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OpenCV Articles
Page 3 of 11
How to detect a face and draw a bounding box around it using OpenCV Python?
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 use haarcascade_frontalface_alt.xml as a "haar cascade" XML file for face detection. How to Download Haarcascades? You can find different haarcascades following the GitHub website address − https://github.com/opencv/opencv/tree/master/data/haarcascades To download the haar cascade for face detection, click on the haarcascade_frontalface_alt.xml file. Open it in raw format, right click and save. ...
Read MoreHow to perform image transpose using OpenCV Python?
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. Steps We could use the following steps to transpose an input image − Import required libraries OpenCV and Matplotlib. Make sure you have already installed them. Read the input image using cv2.imread(). Specify the full path of the image. Assign the image to a variable img. Transpose the input ...
Read MoreColor quantization in an image using K-means in OpenCV Python?
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 clustering for color quantization. Steps To implement color quantization in an image using K-means clustering, you could follow the steps given below − Import required libraries OpenCV and NumPy. Make sure you have already installed them. Read two input images using cv2.imread() method. Specify the full path of the ...
Read MoreHow to create a depth map from stereo images in OpenCV Python?
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 object. Steps To create a depth map from the stereo images, you could follow the steps given below − Import the required libraries OpenCV, Matplotlib and NumPy. Make sure you have already installed them. Read two input images using cv2.imread()method as grayscale images. Specify the full path of ...
Read MoreHow to blur faces in an image using OpenCV Python?
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 can find different haarcascades following the GitHub website address − https://github.com/opencv/opencv/tree/master/data/haarcascades To download a haarcascade for face detection, click the haarcascade_frontalface_alt.xml file. Open it in raw format, right click and save. Steps You could follow the steps given below to blur faces in an image − Import ...
Read MoreHow to implement ORB feature detectors in OpenCV Python?
ORB (Oriented FAST and Rotated BRIEF) is a fusion of FAST keypoint detector and BRIEF descriptors with many changes to enhance the performance. To implement ORB feature detector and descriptors, you could follow the steps given below Import the required libraries OpenCV and NumPy. Make sure you have already installed them. Read the input image using cv2.imread() method. Specify the full path of the image. Convert the input image to grayscale image using cv2.cvtColor() method. Initiate the ORB object with default values using orb=cv2.ORB_create(). Detect and compute the feature keypoints 'kp' and descriptor 'des' in the ...
Read MoreHow to detect and draw FAST feature points in OpenCV Python?
FAST (Features from Accelerated Segment Test) is a high speed corner detection algorithm. We use the FAST algorithm to detect features in the image. We first create a FAST object with cv2.FastFeatureDetector_create(). Then detect the feature points using fast.detect() where fast is the created FAST object. To draw featurepoints, we use cv2.drawKeypoints(). Steps To detect and draw feature points in the input image using the FAST feature detector, you could follow the steps given below Import the required libraries OpenCV and NumPy. Make sure you have already installed them. Read the input image using cv2.imread() method. Specify the full ...
Read MoreOpenCV Python – How to detect and draw keypoints in an image using SIFT?
SIFT (Scale-Invariant Feature Transform ) is scale invariant feature descriptor. It detects keypoints in the image and computes its descriptors. We first create a SIFT object with cv2.SIFT_create(). Then detect the keypoints using sift.detect() where sift is the created SIFT object. To draw keypoints, we use cv2.drawKeypoints(). Steps To detect and draw keypoints in the input image using SIFT algorithm, you could follow the steps given below Import the required libraries OpenCVand NumPy. Make sure you have already installed them. Read the input image using cv2.imread() method. Specify the full path of the image. Convert the ...
Read MoreHow to perform matrix transformation in OpenCV Python?
The cv2.transform() function performs the matrix transformation of each element of the input array. We could apply this transformation directly on the image as the images are NumPy ndarrays in OpenCV. To use this function, we should first define a transformation matrix m. The number of channels in the output will be the same as the number of rows in the transformation matrix m. Steps To find matrix transform of an input image, you could follow the steps given below- Import the required libraries OpenCV and NumPy. Make sure you have already installed them. Read the input ...
Read MoreHow to extract the foreground of an image using OpenCV Python?
We apply the cv2.grabCut() method to extract the foreground in an image. For detailed approach please follow the steps given below − Import the required libraries OpenCV and NumPy. Make sure you have already installed them Read the input image using cv2.imread() method. Specify the full image path. Define the variables: mask, bgdModel and fgdModel. Define the coordinates of a rectangle "rect" which includes the foreground object in the format (x, y, w, h). The correct coordinates are very important to extract the meaningful foreground. Apply grabCut() algorithm to extract the foreground of the input image. Pass mask, ...
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