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| """Sapiens2 surface-normal Gradio Space. | |
| Image → per-pixel surface normals. Visualized by RGB-encoding the unit-length | |
| (x, y, z) normal: r = (x + 1) / 2, g = (y + 1) / 2, b = (z + 1) / 2. | |
| Optionally applies a v1 binary fg/bg mask so background pixels are blacked out. | |
| """ | |
| import sys | |
| import os | |
| sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) | |
| import tempfile | |
| import cv2 | |
| import gradio as gr | |
| import numpy as np | |
| import spaces | |
| import torch | |
| import torch.nn.functional as F | |
| from PIL import Image | |
| from torchvision import transforms | |
| from huggingface_hub import hf_hub_download | |
| from sapiens.dense.models import NormalEstimator, init_model # registers NormalEstimator | |
| _ = NormalEstimator | |
| # ----------------------------------------------------------------------------- | |
| # Config | |
| ASSETS_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "assets") | |
| CONFIGS_DIR = os.path.join(ASSETS_DIR, "configs") | |
| NORMAL_MODELS = { | |
| "0.4B": { | |
| "repo": "facebook/sapiens2-normal-0.4b", | |
| "filename": "sapiens2_0.4b_normal.safetensors", | |
| "config": os.path.join(CONFIGS_DIR, "sapiens2_0.4b_normal_metasim_render_people-1024x768.py"), | |
| }, | |
| "0.8B": { | |
| "repo": "facebook/sapiens2-normal-0.8b", | |
| "filename": "sapiens2_0.8b_normal.safetensors", | |
| "config": os.path.join(CONFIGS_DIR, "sapiens2_0.8b_normal_metasim_render_people-1024x768.py"), | |
| }, | |
| "1B": { | |
| "repo": "facebook/sapiens2-normal-1b", | |
| "filename": "sapiens2_1b_normal.safetensors", | |
| "config": os.path.join(CONFIGS_DIR, "sapiens2_1b_normal_metasim_render_people-1024x768.py"), | |
| }, | |
| "5B": { | |
| "repo": "facebook/sapiens2-normal-5b", | |
| "filename": "sapiens2_5b_normal.safetensors", | |
| "config": os.path.join(CONFIGS_DIR, "sapiens2_5b_normal_metasim_render_people-1024x768.py"), | |
| }, | |
| } | |
| DEFAULT_SIZE = "1B" | |
| # v1 binary fg/bg TorchScript model. | |
| FG_REPO = "facebook/sapiens-seg-foreground-1b-torchscript" | |
| FG_FILENAME = "sapiens_1b_seg_foreground_epoch_8_torchscript.pt2" | |
| BG_OPTIONS = ["fg-bg", "no-bg-removal"] | |
| DEFAULT_BG = "fg-bg" | |
| DEVICE = "cuda" if torch.cuda.is_available() else "cpu" | |
| _fg_transform = transforms.Compose([ | |
| transforms.Resize((1024, 768)), | |
| transforms.ToTensor(), | |
| transforms.Normalize(mean=[123.5 / 255, 116.5 / 255, 103.5 / 255], | |
| std=[58.5 / 255, 57.0 / 255, 57.5 / 255]), | |
| ]) | |
| # ----------------------------------------------------------------------------- | |
| # Model cache | |
| _normal_model_cache: dict = {} | |
| _fg_model = None | |
| def _get_normal_model(size: str): | |
| if size not in _normal_model_cache: | |
| spec = NORMAL_MODELS[size] | |
| ckpt = hf_hub_download(repo_id=spec["repo"], filename=spec["filename"]) | |
| model = init_model(spec["config"], ckpt, device=DEVICE) | |
| _normal_model_cache[size] = model | |
| return _normal_model_cache[size] | |
| def _get_fg_model(): | |
| global _fg_model | |
| if _fg_model is None: | |
| ckpt = hf_hub_download(repo_id=FG_REPO, filename=FG_FILENAME) | |
| _fg_model = torch.jit.load(ckpt).eval().to(DEVICE) | |
| return _fg_model | |
| print("[startup] pre-loading all normal sizes + fg/bg ...") | |
| for _size in NORMAL_MODELS: | |
| _get_normal_model(_size) | |
| _get_fg_model() | |
| print("[startup] ready.") | |
| # ----------------------------------------------------------------------------- | |
| # Inference (mirrors sapiens/dense/tools/vis/vis_normal.py) | |
| def _estimate_normal(image_bgr: np.ndarray, model) -> np.ndarray: | |
| """Returns unit-length normals at the model's NATIVE (post-unpad) resolution | |
