- OpenCV is a Python library that allows you to perform image processing and computer vision tasks. It provides a wide range of features, including object detection, face recognition, and tracking.
- OpenCV is an open-source software library for computer vision and machine learning. The OpenCV full form is Open Source Computer Vision Library.
Install using Anaconda:
conda install -c conda-forge opencv
For Windows:
pip install opencv-python- Check out this repo PythonOpenCVBasics for Basic Code.
- How to Load Data
- Access Image Properties
- Create ImageMask
- Image reshape
- Rotation Image
- Sobel operation
Part 1 Code here.
- Histogram
- GaussianBlur
- cannyBlurWide
- cannyBlurMiddle
- cannyBlurFit
Histograms are useful in OpenCV for a variety of image processing and computer vision tasks, including image analysis, image enhancement, object detection, and image segmentation. They provide important insights into the distribution of pixel intensities in an image and can be used to define a color or texture model of an object of interest. Histograms are a powerful tool for analyzing and processing images in OpenCV, and they are used extensively in many different applications.
Histograms are commonly used to show the correlation between different color channels in an image because they provide a visual representation of the distribution of pixel intensities for each channel. By comparing the histograms for different color channels, we can get an idea of how much overlap there is between the different color channels, and we can identify any patterns or trends that might be present
The Gaussianblur function is often used to reduce image noise and smooth out the image. The function works by convolving the image with a Gaussian kernel, which is a type of filter that emphasizes low-frequency components of the image and suppresses high-frequency components.
The Canny edge detection algorithm is a powerful tool for detecting edges in images and is widely used in many different computer vision applications. It provides accurate and tunable edge detection, is computationally efficient, and can be used as a preprocessing step for other algorithms.
