Common Image Tools
Description
common_image_tools is a repository containing a curated collection of commonly used image manipulation functions for computer vision projects. These tools provide a set of handy functions for performing various image processing tasks, such as resizing, cropping, filtering, and more.
Features
- Resizing: Easily resize images to desired dimensions.
- Cropping: Crop images to focus on specific regions.
- Filtering: Apply various filters to enhance or modify images.
- Conversion: Convert images between different formats.
- Visualization: Tools for visualizing image data.
- Video Source: Flexible interface for handling different types of video inputs.
Installation
To install common_image_tools, run the following command:
pip install common-image-tools
or using uv:
uv add common-image-tools
Usage
Video Source
The VideoSource class provides a flexible interface for handling different types of video inputs with support for various OpenCV backends and hardware acceleration on Jetson devices.
Features
- Support for multiple video source types:
- RTSP streams
- Video files (MP4)
- Webcam devices
- Configurable frame dimensions and FPS
- Multiple backend options:
- OpenCV default
- GStreamer
- GStreamer with Jetson hardware acceleration
- Automatic backend selection based on system capabilities
Basic Usage
from common_image_tools import VideoSource
import cv2
# Create a video source
source = VideoSource(
"video.mp4",
target_frame_height=720,
target_frame_width=1280,
target_fps=30
)
# Use with OpenCV
cap = cv2.VideoCapture(source.parsed_source)
Different Source Types
# RTSP Stream
rtsp_source = VideoSource("rtsp://example.com/stream")
# Video File
file_source = VideoSource("path/to/video.mp4")
# Webcam
webcam_source = VideoSource("/dev/video0")
# Webcam
webcam_source = VideoSource(0)
Backend Selection
from common_image_tools import VideoSource, OpencvBackendMode
# Auto backend selection (default)
source = VideoSource("video.mp4")
# Force OpenCV default backend
source = VideoSource(
"video.mp4",
opencv_backend=OpencvBackendMode.OPENCV_DEFAULT
)
# Force GStreamer backend
source = VideoSource(
"video.mp4",
opencv_backend=OpencvBackendMode.OPENCV_GSTREAMER
)
# Use Jetson-optimized GStreamer backend
source = VideoSource(
"video.mp4",
opencv_backend=OpencvBackendMode.OPENCV_GSTREAMER_JETSON
)
API Reference
VideoSource
VideoSource(
source,
target_frame_height: int = None,
target_frame_width: int = None,
target_fps: int = None,
opencv_backend: OpencvBackendMode = OpencvBackendMode.AUTO
)
Conversion
Operation
Tool
Pixelation (privacy mosaic)
pixelate_person mosaics a person in place, from their keypoints and/or box. The cell size comes
from the whole image (64 cells across its long side by default), so a small or distant person is
censored as hard as a close one, while the silhouette stays readable:
from common_image_tools.tool import pixelate_person
for pose in poses:
# keypoints as {"x", "y", "score"} dicts or (x, y[, score]) rows, bbox as (x, y, w, h),
# both normalized 0-1 (normalized=False for pixels)
pixelate_person(frame, pose.keypoints, pose.bbox)
The building blocks are public too, for other shapes: bbox_from_points builds the padded box
around a set of pixel points, and pixelate_area mosaics any (x0, y0, x1, y1) area.
Verification
Visualization
Contributing
Contributions are welcome! If you have any suggestions, improvements, or new features to add, please open an issue or submit a pull request. Make sure to follow the existing coding style and include appropriate tests.
License
This project is licensed under the MIT License - see the LICENSE file for details.
1 maintainer
Metadata
1 maintainer
Release files for common-image-tools 1.1.0
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