app.py

from matplotlib.pyplot import axis
import gradio as gr
import requests
import numpy as np
from torch import nn
from transformers import SegformerFeatureExtractor, SegformerForSemanticSegmentation
import requests

url1 = 'https://cdn.pixabay.com/photo/2014/09/07/21/52/city-438393_1280.jpg'
r = requests.get(url1, allow_redirects=True)
open("city1.jpg", 'wb').write(r.content)
url2 = 'https://cdn.pixabay.com/photo/2016/02/19/11/36/canal-1209808_1280.jpg'
r = requests.get(url2, allow_redirects=True)
open("city2.jpg", 'wb').write(r.content)

def cityscapes_palette():
    return [[128, 64, 128],[244, 35, 232],[70, 70, 70],[102, 102, 156],[190, 153, 153],
            [153, 153, 153],[250, 170, 30],[220, 220, 0],[107, 142, 35],[152, 251, 152],
            [70, 130, 180], [220, 20, 60], [255, 0, 0], [0, 0, 142], [0, 0, 70],
            [0, 60, 100], [0, 80, 100], [0, 0, 230], [119, 11, 32]]

model_name = "nvidia/segformer-b5-finetuned-cityscapes-1024-1024"

feature_extractor = SegformerFeatureExtractor.from_pretrained(model_name)
model = SegformerForSemanticSegmentation.from_pretrained(model_name)

def inference(image):
    image = image.resize((1024,1024))
    inputs = feature_extractor(images=image, return_tensors="pt")
    outputs = model(**inputs)

    # First, rescale logits to original image size
    logits = nn.functional.interpolate(outputs.logits.detach().cpu(),
                    size=image.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(cityscapes_palette())
    for label, color in enumerate(palette):
        color_seg[seg == label, :] = color

    # Show image + mask
    img = np.array(image) * 0.5 + color_seg * 0.5
    img = img.astype(np.uint8)

    merged = np.concatenate((np.concatenate((np.array(image), color_seg), axis=1), np.concatenate((np.zeros_like(image), img), axis=1)), axis=0)
    return merged



title = "Transformers - SegFormer B5 @ 1024px"
description = "demo for SegFormer. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below.\nModel: nvidia/segformer-b5-finetuned-cityscapes-1024-1024"
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2105.15203'>SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers</a> | <a href='https://huggingface.co/transformers/model_doc/segformer.html#segformerforsemanticsegmentation'>Segformer page</a></p>"

gr.Interface(
    inference, 
    [gr.inputs.Image(type="pil", label="Input")], 
    gr.outputs.Image(type="numpy", label="Output"),
    title=title,
    description=description,
    article=article,
    examples=[
            ["city1.jpg"],
            ["city2.jpg"]
        ]).launch()