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@jzstark
Last active Mar 12, 2018
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Owl's Gist
f93177fcb0ab843e82d2c6b83880d873
#!/usr/bin/env owl
open Owl
module N = Dense.Ndarray.S
let preprocess img =
let img = N.copy img in (*!!!*)
let r = N.get_slice [[];[];[];[0]] img in
let r = N.sub_scalar r 123.68 in
N.set_slice [[];[];[];[0]] img r;
let g = N.get_slice [[];[];[];[1]] img in
let g = N.sub_scalar g 116.779 in
N.set_slice [[];[];[];[1]] img g;
let b = N.get_slice [[];[];[];[2]] img in
let b = N.sub_scalar b 103.939 in
N.set_slice [[];[];[];[2]] img b;
img
let postprocess img =
let img = N.copy img in (*!!!*)
let r = N.get_slice [[];[];[];[0]] img in
let r = N.add_scalar r 123.68 in
N.set_slice [[];[];[];[0]] img r;
let g = N.get_slice [[];[];[];[1]] img in
let g = N.add_scalar g 116.779 in
N.set_slice [[];[];[];[1]] img g;
let b = N.get_slice [[];[];[];[2]] img in
let b = N.add_scalar b 103.939 in
N.set_slice [[];[];[];[2]] img b;
img
let normalise img =
let img = N.div_scalar img 255. in
let img = N.sub_scalar img 0.5 in
let img = N.mul_scalar img 2. in
img
let normalise_to_range a b img=
let max_val = N.max' img in
let min_val = N.min' img in
let img = N.sub_scalar img min_val in
let img = N.div_scalar img (max_val -. min_val) in
let img = N.mul_scalar img (b -. a) in
let img = N.add_scalar img a in
let img = N.round img in
img
let save_image_to_file img outname =
(* let img = N.load arrname in *)
(* Assume img is 3d-array of range [0, 255] *)
(* metadata *)
let shape = N.shape img in
assert (Array.length(shape) = 3);
let h = shape.(0) in
let w = shape.(1) in
let num_col = 255 in
(* divide *)
let r = N.get_slice [[];[];[1]] img in
let r = N.reshape r [|h; w|] in
let g = N.get_slice [[];[];[2]] img in
let g = N.reshape g [|h; w|] in
let b = N.get_slice [[];[];[0]] img in
let b = N.reshape b [|h; w|] in
(* merge r, g, b to one [|h; 3*w|] matrix *)
let img_mat = Dense.Matrix.S.zeros h (3 * w) in
Dense.Matrix.S.set_slice [[];[0;-1;3]] img_mat r;
Dense.Matrix.S.set_slice [[];[1;-1;3]] img_mat g;
Dense.Matrix.S.set_slice [[];[2;-1;3]] img_mat b;
(* rotate *)
let img_mat = Dense.Matrix.S.rotate img_mat 90 in
let img_arr = Dense.Matrix.S.to_arrays img_mat in
(* change to line *)
let img_str = Bytes.make (w * h * 3) ' ' in
let ww = 3 * w in
for i = 0 to ww - 1 do
for j = 0 to h - 1 do
let ch = img_arr.(i).(j) |> int_of_float |> char_of_int in
Bytes.set img_str ((h - 1 -j) * ww + i) ch;
done
done;
let header = "P6\n" ^ string_of_int(h) ^ " " ^ string_of_int(w) ^ "\n" ^ string_of_int(num_col) ^ "\n" in
let img_final = Bytes.concat (Bytes.of_string " ") [header |> Bytes.of_string; img_str] in
Owl_utils.write_file outname (Bytes.to_string img_final)
let save_ppm fname outfname =
let img = N.load fname in
let s = N.shape img in
assert (Array.length s = 4);
let img = N.reshape img [|s.(2);s.(1);s.(3)|] in
let img = N.(clip_by_value ~amin:0. ~amax:255. img |> round) in
save_image_to_file img outfname
let save_ppm_from_arr img outfname =
let s = N.shape img in
assert (Array.length s = 4);
let img = N.reshape img [|s.(2);s.(1);s.(3)|] in
let img = N.(clip_by_value ~amin:0. ~amax:255. img |> round) in
save_image_to_file img outfname
let _read_ppm fname =
(* grayscale binary ppm reading *)
let fp = open_in fname in
let ver = input_line fp in (* version *)
if ver <> "P6" then (* expect a .ppm file *)
raise (Invalid_argument ("Unable to read image: " ^ fname))
else
();
(* This will skip comments. *)
let rec ignore_comments_then_get_w_h () =
let maybe_comment = input_line fp in
if maybe_comment.[0] = '#' then
ignore_comments_then_get_w_h ()
else
(* This line has the width and height in it *)
maybe_comment
in
(* width, height num colors *)
let w_h_line = ignore_comments_then_get_w_h () in
let num_col_line = input_line fp in
let w, h = Scanf.sscanf w_h_line "%d %d" (fun w h -> w, h) in
let num_col = Scanf.sscanf num_col_line "%d" (fun n -> n) in
let img = Bytes.make (w * h * 3) ' ' in
let imf_o = Array.make_matrix (w * 3) h 0.0 in
(* Note that under 32bit OCaml, this will only work when reading strings up
to ~16 megabytes. *)
really_input fp img 0 (w * h * 3); (* end of file *)
close_in fp;
let ww = 3 * w in
for i = 0 to ww - 1 do
for j = 0 to h - 1 do
imf_o.(i).(j) <- float_of_int (int_of_char (Bytes.get img ((h - 1 - j ) * ww + i)));
done
done;
imf_o, w, h, num_col
let load_ppm fname =
let img, w, h, num_col = _read_ppm fname in
let m = Dense.Matrix.S.of_arrays img in
let m = Dense.Matrix.S.rotate m 270 in
(* r,g, b: Mat of size h * w *)
let r = N.get_slice [[];[0;-1;3]] m in
let g = N.get_slice [[];[1;-1;3]] m in
let b = N.get_slice [[];[2;-1;3]] m in
let r' = N.reshape r [|h;w;1|] in
let g' = N.reshape g [|h;w;1|] in
let b' = N.reshape b [|h;w;1|] in
let img = N.zeros [|h;w;3|] in
N.set_fancy [R []; R []; I 0] img r';
N.set_fancy [R []; R []; I 1] img g';
N.set_fancy [R []; R []; I 2] img b';
img
let extend_dim img =
let shape = N.shape img in
let shape = Array.append [|1|] shape in
N.reshape img shape
let _ = ()
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