Spaces:
Runtime error
Runtime error
File size: 1,590 Bytes
4716bb5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 |
import torch
import numpy as np
import gradio as gr
from transformers import pipeline
from PIL import Image
depth_estimator = pipeline(task = 'depth-estimation',
model = 'Intel/dpt-hybrid-midas')
def launch(input_image):
out = depth_estimator(input_image)
# resize the prediction
prediction = torch.nn.functional.interpolate(
out["predicted_depth"].unsqueeze(1),
size=input_image.size[::-1],
mode="bicubic",
align_corners=False,
)
# normalize the prediction
output = prediction.squeeze().numpy()
formatted = (output * 255 / np.max(output)).astype("uint8")
depth = Image.fromarray(formatted)
return depth
depth_interface = gr.Interface(launch,
inputs=gr.Image(type='pil', label = "Input Image"),
outputs=gr.Image(type='pil', label = "Depth Estimation"),
allow_flagging = 'never')
# Add Markdown content
markdown_content_depth_estimation = gr.Markdown(
"""
<div style='text-align: center; font-family: "Times New Roman";'>
<h1 style='color: #FF6347;'>Image Depth Estimation</h1>
<h3 style='color: #4682B4;'>Model: Intel/dpt-hybrid-midas</h3>
<h3 style='color: #32CD32;'>Made By: Md. Mahmudun Nabi</h3>
</div>
"""
)
# Combine the Markdown content and the demo interface
depth_estimation_with_markdown = gr.Blocks()
with depth_estimation_with_markdown:
markdown_content_depth_estimation.render()
depth_interface.render()
|