app.py

"""Sapiens2 pointmap Gradio Space.

Image → per-pixel 3D pointmap (camera frame, metric units). The result is
exported as a .ply point cloud and rendered with Gradio's Model3D component
for interactive 3D viewing. Optionally applies a v1 binary fg/bg mask so only
foreground points end up in the cloud.
"""

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 open3d as o3d
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 PointmapEstimator, init_model  # registers in registry
_ = PointmapEstimator


# -----------------------------------------------------------------------------
# Config

ASSETS_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "assets")
CONFIGS_DIR = os.path.join(ASSETS_DIR, "configs")

POINTMAP_MODELS = {
    "0.4B": {
        "repo": "facebook/sapiens2-pointmap-0.4b",
        "filename": "sapiens2_0.4b_pointmap.safetensors",
        "config": os.path.join(CONFIGS_DIR, "sapiens2_0.4b_pointmap_render_people-1024x768.py"),
    },
    "0.8B": {
        "repo": "facebook/sapiens2-pointmap-0.8b",
        "filename": "sapiens2_0.8b_pointmap.safetensors",
        "config": os.path.join(CONFIGS_DIR, "sapiens2_0.8b_pointmap_render_people-1024x768.py"),
    },
    "1B": {
        "repo": "facebook/sapiens2-pointmap-1b",
        "filename": "sapiens2_1b_pointmap.safetensors",
        "config": os.path.join(CONFIGS_DIR, "sapiens2_1b_pointmap_render_people-1024x768.py"),
    },
    "5B": {
        "repo": "facebook/sapiens2-pointmap-5b",
        "filename": "sapiens2_5b_pointmap.safetensors",
        "config": os.path.join(CONFIGS_DIR, "sapiens2_5b_pointmap_render_people-1024x768.py"),
    },
}
DEFAULT_SIZE = "0.4B"  # iteration mode — only this is preloaded; others lazy-load on click

FG_REPO = "facebook/sapiens-seg-foreground-1b-torchscript"
FG_FILENAME = "sapiens_1b_seg_foreground_epoch_8_torchscript.pt2"

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

_pointmap_model_cache: dict = {}
_fg_model = None


def _get_pointmap_model(size: str):
    if size not in _pointmap_model_cache:
        spec = POINTMAP_MODELS[size]
        ckpt = hf_hub_download(repo_id=spec["repo"], filename=spec["filename"])
        model = init_model(spec["config"], ckpt, device=DEVICE)
        _pointmap_model_cache[size] = model
    return _pointmap_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


# Iteration mode: only preload the default (0.4B) for fast Space boot.
# Re-enable full preload by uncommenting the loop below.
print("[startup] pre-loading 0.4B (iteration mode) + fg/bg ...")
_get_pointmap_model(DEFAULT_SIZE)
_get_fg_model()
# for _size in POINTMAP_MODELS:
#     _get_pointmap_model(_size)
print("[startup] ready.")


# -----------------------------------------------------------------------------
# Inference

def _estimate_pointmap(image_bgr: np.ndarray, model) -> np.ndarray:
    h0, w0 = image_bgr.shape[:2]
    data = model.pipeline(dict(img=image_bgr))
    data = model.data_preprocessor(data)
    inputs, data_samples = data["inputs"], data["data_samples"]
    if inputs.ndim == 3:
        inputs = inputs.unsqueeze(0)

    with torch.no_grad():
        pointmap, scale = model(inputs)
        pointmap = pointmap / scale  # → metric units

    pad = data_samples["meta"]["padding_size"]
    pad_left, pad_right, pad_top, pad_bottom = pad
    pointmap = pointmap[
        :, :,
        pad_top : inputs.shape[2] - pad_bottom,
        pad_left : inputs.shape[3] - pad_right,
    ]
    pointmap = F.interpolate(pointmap, size=(h0, w0), mode="bilinear", align_corners=False)
    return pointmap.squeeze(0).cpu().float().numpy().transpose(1, 2, 0)  # (H, W, 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)
    out = F.interpolate(out, size=(target_h, target_w), mode="bilinear", align_corners=False)
    return (out.argmax(dim=1)[0] > 0).cpu().numpy()


# -----------------------------------------------------------------------------
# Point cloud export

def _camera_marker(radius: float = 0.04, n_points: int = 800,
                   color=(0.20, 0.55, 0.96)) -> o3d.geometry.PointCloud:
    """Small uniformly-blue sphere at the world origin marking the camera.

