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import cv2
import torch
import urllib.request
import gradio as gr
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
urllib.request.urlretrieve(url, filename)
model_type = "DPT_Large" # MiDaS v3 - Large (highest accuracy, slowest inference speed)
#model_type = "DPT_Hybrid" # MiDaS v3 - Hybrid (medium accuracy, medium inference speed)
#model_type = "MiDaS_small" # MiDaS v2.1 - Small (lowest accuracy, highest inference speed)
midas = torch.hub.load("intel-isl/MiDaS", model_type)
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
midas.to(device)
midas.eval()
midas_transforms = torch.hub.load("intel-isl/MiDaS", "transforms")
if model_type == "DPT_Large" or model_type == "DPT_Hybrid":
transform = midas_transforms.dpt_transform
else:
transform = midas_transforms.small_transform
def inference(img):
img = cv2.imread(img.name)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
input_batch = transform(img).to(device)
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()
output = prediction.cpu().numpy()
formatted = (output * 255 / np.max(output)).astype('uint8')
img = Image.fromarray(formatted)
return img
inputs = gr.inputs.Image(type='file', label="Original Image")
outputs = gr.outputs.Image(type="pil",label="Output Image")
title = "DPT-Large"
description = "Gradio demo for DPT-Large:Vision Transformers for Dense Prediction.To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2103.13413' target='_blank'>Vision Transformers for Dense Prediction</a> | <a href='https://github.com/intel-isl/MiDaS' target='_blank'>Github Repo</a></p>"
examples=[['dog.jpg']]
gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, analytics_enabled=False,examples=examples, enable_queue=True).launch(debug=True) |