OpenCV (Open Source Computer Vision) is an open-source computer vision and machine learning software library. It provides a wide range of functionalities for image and video processing, such as image and video capturing, filtering, feature detection, object recognition, tracking, and more. OpenCV was first developed by Intel Corporation in 1999 and was later released as an open-source project under the BSD license.
OpenCV supports various programming languages, including C++, Python, Java, and MATLAB, and can run on different platforms, such as Windows, Linux, macOS, iOS, and Android. It has a large and active community, with many useful resources, tutorials, and sample codes available online.
OpenCV is widely used in different fields, such as robotics, autonomous vehicles, medical imaging, security systems, and more. With its powerful and flexible functionalities, OpenCV provides a valuable tool for developing advanced computer vision and machine learning applications.
⌨️ Reading Images ⌨️ Resizing and Rescaling Frames ⌨️ Drawing Shapes & Putting Text ⌨️ 5 Essential Functions in OpenCV * grayscaling an Image * Blurring an image * edge cascade * Dilating the image * eroding the image * resizing * Cropping ⌨️ Image Transformations ⌨️ Contour Detection
⌨️ Color Spaces ⌨️ Color Channels ⌨️ Blurring ⌨️ BITWISE operations ⌨️ Masking ⌨️ Histogram Computation ⌨️ Thresholding/Binarizing Images ⌨️ Edge Detection
⌨️ Face Detection with Haar Cascades ⌨️ Face Recognition with OpenCV's built-in recognizer
⌨️ Deep Computer Vision: The Simpsons
YOLO (You Only Look Once) is a convolutional neural network that is widely used for real-time object detection.
YOLO detects objects by dividing the input image into a grid and predicting the probability of object presence and its bounding box coordinates for each grid cell. This architecture is much faster than traditional object detection models that require multiple passes through an image.
YOLO also has a high accuracy rate, which makes it popular among researchers and developers.
The newest release of YOLO, YOLOv8, was developed by Alexey Bochkovskiy and his team. YOLOv8 is an advanced version of YOLO that has improved the detection accuracy and is optimized for a wide range of hardware platforms. YOLOv8 has a more powerful feature extractor network that captures finer details and improves the detection of small objects. The architecture also incorporates a multi-scale prediction mechanism that helps in detecting objects at different scales. YOLOv8 has set new standards in object detection accuracy and speed and is expected to be widely adopted in various applications, including self-driving cars, surveillance, and robotics.