Create README.md

Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com>

pytorch/augmentation/README.md

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+
+
+Initial Image: 
+
+![Initial Image](data_augmentation_test_image.png)
+
+```python
+import cv2
+import matplotlib.pyplot as plt
+
+filename: str = "data_augmentation_test_image.jpg"
+
+original_image = cv2.imread(filename)
+
+plt.imshow(original_image)
+plt.show()
+```
+
+![image0](image0.png)
+
+
+```python
+original_image = cv2.imread(filename, cv2.IMREAD_GRAYSCALE)
+
+plt.imshow(original_image, cmap="gray")
+plt.show()
+```
+
+![image1](image1.png)
+
+
+```python
+import numpy as np
+
+original_image = cv2.imread(filename, cv2.IMREAD_COLOR)
+
+# "Convert" from BlueGreenRed (BGR) to RGB (RedGreenBlue)
+# This is a flip in the third dimension.
+original_image = np.flip(original_image, axis=2)
+plt.imshow(original_image)
+plt.show()
+```
+
+### Into PyTorch
+
+![image2](image2.png)
+
+```python
+import torch
+
+torch_image = torch.tensor(
+    np.moveaxis(original_image.astype(dtype=np.float32) / 255.0, 2, 0)
+)
+print(torch_image.shape) # -> torch.Size([3, 1200, 1600])
+```
+
+### Pad 
+
+```python
+import torchvision as tv
+
+pad_transform = tv.transforms.Pad(padding=(50, 100), fill=0.5)
+new_image = pad_transform(torch_image)
+plt.imshow(np.moveaxis(new_image.detach().numpy(), 0, 2))
+plt.show()
+```
+
+![image3](image3.png)
+
+
+### Resize
+
+```python
+resize_transform = tv.transforms.Resize(size=(50, 100))
+new_image = resize_transform(torch_image)
+plt.imshow(np.moveaxis(new_image.detach().numpy(), 0, 2))
+plt.show()
+```
+
+![image4](image4.png)
+
+### CenterCrop
+
+```python
+center_crop_transform = tv.transforms.CenterCrop(size=(250, 200))
+new_image = center_crop_transform(torch_image)
+plt.imshow(np.moveaxis(new_image.detach().numpy(), 0, 2))
+plt.show()
+```
+
+![image5](image5.png)
+
+### FiveCrop
+
+```python
+position = (1, 3, 7, 9, 5)
+five_crop_transform = tv.transforms.FiveCrop(size=(250, 200))
+new_image = five_crop_transform(torch_image)
+
+for i, p in enumerate(position):
+    plt.subplot(3, 3, p)
+    plt.imshow(np.moveaxis(new_image[i].detach().numpy(), 0, 2))
+
+plt.show()
+```
+
+![image6](image6.png)
+
+
+### Grayscale
+
+```python
+gray_transform = tv.transforms.Grayscale()
+new_image = gray_transform(torch_image)
+plt.imshow(new_image.squeeze().detach().numpy(), cmap="gray")
+plt.show()
+```
+
+![image7](image7.png)
+
+
+### ColorJitter
+
+```python
+color_jitter_transform = tv.transforms.ColorJitter(brightness=0.75, hue=0.5)
+for i in range(1, 10):
+    new_image = color_jitter_transform(torch_image)
+    plt.subplot(3, 3, i)
+    plt.imshow(np.moveaxis(new_image.detach().numpy(), 0, 2))
+plt.show()
+```
+
+![image8](image8.png)
+
+
+### Gaussian Blur
+
+```python
+gauss_transform = tv.transforms.GaussianBlur(kernel_size=(101, 101), sigma=(0.1, 10))
+new_image = gauss_transform(torch_image)
+plt.imshow(np.moveaxis(new_image.detach().numpy(), 0, 2))
+plt.show()
+```
+
+![image9](image9.png)
+
+### Random Perspective
+
+```python
+random_perspective_transform = tv.transforms.RandomPerspective(
+    distortion_scale=0.6, p=1.0
+)
+for i in range(1, 10):
+    new_image = random_perspective_transform(torch_image)
+    plt.subplot(3, 3, i)
+    plt.imshow(np.moveaxis(new_image.detach().numpy(), 0, 2))
+plt.show()
+```
+
+![image10](image10.png)
+
+```python
+
+```
+
+![image](image.png)
+
+```python
+
+```
+
+![image](image.png)
+
+
+```python
+
+```
+
+![image](image.png)
+
+
+```python
+
+```
+
+![image](image.png)
+
+
+```python
+
+```
+
+![image](image.png)
+
+
+```python
+
+```
+
+![image](image.png)
+
+# --------------
+
+```python
+
+```
+
+![image](image.png)