Fix: graceful GPU/CPU fallback, remove spaces dependency requirement

README.md

@@ -1,5 +1,5 @@
 ---
-title: AnyCalib GPU
+title: AnyCalib
 emoji: "\U0001F4F7"
 colorFrom: indigo
 colorTo: blue
@@ -14,15 +14,14 @@ tags:
   - computer-vision
   - lens-correction
   - dinov2
-  - gpu
-  - zerogpu
 ---
 
-# AnyCalib — Full-Resolution GPU Camera Calibration
+# AnyCalib — Full-Resolution Camera Calibration
 
-Single-image camera calibration and lens distortion correction running on **ZeroGPU**.
+Single-image camera calibration and lens distortion correction.
 
 No quantization, no resolution limits — full FP32 inference with the complete AnyCalib pipeline.
+Automatically uses GPU when available (ZeroGPU / dedicated), falls back to CPU.
 
 ## What it does
 
@@ -45,5 +44,4 @@ No quantization, no resolution limits — full FP32 inference with the complete
 - **Total**: ~320M parameters, full FP32
 - **Weights**: [SebRincon/anycalib](https://huggingface.co/SebRincon/anycalib)
 - **ONNX**: [SebRincon/anycalib-onnx](https://huggingface.co/SebRincon/anycalib-onnx)
-- **WASM demo**: [SebRincon/anycalib-wasm](https://huggingface.co/spaces/SebRincon/anycalib-wasm)
 - **Source**: [github.com/javrtg/AnyCalib](https://github.com/javrtg/AnyCalib)

app.py

@@ -1,12 +1,13 @@
 """
-AnyCalib — Full-Resolution GPU Camera Calibration & Lens Correction
+AnyCalib — Full-Resolution Camera Calibration & Lens Correction
 
-Gradio Space running the full AnyCalib pipeline on ZeroGPU:
+Gradio Space running the full AnyCalib pipeline:
   1. DINOv2 ViT-L/14 backbone → LightDPT decoder → ConvexTangentDecoder head
   2. RANSAC + Gauss-Newton calibrator → camera intrinsics [f, cx, cy, k1, ...]
   3. Full-resolution undistortion via grid_sample
 
-No resolution limits. No quantization. Full FP32 on a real GPU.
+No resolution limits. No quantization. Full FP32 inference.
+Runs on GPU if available (ZeroGPU / dedicated), falls back to CPU.
 """
 from __future__ import annotations
 
@@ -15,11 +16,18 @@ import time
 
 import gradio as gr
 import numpy as np
-import spaces
 import torch
 
-# ── Load model at startup (on CPU — ZeroGPU moves it to GPU per-call) ──
+# ── GPU decorator (works on ZeroGPU Spaces, no-op elsewhere) ──
+try:
+    import spaces
+    gpu_decorator = spaces.GPU(duration=120)
+except (ImportError, Exception):
+    # Not on a ZeroGPU Space — use identity decorator
+    def gpu_decorator(fn):
+        return fn
 
+# ── Load model at startup ──
 from anycalib.model.anycalib_pretrained import AnyCalib
 from anycalib.cameras.factory import CameraFactory
 
@@ -27,12 +35,10 @@ print("[anycalib] Loading model...")
 t0 = time.time()
 MODEL = AnyCalib(model_id="anycalib_gen")
 MODEL.eval()
-print(f"[anycalib] Model loaded in {time.time() - t0:.1f}s "
-      f"({sum(p.numel() for p in MODEL.parameters()):,} params)")
+TOTAL_PARAMS = sum(p.numel() for p in MODEL.parameters())
+print(f"[anycalib] Model loaded in {time.time() - t0:.1f}s ({TOTAL_PARAMS:,} params)")
 
 
-# ── Undistortion grid builder ──
-
 def _build_undistort_grid(camera, params, h, w, scale=1.0, target_proj="perspective"):
     """Build undistortion sampling grid (mirrors AnyCalibRunner._undistort_grid)."""
     params_b = params[None, ...] if params.ndim == 1 else params
@@ -61,9 +67,7 @@ def _build_undistort_grid(camera, params, h, w, scale=1.0, target_proj="perspect
     return grid, valid
 
 
-# ── Main inference function (runs on GPU via ZeroGPU) ──
-
-@spaces.GPU(duration=60)
+@gpu_decorator
 @torch.no_grad()
 def run_calibration(
     input_image: np.ndarray,
@@ -74,7 +78,7 @@ def run_calibration(
     interp_mode: str,
     k1_threshold: float,
 ):
-    """Full pipeline: predict → fit → undistort at original resolution."""
+    """Full pipeline: predict -> fit -> undistort at original resolution."""
 