| — no upsampling here. The caller upsamples the encoded RGB image instead, | |
| which gives sharper edges than bilinear-upsampling the raw float vectors.""" | |
| data = model.pipeline(dict(img=image_bgr)) # resize + pad | |
| data = model.data_preprocessor(data) # normalize + batch | |
| inputs, data_samples = data["inputs"], data["data_samples"] | |
| with torch.no_grad(): | |
| normal = model(inputs) # (1, 3, padded_H, padded_W) | |
| normal = normal / normal.norm(dim=1, keepdim=True).clamp_min(1e-8) | |
| pad_left, pad_right, pad_top, pad_bottom = data_samples["meta"]["padding_size"] | |
| normal = normal[ | |
| :, :, | |
| pad_top : inputs.shape[2] - pad_bottom, | |
| pad_left : inputs.shape[3] - pad_right, | |
| ] | |
| return normal.squeeze(0).cpu().float().numpy().transpose(1, 2, 0) # (H_native, W_native, 3) | |
| def _foreground_mask(image_pil: Image.Image, target_h: int, target_w: int) -> np.ndarray: | |
| fg = _get_fg_model() | |
| inputs = _fg_transform(image_pil).unsqueeze(0).to(DEVICE) | |
| with torch.no_grad(): | |
| out = fg(inputs) # (1, K, H, W) logits | |
| out = F.interpolate(out, size=(target_h, target_w), mode="bilinear", align_corners=False) | |
| return (out.argmax(dim=1)[0] > 0).cpu().numpy() | |
| def _normal_to_rgb(normal_hwc: np.ndarray) -> np.ndarray: | |
| """(H, W, 3) in [-1, 1] → (H, W, 3) uint8 RGB. NO channel swap (the swap in | |
| vis_normal.py is purely for cv2.imwrite's BGR convention).""" | |
| return (((normal_hwc + 1.0) / 2.0) * 255.0).clip(0, 255).astype(np.uint8) | |
| # ----------------------------------------------------------------------------- | |
| # Gradio handler | |
| def predict(image: Image.Image, size: str, bg_mode: str): | |
| if image is None: | |
| return None, None, None | |
| image_pil = image.convert("RGB") | |
| image_rgb = np.array(image_pil) | |
| image_bgr = cv2.cvtColor(image_rgb, cv2.COLOR_RGB2BGR) | |
| h0, w0 = image_rgb.shape[:2] | |
| model = _get_normal_model(size) | |
| normal_native = _estimate_normal(image_bgr, model) # (H_native, W_native, 3) in [-1, 1] | |
| h_n, w_n = normal_native.shape[:2] | |
| if bg_mode == "fg-bg": | |
| # Mask is computed at native resolution to keep things fast and | |
| # consistent with the normal map's actual pixel grid. | |
| mask_native = _foreground_mask(image_pil, h_n, w_n) | |
| normal_native[~mask_native] = -1.0 # → RGB(0,0,0) after vis | |
| rgb_native = _normal_to_rgb(normal_native) # (H_native, W_native, 3) uint8 | |
| rgb_pil = Image.fromarray(rgb_native).resize((w0, h0), Image.LANCZOS) # upsample IMAGE, lanczos | |
| # Save raw normals at native resolution (NaN where bg removed for completeness). | |
| raw = normal_native.copy() | |
| if bg_mode == "fg-bg": | |
| raw[~mask_native] = np.nan | |
| npy_path = tempfile.NamedTemporaryFile(delete=False, suffix=".npy").name | |
| np.save(npy_path, raw.astype(np.float32)) | |
| # Slider value: (left_image, right_image) — left=input RGB, right=normal map. | |
| return (image_pil, rgb_pil), npy_path | |
| # ----------------------------------------------------------------------------- | |
| # UI | |
| EXAMPLES = sorted( | |
| os.path.join(ASSETS_DIR, "images", n) | |
| for n in os.listdir(os.path.join(ASSETS_DIR, "images")) | |
| if n.lower().endswith((".jpg", ".jpeg", ".png")) | |
| ) | |
| CUSTOM_CSS = """ | |
| :root, body, .gradio-container, button, input, select, textarea, | |
| .gradio-container *:not(code):not(pre) { | |
| font-family: "Helvetica Neue", Helvetica, Arial, sans-serif !important; | |
| -webkit-font-smoothing: antialiased; | |
| -moz-osx-font-smoothing: grayscale; | |
| } | |
| #title { text-align: center; font-size: 44px; font-weight: 700; | |
| letter-spacing: -0.01em; margin: 28px 0 4px; | |
| background: linear-gradient(90deg, #1d4ed8 0%, #6d28d9 50%, #be185d 100%); | |
