Basically, what this does is take an image (let’s call it “input_image”) and turns it into a segmented version of itself with labels assigned to each pixel based on its category (like “cat” or “dog”).
Here’s how it works: first, we load the input image using some fancy Python code that I won’t bore you with. Then, we pass this image through a pretrained deep learning model called DPT (which stands for Deep Pixel Transformer) to generate a set of feature maps at different scales and resolutions.
Next, we apply a segmentation head on top of these feature maps using another fancy neural network architecture that I won’t bore you with either. This segmentation head takes the input features from DPT and outputs a probability distribution over each pixel in the image for every possible category (like “cat” or “dog”).
Finally, we apply some postprocessing steps to clean up any noisy predictions and generate a final output segmentation map that assigns labels to each pixel based on its highest predicted probability. You’ve got yourself a fancy semantic segmentation result for your input image.
Here’s an example of what this might look like in code:
# Import the necessary libraries
from dpt_image_processor import DPTImageProcessor # Import the DPTImageProcessor class from the dpt_image_processor library
import numpy as np # Import the numpy library and alias it as np
import matplotlib.pyplot as plt # Import the pyplot module from the matplotlib library and alias it as plt
# Load the input image and convert it to a NumPy array for processing
input_img = np.load('path/to/your/input/image') # Load the input image as a NumPy array and assign it to the variable input_img
# Create an instance of our fancy DPTImageProcessor class
processor = DPTImageProcessor() # Create an instance of the DPTImageProcessor class and assign it to the variable processor
# Run the segmentation process on the input image and get the output segmentation map
output_map, _ = processor.process(input_img) # Call the process method of the processor instance on the input image and assign the output segmentation map to the variable output_map
# Display the original input image alongside its corresponding segmentation result using Matplotlib
fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(8, 4)) # Create a figure with two subplots and assign them to the variables ax1 and ax2
ax1.imshow(np.uint8(input_img * 255), cmap='gray') # Display the input image on the first subplot, converting it to uint8 and multiplying it by 255 to convert it to grayscale
ax1.set(title="Input Image") # Set the title of the first subplot to "Input Image"
ax2.imshow(output_map) # Display the output segmentation map on the second subplot
ax2.set(title="Segmentation Result") # Set the title of the second subplot to "Segmentation Result"
plt.show() # Display the figure with the two subplots
And that’s it! You now have a fancy semantic segmentation result for your input image using our DPTImageProcessor class.