Save imported image in the same format (WIP)
[master-thesis.git] / ParasitemiaCore / ImgTools / ImgTools.fs
1 module ParasitemiaCore.ImgTools
2
3 open System
4 open System.Drawing
5
6 open Emgu.CV
7 open Emgu.CV.Structure
8
9 /// <summary>
10 /// Normalize an image between 0 and 'upperLimit'.
11 /// FIXME: use to many memory.
12 /// </summary>
13 /// <param name="img"></param>
14 /// <param name="upperLimit"></param>
15 let normalize (img : Image<Gray, float32>) (upperLimit : float) : Image<Gray, float32> =
16 let min = ref [| 0.0 |]
17 let minLocation = ref <| [| Point() |]
18 let max = ref [| 0.0 |]
19 let maxLocation = ref <| [| Point() |]
20 img.MinMax(min, max, minLocation, maxLocation)
21 let normalized = (img - (!min).[0]) / ((!max).[0] - (!min).[0])
22 if upperLimit = 1.0 then
23 normalized
24 else
25 upperLimit * normalized
26
27 let mergeChannels (img : Image<Bgr, float32>) (rgbWeights : float * float * float) : Image<Gray, float32> =
28 match rgbWeights with
29 | 1., 0., 0. -> img.[2]
30 | 0., 1., 0. -> img.[1]
31 | 0., 0., 1. -> img.[0]
32 | redFactor, greenFactor, blueFactor ->
33 let result = new Image<Gray, float32>(img.Size)
34 CvInvoke.AddWeighted(result, 1., img.[2], redFactor, 0., result)
35 CvInvoke.AddWeighted(result, 1., img.[1], greenFactor, 0., result)
36 CvInvoke.AddWeighted(result, 1., img.[0], blueFactor, 0., result)
37 result
38
39 let mergeChannelsWithProjection (img : Image<Bgr, float32>) (v1r : float32, v1g : float32, v1b : float32) (v2r : float32, v2g : float32, v2b : float32) (upperLimit : float) : Image<Gray, float32> =
40 let vr, vg, vb = v2r - v1r, v2g - v1g, v2b - v1b
41 let vMagnitude = sqrt (vr ** 2.f + vg ** 2.f + vb ** 2.f)
42 let project (r : float32) (g : float32) (b : float32) = ((r - v1r) * vr + (g - v1g) * vg + (b - v1b) * vb) / vMagnitude
43 let result = new Image<Gray, float32>(img.Size)
44 // TODO: Essayer en bindant Data pour gagner du temps
45 for i = 0 to img.Height - 1 do
46 for j = 0 to img.Width - 1 do
47 result.Data.[i, j, 0] <- project img.Data.[i, j, 2] img.Data.[i, j, 1] img.Data.[i, j, 0]
48 normalize result upperLimit
49
50 // Normalize image values between 0uy and 255uy.
51 let normalizeAndConvert (img : Image<Gray, 'TDepth>) : Image<Gray, byte> =
52 (normalize (img.Convert<Gray, float32>()) 255.).Convert<Gray, byte>()
53
54 let gaussianFilter (img : Image<'TColor, 'TDepth>) (standardDeviation : float) : Image<'TColor, 'TDepth> =
55 let size = 2 * int (ceil (4.0 * standardDeviation)) + 1
56 img.SmoothGaussian(size, size, standardDeviation, standardDeviation)