continueing data prep pipelien

.ipynb_checkpoints/CONTRIBUTING-checkpoint.md

@@ -0,0 +1,56 @@
+# Contributing to headshot
+
+Thank you for considering contributing to headshot! Please take a moment to review the following guidelines.
+
+## Code of Conduct
+
+This project and everyone participating in it are governed by the [Code of Conduct](CODE_OF_CONDUCT.md). By participating, you agree to uphold this code. Please report unacceptable behavior to [your email or a dedicated email for issues].
+
+## How to Contribute
+
+1. Fork the repository.
+
+2. Clone the forked repository to your local machine:
+
+   ```bash
+   git clone https://github.com/zaibutcooler/headshot.git
+   ```
+
+3. Create a new branch for your feature or bug fix:
+
+   ```bash
+   git checkout -b feature-name
+   ```
+
+4. Make your changes and commit them with a descriptive commit message:
+
+   ```bash
+   git add .
+   git commit -m "Add your descriptive message here"
+   ```
+
+5. Push the changes to your fork:
+
+   ```bash
+   git push origin feature-name
+   ```
+
+6. Create a pull request (PR) from your fork to the main repository.
+
+7. Ensure your PR title and description are clear and concise.
+
+## Reporting Issues
+
+If you find any issues or have suggestions, please open an issue on the [Issue Tracker](https://github.com/zaibutcooler/headshot/issues).
+
+## Style Guide
+
+- Follow the existing coding style.
+- Use meaningful variable and function names.
+- Write clear and concise documentation.
+
+## License
+
+By contributing, you agree that your contributions will be licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
+
+Thank you for contributing to headshot!

.ipynb_checkpoints/training-checkpoint.py

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+num_epochs = 30
+out_dir = 'out'

headshot/.ipynb_checkpoints/data_prep-checkpoint.py

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+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.utils.data import DataLoader, Dataset
+from torchvision import transforms, datasets
+
+data_url = ''
+
+
+class FaceDataset(Dataset):
+    def __init__(self,data,labels,transforms=None):
+        self.tranforms = tranforms
+        self.data = x_data
+        self.labels = y_labels
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, idx):
+        # Load and preprocess the image at the given index
+        image = self.data[idx]
+        label = self.labels[idx]
+        
+        if self.transform:
+            image = self.transform(image)
+        return image,label

headshot/.ipynb_checkpoints/headshot-checkpoint.py

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+import torch
+from torch import nn
+
+class Headshot(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self,x):
+        pass

headshot/.ipynb_checkpoints/robot-checkpoint.py

@@ -0,0 +1 @@
+# to connect with some physical machine

headshot/.ipynb_checkpoints/utils-checkpoint.py

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+import matplotlib.pyplot as plt
+
+def display_img(image):
+    pass

headshot/data_prep.py

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+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.utils.data import DataLoader, Dataset
+from torchvision import transforms, datasets
+
+data_url = ''
+
+
+class FaceDataset(Dataset):
+    def __init__(self,data,labels,transforms=None):
+        self.tranforms = tranforms
+        self.data = x_data
+        self.labels = y_labels
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, idx):
+        # Load and preprocess the image at the given index
+        image = self.data[idx]
+        label = self.labels[idx]
+        
+        if self.transform:
+            image = self.transform(image)
+        return image,label

headshot/robot.py

@@ -0,0 +1 @@
+# to connect with some physical machine

headshot/utils.py

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+import matplotlib.pyplot as plt
+
+def display_img(image):
+    pass

notebooks/.ipynb_checkpoints/detection_pytorch-checkpoint.ipynb

@@ -154,9 +154,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# eval model\n",
-    "def evaluate():\n",
-    "    pass"
+    "# eval model\n"
    ]
   },
   {

notebooks/detection_pytorch.ipynb

@@ -154,9 +154,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# eval model\n",
-    "def evaluate():\n",
-    "    pass"
+    "# eval model\n"
    ]
   },
   {

training.py

@@ -1 +1,2 @@
-num_epochs=30
+num_epochs = 30
+out_dir = 'out'