test

README.md

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-# written_number_classfication
-

识别/.idea/.gitignore

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+# 默认忽略的文件
+/shelf/
+/workspace.xml
+# 基于编辑器的 HTTP 客户端请求
+/httpRequests/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml

识别/.idea/inspectionProfiles/Project_Default.xml

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+<component name="InspectionProjectProfileManager">
+  <profile version="1.0">
+    <option name="myName" value="Project Default" />
+    <inspection_tool class="PyUnresolvedReferencesInspection" enabled="true" level="WARNING" enabled_by_default="true">
+      <option name="ignoredIdentifiers">
+        <list>
+          <option value="mybkauth.views.conn" />
+        </list>
+      </option>
+    </inspection_tool>
+  </profile>
+</component>

识别/.idea/inspectionProfiles/profiles_settings.xml

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+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

识别/.idea/misc.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="Black">
+    <option name="sdkName" value="C:\Users\28326\anaconda3" />
+  </component>
+  <component name="ProjectRootManager" version="2" project-jdk-name="C:\Users\28326\anaconda3" project-jdk-type="Python SDK" />
+</project>

识别/.idea/modules.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/识别.iml" filepath="$PROJECT_DIR$/.idea/识别.iml" />
+    </modules>
+  </component>
+</project>

识别/.idea/识别.iml

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+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="jdk" jdkName="C:\Users\28326\anaconda3" jdkType="Python SDK" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

识别/another-main.py

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+import sys
+import numpy as np
+import torch
+from PIL import Image, ImageQt
+from PyQt5.QtWidgets import QApplication, QMainWindow, QVBoxLayout, QWidget, QPushButton, QLabel
+from PyQt5.QtGui import QPainter, QPen, QImage, QPixmap, QColor
+from PyQt5.QtCore import Qt, QPoint
+from mm import CNN
+
+
+class PaintBoard(QWidget):
+    def __init__(self):
+        super().__init__()
+        self.image = QImage(224, 224, QImage.Format_RGB32)
+        self.image.fill(Qt.black)
+        self.last_point = QPoint()
+        self.initUI()
+
+    def initUI(self):
+        self.setGeometry(0, 0, 224, 224)
+        self.setFixedSize(224, 224)
+        self.setStyleSheet("background-color: black;")
+
+    def clear(self):
+        self.image.fill(Qt.black)
+        self.update()
+
+    def getContentAsQImage(self):
+        return self.image
+
+    def mousePressEvent(self, event):
+        if event.button() == Qt.LeftButton:
+            self.last_point = event.pos()
+
+    def mouseMoveEvent(self, event):
+        if event.buttons() & Qt.LeftButton:
+            painter = QPainter(self.image)
+            pen = QPen(Qt.white, 20, Qt.SolidLine, Qt.RoundCap, Qt.RoundJoin)
+            painter.setPen(pen)
+            painter.drawLine(self.last_point, event.pos())
+            self.last_point = event.pos()
+            self.update()
+
+    def paintEvent(self, event):
+        canvas_painter = QPainter(self)
+        canvas_painter.drawImage(self.rect(), self.image, self.image.rect())
+
+
+class MainWindow(QMainWindow):
+    def __init__(self, model):
+        super().__init__()
+        self.model = model
+        self.model.eval()
+
+        self.setWindowTitle("Handwritten Digit Recognition")
+        self.setGeometry(100, 100, 300, 400)
+        self.paint_board = PaintBoard()
+        self.result_label = QLabel("Prediction: ", self)
+        self.result_label.setGeometry(50, 250, 200, 50)
+        self.result_label.setStyleSheet("font-size: 18px;")
+
+        clear_button = QPushButton("Clear", self)
+        clear_button.setGeometry(50, 300, 100, 50)
+        clear_button.clicked.connect(self.paint_board.clear)
+
+        predict_button = QPushButton("Predict", self)
+        predict_button.setGeometry(150, 300, 100, 50)
+        predict_button.clicked.connect(self.predict)
+
+        #创建垂直布局管理器，把组件放到布局里展示出来
+        layout = QVBoxLayout()
+        layout.addWidget(self.paint_board)
+        layout.addWidget(self.result_label)
+        layout.addWidget(clear_button)
+        layout.addWidget(predict_button)
+        #创建一个QWidget对象作为主容器，然后将垂直布局管理器放在这个主容器中
+        container = QWidget()
+        container.setLayout(layout)
+        self.setCentralWidget(container)
+
+    def predict(self):
+        try:
+            print("Starting prediction...")
+            image = self.paint_board.getContentAsQImage()
+            pil_image = ImageQt.fromqimage(image)
+            pil_image = pil_image.resize((28, 28), Image.ANTIALIAS)
+            pil_image = pil_image.convert('L')
+
+            img_array = np.array(pil_image).reshape(1, 1, 28, 28).astype(np.float32) / 255.0
+            img_tensor = torch.tensor(img_array).to(device)
+
+            with torch.no_grad():
+                output = self.model(img_tensor)
+                prediction = torch.argmax(output, dim=1).item()
+
+            self.result_label.setText(f"Prediction: {prediction}")
+            print(f"Prediction: {prediction}")
+        except Exception as e:
+            print(f"Error during prediction: {e}")
+
+
+if __name__ == "__main__":
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model = CNN().to(device)
+    model.load_state_dict(torch.load('model.pth', map_location=device))
+
+    app = QApplication(sys.argv)
+    main_window = MainWindow(model)
+    main_window.show()
+    sys.exit(app.exec_())

