| import os |
| os.environ["CUDA_VISIBLE_DEVICES"] = "1" |
|
|
| from gradio_client import Client, handle_file |
| from typing import Any, Dict, List, Optional, Tuple, Union |
| import json |
|
|
| |
| svision_client = Client("VeuReu/svision") |
|
|
|
|
| def extract_scenes(video_path: str, threshold: float = 30.0, offset_frames: int = 5, crop_ratio: float = 0.1): |
| """ |
| Call the /scenes_extraction endpoint of the remote Space VeuReu/svision. |
| |
| Parameters |
| ---------- |
| video_path : str |
| Path to the input video file. |
| threshold : float, optional |
| Scene change detection threshold; higher values make detection less sensitive. |
| offset_frames : int, optional |
| Number of frames to include before and after a detected scene boundary. |
| crop_ratio : float, optional |
| Ratio for cropping borders before performing scene detection. |
| |
| Returns |
| ------- |
| Any |
| Response returned by the remote /scenes_extraction endpoint. |
| """ |
| result = svision_client.predict( |
| video_file={"video": handle_file(video_path)}, |
| threshold=threshold, |
| offset_frames=offset_frames, |
| crop_ratio=crop_ratio, |
| api_name="/scenes_extraction" |
| ) |
| return result |
|
|
|
|
| def keyframes_every_second_extraction(video_path: str): |
| """ |
| Call the /keyframes_every_second_extraction endpoint of the remote Space VeuReu/svision. |
| |
| Parameters |
| ---------- |
| video_path : str |
| Path to the input video file. |
| |
| Returns |
| ------- |
| Any |
| Response returned by the remote /keyframes_every_second_extraction endpoint. |
| """ |
| result = svision_client.predict( |
| video_path={"video": handle_file(video_path)}, |
| api_name="/keyframes_every_second_extraction" |
| ) |
| return result |
|
|
|
|
| def add_ocr_and_faces(imagen_path: str, informacion_image: Dict[str, Any], face_col: List[Dict[str, Any]]) -> Dict[str, Any]: |
| """ |
| Call the /add_ocr_and_faces endpoint of the remote Space VeuReu/svision. |
| |
| This function sends an image together with metadata and face collection data |
| to perform OCR, face detection, and annotation enhancement. |
| |
| Parameters |
| ---------- |
| imagen_path : str |
| Path to the input image file. |
| informacion_image : Dict[str, Any] |
| Dictionary containing image-related metadata. |
| face_col : List[Dict[str, Any]] |
| List of dictionaries representing detected faces or face metadata. |
| |
| Returns |
| ------- |
| Dict[str, Any] |
| Processed output containing OCR results, face detection data, and annotations. |
| """ |
| informacion_image_str = json.dumps(informacion_image) |
| face_col_str = json.dumps(face_col) |
| result = svision_client.predict( |
| image=handle_file(imagen_path), |
| informacion_image=informacion_image_str, |
| face_col=face_col_str, |
| api_name="/add_ocr_and_faces" |
| ) |
| return result |
|
|
|
|
| def extract_descripcion_escena(imagen_path: str) -> str: |
| """ |
| Call the /describe_images endpoint of the remote Space VeuReu/svision. |
| |
| This function sends an image to receive a textual description of its visual content. |
| |
| Parameters |
| ---------- |
| imagen_path : str |
| Path to the input image file. |
| |
| Returns |
| ------- |
| str |
| Description generated for the given image. |
| """ |
| print("Calling svision to describe the scene...") |
| result = svision_client.predict( |
| images=[{"image": handle_file(imagen_path)}], |
| api_name="/describe_images" |
| ) |
| return result |
|
|
