drop alg roi files

alg/roi-verify.py

@@ -1,30 +0,0 @@
-#!/usr/bin/env python3
-from PIL import Image
-import os
-import argparse
-import sys
-from roi_lib import *
-
-def parse_args():
-    parser = argparse.ArgumentParser(description='ROI prediction')
-    parser.add_argument('--model', type=str, required=True, help='model path')
-    parser.add_argument('--image', type=str, required=True, help='image file')
-    return parser.parse_args()
-
-# 主函数
-def main():
-    args = parse_args()
-    model = load_model(args.model)
-    image_path = args.image
-    image_tensor = preprocess_image(image_path)
-    predicted_class, probabilities = predict(model, image_tensor)
-    print(f'{image_path} predicted={predicted_class} prob={probabilities}')
-    if predicted_class == 1:
-        print("verify ok")
-        return 0
-    else:
-        print("verify ng")
-        return 1
-
-if __name__ == '__main__':
-    sys.exit(main())

alg/roi_lib.py

@@ -1,35 +0,0 @@
-#!/usr/bin/env python3
-import torch
-import torch.nn as nn
-import torchvision.models as models
-import torchvision.transforms as transforms
-from PIL import Image
-import os
-import argparse
-import random
-
-def load_model(model_path):
-    model = models.resnet18(pretrained=True)
-    num_ftrs = model.fc.in_features
-    model.fc = nn.Linear(num_ftrs, 2)
-    model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))  # 加载模型权重
-    model.eval()
-    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-    model = model.to(device)
-    return model
-
-def preprocess_image(image_path):
-    transform = transforms.Compose([
-        transforms.ToTensor(),                   # 转换为Tensor
-        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])  # 归一化
-    ])
-    image = Image.open(image_path).convert('RGB')  # 打开图像并转换为 RGB
-    image = transform(image).unsqueeze(0)  # 增加 batch 维度
-    return image
-
-def predict(model, image_tensor):
-    with torch.no_grad():  # 禁用梯度计算
-        output = model(image_tensor)
-        _, predicted = torch.max(output, 1)  # 获取预测类别
-        probabilities = torch.nn.functional.softmax(output, dim=1)  # 计算概率
-    return predicted.item(), probabilities.squeeze().tolist()