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Running
on
A10G
| import os | |
| import sys | |
| depth_directory = os.path.abspath(os.path.join(os.path.dirname(__file__), 'depth')) | |
| sys.path.append(depth_directory) | |
| os.chdir(depth_directory) | |
| import cv2 | |
| import numpy as np | |
| import torch | |
| import torch.backends.cudnn as cudnn | |
| from depth.models_depth.model import EVPDepth | |
| from depth.configs.train_options import TrainOptions | |
| from depth.configs.test_options import TestOptions | |
| import glob | |
| import utils | |
| import torchvision.transforms as transforms | |
| from utils_depth.misc import colorize | |
| from PIL import Image | |
| import torch.nn.functional as F | |
| import gradio as gr | |
| import tempfile | |
| css = """ | |
| #img-display-container { | |
| max-height: 50vh; | |
| } | |
| #img-display-input { | |
| max-height: 40vh; | |
| } | |
| #img-display-output { | |
| max-height: 40vh; | |
| } | |
| """ | |
| def create_demo(model, device): | |
| gr.Markdown("### Depth Prediction demo") | |
| with gr.Row(): | |
| input_image = gr.Image(label="Input Image", type='pil', elem_id='img-display-input') | |
| depth_image = gr.Image(label="Depth Map", elem_id='img-display-output') | |
| raw_file = gr.File(label="16-bit raw depth, multiplier:256") | |
| submit = gr.Button("Submit") | |
| def on_submit(image): | |
| transform = transforms.ToTensor() | |
| image = transform(image).unsqueeze(0).to(device) | |
| shape = image.shape | |
| image = torch.nn.functional.interpolate(image, (440,480), mode='bilinear', align_corners=True) | |
| image = F.pad(image, (0, 0, 40, 0)) | |
| with torch.no_grad(): | |
| pred = model(image)['pred_d'] | |
| pred = pred[:,:,40:,:] | |
| pred = torch.nn.functional.interpolate(pred, shape[2:], mode='bilinear', align_corners=True) | |
| pred_d_numpy = pred.squeeze().cpu().numpy() | |
| colored_depth, _, _ = colorize(pred_d_numpy, cmap='gray_r') | |
| tmp = tempfile.NamedTemporaryFile(suffix='.png', delete=False) | |
| raw_depth = Image.fromarray((pred_d_numpy*256).astype('uint16')) | |
| raw_depth.save(tmp.name) | |
| return [colored_depth, tmp.name] | |
| submit.click(on_submit, inputs=[input_image], outputs=[depth_image, raw_file]) | |
| examples = gr.Examples(examples=["test_img.jpg"], | |
| inputs=[input_image]) | |
| def main(): | |
| opt = TestOptions().initialize() | |
| opt.add_argument('--img_path', type=str) | |
| args = opt.parse_args() | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| model = EVPDepth(args=args, caption_aggregation=True) | |
| cudnn.benchmark = True | |
| model.to(device) | |
| model_weight = torch.load(args.ckpt_dir)['model'] | |
| if 'module' in next(iter(model_weight.items()))[0]: | |
| model_weight = OrderedDict((k[7:], v) for k, v in model_weight.items()) | |
| model.load_state_dict(model_weight, strict=False) | |
| model.eval() | |
| title = "# EVP" | |
| description = """Official demo for **EVP: Enhanced Visual Perception using Inverse Multi-Attentive Feature | |
| Refinement and Regularized Image-Text Alignment**. | |
| EVP is a deep learning model for metric depth estimation from a single image. | |
| Please refer to our [paper](https://arxiv.org/abs/2312.08548) or [github](https://github.com/Lavreniuk/EVP) for more details.""" | |
| with gr.Blocks() as demo: | |
| gr.Markdown(title) | |
| gr.Markdown(description) | |
| with gr.Tab("Depth Prediction"): | |
| create_demo(model, device) | |
| gr.HTML('''<br><br><br><center>You can duplicate this Space to skip the queue:<a href="https://huggingface.co/spaces/shariqfarooq/ZoeDepth?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a><br> | |
| <p><img src="https://visitor-badge.glitch.me/badge?page_id=shariqfarooq.zoedepth_demo_hf" alt="visitors"></p></center>''') | |
| demo.queue().launch(share=True) | |
| if __name__ == '__main__': | |
| main() | |