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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).launch(debug=True) |