Copy roi-verify.py to alg/

Makefile

@@ -51,6 +51,9 @@ ALG_FILES = \
 		$(shell find -L \
 			alg/qrtool \
 			alg/wechat_qrcode \
+			alg/roi_lib.py \
+			alg/roi-verify.py \
+			alg/models/resnet18_20250325_114510_94.56.pth \
 			) \
 	)
 

alg/roi-verify.py

@@ -0,0 +1,30 @@
+#!/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

@@ -0,0 +1,35 @@
+#!/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()

api/products/views.py

@@ -687,8 +687,8 @@ class QrVerifyView(BaseView):
             cmd = [qrtool_path, 'side_by_side', img_fn, f.name]
             messages.append("creating side by side image...")
             subprocess.check_call(cmd, cwd=cwd)
-            roi_verify_py = os.path.abspath("../research/roi-verify.py")
-            roi_verify_model = os.path.abspath("../research/models/resnet18_20250325_114510_94.56.pth")
+            roi_verify_py = os.path.abspath("../alg/roi-verify.py")
+            roi_verify_model = os.path.abspath("../alg/models/resnet18_20250325_114510_94.56.pth")
             cmd = [roi_verify_py, '--model', roi_verify_model, '--image', side_by_side_fn]
             messages.append(" ".join(cmd))
             r = subprocess.call(cmd)