""" Depth Anything Comparison Demo (v1 vs v2) Compare different Depth Anything models (v1 and v2) side-by-side or with a slider using Gradio. Inspired by the Stereo Matching Methods Comparison Demo. """ import os import sys import logging import gc import tempfile from pathlib import Path from typing import Optional, Tuple, Dict, List import numpy as np import cv2 import gradio as gr from PIL import Image # Import v1 and v2 model code sys.path.insert(0, os.path.join(os.path.dirname(__file__), "depth_anything")) sys.path.insert(0, os.path.join(os.path.dirname(__file__), "../Depth-Anything-V2/depth_anything_v2")) # v1 imports from depth_anything.dpt import DepthAnything as DepthAnythingV1 from depth_anything.util.transform import Resize, NormalizeImage, PrepareForNet import torch import torch.nn.functional as F from torchvision.transforms import Compose # v2 imports from depth_anything_v2.dpt import DepthAnythingV2 import matplotlib # Logging logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') # Device selection DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu' # Model configs V1_MODEL_CONFIGS = { "vits14": { "model_name": "LiheYoung/depth_anything_vits14", "display_name": "Depth Anything v1 ViT-S (Small, Fastest)" }, "vitb14": { "model_name": "LiheYoung/depth_anything_vitb14", "display_name": "Depth Anything v1 ViT-B (Base, Balanced)" }, "vitl14": { "model_name": "LiheYoung/depth_anything_vitl14", "display_name": "Depth Anything v1 ViT-L (Large, Best Quality)" } } V2_MODEL_CONFIGS = { 'vits': { 'display_name': 'Depth Anything v2 ViT-Small', 'checkpoint': '../Depth-Anything-V2/checkpoints/depth_anything_v2_vits.pth', 'features': 64, 'out_channels': [48, 96, 192, 384] }, 'vitb': { 'display_name': 'Depth Anything v2 ViT-Base', 'checkpoint': '../Depth-Anything-V2/checkpoints/depth_anything_v2_vitb.pth', 'features': 128, 'out_channels': [96, 192, 384, 768] }, 'vitl': { 'display_name': 'Depth Anything v2 ViT-Large', 'checkpoint': '../Depth-Anything-V2/checkpoints/depth_anything_v2_vitl.pth', 'features': 256, 'out_channels': [256, 512, 1024, 1024] } } # Model cache _v1_models = {} _v2_models = {} # v1 transform v1_transform = Compose([ Resize(width=518, height=518, resize_target=False, keep_aspect_ratio=True, ensure_multiple_of=14, resize_method='lower_bound', image_interpolation_method=cv2.INTER_CUBIC), NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), PrepareForNet(), ]) def load_v1_model(key: str): if key in _v1_models: return _v1_models[key] model = DepthAnythingV1.from_pretrained(V1_MODEL_CONFIGS[key]["model_name"]).to(DEVICE).eval() _v1_models[key] = model return model def load_v2_model(key: str): if key in _v2_models: return _v2_models[key] config = V2_MODEL_CONFIGS[key] model = DepthAnythingV2(encoder=key, features=config['features'], out_channels=config['out_channels']) state_dict = torch.load(config['checkpoint'], map_location=DEVICE) model.load_state_dict(state_dict) model = model.to(DEVICE).eval() _v2_models[key] = model return model def predict_v1(model, image: np.ndarray) -> np.ndarray: h, w = image.shape[:2] image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) / 255.0 image = v1_transform({'image': image})['image'] image = torch.from_numpy(image).unsqueeze(0).to(DEVICE) with torch.no_grad(): depth = model(image) depth = F.interpolate(depth[None], (h, w), mode='bilinear', align_corners=False)[0, 0] return depth.cpu().numpy() def predict_v2(model, image: np.ndarray) -> np.ndarray: with torch.no_grad(): depth = model.infer_image(image[:, :, ::-1]) # BGR to RGB return depth def colorize_depth(depth: np.ndarray) -> np.ndarray: depth_norm = (depth - depth.min()) / (depth.max() - depth.min() + 1e-8) depth_uint8 = (depth_norm * 255).astype(np.uint8) cmap = matplotlib.colormaps.get_cmap('Spectral_r') colored = (cmap(depth_uint8)[:, :, :3] * 255).astype(np.uint8) return colored def get_model_choices() -> List[Tuple[str, str]]: choices = [] for k, v in V1_MODEL_CONFIGS.items(): choices.append((v['display_name'], f'v1_{k}')) for k, v in V2_MODEL_CONFIGS.items(): choices.append((v['display_name'], f'v2_{k}')) return choices def run_model(model_key: str, image: np.ndarray) -> Tuple[np.ndarray, str]: if model_key.startswith('v1_'): key = model_key[3:] model = load_v1_model(key) depth = predict_v1(model, image) label = V1_MODEL_CONFIGS[key]['display_name'] else: key = model_key[3:] model = load_v2_model(key) depth = predict_v2(model, image) label = V2_MODEL_CONFIGS[key]['display_name'] colored = colorize_depth(depth) return colored, label def compare_models(image: np.ndarray, model1: str, model2: str, progress=gr.Progress()) -> Tuple[np.ndarray, str]: