Desliza para mostrar el menú

Corner and Blob Detection

Corner Detection

Corner detection is used to identify sharp changes in intensity where two edges meet. It helps in feature matching, object tracking, and structure recognition.

Popular Methods:

Harris corner detector (cv2.cornerHarris): detects corners based on gradient changes;

Shi-Tomasi corner detector (cv2.goodFeaturesToTrack): selects the strongest corners in an image;

Blob Detection

Blob detection finds regions of similar intensity in an image, useful for object detection and tracking.

One of the popular methods for blob detection is SimpleBlobDetector

cv2.SimpleBlobDetector: detects keypoints representing blobs based on size, shape, and intensity.

Tarea

Swipe to start coding

You are given the images of factory (factory) and sunflowers (sunflowers):

Convert factory image to grayscale and store in gray_factory variable;
Convert sunflowers image to grayscale and store in gray_sunflowers variable;
It is necessary for Harris Detector to convert image matrix to float32, do it and store in gray_float;
Apply Harris corner detection and store in harris_corners (recommended parameters blockSize=2, ksize=3, k=0.04);
Use dilate() to improve visibility of harris_corners;
Apply Shi-Tomasi corner detection to image and store in shi_tomasi_corners (recommended parameters gray_factory, maxCorners=100, qualityLevel=0.01, minDistance=10)
Create SimpleBlobDetector_Params object to initialize the parameters and store in params;
Create a blob detector with specified parameters and store in detector;
Detect blob keypoints and store in keypoints.

Solución

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

¿Todo estuvo claro?

¡Gracias por tus comentarios!

Sección 2. Capítulo 8

Pregunte a AI

Pregunte lo que quiera o pruebe una de las preguntas sugeridas para comenzar nuestra charla

Corner and Blob Detection

Corner Detection

Corner detection is used to identify sharp changes in intensity where two edges meet. It helps in feature matching, object tracking, and structure recognition.

Popular Methods:

Harris corner detector (cv2.cornerHarris): detects corners based on gradient changes;

Shi-Tomasi corner detector (cv2.goodFeaturesToTrack): selects the strongest corners in an image;

Blob Detection

Blob detection finds regions of similar intensity in an image, useful for object detection and tracking.

One of the popular methods for blob detection is SimpleBlobDetector

cv2.SimpleBlobDetector: detects keypoints representing blobs based on size, shape, and intensity.

Tarea

Swipe to start coding

You are given the images of factory (factory) and sunflowers (sunflowers):

Convert factory image to grayscale and store in gray_factory variable;
Convert sunflowers image to grayscale and store in gray_sunflowers variable;
It is necessary for Harris Detector to convert image matrix to float32, do it and store in gray_float;
Apply Harris corner detection and store in harris_corners (recommended parameters blockSize=2, ksize=3, k=0.04);
Use dilate() to improve visibility of harris_corners;
Apply Shi-Tomasi corner detection to image and store in shi_tomasi_corners (recommended parameters gray_factory, maxCorners=100, qualityLevel=0.01, minDistance=10)
Create SimpleBlobDetector_Params object to initialize the parameters and store in params;
Create a blob detector with specified parameters and store in detector;
Detect blob keypoints and store in keypoints.

Solución

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

¿Todo estuvo claro?

¡Gracias por tus comentarios!

Sección 2. Capítulo 8

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones