Camshowrecordings/model/sam_samantha/5 ((new)) -

# 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

# ------------------------------------------------------------------ # 4️⃣ Pre‑process a single frame (example uses OpenCV) # ------------------------------------------------------------------ def preprocess(img: np.ndarray, cfg) -> torch.Tensor: # Resize while keeping aspect ratio (optional) target_sz = cfg["model"]["image_size"] img_resized = cv2.resize(img, (target_sz, target_sz)) camshowrecordings/model/sam_samantha/5

Most projects ship a requirements.txt or environment.yml . After cloning the repo, just run pip install -r requirements.txt (or conda env create -f environment.yml ). # Normalize img_norm = img_rgb

cd model/sam_samantha/5 ls -l

cap.release() out.release() print(f"Finished → out_path") camshowrecordings/model/sam_samantha/5

# 2️⃣ Create & activate a virtual env (optional but recommended) python -m venv .venv source .venv/bin/activate # Windows: .venv\Scripts\activate