Delete tabs/tab_fft.py

tabs/tab_fft.py

@@ -1,101 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Help Tab
-
-Created on Sat Nov  8 11:58:29 2025
-@author: standarduser
-"""
-
-import gradio as gr
-        
-        
-def create_tab_image_fft(tab_label):
-    """Creates a tab for help text"""
-    with gr.TabItem(tab_label):
-        gr.Markdown("""
-# SVAT - Synthetic Video Analyze Tool
-
-## Quick Start
-
-1. **Load Video:** Go to "Video-Frames" tab and upload a video
-2. **Navigate Frames:** Use slider or buttons to move through frames
-3. **Apply Transformations:** Click transformation buttons to analyze frames
-4. **Annotate:** Draw on frames in "Annotations" tab
-5. **Analyze Video:** Use "Video Analysis" tab for global analysis
-
-## Frame Transformations
-
-### Laplacian High-Pass
-Emphasizes high-frequency details and edges. Useful for detecting sharpness artifacts.
-
-### FFT Spectrum
-Shows frequency domain representation with viridis colormap (blue-green-yellow).
-Reveals periodic patterns and compression artifacts.
-
-### Error Level Analysis (ELA)
-Detects JPEG compression artifacts by re-compressing the image.
-Lower quality = more visible differences in manipulated areas.
-
-### Wavelet Decomposition
-Multi-scale frequency analysis showing LL, LH, HL, HH subbands.
-Reveals different frequency components.
-
-### Noise Extraction
-Isolates high-frequency noise via high-pass filtering.
-Shows noise patterns that might indicate generation artifacts.
-
-### YCbCr Channels
-Separates luminance (Y) and chrominance (Cb, Cr) channels.
-Useful for detecting color space artifacts.
-
-### Gradient Magnitude
-Visualizes edge strength using Sobel operator.
-Shows edge consistency.
-
-### Histogram Stretching (CLAHE)
-Adaptive contrast enhancement that preserves local details.
-
-## Video Analysis
-
-### Mean FFT
-Calculates average FFT across all frames to detect:
-- Consistent frequency patterns in AI-generated videos
-- Generator-specific fingerprints
-- Temporal artifacts
-
-## Annotation Modes
-
-**Per Frame (A):** Separate drawings for each frame  
-**Global (B):** One drawing overlaid on all frames
-
-## Tips for AI Detection
-
-- Look for **repeating patterns** in FFT spectrum
-- Check **ELA** for inconsistent compression levels
-- Use **Mean FFT** to find generator fingerprints
-- Compare **noise patterns** between frames
-- Watch for **unnatural frequency distributions**
-
-## Keyboard Shortcuts
-
-*Navigation:*
-- Use frame slider for quick navigation
-- Click ◀/▶ buttons for precise frame control
-
-## System Requirements
-
-- Python 3.8+
-- Gradio 6.x
-- OpenCV
-- NumPy 2.x
-- Pillow
-- Matplotlib
-
-## About
-
-SVAT is designed to help identify synthetic/AI-generated video content through various image analysis techniques.
-
-Version: 1.0  
-Updated: 2025
-        """)