PipeLine, ScopedTiming
from libs.AIBase import AIBase
from libs.AI2D import Ai2d
import os
import ujson
from media.media import *
from time import *
import nncase_runtime as nn
import ulab.numpy as np
import time
import utime
import image
import random
import gc
import sys
import aicube
person_det = None
from libs.YbProtocol import YbProtocol
from ybUtils.YbUart import YbUart
# uart = None
uart = YbUart(baudrate=115200)
pto = YbProtocol()
# 自定义人体检测类
# (Custom person detection class)
class PersonDetectionApp(AIBase):
def __init__(self, kmodel_path, model_input_size, labels, anchors, confidence_threshold=0.2, nms_threshold=0.5, nms_option=False, strides=[8, 16, 32], rgb888p_size=[224, 224], display_size=[640, 360], debug_mode=0):
"""
初始化人体检测应用类
(Initialize the person detection application class)
参数:
kmodel_path: 模型文件路径
(kmodel_path: Path to the model file)
model_input_size: 模型输入分辨率
(model_input_size: Model input resolution)
labels: 标签列表
(labels: List of labels)
anchors: 检测anchors设置
(anchors: Detection anchor settings)
confidence_threshold: 置信度阈值
(confidence_threshold: Confidence threshold)
nms_threshold: nms阈值
(nms_threshold: NMS threshold)
nms_option: nms是否开启
(nms_option: Whether NMS is enabled)
strides: 特征图降采样倍数
(strides: Feature map downsampling factor)
rgb888p_size: 传感器输入图像分辨率
(rgb888p_size: Sensor input image resolution)
display_size: 显示分辨率
(display_size: Display resolution)
debug_mode: 调试模式级别
(debug_mode: Debug mode level)
"""
super().__init__(kmodel_path, model_input_size, rgb888p_size, debug_mode)
self.kmodel_path = kmodel_path
# 模型输入分辨率
# (Model input resolution)
self.model_input_size = model_input_size
# 标签
# (Labels)
self.labels = labels
# 检测anchors设置
# (Detection anchor settings)
self.anchors = anchors
# 特征图降采样倍数
# (Feature map downsampling factor)
self.strides = strides
# 置信度阈值设置
# (Confidence threshold setting)
self.confidence_threshold = confidence_threshold
# nms阈值设置
# (NMS threshold setting)
self.nms_threshold = nms_threshold
self.nms_option = nms_option
# sensor给到AI的图像分辨率
# (Image resolution from sensor to AI)
self.rgb888p_size = [ALIGN_UP(rgb888p_size[0], 16), rgb888p_size[1]]
# 显示分辨率
# (Display resolution)
self.display_size = [ALIGN_UP(display_size[0], 16), display_size[1]]
self.debug_mode = debug_mode
# Ai2d实例，用于实现模型预处理
# (Ai2d instance for model preprocessing)
self.ai2d = Ai2d(debug_mode)
# 设置Ai2d的输入输出格式和类型
# (Set Ai2d input and output formats and types)
self.ai2d.set_ai2d_dtype(nn.ai2d_format.NCHW_FMT, nn.ai2d_format.NCHW_FMT, np.uint8, np.uint8)
# 配置预处理操作，这里使用了pad和resize，Ai2d支持crop/shift/pad/resize/affine，具体代码请打开/sdcard/app/libs/AI2D.py查看
# (Configure preprocessing operations, using pad and resize here, Ai2d supports crop/shift/pad/resize/affine)
def config_preprocess(self, input_image_size=None):
"""
配置模型输入数据的预处理操作
(Configure preprocessing operations for model input data)
参数:
input_image_size: 输入图像尺寸，如果为None则使用默认rgb888p_size
(input_image_size: Input image size, if None uses default rgb888p_size)
"""
with ScopedTiming("set preprocess config", self.debug_mode > 0):
