Camshowrecordings/model/sam_samantha/5 Best 〈90% Genuine〉

python run_inference.py path/to/your/frame.jpg

import argparse import cv2 from pathlib import Path from run_inference import infer, model, preprocess, cfg # reuse the functions above

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 ).

# ------------------------------------------------------------------ # 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 camshowrecordings/model/sam_samantha/5

# ------------------------------------------------------------------ # 3️⃣ Load checkpoint # ------------------------------------------------------------------ ckpt_path = Path(__file__).parent / "model" / "sam_samantha" / "5" / "model.ckpt" model = build_model(cfg) state_dict = torch.load(ckpt_path, map_location="cpu") model.load_state_dict(state_dict) model.eval()

python video_segment.py recordings/2024-03-15.mp4 recordings/2024-03-15_segmented.mp4 --stride 3

def process_video(in_path: Path, out_path: Path, stride: int = 5): cap = cv2.VideoCapture(str(in_path)) if not cap.isOpened(): raise RuntimeError(f"Cannot open in_path") python run_inference

# 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

# ----------------------------------------------------------- # file: run_inference.py # ----------------------------------------------------------- import yaml import torch import cv2 import numpy as np from pathlib import Path

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 │ ├─ model/ │ └─ sam_samantha/ │ ├─

: Global audiences can access streams missed during live broadcasts.

Decoding Adult Webcam Archives: A Deep Dive into Model Content Categorization

If you have a GPU, make sure torch.cuda.is_available() returns True . The script will automatically use the device defined in config.yaml .