import os import sys sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), 'depth'))) sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), 'refer'))) sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), 'stable-diffusion'))) sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), 'taming-transformers'))) os.chdir(os.path.abspath(os.path.join(os.path.dirname(__file__), 'depth'))) import cv2 import numpy as np import torch from depth.models_depth.model import EVPDepth from models_refer.model import EVPRefer 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 from transformers import CLIPTokenizer css = """ #img-display-container { max-height: 50vh; } #img-display-input { max-height: 40vh; } #img-display-output { max-height: 40vh; } """ def create_depth_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=["imgs/test_img1.jpg", "imgs/test_img2.jpg", "imgs/test_img3.jpg", "imgs/test_img4.jpg", "imgs/test_img5.jpg"], inputs=[input_image]) def create_refseg_demo(model, tokenizer, device): gr.Markdown("### Referring Segmentation demo") with gr.Row(): input_image = gr.Image(label="Input Image", type='pil', elem_id='img-display-input') refseg_image = gr.Image(label="Output Mask", elem_id='img-display-output') input_text = gr.Textbox(label='Prompt', placeholder='Please upload your image first', lines=2) submit = gr.Button("Submit") def on_submit(image, text): image = np.array(image) image_t = transforms.ToTensor()(image).unsqueeze(0).to(device) image_t = transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])(image_t) shape = image_t.shape image_t = torch.nn.functional.interpolate(image_t, (512,512), mode='bilinear', align_corners=True) input_ids = tokenizer(text=text, truncation=True, max_length=40, return_length=True, return_overflowing_tokens=False, padding="max_length", return_tensors="pt")['input_ids'].to(device) with torch.no_grad(): pred = model(image_t, input_ids) pred = torch.nn.functional.interpolate(pred, shape[2:], mode='bilinear', align_corners=True) output_mask = pred.cpu().argmax(1).data.numpy().squeeze() alpha = 0.65 image[output_mask == 0] = (image[output_mask == 0]*alpha).astype(np.uint8) contours, _ = cv2.findContours(output_mask.astype(np.uint8), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) cv2.drawContours(image, contours, -1, (0, 255, 0), 2) return Image.fromarray(image) submit.click(on_submit, inputs=[input_image, input_text], outputs=refseg_image) examples = gr.Examples(examples=[["imgs/test_img2.jpg", "green plant"], ["imgs/test_img3.jpg", "chair"], ["imgs/test_img4.jpg", "left green plant"], ["imgs/test_img5.jpg", "man walking on foot"], ["imgs/test_img5.jpg", "the rightest camel"]], inputs=[input_image, input_text]) def main(): opt = TestOptions().initialize() args = opt.parse_args() device = torch.device("cuda" if torch.cuda.is_available() else "cpu") #model = EVPDepth(args=args, caption_aggregation=True) #model.to(device) #model_weight = torch.load('best_model_nyu.ckpt', map_location=device)['model'] #model.load_state_dict(model_weight, strict=False) #model.eval() tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14") model_refseg = EVPRefer() model_refseg.to(device) model_weight = torch.load('best_model_refcoco.pth', map_location=device)['model'] model_refseg.load_state_dict(model_weight, strict=False) model_refseg.eval() del model_weight print('Models uploaded successfully') 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 as well as referring segmentation. Please refer to our [project page](https://lavreniuk.github.io/EVP) or [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_depth_demo(model_refseg, device) with gr.Tab("Referring Segmentation"): create_refseg_demo(model_refseg, tokenizer, device) gr.HTML('''


You can duplicate this Space to skip the queue:Duplicate Space

visitors

''') demo.queue().launch(share=True) if __name__ == '__main__': main()