# 初始化ai2d预处理配置，默认为sensor给到AI的尺寸，您可以通过设置input_image_size自行修改输入尺寸
# (Initialize ai2d preprocessing configuration, default is the size from sensor to AI)
ai2d_input_size = input_image_size if input_image_size else self.rgb888p_size
top, bottom, left, right = self.get_padding_param()
self.ai2d.pad([0, 0, 0, 0, top, bottom, left, right], 0, [0, 0, 0])
self.ai2d.resize(nn.interp_method.tf_bilinear, nn.interp_mode.half_pixel)
self.ai2d.build([1, 3, ai2d_input_size[1], ai2d_input_size[0]], [1, 3, self.model_input_size[1], self.model_input_size[0]])
# 自定义当前任务的后处理
# (Custom post-processing for the current task)
def postprocess(self, results):
"""
对模型输出进行后处理
(Post-process the model output)
参数:
results: 模型的原始输出
(results: Raw output from the model)
返回:
处理后的检测结果
(Processed detection results)
"""
with ScopedTiming("postprocess", self.debug_mode > 0):
# 这里使用了aicube模型的后处理接口anchorbasedet_post_preocess
# (Use the anchorbasedet_post_process interface from the aicube model for post-processing)
dets = aicube.anchorbasedet_post_process(results[0], results[1], results[2], self.model_input_size, self.rgb888p_size, self.strides, len(self.labels), self.confidence_threshold, self.nms_threshold, self.anchors, self.nms_option)
return dets
# 绘制结果
# (Draw results)
def draw_result(self, pl, dets):
"""
在画布上绘制人体检测结果
(Draw person detection results on the canvas)
参数:
pl: 画布对象
(pl: Canvas object)
dets: 检测结果
(dets: Detection results)
"""
with ScopedTiming("display_draw", self.debug_mode > 0):
if dets:
pl.osd_img.clear()
for det_box in dets:
# 计算检测框在显示分辨率下的坐标和尺寸
# (Calculate the coordinates and size of the detection box in the display resolution)
x1, y1, x2, y2 = det_box[2], det_box[3], det_box[4], det_box[5]
w = float(x2 - x1) * self.display_size[0] // self.rgb888p_size[0]
h = float(y2 - y1) * self.display_size[1] // self.rgb888p_size[1]
x1 = int(x1 * self.display_size[0] // self.rgb888p_size[0])
y1 = int(y1 * self.display_size[1] // self.rgb888p_size[1])
x2 = int(x2 * self.display_size[0] // self.rgb888p_size[0])
y2 = int(y2 * self.display_size[1] // self.rgb888p_size[1])
# 过滤掉尺寸太小的检测框
# (Filter out detection boxes that are too small)
if (h < (0.1 * self.display_size[0])):
continue
if (w < (0.25 * self.display_size[0]) and ((x1 < (0.03 * self.display_size[0])) or (x2 > (0.97 * self.display_size[0])))):
continue
if (w < (0.15 * self.display_size[0]) and ((x1 < (0.01 * self.display_size[0])) or (x2 > (0.99 * self.display_size[0])))):
continue
# 在画布上绘制检测框和标签
# (Draw the detection box and label on the canvas)
pl.osd_img.draw_rectangle(x1, y1, int(w), int(h), color=(255, 0, 255, 0), thickness=2)
# uart.send(f"${x1},{y1},{w},{h}#")
pl.osd_img.draw_string_advanced(x1, y1 - 50, 32, " " + self.labels[det_box[0]] + " " + str(round(det_box[1], 2)), color=(255, 0, 255, 0))
pto_data = pto.get_person_detect_data(x1, y1, w, h)
uart.send(pto_data)
print(pto_data)
else:
pl.osd_img.clear()
# 计算padding参数
# (Calculate padding parameters)
def get_padding_param(self):
"""
计算将输入图像等比缩放到模型输入尺寸时需要的padding参数
(Calculate the padding parameters required to proportionally scale the input image to the model input size)
返回:
padding参数 [top, bottom, left, right]
