add old project

mnist/.gitignore

@@ -0,0 +1,10 @@
+# Python-generated files
+__pycache__/
+*.py[oc]
+build/
+dist/
+wheels/
+*.egg-info
+
+# Virtual environments
+.venv

mnist/.python-version

@@ -0,0 +1 @@
+3.11

mnist/example.py

@@ -0,0 +1,27 @@
+from datasets import load_dataset
+import numpy as np
+import matplotlib.pyplot as plt
+
+def main():
+    dataset = load_dataset('parquet', data_files={
+        'train': r"D:\AiData\Dataset\mnist\mnist\train-00000-of-00001.parquet",
+        'test': r"D:\AiData\Dataset\mnist\mnist\test-00000-of-00001.parquet",
+    })
+    train_dataset = dataset['train']
+    first_sample = train_dataset[0]
+
+    print("Label:", first_sample['label'])
+
+    image = first_sample['image']
+    image_array = np.array(image)
+    print("Image shape:", image_array.shape)
+
+    plt.imshow(image_array, cmap='gray')
+    plt.show()
+
+    print("First few rows of pixel values:")
+    print(image_array[:5, :5])
+
+
+if __name__ == "__main__":
+    main()

mnist/main.py

@@ -0,0 +1,196 @@
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.utils.data import DataLoader
+from datasets import load_dataset
+import numpy as np
+import matplotlib.pyplot as plt
+import time
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+print(f"Using device: {device}")
+
+class SimpleMLP(nn.Module):
+    def __init__(self, input_size=784, hidden_size=128, num_classes=10):
+        super(SimpleMLP, self).__init__()
+        self.fc1 = nn.Linear(input_size, hidden_size)
+        self.relu = nn.ReLU()
+        self.fc2 = nn.Linear(hidden_size, num_classes)
+        
+    def forward(self, x):
+        x = x.view(x.size(0), -1)
+        x = self.fc1(x)
+        x = self.relu(x)
+        x = self.fc2(x)
+        return x
+
+def preprocess_data(dataset):
+    """将数据集转换为PyTorch张量格式"""
+    def transform_sample(example):
+        # 转换图像：归一化并转为 float32
+        image = np.array(example['image']).astype(np.float32) / 255.0
+        # 注意：这里返回 numpy array，稍后统一转为 tensor
+        return {
+            'image': image,  # 保持为 numpy array
+            'label': example['label']
+        }
+    
+    # 先应用转换
+    dataset = dataset.map(transform_sample, remove_columns=dataset.column_names)
+    
+    # 关键：设置格式为 "torch"，并指定列类型
+    dataset = dataset.with_format(
+        "torch",
+        columns=["image", "label"],
+        output_all_columns=False
+    )
+    return dataset
+
+# 不再需要自定义 collate_fn！
+# 因为 with_format("torch") 后，DataLoader 会自动处理张量批处理
+
+def train_model(model, train_loader, test_loader, num_epochs=10, learning_rate=0.001):
+    criterion = nn.CrossEntropyLoss()
+    optimizer = optim.Adam(model.parameters(), lr=learning_rate)
+    
+    model.to(device)
+    model.train()
+    
+    train_losses = []
+    train_accuracies = []
+    test_accuracies = []
+    
+    for epoch in range(num_epochs):
+        epoch_start_time = time.time()
+        running_loss = 0.0
+        correct_train = 0
+        total_train = 0
+        
+        for batch_idx, batch in enumerate(train_loader):
+            # batch 是一个字典：{'image': tensor, 'label': tensor}
+            images = batch['image'].to(device)
+            labels = batch['label'].to(device)
+            
+            # 确保图像是正确的形状 (B, 1, 28, 28)
+            if images.dim() == 3:
+                images = images.unsqueeze(1)  # 添加通道维度
+            
+            outputs = model(images)
+            loss = criterion(outputs, labels)
+            
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+            
+            running_loss += loss.item()
+            _, predicted = torch.max(outputs.data, 1)
+            total_train += labels.size(0)
+            correct_train += (predicted == labels).sum().item()
+            
+            if batch_idx % 100 == 0:
+                print(f'Epoch [{epoch+1}/{num_epochs}], Step [{batch_idx}/{len(train_loader)}], '
+                      f'Loss: {loss.item():.4f}')
+        
+        train_accuracy = 100 * correct_train / total_train
+        avg_loss = running_loss / len(train_loader)
+        test_accuracy = evaluate_model(model, test_loader)
