Camshowrecordings/model/sam_samantha/5 · Tested & Working

cd model/sam_samantha/5 ls -l Typical files you’ll see:

In short: camshowrecordings/model/sam_samantha/5 points to the of the SAM‑Samantha model inside the CamShowRecordings codebase. 2️⃣ Prerequisites | Requirement | Why You Need It | Quick Install | |-------------|----------------|---------------| | Python ≥ 3.8 | Most model code (PyTorch / TensorFlow) runs on modern Python. | python -V sudo apt-get install python3 (Linux) | | Git | To clone the repo or pull updates. | git --version | | Virtual Environment (venv, conda, poetry) | Keeps dependencies isolated. | python -m venv venv && source venv/bin/activate | | PyTorch ≥ 2.0 (or TensorFlow if the repo uses TF) | Underlying deep‑learning framework. | pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118 | | OpenCV | For loading video frames and visualising results. | pip install opencv-python | | NumPy, tqdm | Common utilities. | pip install numpy tqdm | | Optional: CUDA Toolkit | If you want GPU acceleration. | Follow NVIDIA’s installer for your GPU. | Tip: 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 ). 3️⃣ Repository Layout (Typical) camshowrecordings/ │ ├─ data/ # Raw video recordings, annotation files, etc. │ ├─ model/ │ └─ sam_samantha/ │ ├─ 5/ │ │ ├─ config.yaml # Model hyper‑parameters & architecture │ │ ├─ model.ckpt # Serialized weights (PyTorch checkpoint) │ │ ├─ tokenizer/ # If the model uses any tokenizers │ │ └─ README.md # Model‑specific notes │ ├─ 4/ ... (older versions) │ └─ latest -> 5/ # Symlink to the newest version │ ├─ scripts/ # Example utilities, e.g., run_inference.py │ ├─ notebooks/ # Jupyter notebooks for exploration │ └─ README.md If you only see latest pointing to 5 , you’re already looking at the most recent release. 4️⃣ Getting the Code # 1️⃣ Clone the repo (replace URL with the actual one) git clone https://github.com/yourorg/camshowrecordings.git cd camshowrecordings camshowrecordings/model/sam_samantha/5

# ------------------------------------------------------------------ # 5️⃣ Run inference # ------------------------------------------------------------------ def infer(frame: np.ndarray): x = preprocess(frame, cfg) with torch.no_grad(): # The exact call depends on the model; many SAM‑style models return a mask mask = model(x) # → (B, 1, H, W) logits or probabilities # Post‑process: convert logits → binary mask mask = torch.sigmoid(mask) > 0.5 mask_np = mask.squeeze().cpu().numpy().astype(np.uint8) * 255 return mask_np cd model/sam_samantha/5 ls -l Typical files you’ll see:

# ------------------------------------------------------------------ # 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 | git --version | | Virtual Environment (venv,

device = torch.device(cfg.get("device", "cpu")) model.to(device)

# HWC → CHW and add batch dim tensor = torch.from_numpy(img_norm).permute(2, 0, 1).unsqueeze(0) return tensor.to(device)

if frame_idx % stride == 0: mask = infer(frame) # binary mask (0/255) overlay = cv2.addWeighted(frame, 0.7, cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR), 0.3, 0) out.write(overlay) else: out.write(frame) # write raw frame for non‑processed indices