fps = cap.get(cv2.CAP_PROP_FPS) w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
| File | Description | |------|-------------| | config.yaml | Human‑readable config (input size, number of classes, preprocessing steps). | | model.ckpt | Serialized weights (PyTorch format). | | tokenizer/ | Byte‑pair‑encoding files if the model uses textual prompts. | | README.md | Often contains version‑specific notes, e.g., known bugs or recommended hardware. |
# ------------------------------------------------------------------ # 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
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. |
cd model/sam_samantha/5 ls -l Typical files you’ll see:
# Visualise side‑by‑side overlay = cv2.addWeighted(img, 0.7, cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR), 0.3, 0) cv2.imshow("Original", img) cv2.imshow("Mask", mask) cv2.imshow("Overlay", overlay) cv2.waitKey(0) cv2.destroyAllWindows()
# ------------------------------------------------------------------ # 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))