+        epoch_time = time.time() - epoch_start_time
+        
+        train_losses.append(avg_loss)
+        train_accuracies.append(train_accuracy)
+        test_accuracies.append(test_accuracy)
+        
+        print(f'Epoch [{epoch+1}/{num_epochs}] completed in {epoch_time:.2f}s')
+        print(f'Train Loss: {avg_loss:.4f}, Train Accuracy: {train_accuracy:.2f}%, '
+              f'Test Accuracy: {test_accuracy:.2f}%')
+        print('-' * 60)
+    
+    return train_losses, train_accuracies, test_accuracies
+
+def evaluate_model(model, test_loader):
+    model.eval()
+    correct = 0
+    total = 0
+    
+    with torch.no_grad():
+        for batch in test_loader:
+            images = batch['image'].to(device)
+            labels = batch['label'].to(device)
+            
+            if images.dim() == 3:
+                images = images.unsqueeze(1)
+                
+            outputs = model(images)
+            _, predicted = torch.max(outputs.data, 1)
+            total += labels.size(0)
+            correct += (predicted == labels).sum().item()
+    
+    model.train()
+    return 100 * correct / total
+
+def main():
+    dataset = load_dataset('parquet', data_files={
+        'train': r"D:\AiData\Dataset\mnist\mnist\train-00000-of-00001.parquet",
+        'test': r"D:\AiData\Dataset\mnist\mnist\test-00000-of-00001.parquet",
+    })
+    
+    print("Preprocessing training data...")
+    train_dataset = preprocess_data(dataset['train'])
+    print("Preprocessing test data...")
+    test_dataset = preprocess_data(dataset['test'])
+    
+    batch_size = 64
+    train_loader = DataLoader(
+        train_dataset, 
+        batch_size=batch_size, 
+        shuffle=True,
+        num_workers=0
+    )
+    test_loader = DataLoader(
+        test_dataset, 
+        batch_size=batch_size, 
+        shuffle=False,
+        num_workers=0
+    )
+    
+    model = SimpleMLP()
+    print(f"Model: {model}")
+    print(f"Total parameters: {sum(p.numel() for p in model.parameters()):,}")
+    
+    print("\nStarting training...")
+    train_losses, train_accuracies, test_accuracies = train_model(
+        model, train_loader, test_loader, num_epochs=10, learning_rate=0.001
+    )
+    
+    # 绘制训练曲线
+    plt.figure(figsize=(15, 5))
+    plt.subplot(1, 3, 1)
+    plt.plot(train_losses)
+    plt.title('Training Loss')
+    plt.xlabel('Epoch')
+    plt.ylabel('Loss')
+    
+    plt.subplot(1, 3, 2)
+    plt.plot(train_accuracies, label='Train')
+    plt.plot(test_accuracies, label='Test')
+    plt.title('Accuracy')
+    plt.xlabel('Epoch')
+    plt.ylabel('Accuracy (%)')
+    plt.legend()
+    
+    plt.subplot(1, 3, 3)
+    plt.plot(train_accuracies, 'b-', label='Train Accuracy')
+    plt.plot(test_accuracies, 'r--', label='Test Accuracy')
+    plt.title('Train vs Test Accuracy')
+    plt.xlabel('Epoch')
+    plt.ylabel('Accuracy (%)')
+    plt.legend()
+    
+    plt.tight_layout()
+    plt.show()
+    
+    final_test_acc = evaluate_model(model, test_loader)
+    print(f"\nFinal Test Accuracy: {final_test_acc:.2f}%")
+
+if __name__ == "__main__":
+    main()

mnist/pyproject.toml

@@ -0,0 +1,11 @@
+[project]
+name = "mnist"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.11"
+dependencies = [
+    "datasets>=4.3.0",
+    "matplotlib>=3.10.7",
+    "numpy>=2.3.4",
+]

mv-and-ip/car_plate.py

@@ -47,8 +47,12 @@ def _uniform_car_plate(img: cv.typing.MatLike) -> cv.typing.MatLike:
 
 @dataclass
 class CarPlateHsvBoundary:
+    """HSV boundary for car plate color detection."""
+
     lower_bound: cv.typing.MatLike
+    """Lower bound of HSV range for car plate color detection."""
     upper_bound: cv.typing.MatLike
+    """Upper bound of HSV range for car plate color detection."""
     need_revert: bool
     """是否取反黑白颜色，因为蓝牌和黄牌的操作正好是反的"""
 
@@ -65,14 +69,24 @@ CAR_PLATE_HSV_BOUNDARIES: tuple[CarPlateHsvBoundary, ...] = (
 
 @dataclass
 class CarPlateMask:
+    """Car plate mask result."""
+
     mask: cv.typing.MatLike
+    """The masked image in U8 format."""
     need_revert: bool
     """是否对颜色取反，与CarPlateHsvBoundary中的同名字段含义一致"""
 
