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refactor/fix: 优化运行时异常处理、调整 nncase 导入时机及修复 YOLO11/路径兼容性问题 #4

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refactor/fix: 优化运行时异常处理、调整 nncase 导入时机及修复 YOLO11/路径兼容性问题 #4

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43 changes: 30 additions & 13 deletions app.py
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -22,7 +22,7 @@
from PyQt5.QtGui import QPixmap
from PyQt5.QtCore import QThread, pyqtSignal
import io
sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8')
sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8', write_through=True)

lang_id = 0
lang = {
Expand Down Expand Up @@ -82,9 +82,15 @@
"yolov8n-cls": ["yolov8n-cls", "object-classification-classifier"],
"yolov8n-seg": ["yolov8n-seg", "object-segmentation-segment"],
"yolov8n": ["yolov8n-det", "object-detection-detector"],
"yolo11n-cls": ["yolo11n-cls", "object-classification-classifier"],
"yolo11n-seg": ["yolo11n-seg", "object-segmentation-segment"],
"yolo11n": ["yolo11n-det", "object-detection-detector"],

"yolo11n-cls": ["yolov8n-cls", "object-classification-classifier"],
"yolov11n-cls": ["yolov8n-cls", "object-classification-classifier"],

"yolo11n-seg": ["yolov8n-seg", "object-segmentation-segment"],
"yolov11n-seg": ["yolov8n-seg", "object-segmentation-segment"],

"yolo11n": ["yolov8n-det", "object-detection-detector"],
"yolov11n": ["yolov8n-det", "object-detection-detector"],
}

def clean_name(name):
Expand Down Expand Up @@ -292,14 +298,19 @@ def __init__(self, onnx_path, kmodel_path, dataset_path, conf_path, output_zip_f
self.output_zip_file = output_zip_file

def run(self):
import convertor
# 耗时操作放在这里
shapes = get_input_shape(self.onnx_path)
if 1 == len(shapes):
shape = list(shapes.values())[0]
convertor.make(self.onnx_path, self.kmodel_path, self.dataset_path, self.conf_path,shape)
file_path = zip_with_md5(base_name=self.output_zip_file)
self.finished.emit(file_path) # 发射信号通知主线程
try:
import convertor
# 耗时操作放在这里
shapes = get_input_shape(self.onnx_path)
if 1 == len(shapes):
shape = list(shapes.values())[0]
convertor.make(self.onnx_path, self.kmodel_path, self.dataset_path, self.conf_path, shape)
file_path = zip_with_md5(base_name=self.output_zip_file)
self.finished.emit(file_path) # 发射信号通知主线程
except Exception as e:
import traceback
traceback.print_exc()
print(f"!!! 子线程发生严重错误: {e} !!!", flush=True)


class ModelExportApp(QWidget):
Expand Down Expand Up @@ -705,7 +716,7 @@ def export_model(self):

icon_file = self._conf["comm"]["icon_file"]
if os.path.exists(icon_file):
shutil.copy(icon_file, os.path.join("model_output/icon.png"))
shutil.copy(icon_file, os.path.join("model_output", "icon.png"))

# 创建空文件
open(f"model_output/app.{conf_data['conf']['application']}", "w").close()
Expand Down Expand Up @@ -736,6 +747,12 @@ def pack(self):
print("转换完成！")

if __name__ == "__main__":
import traceback
# 拦截 PyQt5 的主线程异常，防止 0xC0000409 闪退
def exception_hook(type, value, tb):
traceback.print_exception(type, value, tb)
print(f"\n[程序内部报错] {value}", flush=True)
sys.excepthook = exception_hook
language_code = locale.getdefaultlocale()[0]
print(f"默认语言环境: {language_code}")
if language_code.startswith("zh_CN"):
Expand Down
50 changes: 25 additions & 25 deletions convertor.py
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2,35 +2,27 @@

import subprocess
import argparse
import nncase
import tomlkit
import sys
import os
import cv2
import numpy as np
from pathlib import Path

swapRB = False
preprocess = False

input_type = np.uint8

templs_shape = 640


# setup env
result = subprocess.run(["pip", "show", "nncase"], capture_output=True)
result = subprocess.run([sys.executable, "-m", "pip", "show", "nncase"], capture_output=True)
line_break = "\n"
if sys.platform == "win32":
line_break = "\r\n"
location_s = [i for i in result.stdout.decode().split(
line_break) if i.startswith("Location:")]
location = location_s[0].split(": ")[1]
if "PATH" in os.environ:
os.environ["PATH"] += os.pathsep + location
else:
os.environ["PATH"] = location
os.environ["NNCASE_PLUGIN_PATH"] = location
location_s = [i for i in result.stdout.decode().split(line_break) if i.startswith("Location:")]
if location_s:
location = location_s[0].split(": ")[1]
if "PATH" in os.environ:
os.environ["PATH"] += os.pathsep + location
else:
os.environ["PATH"] = location
os.environ["NNCASE_PLUGIN_PATH"] = location

import nncase
import tomlkit


class Convertor(nncase.Compiler):
Expand Down Expand Up @@ -134,30 +126,38 @@ def gen(_dir):
'''

def gen(_dir):
path = Path(_dir)

path = Path(_dir).resolve()
count = 0
for f in path.rglob('*'):
if not f.is_file():
continue

img_path = str(f) # �ؼ�����Ҫ f.name
img_path = str(f)

data = np.fromfile(img_path, dtype=np.uint8)
templ = cv2.imdecode(data, cv2.IMREAD_COLOR)

if templ is None:
print(f"[WARN] read failed: {img_path}")
continue

templ = process_img(templ)
count += 1
print(f" -> 成功载入校准图 [{count}/100]: {f.name}", flush=True)
yield templ

def make(onnx_file, kmodel_file, dataset, toml_file, input_shape):
global templs_shape
templs_shape = input_shape[2]
calib = []
max_calib_images = 100
for t in gen(dataset):
calib.append(t)
if len(calib) >= max_calib_images:
break

if len(calib) == 0:
print(f"\n[错误] 严重警告：在目录 '{dataset}' 下未找到任何有效的图片！", flush=True)
print("请检查该文件夹是否存在，且里面是否确实含有 .jpg 或 .png 图片。\n", flush=True)
raise FileNotFoundError(f"未能在 {dataset} 中找到校准图片")

npcalib = np.array(calib).astype(np.uint8)

Expand Down