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import os
import pandas as pd
import numpy as np
import torch
from transformers import DPTFeatureExtractor, DPTForSemanticSegmentation
from PIL import Image
from torch import nn
import requests
import streamlit as st
img_path = None
st.title('Semantic Segmentation using Beit')
file_upload = st.file_uploader('Raw Input Image')
image_path = st.selectbox(
'Choose any one image for inference',
('Select image', 'image1.jpg', 'image2.jpg', 'image3.jpg'))
if file_upload is None:
raw_image = image_path
else:
raw_image = file_upload
if raw_image != 'Select image':
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)
with st.spinner('Loading Model...'):
feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-large-ade")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade",ignore_mismatched_sizes=True,num_labels=len(id2label), id2label=id2label, label2id=label2id,reshape_last_stage=True)
model = model.to(device)
model.eval()
st.success("Success")
#url = "http://images.cocodataset.org/val2017/000000039769.jpg"
#image = Image.open(requests.get(url, stream=True).raw)
#st.success("Image open: Success")
#feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-large-ade")
#model = DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade")
#st.success("Load model: Success")
#inputs = feature_extractor(images=image, return_tensors="pt")
#st.success("Feature extraction: Success")
#outputs = model(**inputs)
#logits = outputs.logits
#st.text(str(logits))
#st.success("Success") |