识别/basic.py

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+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torch.nn.functional as F
+import torchvision
+import matplotlib.pyplot as plt
+import numpy as np
+
+input_size = 28
+num_classes = 10
+num_epochs = 40
+batch_size = 64
+
+# 确保 CUDA 可用并且使用它
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+train_dataset = torchvision.datasets.MNIST(
+    root='./data', train=True, transform=torchvision.transforms.ToTensor(), download=True
+)
+test_dataset = torchvision.datasets.MNIST(root='./data', train=False, transform=torchvision.transforms.ToTensor())
+
+train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
+                                            batch_size=batch_size,
+                                            shuffle=True)
+test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
+                                           batch_size=batch_size,
+                                           shuffle=True)
+
+#定义了一个CNN类作为神经网络模型
+# in_channels：输入数据的通道数。在第一层卷积中，输入的MNIST图像是灰度图像，因此 in_channels=1。
+# out_channels：输出的特征图通道数，即卷积核的数量。每个卷积核生成一个特征图。
+# kernel_size：卷积核的大小。例如，kernel_size=5 表示卷积核大小为5x5。
+# stride：卷积核的步幅。在代码中设置为1，表示卷积核每次移动1个像素。
+# padding：填充的像素数。在代码中设置为2，表示在输入数据周围填充2个像素。
+class CNN(nn.Module):
+    def __init__(self):
+        super(CNN, self).__init__()
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(1, 16, 5, 1, 2),
+            nn.ReLU(),
+            nn.MaxPool2d(2)
+        )
+        self.conv2 = nn.Sequential(
+            nn.Conv2d(16, 32, 5, 1, 2),
+            nn.ReLU(),
+            nn.MaxPool2d(2)
+        )
+        self.conv3 = nn.Sequential(
+            nn.Conv2d(32, 64, 5, 1, 2),
+            nn.ReLU(),
+            nn.MaxPool2d(2)
+        )
+        self.conv4 = nn.Sequential(
+            nn.Conv2d(64, 32, 5, 1, 2),
+            nn.ReLU(),
+            nn.MaxPool2d(2)
+        )
+        self.out = nn.Linear(32, num_classes)  # 确保输出类别数正确
+
+    def forward(self, x):
+        x = self.conv1(x).to(device)  # 将数据迁移到GPU
+        x = self.conv2(x).to(device)
+        x=self.conv3(x).to(device)
+        x = self.conv4(x).to(device)
+        x = x.view(x.size(0), -1).to(device)
+        x = self.out(x).to(device)
+        return x
+
+def accuracy(prediction, label):
+    pred = torch.max(prediction.data, 1)[1]
+    rights = pred.eq(label.data.view_as(pred)).sum()
+    return rights, len(label)
+
+net = CNN().to(device)  # 将模型迁移到GPU
+criterion = nn.CrossEntropyLoss().to(device)  # 将损失函数迁移到GPU
+optimizer = optim.Adam(net.parameters(), lr=0.001)
+
+tr = []
+for epoch in range(40):
+    train_rights = []
+    for batch_id, (data, target) in enumerate(train_loader):
+        data, target = data.to(device), target.to(device)  # 将数据迁移到GPU
+        optimizer.zero_grad()
+        output = net(data)
+        loss = criterion(output, target)
+        loss.backward()
+        optimizer.step()
+        right = accuracy(output, target)
+        train_rights.append(right)
+        tr.append(loss)
+        if batch_id % 100 == 0:
+            val_right = []
+            for (data, target) in test_loader:
+                data, target = data.to(device), target.to(device)  # 将数据迁移到GPU
+                output = net(data)
+                right = accuracy(output, target)
+                val_right.append(right)
+            train_r = (sum(t[0] for t in train_rights), sum(t[1] for t in train_rights))
+            val_r = (sum(t[0] for t in val_right), sum(t[1] for t in val_right))
+            print("train_acc {}, test_acc {}".format(train_r[0] / train_r[1], val_r[0] / val_r[1]))
+
+# 注意，绘图时不需要将数据迁移到GPU
+plt.plot(list(range(len([tensor.detach().cpu().numpy() for tensor in tr]))), [tensor.detach().cpu().numpy() for tensor in tr])
+plt.show()
+torch.save(net.state_dict(), 'model.pth')

