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All images I have tried image_surf on return a surf vector of 64 dimension, but only the first dimension has a non-zero value. The function runs without warning or errors, and I have successfully used the exact same images to run the image_fhog function in the same package
This is true as well for the example code you give in the "computer vision for R users" presentation:
All images I have tried image_surf on return a surf vector of 64 dimension, but only the first dimension has a non-zero value. The function runs without warning or errors, and I have successfully used the exact same images to run the image_fhog function in the same package
This is true as well for the example code you give in the "computer vision for R users" presentation:
f <- system.file("extdata", "cruise_boat.bmp", package="image.dlib")
surf_blobs <- image_surf(f, max_points = 10000, detection_threshold = 50)
e.g., on the above example, from skimr
skim(as.data.frame(surf_blobs$surf))
Variable type: numeric
variable missing complete n mean sd p0 p25 median p75 p100 hist
V1 0 296 296 0.018 0.055 -0.089 0 0 0 0.32 ▁▇▁▁▁▁▁▁
V10 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V11 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V12 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V13 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V14 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V15 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V16 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V17 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V18 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V19 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V2 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V20 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V21 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V22 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V23 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V24 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V25 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V26 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V27 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V28 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V29 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V3 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V30 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V31 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V32 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V33 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V34 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V35 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V36 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V37 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V38 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V39 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V4 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V40 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V41 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V42 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V43 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V44 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V45 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V46 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V47 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V48 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V49 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V5 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V50 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V51 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V52 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V53 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V54 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V55 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V56 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V57 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V58 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V59 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V6 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V60 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V61 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V62 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V63 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V64 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V7 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V8 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
V9 0 296 296 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
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