+
 def _batchly_mask_car_plate(
     hsv: cv.typing.MatLike,
 ) -> typing.Iterator[CarPlateMask]:
-    """ """
+    """
+    Iterate over each car plate HSV boundary and apply mask to the given HSV image.
+
+    :param hsv: The HSV image to apply mask.
+    :return: An iterator of CarPlateMask.
+    """
+
     for boundary in CAR_PLATE_HSV_BOUNDARIES:
         # 以给定HSV范围检测符合该颜色的位置
         mask = cv.inRange(hsv, boundary.lower_bound, boundary.upper_bound)
@@ -89,6 +103,8 @@ def _batchly_mask_car_plate(
 
 @dataclass
 class CarPlateRegion:
+    """Car plate region result."""
+
     x: int
     y: int
     w: int
@@ -98,32 +114,46 @@ class CarPlateRegion:
 
 
 MIN_AREA: float = 3000
+"""Minimum area for car plate region."""
 MIN_ASPECT_RATIO: float = 1.5
+"""Minimum aspect ratio for car plate region."""
 MAX_ASPECT_RATIO: float = 6.0
+"""Maximum aspect ratio for car plate region."""
 BEST_ASPECT_RATIO: float = 3.5
+"""Best aspect ratio for car plate region."""
 
 
 def _analyse_car_plate_connection(
-    mask: CarPlateMask,
+    masks: typing.Iterator[CarPlateMask],
 ) -> typing.Optional[CarPlateRegion]:
-    # 连通域分析，筛选最符合车牌长宽比的区域
+    """
-    num_labels, labels, stats, _ = cv.connectedComponentsWithStats(mask.mask, connectivity=8)
+    Analyse car plate connection in given masks.
+
+    :param masks: An iterator of CarPlateMask to analyse.
+    :return: The car plate region if succeed, otherwise None.
+    """
 
     best: typing.Optional[CarPlateRegion] = None
     best_score = 0
 
-    for i in range(1, num_labels):
+    for mask in masks:
-        x, y, w, h, area = stats[i]
+        # 连通域分析，筛选最符合车牌长宽比的区域
-        # 检查面积
+        num_labels, labels, stats, _ = cv.connectedComponentsWithStats(
-        if area < MIN_AREA:
+            mask.mask, connectivity=8
-            continue
+        )
-        # 标准车牌宽高比约 3:1 ~ 5:1
+
-        ratio = w / (h + 1e-5)
+        for i in range(1, num_labels):
-        if ratio >= MIN_ASPECT_RATIO and ratio <= MAX_ASPECT_RATIO:
+            x, y, w, h, area = stats[i]
-            score = area * (1 - abs(ratio - BEST_ASPECT_RATIO) / BEST_ASPECT_RATIO)
+            # 检查面积
-            if score > best_score:
+            if area < MIN_AREA:
-                best_score = score
+                continue
-                best = CarPlateRegion(x, y, w, h, mask.need_revert)
+            # 标准车牌宽高比约 3:1 ~ 5:1
+            ratio = w / (h + 1e-5)
+            if ratio >= MIN_ASPECT_RATIO and ratio <= MAX_ASPECT_RATIO:
+                score = area * (1 - abs(ratio - BEST_ASPECT_RATIO) / BEST_ASPECT_RATIO)
+                if score > best_score:
+                    best_score = score
+                    best = CarPlateRegion(x, y, w, h, mask.need_revert)
 
     return best
 
@@ -135,20 +165,15 @@ def extract_car_plate(img: cv.typing.MatLike) -> typing.Optional[cv.typing.MatLi
     :param img: The image containing car plate in BGR format.
     :return: The image of binary car plate in U8 format if succeed, otherwise None.
     """
+    # 统一图片大小
     img = _uniform_car_plate(img)
 
     # 转换到HSV空间
     hsv = cv.cvtColor(img, cv.COLOR_BGR2HSV)
 
     # 利用车牌颜色在 HSV 空间定位车牌
-    candidate: typing.Optional[CarPlateRegion] = None
+    masks = _batchly_mask_car_plate(hsv)
-    for mask in _batchly_mask_car_plate(hsv):
+    candidate = _analyse_car_plate_connection(masks)
-        # 连通域分析，筛选最符合车牌长宽比的区域作为车牌
-        candidate = _analyse_car_plate_connection(mask)
-        # 找到任意一个就退出
-        if candidate is not None:
-            break
-
     if candidate is None:
         logging.error("Can not find any car plate.")
         return None