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USAGE.md

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+# How to use the remove function
+
+## Load the Image
+```python
+from PIL import Image
+from rembg import new_session, remove
+
+input_path = 'input.png'
+output_path = 'output.png'
+
+input = Image.open(input_path)
+```
+## Removing the background
+
+### Without additional arguments
+This defaults to the `u2net` model.
+```python
+output = remove(input)
+output.save(output_path)
+```
+
+### With a specific model
+You can use the `new_session` function to create a session with a specific model.
+```python
+model_name = "isnet-general-use"
+session = new_session(model_name)
+output = session.remove(input, session=session)
+```
+
+### With alpha metting
+Alpha metting is a post processing step that can be used to improve the quality of the output.
+```python
+output = remove(input, alpha_matting=True, alpha_matting_foreground_threshold=270,alpha_matting_background_threshold=20, alpha_matting_erode_size=11)
+```
+
+### Only mask
+If you only want the mask, you can use the `only_mask` argument.
+```python
+output = remove(input, only_mask=True)
+```
+
+### With post processing
+You can use the `post_process_mask` argument to post process the mask to get better results.
+```python
+output = remove(input, post_process_mask=True)
+```
+
+### Replacing the background color
+You can use the `bgcolor` argument to replace the background color.
+```python
+output = remove(input, bgcolor=(255, 255, 255))
+```
+
+### Using input points
+You can use the `input_point` and `input_label` argument to specify the points that should be used for the masks. This only works with the `sam` model.
+```python
+import numpy as np
+# Define the points and labels
+# The points are defined as [y, x]
+input_point = np.array([[400, 350], [700, 400], [200, 400]])
+input_label = np.array([1, 1, 2])
+
+image = remove(image,session=session, input_point=input_point, input_label=input_label)
+```
+
+## Save the image
+```python
+output.save(output_path)
+```
+
+