识别/mm.py

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+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torch.nn.functional as F
+import torchvision
+import matplotlib.pyplot as plt
+import numpy as np
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+class CNN(nn.Module):
+    def __init__(self):
+        super(CNN, self).__init__()
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(1, 16, 5, 1, 2),
+            nn.ReLU(),
+            nn.MaxPool2d(2)
+        )
+        self.conv2 = nn.Sequential(
+            nn.Conv2d(16, 32, 5, 1, 2),
+            nn.ReLU(),
+            nn.MaxPool2d(2)
+        )
+        self.conv3 = nn.Sequential(
+            nn.Conv2d(32, 64, 5, 1, 2),
+            nn.ReLU(),
+            nn.MaxPool2d(2)
+        )
+        self.conv4 = nn.Sequential(
+            nn.Conv2d(64, 32, 5, 1, 2),
+            nn.ReLU(),
+            nn.MaxPool2d(2)
+        )
+        self.out = nn.Linear(32, 10)  # 确保输出类别数正确
+
+    def forward(self, x):
+        x = self.conv1(x).to(device)  # 将数据迁移到GPU
+        x = self.conv2(x).to(device)
+        x=self.conv3(x).to(device)
+        x = self.conv4(x).to(device)
+        x = x.view(x.size(0), -1).to(device)
+        x = self.out(x).to(device)
+        return x

识别/requirements.txt

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+certifi==2024.6.2
+charset-normalizer==3.3.2
+contourpy==1.2.1
+cycler==0.12.1
+filelock==3.15.1
+fonttools==4.53.0
+idna==3.7
+Jinja2==3.1.4
+kiwisolver==1.4.5
+MarkupSafe==2.1.5
+matplotlib==3.6.3
+mpmath==1.3.0
+networkx==3.2.1
+numpy==1.22.4
+packaging==24.1
+Pillow==9.1.0
+pyparsing==3.1.2
+PyQt5==5.15.6
+PyQt5-Qt5==5.15.2
+PyQt5-sip==12.13.0
+python-dateutil==2.9.0.post0
+requests==2.32.3
+six==1.16.0
+sympy==1.12.1
+torch==1.13.1
+torchvision==0.14.0
+typing_extensions==4.12.2
+urllib3==2.2.1