opencv / opencv/opencv-python

Orb Cuda computeAsync not using updated keypoints

Abierto
#950 1 comentario 0 reacciones 1 asignado Ver en GitHub

@asmorkalov ya está trabajando en esto.

Desde el 21/5/2024.

Lenguaje dominante
Python
Estrellas
5.4k
Forks
1k
Merge medio
22 h 17 min
PR fusionados (30 d)
3

Descripción

I am using the ORB cuda CV2 detector. I need to filter the keypoint returned from detect and then compute the descriptors using the updated keypoints.

Unfortunately it seems that the computeAsync uses the keypoints received from the detect.
The number of keypoints returned from detectAsync is 3000 (max number defined in orb cuda create). The filtered keypoints is 1700 but the number of descriptors is 3000.

I know that I can use detectAndComputeAsync and filter after but is much time consuming.

I am using opencV 4.8.0 with python 3.8 on windows 64bit PC.
thanks,
Iliya

Code example
##################################

            #detect keypoints
            cuMat1 = cv2.cuda_GpuMat()
            cuMat1.upload(image)
            keypoints_detected_GPU = detector.detectAsync(cuMat1, mask=None)
            keypoints_detected = detector.convert(keypoints_detected_GPU)
            
            #filter keypoints
            keypoints = FilterFunction(keypoints_detected )
            
            #upload filtered keypoints
            keypoints_np = np.array([
                [kp.pt[0] for kp in keypoints],  # x-coordinate
                [kp.pt[1] for kp in keypoints],  # y-coordinate
                [kp.response for kp in keypoints],  # response
                [kp.angle for kp in keypoints],  # angle
                [kp.octave for kp in keypoints],  # octave
                [kp.size for kp in keypoints],  # size
            ], dtype=np.float32)
            
            keypointsGpu = cv2.cuda_GpuMat()
            keypointsGpu.upload(keypoints_np)

            #Compute descriptor
            keypointsGpuCompute, descriptorsGpuCompute = detector.computeAsync(cuMat1, keypointsGpu)
            keypoints_np1 = keypointsGpuCompute.download()
            
            keypoints_list = []
            num_keypoints = keypoints_np1.shape[1]
            
            for i in range(num_keypoints):
                x = keypoints_np1[0, i]
                y = keypoints_np1[1, i]
                response = keypoints_np1[2, i]
                angle = keypoints_np1[3, i]
                octave = keypoints_np1[4, i]
                size = keypoints_np1[5, i]
            
                # Create cv2.KeyPoint object and append to the list
                kp = cv2.KeyPoint(x, y, size, angle, response, int(octave), -1)
                keypoints_list.append(kp)
            keypoints = keypoints_list

            #download descriptors
            descriptors = descriptorsGpuCompute.download()

Guía de contribución

Abrir la guía de contribución

Primeros pasos

  1. Lee el issue completo y luego la guía de contribución del proyecto.
  2. Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
  3. Haz un fork del repositorio y trabaja en una rama.
  4. Abre un pull request que haga referencia al número del issue.

Evaluación

Este issue todavía no se ha evaluado.

Recibe los nuevos issues en tu correo

Un resumen breve de issues de GitHub para principiantes.