Prune unused helpers and video stubs

README.md

@@ -9,7 +9,7 @@ Analyze aerial RGB imagery to detect safe drone landing sites. Combines monocula
 </p>
 
 ## What’s inside
-- **Main app (`landing_app.py`)** — runs full inference with adjustable thresholds, overlays, and camera assumptions; >8GB VRAM required.
+- **Main app (`landing_app.py`)** — runs full inference with adjustable thresholds, overlays, and camera assumptions; requires >8GB VRAM (assuming default 1024px processing resolution); runtime is ~1000ms per image.
 - **Curated gallery (`demo/curated_app.py`)** — precomputed PNG/JPG/JSON artifacts for fast, zero-GPU browsing.
 
 ## Prereqs

app/config.py

@@ -5,9 +5,7 @@ from pathlib import Path
 
 VISLOC_DIR = Path("data/Image/VISLOC")
 IMAGE_ROOT = Path("data/Image")
-VIDEO_DIR = Path("data/Video")
 IMAGE_EXTS = (".jpg", ".jpeg", ".png", ".JPG", ".JPEG", ".PNG")
-VIDEO_EXTS = {".mp4", ".avi", ".mov", ".mkv", ".flv", ".wmv", ".webm", ".m4v"}
 DEFAULT_ALTITUDE_M = 450.0
 ASSUMED_FOV_DEG = 90.0
 DEFAULT_MODEL_ID = "depth-anything/DA3MONO-LARGE"
@@ -51,9 +49,7 @@ DEFAULT_ANALYZER_SETTINGS = AnalyzerSettings()
 __all__ = [
     "VISLOC_DIR",
     "IMAGE_ROOT",
-    "VIDEO_DIR",
     "IMAGE_EXTS",
-    "VIDEO_EXTS",
     "DEFAULT_ALTITUDE_M",
     "ASSUMED_FOV_DEG",
     "DEFAULT_MODEL_ID",

app/data_sources.py

@@ -3,7 +3,7 @@ from __future__ import annotations
 from functools import lru_cache
 from pathlib import Path
 
-from .config import IMAGE_EXTS, IMAGE_ROOT, VIDEO_DIR, VIDEO_EXTS, VISLOC_DIR
+from .config import IMAGE_EXTS, IMAGE_ROOT, VISLOC_DIR
 
 
 @lru_cache(maxsize=1)
@@ -14,14 +14,6 @@ def list_visloc_images() -> list[Path]:
     return sorted(files)
 
 
-@lru_cache(maxsize=1)
-def list_videos() -> list[Path]:
-    if not VIDEO_DIR.exists():
-        return []
-    files = [p for p in VIDEO_DIR.iterdir() if p.suffix.lower() in VIDEO_EXTS]
-    return sorted(files)
-
-
 @lru_cache(maxsize=1)
 def list_all_data_inputs() -> list[str]:
     paths: list[Path] = []
@@ -46,13 +38,11 @@ def list_all_data_inputs() -> list[str]:
 
 def clear_caches() -> None:
     list_visloc_images.cache_clear()
-    list_videos.cache_clear()
     list_all_data_inputs.cache_clear()
 
 
 __all__ = [
     "list_visloc_images",
-    "list_videos",
     "list_all_data_inputs",
     "clear_caches",
 ]

app/depth_pipeline.py

@@ -48,27 +48,6 @@ def compute_roof_mask_depth(depth: np.ndarray, aggressiveness: float = 1.3, morp
     return mask > 0
 
 
-def fit_plane_ransac(points: np.ndarray, values: np.ndarray, iterations: int = 200, threshold: float = 0.01):
-    best_coef = None
-    best_inliers = -1
-    n_samples = points.shape[0]
-    if n_samples < 3:
-        return None
-    for _ in range(iterations):
-        idx = np.random.choice(n_samples, 3, replace=False)
-        A = np.concatenate([points[idx], np.ones((3, 1))], axis=1)
-        try:
-            coef = np.linalg.lstsq(A, values[idx], rcond=None)[0]
-        except np.linalg.LinAlgError:
-            continue
-        residuals = np.abs(points[:, 0] * coef[0] + points[:, 1] * coef[1] + coef[2] - values.flatten())
-        inliers = np.sum(residuals < threshold)
-        if inliers > best_inliers:
-            best_inliers = inliers
-            best_coef = coef
-    return best_coef
-
-
 def remove_global_plane(depth: np.ndarray, method: str = "least_squares") -> np.ndarray:
     if depth.ndim != 2:
         return depth
@@ -205,7 +184,6 @@ __all__ = [
     "DepthEngine",
     "compute_roof_mask_depth",
     "crop_nonblack",
-    "fit_plane_ransac",
     "pick_flat_patch",
     "remove_global_plane",
     "smooth_depth",