Topic: camshowrecordings/model/sam_samantha/5 This guide walks you through everything you need to know to locate, load, and work with the SAM‑Samantha model (version 5) that lives inside the camshowrecordings project. It assumes a typical development environment (Linux/macOS/Windows) and a basic familiarity with Python and machine‑learning model handling. 1️⃣ What Is This Path About? | Part of the Path | Meaning | |------------------|---------| | camshowrecordings | Top‑level repository / package that stores camera‑related recordings, utilities, and ML models used for analysis (e.g., object detection, segmentation, activity classification). | | model | Directory that groups all trained models and their supporting files. | | sam_samantha | Specific model family. “SAM” often stands for Segment Anything Model , and “Samantha” is the custom‑trained variant. | | 5 | The version number (v5). Newer releases may appear as 6 , 7 , … and usually contain performance or architecture upgrades. |
# ------------------------------------------------------------------ # 2️⃣ Helper: build the model (adjust to your repo's factory function) # ------------------------------------------------------------------ def build_model(cfg): """ Replace the body of this function with the actual model‑building logic from camshowrecordings. """ from camshowrecordings.models.sam import SamSamantha # Example import model = SamSamantha( backbone=cfg["model"]["backbone"], img_size=cfg["model"]["image_size"], num_classes=cfg["model"]["num_classes"] ) return model camshowrecordings/model/sam_samantha/5
img = cv2.imread(args.image_path) if img is None: raise FileNotFoundError(f"Cannot read args.image_path") | Part of the Path | Meaning |
# 2️⃣ Create & activate a virtual env (optional but recommended) python -m venv .venv source .venv/bin/activate # Windows: .venv\Scripts\activate “SAM” often stands for Segment Anything Model ,
# Normalize img_norm = img_rgb.astype(np.float32) / 255.0 mean = np.array(cfg["preprocess"]["mean"]) std = np.array(cfg["preprocess"]["std"]) img_norm = (img_norm - mean) / std
if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("input_video", type=Path) parser.add_argument("output_video", type=Path) parser.add_argument("--stride", type=int, default=5, help="Run inference every N frames (default=5)") args = parser.parse_args() process_video(args.input_video, args.output_video, args.stride)
mask = infer(img)