if image is None: return None, "❌ Please upload an image." progress(0.1, desc=f"Running {model1}") out1, label1 = run_model(model1, image) progress(0.5, desc=f"Running {model2}") out2, label2 = run_model(model2, image) h, w = out1.shape[:2] canvas = np.ones((h + 40, w * 2 + 20, 3), dtype=np.uint8) * 255 canvas[40:40+h, 10:10+w] = out1 canvas[40:40+h, w+20:w*2+20] = out2 font = cv2.FONT_HERSHEY_SIMPLEX font_scale = 0.7 thickness = 2 size1 = cv2.getTextSize(label1, font, font_scale, thickness)[0] size2 = cv2.getTextSize(label2, font, font_scale, thickness)[0] cv2.putText(canvas, label1, (10 + (w - size1[0]) // 2, 28), font, font_scale, (0,0,0), thickness) cv2.putText(canvas, label2, (w+20 + (w - size2[0]) // 2, 28), font, font_scale, (0,0,0), thickness) progress(1.0, desc="Done") return canvas, f"**{label1}** vs **{label2}**" def slider_compare(image: np.ndarray, model1: str, model2: str, progress=gr.Progress()): if image is None: return None, "❌ Please upload an image." progress(0.1, desc=f"Running {model1}") out1, label1 = run_model(model1, image) progress(0.5, desc=f"Running {model2}") out2, label2 = run_model(model2, image) def add_label(img, label): h, w = img.shape[:2] canvas = np.ones((h+40, w, 3), dtype=np.uint8) * 255 canvas[40:, :] = img font = cv2.FONT_HERSHEY_SIMPLEX font_scale = 0.7 thickness = 2 size = cv2.getTextSize(label, font, font_scale, thickness)[0] cv2.putText(canvas, label, ((w-size[0])//2, 28), font, font_scale, (0,0,0), thickness) return canvas return (add_label(out1, label1), add_label(out2, label2)), f"Slider: **{label1}** vs **{label2}**" def single_inference(image: np.ndarray, model: str, progress=gr.Progress()): if image is None: return None, "❌ Please upload an image." progress(0.1, desc=f"Running {model}") out, label = run_model(model, image) progress(1.0, desc="Done") return out, f"**{label}**" def get_example_images() -> List[str]: ex_dir = os.path.join(os.path.dirname(__file__), "assets/examples") if not os.path.exists(ex_dir): return [] files = [os.path.join(ex_dir, f) for f in sorted(os.listdir(ex_dir)) if f.lower().endswith(('.png', '.jpg', '.jpeg'))] return files[:6] def create_app(): model_choices = get_model_choices() default1 = model_choices[0][1] default2 = model_choices[1][1] with gr.Blocks(title="Depth Anything v1 vs v2 Comparison", theme=gr.themes.Soft()) as app: gr.Markdown(""" # Depth Anything v1 vs v2 Comparison Compare different Depth Anything models (v1 and v2) side-by-side or with a slider. """) with gr.Tabs(): with gr.Tab("🔍 Method Comparison"): with gr.Row(): img_input = gr.Image(label="Input Image", type="numpy") with gr.Column(): m1 = gr.Dropdown(choices=model_choices, label="Model 1", value=default1) m2 = gr.Dropdown(choices=model_choices, label="Model 2", value=default2) btn = gr.Button("Compare", variant="primary") out_img = gr.Image(label="Comparison Result") out_status = gr.Markdown() btn.click(compare_models, inputs=[img_input, m1, m2], outputs=[out_img, out_status], show_progress=True) # Examples ex_imgs = get_example_images() if ex_imgs: gr.Examples(examples=[[f] for f in ex_imgs], inputs=[img_input], label="Example Images") with gr.Tab("🎚️ Slider Comparison"): with gr.Row(): img_input2 = gr.Image(label="Input Image", type="numpy") with gr.Column(): m1s = gr.Dropdown(choices=model_choices, label="Model A", value=default1) m2s = gr.Dropdown(choices=model_choices, label="Model B", value=default2) btn2 = gr.Button("Slider Compare", variant="primary") slider = gr.ImageSlider(label="Model Comparison Slider") slider_status = gr.Markdown() btn2.click(slider_compare, inputs=[img_input2, m1s, m2s], outputs=[slider, slider_status], show_progress=True) if ex_imgs: gr.Examples(examples=[[f] for f in ex_imgs], inputs=[img_input2], label="Example Images") with gr.Tab("🎯 Single Model"): with gr.Row(): img_input3 = gr.Image(label="Input Image", type="numpy") m_single = gr.Dropdown(choices=model_choices, label="Model", value=default1) btn3 = gr.Button("Run", variant="primary") out_single = gr.Image(label="Depth Result") out_single_status = gr.Markdown() btn3.click(single_inference, inputs=[img_input3, m_single], outputs=[out_single, out_single_status], show_progress=True) if ex_imgs: gr.Examples(examples=[[f] for f in ex_imgs], inputs=[img_input3], label="Example Images") gr.Markdown(""" --- - **v1**: [Depth Anything v1](https://github.com/LiheYoung/Depth-Anything) - **v2**: [Depth Anything v2](https://github.com/DepthAnything/Depth-Anything-V2) """) return app def main(): logging.info("🚀 Starting Depth Anything Comparison App...") app = create_app() app.queue().launch(show_error=True) if __name__ == "__main__": main()