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from transformers import pipeline
from PIL import Image
import torch
import numpy as np
import gradio as gr
depth_estimator = pipeline(task="depth-estimation", model="Intel/dpt-hybrid-midas")
def generate_depth_image(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
demo = gr.Interface(title = "Depth Estimation of the Detected Objects in the Image - Test & Demo App by Srinivas.v..",
description='Upload an image that has some vivid foreground objects and submit',
fn = generate_depth_image, inputs=gr.Image(type='pil'), outputs=gr.Image(type='pil'))
demo.launch(share=True,debug=True) |