python/imtool.py

#!/usr/bin/env python3

import os
import math
import cv2
import numpy as np
from typing import NamedTuple, Tuple, List

from entity import Entity
from common import mkdir

TILE_SIZE = 416
TILE_OVERLAP = 0.8

class BoundingBox(NamedTuple):
    x: float = 0.0
    y: float = 0.0
    w: float = 0.0
    h: float = 0.0

    @classmethod
    def from_centroid(cls, c, shape):
        (ih, iw, ic) = shape
        self = cls(x=math.floor(w*(c.x - c.w/2))
                   , y=math.floor(h*(c.y - c.h/2))
                   , w=math.ceil(w*c.w)
                   , h=math.ceil(h*c.h))
        return self

    @classmethod
    def from_dict(cls, d):
        self = cls(x=d['x'], y=d['y'], w=d['width'], h=d['height'])
        return self

    @property
    def start(self):
        return floor_point(self.x, self.y)

    @property
    def end(self):
        return floor_point(self.x + self.w, self.y + self.h)

    def to_centroid(self, shape):
        (h, w, c) = shape
        return Centroid(x=math.floor(self.x + self.w/2)/w
                   , y=math.floor(self.y + self.h/2)/h
                   , w=math.ceil(self.w)/w
                   , h=math.ceil(self.h)/h)

    def intersect(self, f, r: float = 0.8):
        six = self.x - f.x
        siy = self.y - f.y
        eix = six + self.w
        eiy = siy + self.h

        if six < 0:
            if six + self.w < 0:
                return None
            six = 0
        if siy < 0:
            if siy + self.h < 0:
                return None
            siy = 0
        if eix > f.w:
            if eix - self.w > f.w:
                return None
            eix = f.w
        if eiy > f.h:
            if eiy - self.h > f.h:
                return None
            eiy = f.h

        i = BoundingBox(six, siy, eix - six, eiy - siy)
        if (i.w*i.h) < (self.w*self.h)*r:
            return None

        return i

class Centroid(BoundingBox):
    def to_bounding_box(self, shape):
        (h, w, c) = shape

        return BoundingBox(
            x=math.floor(w*(self.x - self.w/2))
            , y=math.floor(h*(self.y - self.h/2))
            , w=math.ceil(w*self.w)
            , h=math.ceil(h*self.h))

    def to_anotation(self, id: int):
        return f'{id} {self.x} {self.y} {self.w} {self.h}'

def read_marker(filename: str, Type: type):
    ret = []
    bco = None
    with open(filename, 'r') as f:
        lines = f.readlines()
        for l in lines:
            (b, x,y,w,h) = [float(i) for i in l.split(' ')]
            bco = int(b)
            ret.append(Type(x,y,w,h))
    return bco, ret

def read_bounding_boxes(filename: str):
    return read_marker(filename, BoundingBox)

def read_centroids(filename: str):
    return read_marker(filename, Centroid)

def coord_dict_to_point(c: dict):
    return coord_to_point(c['x'], c['y'], c['width'], c['height'])

def coord_to_point(cx, cy, cw, ch):
    x = math.floor(cx + cw/2)
    y = math.floor(cy + ch/2)
    return f"{x} {y} {math.ceil(cw)} {math.ceil(ch)}"

def floor_point(x: float, y: float):
    return (math.floor(x), math.floor(y))

def cut_img(im, s: Tuple[float, float], e: Tuple[float, float]):
    x1 = math.floor(s[0])
    y1 = math.floor(s[1])
    x2 = math.floor(e[0])
    y2 = math.floor(e[1])

    return im[y1:y2, x1:x2]

def cut_logo(im, l):
    (x, y, w, h) = floor_logo(l)
    return im[y:y+h, x:x+w]

def add_alpha(img):
    b, g, r = cv2.split(img)
    a = np.ones(b.shape, dtype=b.dtype) * 50
    return cv2.merge((b,g,r,a))

def remove_white(img):
    gray = cv2.cvtColor(img, cv2.COLOR_BGRA2GRAY)
    gray = 255*(gray<128)
    coords = cv2.findNonZero(gray)
    x, y, w, h = cv2.boundingRect(coords) # Find minimum spanning bounding box
    rect = img[y:y+h, x:x+w] # Crop the image - note we do this on the original image

