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import gradio as gr
import matplotlib.pyplot as plt
import cv2
import torch
import timm
import numpy as np
midas = torch.hub.load('intel-isl/MiDaS', 'DPT_Hybrid')
midas.to('cpu')
midas.eval()
transforms = torch.hub.load('intel-isl/MiDaS', 'transforms')
transform = transforms.dpt_transform
def predict_image(img):
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
input_batch = transform(img).to('cpu')
with torch.no_grad():
prediction = midas(input_batch)
prediction = torch.nn.functional.interpolate(
prediction.unsqueeze(1),
size=img.shape[:2],
mode="bicubic",
align_corners=False,
).squeeze()
img = prediction.cpu().numpy()
a = img.max()
img = (img / a)*255
out = (img).astype(np.uint8)
return out
image = gr.inputs.Image()
label = gr.outputs.Label('ok')
gr.Interface(fn=predict_image, inputs=image, outputs=image).launch(debug='True') |