app.py

from torch import nn
import numpy as np
from PIL import Image 
import gradio as gr 
from transformers import AutoFeatureExtractor, SegformerForSemanticSegmentation

def ade_palette():
    """ADE20K palette that maps each class to RGB values."""
    return [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50],
            [4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255],
            [230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7],
            [150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82],
            [143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3],
            [0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255],
            [255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220],
            [255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224],
            [255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255],
            [224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7],
            [255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153],
            [6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255],
            [140, 140, 140], [250, 10, 15], [20, 255, 0], [31, 255, 0],
            [255, 31, 0], [255, 224, 0], [153, 255, 0], [0, 0, 255],
            [255, 71, 0], [0, 235, 255], [0, 173, 255], [31, 0, 255],
            [11, 200, 200], [255, 82, 0], [0, 255, 245], [0, 61, 255],
            [0, 255, 112], [0, 255, 133], [255, 0, 0], [255, 163, 0],
            [255, 102, 0], [194, 255, 0], [0, 143, 255], [51, 255, 0],
            [0, 82, 255], [0, 255, 41], [0, 255, 173], [10, 0, 255],
            [173, 255, 0], [0, 255, 153], [255, 92, 0], [255, 0, 255],
            [255, 0, 245], [255, 0, 102], [255, 173, 0], [255, 0, 20],
            [255, 184, 184], [0, 31, 255], [0, 255, 61], [0, 71, 255],
            [255, 0, 204], [0, 255, 194], [0, 255, 82], [0, 10, 255],
            [0, 112, 255], [51, 0, 255], [0, 194, 255], [0, 122, 255],
            [0, 255, 163], [255, 153, 0], [0, 255, 10], [255, 112, 0],
            [143, 255, 0], [82, 0, 255], [163, 255, 0], [255, 235, 0],
            [8, 184, 170], [133, 0, 255], [0, 255, 92], [184, 0, 255],
            [255, 0, 31], [0, 184, 255], [0, 214, 255], [255, 0, 112],
            [92, 255, 0], [0, 224, 255], [112, 224, 255], [70, 184, 160],
            [163, 0, 255], [153, 0, 255], [71, 255, 0], [255, 0, 163],
            [255, 204, 0], [255, 0, 143], [0, 255, 235], [133, 255, 0],
            [255, 0, 235], [245, 0, 255], [255, 0, 122], [255, 245, 0],
            [10, 190, 212], [214, 255, 0], [0, 204, 255], [20, 0, 255],
            [255, 255, 0], [0, 153, 255], [0, 41, 255], [0, 255, 204],
            [41, 0, 255], [41, 255, 0], [173, 0, 255], [0, 245, 255],
            [71, 0, 255], [122, 0, 255], [0, 255, 184], [0, 92, 255],
            [184, 255, 0], [0, 133, 255], [255, 214, 0], [25, 194, 194],
            [102, 255, 0], [92, 0, 255]]
            
def resize_image(image, new_size, sdxl_resize=None):
    """
    Resizes the given image while maintaining its aspect ratio.

    Args:
        image (PIL.Image): The image to be resized.
        new_size (int): The new size (width or height) to resize the image to.
        sdxl_resize (bool, optional): Flag indicating whether to resize based on \
            the larger dimension. Default is None.

    Returns:
        PIL.Image: The resized image.
    """
    original_width, original_height = image.size

    if sdxl_resize:
        value = max(original_height, original_width)
    else:
        value = min(original_height, original_width)

    # Determine which side to fix based on minimum width or height
    if value == original_height:
        aspect_ratio = original_width / original_height
        new_height = new_size
        new_width = int(new_height * aspect_ratio)
    else:
        aspect_ratio = original_height / original_width
        new_width = new_size
        new_height = int(new_width * aspect_ratio)

    resized_image = image.resize((new_width, new_height))

    # Ensure that both dimensions are multiples of 64
    w, h = resized_image.size
    w, h = map(lambda x: x - x % 64, (w, h))
    resized_image = resized_image.resize((w, h))

    return resized_image      
            
            
def run(img):
  extractor = AutoFeatureExtractor.from_pretrained("mohit-mahavar/segformer-b0-finetuned-segments-sidewalk-july-24")
  model = SegformerForSemanticSegmentation.from_pretrained("mohit-mahavar/segformer-b0-finetuned-segments-sidewalk-july-24")
  
  if min(img.size) >= 768:
     img = resize_image(img, 768)
  elif max(img.size) >= 1024:
     img = resize_image(img, 1024, sdxl_resize=True)
  elif min(img.size) >= 512:
    img = resize_image(img, 512)
  elif max(img.size) >= 768:
    img = resize_image(img, 768, sdxl_resize=True)
  elif max(img.size) >= 512:
    img = resize_image(img, 512, sdxl_resize=True)

  pixel_values = extractor(img, return_tensors="pt").pixel_values.to("cpu")
  outputs = model(pixel_values)
  logits = outputs.logits
  logits = nn.functional.interpolate(outputs.logits.detach().cpu(),
                size=img.size[::-1], # (height, width)
                mode='bilinear',
                align_corners=False)
    # Second, apply argmax on the class dimension
  seg = logits.argmax(dim=1)[0]
  color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8) # height, width, 3
  palette = np.array(ade_palette())
  for label, color in enumerate(palette):
      color_seg[seg == label, :] = color
  # Convert to BGR
  color_seg = color_seg[..., ::-1]

  # Show image + mask
  img = np.array(img) * 0.5 + color_seg * 0.5
  img = img.astype(np.uint8)
  img = Image.fromarray(img)
  return img 
# Create a Gradio interface
iface = gr.Interface(
    fn=run,
    inputs=gr.Image(label="Input image", type="pil"),
    examples=["1.jpg" , "2.jpg"] , 
    outputs=gr.Image(label="Output image with predicted instance Masks", type="pil"),
    title="Image Segmentation with Segments-Sidewalk-SegFormer-B0",
    description="Upload an image, and this app will perform image segmentation and display the result",
)

# Launch the app
iface.launch(debug=True)