A "haar cascade classifier" is an effective machine learning based approach for object detection. To train a haar cascade classifier for eye detection, the algorithm initially needs a lot of positive images (images of eyes) and negative images (images without eyes). Then the classifier is trained from these positive and ... Read More

OpenCV provides us with different types of mouse events. There are different types of muse events such as left or right button click, mouse move, left button double click etc. A mouse event returns the coordinates (x, y) of the mouse event. To perform an action when an event is ... Read More

We detect a face in an image using a haar cascade classifier. A haar cascade classifier is an effective machine learning based approach for object detection. We can train our own haar cascade for training data but here we use already trained haar cascades for face detection. We will ... Read More

In OpenCV, the image is NumPy ndarray. The image transpose operation in OpenCV is performed as the transpose of a NumPy 2D array (matrix). A matrix is transposed along its major diagonal. A transposed image is a flipped image over its diagonal. We use cv2.transpose() to transpose an image. ... Read More

In the process of Color Quantization the number of colors used in an image is reduced. One reason to do so is to reduce the memory. Sometimes, some devices can produce only a limited number of colors. In these cases, color quantization is performed. We use cv2.kmeans() to apply k-means ... Read More

A depth map can be created using stereo images. To construct a depth map from the stereo images, we find the disparities between the two images. For this we create an object of the StereoBM class using cv2.StereoBM_create() and compute the disparity using stereo.comput(). Where stereo is the created StereoBM ... Read More

We implement feature matching between two images using Scale Invariant Feature Transform (SIFT) and FLANN (Fast Library for Approximate Nearest Neighbors). The SIFT is used to find the feature keypoints and descriptors. A FLANN based matcher with knn is used to match the descriptors in both images. We use cv2.FlannBasedMatcher() ... Read More

We use Scale Invariant Feature Transform (SIFT) feature descriptor and Brute Force feature matcher to implement feature matching between two images. The SIFT is used to find the feature keypoints and descriptors in the images. A Brute Force matcher is used to match the descriptors in both images. Steps To ... Read More

To match the keypoints of two images, we use ORB (Oriented FAST and Rotated BRIEF) to detect and compute the feature keypoints and descriptors and Brute Force matcher to match the descriptors in both images. Steps To match keypoints of two images using the ORB feature detector and Brute ... Read More

To blur faces in an image first we detect the faces using a haar cascade classifier. OpenCV provides us with different types of trained haarcascades for object detection. We use haarcascade_frontalface_alt.xml as a haarcascade xml file. To blur the face area, we apply the cv2.GaussianBlur(). How to Download Haarcascade? You ... Read More