(Padding parameters [top, bottom, left, right])
"""
dst_w = self.model_input_size[0]
dst_h = self.model_input_size[1]
input_width = self.rgb888p_size[0]
input_high = self.rgb888p_size[1]
ratio_w = dst_w / input_width
ratio_h = dst_h / input_high
if ratio_w < ratio_h:
ratio = ratio_w
else:
ratio = ratio_h
new_w = (int)(ratio * input_width)
new_h = (int)(ratio * input_high)
dw = (dst_w - new_w) / 2
dh = (dst_h - new_h) / 2
top = int(round(dh - 0.1))
bottom = int(round(dh + 0.1))
left = int(round(dw - 0.1))
right = int(round(dw - 0.1))
return top, bottom, left, right
def exce_demo(pl):
"""
执行人体检测演示
(Execute person detection demo)
参数:
pl: PipeLine对象
(pl: PipeLine object)
"""
global person_det
display_mode = pl.display_mode
rgb888p_size = pl.rgb888p_size
display_size = pl.display_size
# 设置模型路径和其他参数
# (Set model path and other parameters)
kmodel_path = "/data/best.kmodel"
# 其它参数设置
# (Other parameter settings)
confidence_threshold = 0.2
nms_threshold = 0.6
labels = ["stm32"]
anchors = [10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 373, 326]
try:
# 初始化自定义人体检测实例
# (Initialize the custom person detection instance)
person_det = PersonDetectionApp(kmodel_path, model_input_size=[640, 640], labels=labels, anchors=anchors, confidence_threshold=confidence_threshold, nms_threshold=nms_threshold, nms_option=False, strides=[8, 16, 32], rgb888p_size=rgb888p_size, display_size=display_size, debug_mode=0)
person_det.config_preprocess()
while True:
with ScopedTiming("total", 1):
# 获取当前帧数据
# (Get the current frame data)
img = pl.get_frame()
# 推理当前帧
# (Inference on the current frame)
res = person_det.run(img)
# 绘制结果到PipeLine的osd图像
# (Draw the results on the PipeLine's osd image)
person_det.draw_result(pl, res)
# 显示当前的绘制结果
# (Display the current drawing results)
pl.show_image()
gc.collect()
except Exception as e:
print("stm32检测功能退出")
# (Person detection function exited)
finally:
person_det.deinit() # 反初始化
# (Deinitialize)
def exit_demo():
"""
退出人体检测演示
(Exit person detection demo)
"""
global person_det
person_det.deinit()
if __name__ == "__main__":
rgb888p_size = [640, 360]
display_size = [640, 480]
display_mode = "lcd"
# 初始化PipeLine，用于图像处理流程
# (Initialize PipeLine for image processing)
pl = PipeLine(rgb888p_size=rgb888p_size, display_size=display_size, display_mode=display_mode)
pl.create() # 创建PipeLine实例
# (Create PipeLine instance)
# 初始化自定义人脸检测实例
# (Initialize the custom person detection instance)
exce_demo(pl)
期待结果和实际结果
两个问题：1.如果要本地训练在环境确认是没有问题之后还应该注意哪些事项；2.有没有通用的能运行kmodel的例程。我在下面附上了我改动过的代码
软硬件版本信息
错误日志
Traceback (most recent call last):
File "", line 97, in run_demo
File "/sdcard/libs/AIBase.py", line 74, in run
File "", line 44, in postprocess
IndexError: list index out of range
MPY: soft reboot
CanMV v1.3.5(based on Micropython e00a144) on 2025-06-30; k230_canmv_yahboom with K230
find sensor gc2093_csi2, type 8, output 1920x1080@30
sensor(0), mode 0, buffer_num 4, buffer_size 0
total took 21.07 ms
人体检测功能退出
MPY: soft reboot
CanMV v1.3.5(based on Micropython e00a144) on 2025-06-30; k230_canmv_yahboom with K230
[DEBUG] main IDE interrupt
尝试解决过程
补充材料