    Manual Fibonacci-sphere sampling — instant, vs Open3D's poisson-disk which
    can take seconds per call.
    """
    rng = np.random.default_rng(0)
    i = np.arange(n_points)
    phi = np.arccos(1 - 2 * (i + 0.5) / n_points)             # latitude
    theta = np.pi * (1 + 5 ** 0.5) * (i + 0.5)                # golden-angle longitude
    pts = np.stack([
        radius * np.sin(phi) * np.cos(theta),
        radius * np.sin(phi) * np.sin(theta),
        radius * np.cos(phi),
    ], axis=1)
    pc = o3d.geometry.PointCloud()
    pc.points = o3d.utility.Vector3dVector(pts.astype(np.float64))
    pc.colors = o3d.utility.Vector3dVector(np.tile(color, (n_points, 1)).astype(np.float64))
    return pc


def _make_ply(image_rgb: np.ndarray, pointmap_hwc: np.ndarray, mask_hw: np.ndarray | None = None,
              max_points: int = 200_000) -> str:
    pts = pointmap_hwc.reshape(-1, 3)
    cols = (image_rgb.reshape(-1, 3).astype(np.float32) / 255.0)

    z = pts[:, 2]
    finite = np.isfinite(pts).all(axis=1) & (z > 0.05) & (z < 25.0)
    if mask_hw is not None:
        finite &= mask_hw.reshape(-1)
    pts, cols = pts[finite], cols[finite]

    if len(pts) > max_points:
        idx = np.random.default_rng(0).choice(len(pts), size=max_points, replace=False)
        pts, cols = pts[idx], cols[idx]

    pc = o3d.geometry.PointCloud()
    pc.points = o3d.utility.Vector3dVector(pts.astype(np.float64))
    pc.colors = o3d.utility.Vector3dVector(cols.astype(np.float64))

    # Add the camera marker (blue ball at origin) so users see where the
    # observer is in the reconstructed 3D scene.
    pc += _camera_marker()

    out_path = tempfile.NamedTemporaryFile(delete=False, suffix=".ply").name
    o3d.io.write_point_cloud(out_path, pc, write_ascii=False)
    return out_path


# -----------------------------------------------------------------------------
# Gradio handler

@spaces.GPU(duration=180)
def predict(image: Image.Image, size: str):
    if image is None:
        return 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_pointmap_model(size)
    pointmap = _estimate_pointmap(image_bgr, model)

    # Foreground masking is mandatory — keeps the cloud clean and the camera
    # marker meaningful (background depth is unreliable).
    mask = _foreground_mask(image_pil, h0, w0)
    ply_path = _make_ply(image_rgb, pointmap, mask)

    return ply_path, ply_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: Pointmap</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://openreview.net/pdf?id=IVAlYCqdvW" 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 Pointmap", 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_ply = gr.Model3D(
            label="Point cloud — drag to rotate, scroll to zoom, shift+drag to pan",
            height=640,
            clear_color=[0.07, 0.09, 0.13, 1.0],   # subtle slate-900 backdrop
            display_mode="point_cloud",
            zoom_speed=0.7,
            pan_speed=0.5,
        )

    with gr.Row():
        size = gr.Radio(
            choices=["0.4B"],  # iteration mode — re-add other sizes when shipping
            value=DEFAULT_SIZE,
            label="Model",
            scale=4,
        )
        run = gr.Button("Run", variant="primary", size="lg", scale=1)

    gr.Examples(examples=EXAMPLES, inputs=inp, examples_per_page=14)

    with gr.Accordion("Raw Pointmap", open=False):
        out_ply_file = gr.File(label="Point cloud (.ply — open in MeshLab/CloudCompare/Blender)")

    run.click(predict, inputs=[inp, size], outputs=[out_ply, out_ply_file])


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)