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Hough transform: Difference between revisions
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* see [[Example:Hough transform/MATLAB]]
=={{header|Nim}}==
{{trans|D}}
{{libheader|nimPNG}}
We use the modules from tasks “Bitmap” and “Grayscale image”, adding necessary conversions to read and write PNG files.
<lang Nim>import lenientops, math
import grayscale_image
const White = 255
func houghTransform*(img: GrayImage; hx = 460; hy = 360): GrayImage =
assert not img.isNil
assert hx > 0 and hy > 0
assert (hy and 1) == 0, "hy argument must be even"
result = newGrayImage(hx, hy)
result.fill(White)
let rMax = hypot(img.w.toFloat, img.h.toFloat)
let dr = rMax / (hy / 2)
let dTh = PI / hx
for y in 0..<img.h:
for x in 0..<img.w:
if img[x, y] == White: continue
for iTh in 0..<hx:
let th = dTh * iTh
let r = x * cos(th) + y * sin(th)
let iry = hy div 2 - (r / dr).toInt
if result[iTh, iry] > 0:
result[iTh, iry] = result[iTh, iry] - 1
when isMainModule:
import nimPNG
import bitmap
const Input = "Pentagon.png"
const Output = "Hough.png"
let pngImage = loadPNG24(seq[byte], Input).get()
let grayImage = newGrayImage(pngImage.width, pngImage.height)
# Convert to grayscale.
for i in 0..grayImage.pixels.high:
grayImage.pixels[i] = Luminance(0.2126 * pngImage.data[3 * i] +
0.7152 * pngImage.data[3 * i + 1] +
0.0722 * pngImage.data[3 * i + 2] + 0.5)
# Apply Hough transform and convert to an RGB image.
let houghImage = grayImage.houghTransform().toImage()
# Save into a PNG file.
# As nimPNG expects a sequence of bytes, not a sequence of colors, we have to make a copy.
var data = newSeqOfCap[byte](houghImage.pixels.len * 3)
for color in houghImage.pixels:
data.add([color.r, color.g, color.b])
discard savePNG24(Output, data, houghImage.w, houghImage.h)</lang>
=={{header|Perl}}==
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