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import streamlit as st
from PIL import Image
from transformers import pipeline
import numpy as np
import cv2
import matplotlib.cm as cm
semantic_segmentation = pipeline("image-segmentation", "nvidia/segformer-b1-finetuned-cityscapes-1024-1024")
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png"])
def draw_masks_fromDict(image, results):
masked_image = image.copy()
colormap = cm.get_cmap('nipy_spectral')
for i, result in enumerate(results):
mask = np.array(result['mask'])
mask = np.repeat(mask[:, :, np.newaxis], 3, axis=2)
color = colormap(i / len(results))[:3]
color = tuple(int(c * 255) for c in color)
masked_image = np.where(mask, color, masked_image)
masked_image = masked_image.astype(np.uint8)
return cv2.addWeighted(image, 0.3, masked_image, 0.7, 0)
if uploaded_file is not None:
image = Image.open(uploaded_file)
st.image(image, caption='Uploaded Image.', use_column_width=True)
st.write("")
results = semantic_segmentation(image)
st.json(results)
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