visionmagic 0.2.0

Collection of vision & graphics algorithms
Documentation 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133

//! Processor to group clusters together by the disjoint set algorithm
use std::collections::HashMap;
use visioncortex::{Color, ColorImage, ColorSum};
use visioncortex::color_clusters::{Cluster, Clusters, ClusterIndex};
use visioncortex::disjoint_sets::Forests;
use crate::pipeline::Processor as ProcessorTrait;

#[derive(Default)]
pub struct Processor {
    params: Params,
    clusters: Option<Input>,
    forests: Forests<ClusterIndex>,
    counter: usize,
}

/// [`Clusters`]
pub type Input = Clusters;

/// [`ColorImage`]
pub type Output = ColorImage;

#[derive(Default)]
pub struct Params {
    /// Allowed color difference between shapes in same set
    pub deviation: f64,
}

impl ProcessorTrait for Processor {

    type Input = Input;
    type Output = Output;
    type Params = Params;

    fn new() -> Self {
        Self::default()
    }

    fn config(&mut self, params: Params) -> bool {
        self.params = params;
        if self.clusters.is_some() {
            let cluster = self.clusters.take().unwrap();
            self.input(cluster);
        }
        true
    }

    fn input(&mut self, input: Input) -> bool {
        self.clusters = Some(input);
        let view = self.clusters.as_ref().unwrap().view();
        let len = view.clusters_output.len();
        self.counter = if len > 0 { len - 1 } else { 0 };
        self.forests = Forests::new();
        for index in view.clusters_output.iter() {
            self.forests.make_set(*index);
        }
        len > 0
    }

    fn tick(&mut self) -> bool {
        let view = self.clusters.as_ref().unwrap().view();
        let myselfi = view.clusters_output[self.counter];
        let myself = view.get_cluster(myselfi);
        let mut votes: Vec<(ClusterIndex, i32)> = myself.neighbours(&view).iter().map(|otheri| {
            let other = view.get_cluster(*otheri);
            (*otheri, Self::color_distance(myself, other))
        }).collect();
        votes.sort_by_key(|v| v.1);
        for (i, v) in votes.iter().enumerate() {
            let diff = v.1 as f64 / 10000.0;
            if  i == 0 && diff > self.params.deviation ||
                i == 1 && diff > self.params.deviation * 0.5 ||
                diff > self.params.deviation * 0.25 {
                break;
            }
            self.forests.union(&myselfi, &v.0);
        }
        if self.counter > 0 {
            self.counter -= 1;
            false
        } else {
            true
        }
    }

    fn progress(&self) -> u32 {
        let total = self.clusters.as_ref().unwrap().output_len() - 1;
        100 - 100 * self.counter as u32 / total as u32
    }

    /// to be called once only after process ends
    fn output(&mut self) -> Output {
        let mut aggregate = HashMap::new();
        let mut pairs = Vec::new();
        let view = self.clusters.as_ref().unwrap().view();
        for index in view.clusters_output.iter() {
            let label = self.forests.find_set(&index).unwrap();
            pairs.push((*index, label));
            let cluster = view.get_cluster(*index);
            (*aggregate.entry(label).or_insert_with(ColorSum::new)).add(&cluster.residue_color());
        }
        let mut image = ColorImage::new_w_h(view.width as usize, view.height as usize);
        for (index, label) in pairs.iter() {
            let cluster = view.get_cluster(*index);
            let color = aggregate.get(label).unwrap().average();
            cluster.render_to_color_image_with_color(&view, &mut image, &color);
        }
        image
    }

}

impl Processor {
    fn color_distance(myself: &Cluster, other: &Cluster) -> i32 {
        let mycolor = myself.residue_color();
        let otcolor = other.residue_color();
        (10000.0 * Self::color_diff_hsv(mycolor, otcolor)) as i32
    }

    fn color_diff_hsv(a: Color, b: Color) -> f64 {
        let a = a.to_hsv();
        let b = b.to_hsv();
        return 2.0 * wrap(a.h, b.h) + (a.s - b.s).abs() + (a.v - b.v).abs();

        fn wrap(x: f64, y: f64) -> f64 {
            let d = (x - y).abs();
            if d < 0.5 {
                d
            } else {
                1.0 - d
            }
        }
    }
}