import gradio as gr import cv2 import matplotlib import numpy as np import os from PIL import Image import spaces import torch import tempfile from gradio_imageslider import ImageSlider from huggingface_hub import hf_hub_download # from depth_anything_v2.dpt import DepthAnythingV2 from Marigold.marigold import MarigoldPipeline from diffusers import AutoencoderKL, DDIMScheduler, UNet2DConditionModel from transformers import CLIPTextModel, CLIPTokenizer # import xformers css = """ #img-display-container { max-height: 100vh; } #img-display-input { max-height: 80vh; } #img-display-output { max-height: 80vh; } #download { height: 62px; } """ DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu' dtype = torch.float32 variant = None checkpoint_path = "GonzaloMG/marigold-e2e-ft-depth" unet = UNet2DConditionModel.from_pretrained(checkpoint_path, subfolder="unet") vae = AutoencoderKL.from_pretrained(checkpoint_path, subfolder="vae") text_encoder = CLIPTextModel.from_pretrained(checkpoint_path, subfolder="text_encoder") tokenizer = CLIPTokenizer.from_pretrained(checkpoint_path, subfolder="tokenizer") scheduler = DDIMScheduler.from_pretrained(checkpoint_path, timestep_spacing="trailing", subfolder="scheduler") pipe = MarigoldPipeline.from_pretrained(pretrained_model_name_or_path = checkpoint_path, unet=unet, vae=vae, scheduler=scheduler, text_encoder=text_encoder, tokenizer=tokenizer, variant=variant, torch_dtype=dtype, ) # try: # pipe.enable_xformers_memory_efficient_attention() # except ImportError: # pass # run without xformers pipe = pipe.to(DEVICE) pipe.unet.eval() # model_configs = { # 'vits': {'encoder': 'vits', 'features': 64, 'out_channels': [48, 96, 192, 384]}, # 'vitb': {'encoder': 'vitb', 'features': 128, 'out_channels': [96, 192, 384, 768]}, # 'vitl': {'encoder': 'vitl', 'features': 256, 'out_channels': [256, 512, 1024, 1024]}, # 'vitg': {'encoder': 'vitg', 'features': 384, 'out_channels': [1536, 1536, 1536, 1536]} # } # encoder2name = { # 'vits': 'Small', # 'vitb': 'Base', # 'vitl': 'Large', # 'vitg': 'Giant', # we are undergoing company review procedures to release our giant model checkpoint # } # encoder = 'vitl' # model_name = encoder2name[encoder] # model = DepthAnythingV2(**model_configs[encoder]) # filepath = hf_hub_download(repo_id=f"depth-anything/Depth-Anything-V2-{model_name}", filename=f"depth_anything_v2_{encoder}.pth", repo_type="model") # state_dict = torch.load(filepath, map_location="cpu") # model.load_state_dict(state_dict) # model = model.to(DEVICE).eval() title = "# ..." description = """... **...**""" # def predict_depth(image): # return model.infer_image(image) @spaces.GPU def predict_depth(image): #, processing_res, model_choice, current_model): with torch.no_grad(): pipe_out = pipe(image, denoising_steps=1, ensemble_size=1, noise="zeros", normals=False, processing_res=768, match_input_res=True) pred = pipe_out.depth_np pred_colored = pipe_out.depth_colored return pred, pred_colored with gr.Blocks(css=css) as demo: gr.Markdown(title) gr.Markdown(description) gr.Markdown("### Depth Prediction demo") with gr.Row(): input_image = gr.Image(label="Input Image", type='numpy', elem_id='img-display-input') depth_image_slider = ImageSlider(label="Depth Map with Slider View", elem_id='img-display-output', position=0.5) withgr.Row() submit = gr.Button(value="Compute Depth") processing_res_choice = gr.Radio( [ ("Recommended (768)", 768), ("Native", 0), ], label="Processing resolution", value=768, ) gray_depth_file = gr.File(label="Grayscale depth map", elem_id="download",) raw_file = gr.File(label="16-bit raw output (can be considered as disparity)", elem_id="download",) cmap = matplotlib.colormaps.get_cmap('Spectral_r') def on_submit(image): if image is None: print("No image uploaded.") return None pil_image = Image.fromarray(image.astype('uint8')) depth_npy, depth_colored = predict_depth(pil_image) # Save the npy data (raw depth map) # tmp_npy_depth = tempfile.NamedTemporaryFile(suffix='.npy', delete=False) # np.save(tmp_npy_depth.name, depth_npy) # Save the grayscale depth map depth_gray = (depth_npy * 65535.0).astype(np.uint16) tmp_gray_depth = tempfile.NamedTemporaryFile(suffix='.png', delete=False) Image.fromarray(depth_gray).save(tmp_gray_depth.name, mode="I;16") # Save the colored depth map tmp_colored_depth = tempfile.NamedTemporaryFile(suffix='.png', delete=False) depth_colored.save(tmp_colored_depth.name) return [(image, depth_colored), tmp_gray_depth.name, tmp_colored_depth.name] # h, w = image.shape[:2] # depth = predict_depth(image[:, :, ::-1]) # raw_depth = Image.fromarray(depth.astype('uint16')) # tmp_raw_depth = tempfile.NamedTemporaryFile(suffix='.png', delete=False) # raw_depth.save(tmp_raw_depth.name) # depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0 # depth = depth.astype(np.uint8) # colored_depth = (cmap(depth)[:, :, :3] * 255).astype(np.uint8) # gray_depth = Image.fromarray(depth) # tmp_gray_depth = tempfile.NamedTemporaryFile(suffix='.png', delete=False) # gray_depth.save(tmp_gray_depth.name) # return [(original_image, colored_depth), tmp_gray_depth.name, tmp_raw_depth.name] submit.click(on_submit, inputs=[input_image, processing_res_choice], outputs=[depth_image_slider, gray_depth_file, raw_file]) example_files = os.listdir('assets/examples') example_files.sort() example_files = [os.path.join('assets/examples', filename) for filename in example_files] example_files = [[image, 768] for image in example_files] examples = gr.Examples(examples=example_files, inputs=[input_image], outputs=[depth_image_slider, gray_depth_file, raw_file], fn=on_submit) if __name__ == '__main__': demo.queue().launch(share=True)