SegFormer / app.py
Karol Majek
nonsense 9dcc578
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()