    return rect


def mix(a, b, fx, fy):
    alpha = b[:, :, 3]/255

    return _mix_alpha(a, b, alpha, fx, fy)

def mix_alpha(a, b, ba, fx, fy):
    (ah, aw, ac) = a.shape
    (bh, bw, bc) = b.shape

    p = 0.2
    if (aw*p < bw or ah*p < bh):
        f = min(p*aw/bw, p*ah/bh)
        nw, nh = floor_point(bw*f, bh*f)
        print(f'resizing to fit in {aw}x{ah}\t {bw}x{bh}\t=> {nw}x{nh}\tfactor {f}')
        r = cv2.resize(b, (nw, nh), interpolation = cv2.INTER_LINEAR)
        rba = cv2.resize(ba, (nw, nh), interpolation = cv2.INTER_LINEAR)

        return mix_alpha(a, r, rba, fx, fy)

    assert bw > 10, f'b({bw}) too small'
    assert bh > 10, f'b({bh}) too small'

    return _mix_alpha(a, b, ba, fx, fy)

def _mix_alpha(a, b, ba, fx, fy):
    (ah, aw, ac) = a.shape
    (bh, bw, bc) = b.shape

    x = math.floor(fx*(aw - bw))
    y = math.floor(fy*(ah - bh))

    # handle transparency
    mat = a[y:y+bh,x:x+bw]
    cols = b[:, :, :3]
    mask = np.dstack((ba, ba, ba))

    a[y:y+bh,x:x+bw] = mat * (1 - mask) + cols * mask

    return BoundingBox(x, y, bw, bh)

def crop(id, fn, logos: List[Centroid], out = './data/squares'):
    basename = os.path.basename(fn).replace('.png', '')
    img_out = f"{out}/images"
    txt_out = f"{out}/labels"
    debug_out = f"{out}/debug"
    mkdir.make_dirs([debug_out, img_out, txt_out])

    im = cv2.imread(fn)
    rim = cv2.imread(fn)

    (h, w, c) = im.shape
    (tw, th) = (min(w, TILE_SIZE), min(h, TILE_SIZE))
    (tx, ty)= (
        math.ceil(w/(tw*TILE_OVERLAP)),
        math.ceil(h/(th*TILE_OVERLAP))
    )

    print('shape', basename, tx, ty, w, h)
    for x in range(tx):
        for y in range(ty):
            color = (0,x*(255/tx),y*(255/ty))
            logo_color = (255, 0, 0)

            if tx < 2:
                xs = 0
            else:
                xs = (w - tw)*x/(tx - 1)
            if ty < 2:
                ys = 0
            else:
                ys = (h - th)*y/(ty - 1)

            f = BoundingBox(xs, ys, tw, th)

            start = floor_point(f.x, f.y)
            end = floor_point(f.x + f.w, f.y + f.h)

            rim = cv2.rectangle(rim, start, end, color, 10)
            li = []
            for l in logos:
                bl = l.to_bounding_box(im.shape)
                rim = cv2.rectangle(rim, bl.start, bl.end, logo_color, 5)
                p = bl.intersect(f, 0.5)
                if p:
                    li.append(p)

            nim = cut_img(im, start, end)
            rnim = cut_img(rim, start, end)
            img_name =f"{img_out}/{basename}-x{x}y{y}.jpg"
            txt_name =f"{txt_out}/{basename}-x{x}y{y}.txt"

            cv2.imwrite(img_name, nim)
            if len(li):
                with open(txt_name, 'w') as label:
                    for p in li:
                        dim = cv2.rectangle(rnim, p.start, p.end, logo_color, 5)
                        lc = p.to_centroid((TILE_SIZE, TILE_SIZE, 3))

                        a = f"{int(id)} {lc.x} {lc.y} {lc.w} {lc.h}"
                        label.write(a)
                        cv2.imwrite(f'{debug_out}/{basename}{x}{y}.debug.png', dim)

    cv2.imwrite(f'{debug_out}/{basename}.debug.png', rim)

if __name__ == '__main__':
    i = 0
    with os.scandir('./data/') as it:
        for e in it:
            if e.name.endswith('.txt') and e.is_file():
                print(e.name)
                try:
                    i+=1
                    bco, boxes = read_bounding_boxes(e.path)
                    crop(i, e.path.replace('.txt', '.png'), boxes)
                except Exception as err:
                    print(err)