added cnn with tests and tensoboard

.gitignore

@@ -64,3 +64,6 @@ Thumbs.db.meta
 
 data/*
 venv/*
+new_data/*
+cnn/runs/*
+cnn/new_data/*

cnn/main.py

@@ -0,0 +1,113 @@
+import os
+import cv2
+from torch.utils.data import DataLoader, Dataset
+from torch.utils.data import random_split
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+import torchvision
+
+class TreesDataset(Dataset):
+    def __init__(self, data_links) -> None:
+        self.X, self.Y = readData(data_links)
+
+    def __len__(self):
+        return len(self.X)
+
+    def __getitem__(self, index):
+        return (self.X[index], self.Y[index])
+
+
+class Net(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.conv1 = nn.Conv2d(3, 6, 5)
+        self.pool = nn.MaxPool2d(2, 2)
+        self.conv2 = nn.Conv2d(6, 16, 5)
+        self.fc1 = nn.Linear(3264, 120)
+        self.fc2 = nn.Linear(120, 84)
+        self.fc3 = nn.Linear(84, 10)
+
+    def forward(self, x):
+        x = self.pool(F.relu(self.conv1(x)))
+        x = self.pool(F.relu(self.conv2(x)))
+        x = torch.flatten(x, 1)
+        x = F.relu(self.fc1(x))
+        x = F.relu(self.fc2(x))
+        x = self.fc3(x)
+        return x
+
+def create_datalinks(root_dir):
+    data_links = os.listdir(root_dir)
+    data_links = [root_dir + "/" + x for x in data_links]
+    return data_links
+
+def preprocess(img):
+    scale_percent = 10
+    width = int(img.shape[1] * scale_percent / 100)
+    height = int(img.shape[0] * scale_percent / 100)
+    dim = (width, height)
+    resized = cv2.resize(img, dim, interpolation = cv2.INTER_AREA)
+    resized = torchvision.transforms.functional.to_tensor(resized)
+    return resized
+
+def readData(data_links):
+    x, y = [], []
+    for link in data_links:
+        img = cv2.imread(link, cv2.IMREAD_COLOR)
+        img = preprocess(img)
+        if("ground" in link):
+            label = 1
+        elif("AS12" in link):
+            label = 0
+        x.append(img)
+        y.append(label)
+
+    return x, y
+
+links_3_plus_ground = create_datalinks("new_data/AS12_3") + create_datalinks("new_data/ground")
+
+dataset = TreesDataset(links_3_plus_ground)
+
+train_set, test_set = random_split(dataset, [300, 50], generator=torch.Generator().manual_seed(42))
+
+trainloader = DataLoader(train_set, batch_size=10, shuffle=True, num_workers=2)
+testloader = DataLoader(test_set, batch_size=10, shuffle=True, num_workers=2)
+
+classes = ('tree', 'ground')
+epochs_num = 15
+
+net = Net()
+criterion = nn.CrossEntropyLoss()
+optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
+
+for epoch in range(epochs_num):
+
+    running_loss = 0.0
+    for i, data in enumerate(trainloader, 0):
+        inputs, labels = data
+        optimizer.zero_grad()
+        outputs = net(inputs)
+        loss = criterion(outputs, labels)
+        loss.backward()
+        optimizer.step()
+        running_loss += loss.item()
+        if i % 10 == 0:  
+            print('[%d, %5d] loss: %.3f' %
+                  (epoch + 1, i + 1, running_loss / 10))
+            running_loss = 0.0
+
+print('Finished Training')
+
+correct = 0
+total = 0
+with torch.no_grad():
+    for data in testloader:
+        images, labels = data
+        outputs = net(images)
+        _, predicted = torch.max(outputs.data, 1)
+        total += labels.size(0)
+        correct += (predicted == labels).sum().item()
+
+print('Accuracy : %d %%' % (100 * correct / total))