| -webkit-background-clip: text; -webkit-text-fill-color: transparent; | |
| background-clip: text; } | |
| #subtitle { text-align: center; font-size: 12px; color: #64748b; | |
| letter-spacing: 0.18em; margin: 0 0 14px; text-transform: uppercase; | |
| font-weight: 500; } | |
| #badges { display: flex; justify-content: center; flex-wrap: wrap; | |
| gap: 8px; margin: 0 0 32px; } | |
| .pill { display: inline-flex; align-items: center; gap: 6px; | |
| padding: 7px 14px; border-radius: 999px; | |
| background: #f1f5f9; color: #0f172a !important; | |
| font-size: 13px; font-weight: 500; letter-spacing: 0.01em; | |
| text-decoration: none !important; border: 1px solid #e2e8f0; | |
| transition: background 150ms ease, transform 150ms ease, border-color 150ms ease; } | |
| .pill:hover { background: #0f172a; color: #f8fafc !important; | |
| border-color: #0f172a; transform: translateY(-1px); } | |
| .pill svg { width: 14px; height: 14px; } | |
| """ | |
| HEADER_HTML = """ | |
| <div id="title">Sapiens2: Normal</div> | |
| <div id="subtitle">ICLR 2026</div> | |
| <div id="badges"> | |
| <a class="pill" href="https://github.com/facebookresearch/sapiens2" target="_blank" rel="noopener"> | |
| <svg viewBox="0 0 24 24" fill="currentColor"><path d="M12 .3a12 12 0 0 0-3.8 23.4c.6.1.8-.3.8-.6v-2c-3.3.7-4-1.6-4-1.6-.6-1.4-1.4-1.8-1.4-1.8-1.1-.7.1-.7.1-.7 1.3.1 2 1.3 2 1.3 1.1 1.9 3 1.4 3.7 1 .1-.8.4-1.4.8-1.7-2.7-.3-5.5-1.3-5.5-5.9 0-1.3.5-2.4 1.3-3.2-.1-.4-.6-1.6.1-3.2 0 0 1-.3 3.3 1.2a11.5 11.5 0 0 1 6 0c2.3-1.5 3.3-1.2 3.3-1.2.7 1.6.2 2.8.1 3.2.8.8 1.3 1.9 1.3 3.2 0 4.6-2.8 5.6-5.5 5.9.4.4.8 1.1.8 2.2v3.3c0 .3.2.7.8.6A12 12 0 0 0 12 .3"/></svg> | |
| Code | |
| </a> | |
| <a class="pill" href="https://huggingface.co/facebook/sapiens2" target="_blank" rel="noopener"> | |
| 🤗 Models | |
| </a> | |
| <a class="pill" href="https://arxiv.org/pdf/2604.21681" target="_blank" rel="noopener"> | |
| <svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><path d="M14 2H6a2 2 0 0 0-2 2v16a2 2 0 0 0 2 2h12a2 2 0 0 0 2-2V8z"/><polyline points="14 2 14 8 20 8"/><line x1="9" y1="13" x2="15" y2="13"/><line x1="9" y1="17" x2="15" y2="17"/></svg> | |
| Paper | |
| </a> | |
| <a class="pill" href="https://rawalkhirodkar.github.io/sapiens2" target="_blank" rel="noopener"> | |
| <svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><circle cx="12" cy="12" r="10"/><line x1="2" y1="12" x2="22" y2="12"/><path d="M12 2a15.3 15.3 0 0 1 4 10 15.3 15.3 0 0 1-4 10 15.3 15.3 0 0 1-4-10 15.3 15.3 0 0 1 4-10z"/></svg> | |
| Project | |
| </a> | |
| </div> | |
| """ | |
| with gr.Blocks(title="Sapiens2 Normal", theme=gr.themes.Soft(), css=CUSTOM_CSS) as demo: | |
| gr.HTML(HEADER_HTML) | |
| with gr.Row(equal_height=True): | |
| inp = gr.Image(label="Input", type="pil", height=640) | |
| out_img = gr.ImageSlider( | |
| label="Input ↔ Normal", | |
| type="pil", | |
| height=640, | |
| max_height=640, | |
| slider_position=50, | |
| ) | |
| with gr.Row(): | |
| size = gr.Radio( | |
| choices=list(NORMAL_MODELS.keys()), | |
| value=DEFAULT_SIZE, | |
| label="Model", | |
| scale=2, | |
| ) | |
| bg = gr.Radio( | |
| choices=BG_OPTIONS, | |
| value=DEFAULT_BG, | |
| label="Background", | |
| scale=2, | |
| ) | |
| run = gr.Button("Run", variant="primary", size="lg", scale=1) | |
| gr.Examples(examples=EXAMPLES, inputs=inp, examples_per_page=16) | |
| with gr.Accordion("Raw Normals", open=False): | |
| out_npy = gr.File(label="Raw normals (.npy float32 [-1, 1]; NaN where bg removed)") | |
| run.click(predict, inputs=[inp, size, bg], outputs=[out_img, out_npy]) | |
| if __name__ == "__main__": | |
| if torch.cuda.is_available(): | |
| torch.backends.cuda.matmul.allow_tf32 = True | |
| torch.backends.cudnn.allow_tf32 = True | |
| demo.launch(share=False) | |