NumPy Mini Project

 Import the required libraries:

• Library: PIL (Pillow) for loading and saving the image.
• Library: numpy for processing the image data.

 Load the image as a pixel matrix using Pillow:

• Function: Image.open("image_path")
• Convert to an array: Use np.array(image) to store pixel values in a NumPy array.

 Extract the R, G, and B channels from the NumPy array:

• If the image is in RGB format, the NumPy array will have a shape of (height, width, 3), where the last dimension corresponds to the three color channels.
• Extract individual channels:
  • R = image_array[:, :, 0] (Red channel)
  • G = image_array[:, :, 1] (Green channel)
  • B = image_array[:, :, 2] (Blue channel)

 Process the pixel data to grayscale manually:

• Apply the grayscale conversion formula:

Gray=0.2989R+0.5870G+0.1140B

• Use NumPy operations to apply the formula element-wise across the image array.

 Convert the processed grayscale array back to an image:

• Ensure the grayscale array is converted to uint8 type.
• Function: Image.fromarray(grayscale_array, mode="L") to convert it back to a grayscale image.

 Save or display the grayscale image using Pillow:

• Function: .save("output_path") to save the image.