| I'll analyze the FrameVis script and break down its key functionality. | |
| ### Core Purpose | |
| FrameVis is a Python script that creates a visual representation of a video by extracting frames at regular intervals and combining them into a single image. It's essentially creating what's sometimes called a "movie barcode." | |
| ### Key Components | |
| 1. **Main Class: FrameVis** | |
| - Primary class that handles the video frame extraction and visualization | |
| - Default settings for frame dimensions and concatenation direction | |
| - Main method: `visualize()` which does the heavy lifting | |
| 2. **Supporting Classes:** | |
| - `MatteTrimmer`: Handles detection and removal of black bars (letterboxing/pillarboxing) | |
| - `ProgressBar`: Provides console feedback during processing | |
| ### Core Workflow | |
| 1. **Input Processing** | |
| ```python | |
| # Takes key parameters: | |
| - source: video file path | |
| - nframes: number of frames to extract | |
| - height/width: dimensions for output frames | |
| - direction: horizontal/vertical concatenation | |
| - trim: whether to remove black bars | |
| ``` | |
| 2. **Frame Extraction Logic** | |
| ```python | |
| # Calculates frame interval | |
| video_total_frames = video.get(cv2.CAP_PROP_FRAME_COUNT) | |
| keyframe_interval = video_total_frames / nframes | |
| # Frames are extracted at regular intervals: | |
| next_keyframe = keyframe_interval / 2 # Starts from middle of first interval | |
| ``` | |
| 3. **Frame Processing** | |
| - Reads frames using OpenCV | |
| - Optionally trims black bars | |
| - Resizes frames to specified dimensions | |
| - Concatenates frames either horizontally or vertically | |
| 4. **Post-Processing Options** | |
| - `average_image()`: Creates a color average across frames | |
| - `motion_blur()`: Applies directional blur effect | |
| ### Special Features | |
| 1. **Matte Detection** | |
| - Automatically detects and removes letterboxing/pillarboxing | |
| - Samples multiple frames to ensure consistent detection | |
| - Uses threshold-based detection for black bars | |
| 2. **Flexible Output** | |
| - Can output horizontally (traditional movie barcode) | |
| - Can output vertically (stacked frames) | |
| - Supports automatic dimension calculation | |
| 3. **Time-Based Frame Selection** | |
| ```python | |
| # Can specify frames either by: | |
| - Total number of frames (-n/--nframes) | |
| - Time interval between frames (-i/--interval) | |
| ``` | |
| ### Command Line Interface | |
| ```python | |
| # Supports various arguments: | |
| -n/--nframes: Number of frames | |
| -i/--interval: Time between frames | |
| -h/--height: Frame height | |
| -w/--width: Frame width | |
| -d/--direction: Concatenation direction | |
| -t/--trim: Auto-trim black bars | |
| -a/--average: Average colors | |
| -b/--blur: Apply motion blur | |
| -q/--quiet: Suppress output | |
| ``` | |
| ### Error Handling | |
| - Validates input parameters | |
| - Checks for file existence | |
| - Ensures frame counts are valid | |
| - Handles video reading errors | |
| ### Performance Considerations | |
| - Uses OpenCV for efficient video processing | |
| - Maintains float precision for frame intervals | |
| - Only loads required frames rather than entire video | |
| This is a well-structured script that provides a robust set of features for video frame visualization while maintaining good error handling and user feedback through progress bars and console output. |