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| 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__), 'src/taming-transformers'))) | |
| sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), 'src/clip'))) | |
| 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, AutoModel | |
| 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 = torch.from_numpy(pred).to(device).float() | |
| if pred.dim() == 2: # H×W | |
| pred = pred.unsqueeze(0).unsqueeze(0) | |
| 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): | |
| # Convert PIL -> np array | |
| image_np = np.array(image).copy() | |
| transform = transforms.ToTensor() | |
| image_t = transform(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) | |
| image_t = torch.nn.functional.interpolate(image_t, (512, 512), mode='bilinear', align_corners=True) | |
| with torch.no_grad(): | |
| out = model(image_t, text) | |
| # Ensure numpy mask | |
| if isinstance(out, torch.Tensor): | |
| mask = out.squeeze().detach().cpu().numpy() | |
| else: | |
| mask = out | |
| # Convert to binary mask | |
| if mask.ndim > 2: | |
| mask = np.argmax(mask, axis=0) | |
| mask = (mask > 0).astype(np.uint8) | |
| # Resize mask to original image size | |
| mask = cv2.resize(mask, (image_np.shape[1], image_np.shape[0]), interpolation=cv2.INTER_NEAREST) | |
| # Overlay mask | |
| alpha = 0.65 | |
| overlay = image_np.copy() | |
| overlay[mask == 0] = (overlay[mask == 0] * alpha).astype(np.uint8) | |
| # Draw contours | |
| contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) | |
| cv2.drawContours(overlay, contours, -1, (0, 255, 0), 2) | |
| return Image.fromarray(overlay) | |
| 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(): | |
| upload_2_models = True | |
| opt = TestOptions().initialize() | |
| args = opt.parse_args() | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| if upload_2_models: | |
| model = AutoModel.from_pretrained("MykolaL/evp_depth", trust_remote_code=True).to(device).eval() | |
| tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14") | |
| model_refseg = AutoModel.from_pretrained("MykolaL/evp_refer", trust_remote_code=True).to(device).eval() | |
| 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) | |
| if upload_2_models: | |
| with gr.Tab("Depth Prediction"): | |
| create_depth_demo(model, device) | |
| with gr.Tab("Referring Segmentation"): | |
| create_refseg_demo(model_refseg, tokenizer, device) | |
| gr.HTML('''<br><br><br><center>You can duplicate this Space to skip the queue:<a href="https://huggingface.co/spaces/MykolaL/evp?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a><br> | |
| <p><img src="https://visitor-badge.glitch.me/badge?page_id=MykolaL/evp" alt="visitors"></p></center>''') | |
| demo.queue().launch(share=True) | |
| if __name__ == '__main__': | |
| main() | |