Tools to experiment with computer vision algorithms. Use ocv_read and ocv_write to load/save images on disk, or use ocv_picture / ocv_video to use your webcam. In RSudio IDE the image objects will automatically be displayed in the viewer pane.
Usage
ocv_face(image)
ocv_facemask(image)
ocv_read(path)
ocv_write(image, path)
ocv_destroy(image)
ocv_bitmap(image)
ocv_edges(image)
ocv_picture()
ocv_resize(image, width = 0, height = 0)
ocv_mog2(image)
ocv_knn(image)
ocv_hog(image)
ocv_blur(image, ksize = 5)
ocv_sketch(image, color = TRUE)
ocv_stylize(image)
ocv_markers(image)
ocv_info(image)
ocv_copyto(image, target, mask)
ocv_display(image)
ocv_video(filter, stop_on_result = FALSE)
ocv_grayscale(image)
ocv_version()Arguments
- image
an ocv image object created from e.g.
ocv_read()- path
image file such as png or jpeg
- width
output width in pixels
- height
output height in pixels
- ksize
size of blurring matrix
- color
true or false
- target
the output image
- mask
only copy pixels from the mask
- filter
an R function that takes and returns an opecv image
- stop_on_result
stop if an object is detected
Examples
# Silly example
mona <- ocv_read('https://jeroen.github.io/images/monalisa.jpg')
# Edge detection
ocv_edges(mona)
#> <pointer: 0x558098b937e0>
#> attr(,"class")
#> [1] "opencv-image"
ocv_markers(mona)
#> <pointer: 0x558098702560>
#> attr(,"class")
#> [1] "opencv-image"
# Find face
faces <- ocv_face(mona)
# To show locations of faces
facemask <- ocv_facemask(mona)
attr(facemask, 'faces')
#> radius x y
#> 1 174 582 478
# This is not strictly needed
ocv_destroy(mona)