diff --git a/app.py b/app.py index 9c2c7fa..7376c3b 100644 --- a/app.py +++ b/app.py @@ -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 = { @@ -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): @@ -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): @@ -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() @@ -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"): diff --git a/convertor.py b/convertor.py index 79a9f1b..e8d0748 100644 --- a/convertor.py +++ b/convertor.py @@ -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): @@ -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) # ؼҪ 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)