     if input_image is None:
         raise gr.Error("Please upload an image.")
@@ -85,19 +89,19 @@ def run_calibration(
     h, w = input_image.shape[:2]
     t_total = time.time()
 
-    # ── Preprocess ──
+    # Preprocess
     x = input_image.astype("float32") / 255.0
-    x = np.transpose(x, (2, 0, 1))  # HWC → CHW
+    x = np.transpose(x, (2, 0, 1))
     x_t = torch.from_numpy(x).to(device)
 
-    # ── Neural network inference ──
+    # Neural network inference
     t0 = time.time()
     out = MODEL.predict(x_t, cam_id=cam_id)
     intrinsics = out["intrinsics"]
     pred_size = out.get("pred_size")
     t_infer = time.time() - t0
 
-    # ── Parse intrinsics ──
+    # Parse intrinsics
     camera = CameraFactory.create_from_id(cam_id)
     num_f = int(camera.NUM_F)
     intr_list = intrinsics.detach().cpu().numpy().astype(np.float64).tolist()
@@ -106,12 +110,10 @@ def run_calibration(
     cx_val, cy_val = intr_list[num_f], intr_list[num_f + 1]
     k1_val = intr_list[num_f + 2] if len(intr_list) > num_f + 2 else 0.0
 
-    # FOV
     f_px = focal[0]
     fov_h = float(2 * np.degrees(np.arctan(w / (2 * f_px)))) if f_px > 0 else 0
     fov_v = float(2 * np.degrees(np.arctan(h / (2 * f_px)))) if f_px > 0 else 0
 
-    # Distortion type
     if k1_val < -0.001:
         dist_type = "Barrel (k1 < 0)"
     elif k1_val > 0.001:
@@ -119,7 +121,6 @@ def run_calibration(
     else:
         dist_type = "Negligible"
 
-    # ── k1 gating ──
     skip_undistort = k1_threshold > 0 and abs(k1_val) < k1_threshold
 
     if skip_undistort:
@@ -127,7 +128,6 @@ def run_calibration(
         valid_frac = 1.0
         t_undistort = 0.0
     else:
-        # ── Undistortion at full resolution ──
         t0 = time.time()
         grid, valid = _build_undistort_grid(
             camera, intrinsics, h, w,
@@ -149,7 +149,8 @@ def run_calibration(
 
     t_total_elapsed = time.time() - t_total
 
-    # ── Build params table ──
+    hw_label = "GPU" if device.type == "cuda" else "CPU"
+
     params_md = f"""
 ### Camera Intrinsics
 
@@ -174,17 +175,16 @@ def run_calibration(
 | **Scale** | `{scale}` |
 | **Target projection** | `{target_proj}` |
 
-### Timing
+### Timing ({hw_label})
 
 | Stage | Time |
 |-------|------|
 | Neural net inference | `{t_infer*1000:.0f}` ms |
 | Undistortion (grid_sample) | `{t_undistort*1000:.0f}` ms |
 | **Total** | **`{t_total_elapsed*1000:.0f}` ms** |
-| Device | `{device}` |
+| Hardware | `{device}` ({hw_label}) |
 """
 
-    # ── Raw JSON ──
     raw_json = json.dumps({
         "intrinsics": {
             "focal_length_px": focal,
@@ -225,12 +225,12 @@ def run_calibration(
 with gr.Blocks() as demo:
 
     gr.Markdown("""
-# AnyCalib — Full-Resolution GPU Camera Calibration
+# AnyCalib — Full-Resolution Camera Calibration
 
 Single-image lens calibration & distortion correction powered by
 [AnyCalib](https://github.com/javrtg/AnyCalib) (DINOv2 ViT-L/14 + LightDPT + ConvexTangentDecoder, ~320M params).
 
-Running on **GPU via ZeroGPU** — no quantization, no resolution limits, full FP32 inference.
+Full FP32 inference, no quantization, no resolution limits. Automatically uses GPU when available.
 
 Upload any image and get the **corrected (undistorted) image** at original resolution,
 plus camera intrinsics, FOV, distortion parameters, and timing.
@@ -302,8 +302,6 @@ plus camera intrinsics, FOV, distortion parameters, and timing.
 4. Image is **undistorted at full resolution** via differentiable grid_sample
 5. All parameters and raw JSON output are displayed
 
-Runs in ~100-500ms on GPU depending on image size.
-
 ### Links
 
 - Raw weights: [SebRincon/anycalib](https://huggingface.co/SebRincon/anycalib) (safetensors)

requirements.txt

@@ -4,4 +4,3 @@ numpy>=1.26.0
 opencv-python-headless>=4.9.0
 anycalib @ git+https://github.com/javrtg/AnyCalib.git@3cf2e5dda92faf80f3548adaa0a8515f807848aa
 safetensors>=0.4.0
-gradio>=4.0.0