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import warnings
warnings.filterwarnings("ignore")
import gradio as gr
import re
from typing import Dict, List
import csv
import os
import torch
from src.video_model import describe_video
from src.utils import parse_string, parse_annotations
# Function to save data to a CSV file
def save_to_csv(observations: List[Dict], output_dir: str = "outputs") -> str:
if not os.path.exists(output_dir):
os.makedirs(output_dir)
csv_file = os.path.join(output_dir, "video_observations.csv")
with open(csv_file, mode='w', newline='') as file:
writer = csv.writer(file)
writer.writerow(["video_name", "standing", "hands.free", "indoors", "screen.interaction_yes"])
for observation in observations:
writer.writerow([
observation['video_name'],
observation['standing'],
observation['hands.free'],
observation['indoors'],
observation['screen.interaction_yes']
])
return csv_file
# Function to process a single video and return the observation data
def process_single_video(video_path: str, sitting, hands, location, screen) -> Dict:
video_name = os.path.basename(video_path) # Extract video name from the path
query = "Describe this video in detail and answer the questions"
additional_info = []
if sitting:
additional_info.append("Is the subject in the video standing or sitting?")
if hands:
additional_info.append("Is the subject holding any object in their hands, if so the hands are not free else they are free?")
if location:
additional_info.append("Is the subject present indoors or outdoors?")
if screen:
additional_info.append("Is the subject interacting with a screen in the background by facing the screen?")
end_query = """Provide the results in <annotation> tags, where 0 indicates False, 1 indicates True, and None indicates that no information is present. Follow the below examples:
<annotation>indoors: 0</annotation>
<annotation>standing: 1</annotation>
<annotation>hands.free: 0</annotation>
<annotation>screen.interaction_yes: 0</annotation>
"""
final_query = query + " " + " ".join(additional_info)
final_prompt = final_query + " " + end_query
# Assuming your describe_video function handles the video processing
final_response = describe_video(video_path, final_prompt)
conditions = {
'standing': (standing, 'standing: 1', 'standing: None'),
'hands': (hands, 'hands.free: 1', 'hands.free: None'),
'location': (location, 'indoors: 1', 'indoors: None'),
'screen': (screen, 'screen.interaction_yes: 1', 'screen.interaction_yes: None')
}
for key, (condition, to_replace, replacement) in conditions.items():
if not condition:
final_response = final_response.replace(to_replace, replacement)
return final_response
# Function to process all videos in a folder
def process_multiple_videos(video_files: List[str], sitting, hands, location, screen):
all_observations = []
for video_path in video_files:
observation = process_single_video(video_path, sitting, hands, location, screen)
if "error" not in observation:
all_observations.append(observation)
else:
print(observation["error"]) # Log any errors
# Clear GPU cache
torch.cuda.empty_cache()
# Save all observations to a CSV file and return the file path
csv_file = save_to_csv(all_observations)
return "Processing completed. Download the CSV file.", csv_file
# Gradio interface
def gradio_interface(video_files, sitting, hands, location, screen):
return process_multiple_videos(video_files, sitting, hands, location, screen)
# Inputs
video_files = gr.File(file_count="multiple", file_types=["video"], label="Upload multiple videos")
sitting = gr.Checkbox(label="Sitting/Standing")
hands = gr.Checkbox(label="Hands Free/Not Free")
location = gr.Checkbox(label="Indoors/Outdoors")
screen = gr.Checkbox(label="Screen Interaction")
# Outputs
response = gr.Textbox(label="Status")
download_link = gr.File(label="Download CSV")
# Gradio interface setup
interface = gr.Interface(
fn=gradio_interface,
inputs=[video_files, sitting, hands, location, screen],
outputs=[response, download_link],
title="Batch Video Annotation",
description="Upload multiple videos and process them sequentially, saving the results to a downloadable CSV file.",
theme=gr.themes.Soft(primary_hue="red", secondary_hue="red"),
allow_flagging="never"
)
# Launch interface
interface.launch(debug=False)
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