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import os
import pandas as pd
import numpy as np
import torch
from PIL import Image
from transformers import SegformerForSemanticSegmentation, SegformerFeatureExtractor
from torch import nn
import streamlit as st
st.title('Semantic Segmentation using SegFormer')
raw_image = st.file_uploader('Raw Input Image')
if raw_image is not None:
df = pd.read_csv('class_dict_seg.csv')
classes = df['name']
palette = df[[' r', ' g', ' b']].values
id2label = classes.to_dict()
label2id = {v: k for k, v in id2label.items()}
image = Image.open(raw_image)
image = np.asarray(image)
feature_extractor = SegformerFeatureExtractor(align=False, reduce_zero_label=False)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = SegformerForSemanticSegmentation.from_pretrained("deep-learning-analytics/segformer_semantic_segmentation",
ignore_mismatched_sizes=True,
num_labels=len(id2label), id2label=id2label, label2id=label2id,
reshape_last_stage=True)
model = model.to(device)
# prepare the image for the model (aligned resize)
feature_extractor_inference = SegformerFeatureExtractor(do_random_crop=False, do_pad=False)
pixel_values = feature_extractor_inference(image, return_tensors="pt").pixel_values.to(device)
model.eval()
with st.spinner('Running inference...'):
outputs = model(pixel_values=pixel_values)# logits are of shape (batch_size, num_labels, height/4, width/4)
logits = outputs.logits.cpu()
# First, rescale logits to original image size
upsampled_logits = nn.functional.interpolate(logits,
size=image.shape[:-1], # (height, width)
mode='bilinear',
align_corners=False)
# Second, apply argmax on the class dimension
seg = upsampled_logits.argmax(dim=1)[0]
color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8) # height, width, 3\
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(image) * 0.5 + color_seg * 0.5
img = img.astype(np.uint8)
st.image(img, caption="Segmented Image") |