lcnn/dataset/york.py

#!/usr/bin/env python3
"""Process YorkUrban dataset for L-CNN network
Usage:
    dataset/york.py <src> <dst>
    dataset/york.py (-h | --help )

Examples:
    python dataset/york.py /datadir/york data/york

Arguments:
    <src>                Original data directory of YorkUrban
    <dst>                Directory of the output

Options:
   -h --help             Show this screen.
"""

import os
import sys
import glob
import json
import os.path as osp
from itertools import combinations

import cv2
import numpy as np
import skimage.draw
import matplotlib.pyplot as plt
from docopt import docopt
from scipy.io import loadmat
from scipy.ndimage import zoom

try:
    sys.path.append(".")
    sys.path.append("..")
    from lcnn.utils import parmap
except Exception:
    raise


def inrange(v, shape):
    return 0 <= v[0] < shape[0] and 0 <= v[1] < shape[1]


def to_int(x):
    return tuple(map(int, x))


def save_heatmap(prefix, image, lines):
    im_rescale = (512, 512)
    heatmap_scale = (128, 128)

    fy, fx = heatmap_scale[1] / image.shape[0], heatmap_scale[0] / image.shape[1]
    jmap = np.zeros((1,) + heatmap_scale, dtype=np.float32)
    joff = np.zeros((1, 2) + heatmap_scale, dtype=np.float32)
    lmap = np.zeros(heatmap_scale, dtype=np.float32)

    lines[:, :, 0] = np.clip(lines[:, :, 0] * fx, 0, heatmap_scale[0] - 1e-4)
    lines[:, :, 1] = np.clip(lines[:, :, 1] * fy, 0, heatmap_scale[1] - 1e-4)
    lines = lines[:, :, ::-1]

    junc = []
    jids = {}

    def jid(jun):
        jun = tuple(jun[:2])
        if jun in jids:
            return jids[jun]
        jids[jun] = len(junc)
        junc.append(np.array(jun + (0,)))
        return len(junc) - 1

    lnid = []
    lpos, lneg = [], []
    for v0, v1 in lines:
        lnid.append((jid(v0), jid(v1)))
        lpos.append([junc[jid(v0)], junc[jid(v1)]])

        vint0, vint1 = to_int(v0), to_int(v1)
        jmap[0][vint0] = 1
        jmap[0][vint1] = 1
        rr, cc, value = skimage.draw.line_aa(*to_int(v0), *to_int(v1))
        lmap[rr, cc] = np.maximum(lmap[rr, cc], value)

    for v in junc:
        vint = to_int(v[:2])
        joff[0, :, vint[0], vint[1]] = v[:2] - vint - 0.5

    llmap = zoom(lmap, [0.5, 0.5])
    lineset = set([frozenset(l) for l in lnid])
    for i0, i1 in combinations(range(len(junc)), 2):
        if frozenset([i0, i1]) not in lineset:
            v0, v1 = junc[i0], junc[i1]
            vint0, vint1 = to_int(v0[:2] / 2), to_int(v1[:2] / 2)
            rr, cc, value = skimage.draw.line_aa(*vint0, *vint1)
            lneg.append([v0, v1, i0, i1, np.average(np.minimum(value, llmap[rr, cc]))])
            # assert np.sum((v0 - v1) ** 2) > 0.01

    assert len(lneg) != 0
    lneg.sort(key=lambda l: -l[-1])

    junc = np.array(junc, dtype=np.float32)
    Lpos = np.array(lnid, dtype=np.int)
    Lneg = np.array([l[2:4] for l in lneg][:4000], dtype=np.int)
    lpos = np.array(lpos, dtype=np.float32)
    lneg = np.array([l[:2] for l in lneg[:2000]], dtype=np.float32)

    image = cv2.resize(image, im_rescale)

    # plt.subplot(131), plt.imshow(lmap)
    # plt.subplot(132), plt.imshow(image)
    # for i0, i1 in Lpos:
    #     plt.scatter(junc[i0][1] * 4, junc[i0][0] * 4)
    #     plt.scatter(junc[i1][1] * 4, junc[i1][0] * 4)
    #     plt.plot([junc[i0][1] * 4, junc[i1][1] * 4], [junc[i0][0] * 4, junc[i1][0] * 4])
    # plt.subplot(133), plt.imshow(lmap)
    # for i0, i1 in Lneg[:150]:
    #     plt.plot([junc[i0][1], junc[i1][1]], [junc[i0][0], junc[i1][0]])
    # plt.show()

    np.savez_compressed(
        f"{prefix}_label.npz",
        aspect_ratio=image.shape[1] / image.shape[0],
        jmap=jmap,  # [J, H, W]
        joff=joff,  # [J, 2, H, W]
        lmap=lmap,  # [H, W]
        junc=junc,  # [Na, 3]
        Lpos=Lpos,  # [M, 2]
        Lneg=Lneg,  # [M, 2]
        lpos=lpos,  # [Np, 2, 3]   (y, x, t) for the last dim
        lneg=lneg,  # [Nn, 2, 3]
    )
    cv2.imwrite(f"{prefix}.png", image)

    # plt.imshow(jmap[0])
    # plt.savefig("/tmp/1jmap0.jpg")
    # plt.imshow(jmap[1])
    # plt.savefig("/tmp/2jmap1.jpg")
    # plt.imshow(lmap)
    # plt.savefig("/tmp/3lmap.jpg")
    # plt.imshow(Lmap[2])
    # plt.savefig("/tmp/4ymap.jpg")
    # plt.imshow(jwgt[0])
    # plt.savefig("/tmp/5jwgt.jpg")
    # plt.cla()
    # plt.imshow(jmap[0])
    # for i in range(8):
    #     plt.quiver(
    #         8 * jmap[0] * cdir[i] * np.cos(2 * math.pi / 16 * i),
    #         8 * jmap[0] * cdir[i] * np.sin(2 * math.pi / 16 * i),
    #         units="xy",
    #         angles="xy",
    #         scale_units="xy",
    #         scale=1,
    #         minlength=0.01,
    #         width=0.1,
    #         zorder=10,
    #         color="w",
    #     )
    # plt.savefig("/tmp/6cdir.jpg")
    # plt.cla()
    # plt.imshow(lmap)
    # plt.quiver(
    #     2 * lmap * np.cos(ldir),
    #     2 * lmap * np.sin(ldir),
    #     units="xy",
    #     angles="xy",
    #     scale_units="xy",
    #     scale=1,
    #     minlength=0.01,
    #     width=0.1,
    #     zorder=10,
    #     color="w",
    # )
    # plt.savefig("/tmp/7ldir.jpg")
    # plt.cla()
    # plt.imshow(jmap[1])
    # plt.quiver(
    #     8 * jmap[1] * np.cos(tdir),
    #     8 * jmap[1] * np.sin(tdir),
    #     units="xy",
    #     angles="xy",
    #     scale_units="xy",
    #     scale=1,
    #     minlength=0.01,
    #     width=0.1,
    #     zorder=10,
    #     color="w",
    # )
    # plt.savefig("/tmp/8tdir.jpg")


def main():
    args = docopt(__doc__)
    data_root = args["<src>"]
    data_output = args["<dst>"]
    os.makedirs(data_output, exist_ok=True)

    dataset = sorted(glob.glob(osp.join(data_root, "*/*.jpg")))

    def handle(iname):
        prefix = osp.split(iname)[1].replace(".jpg", "")
        im = cv2.imread(iname)
        mat = loadmat(iname.replace(".jpg", "LinesAndVP.mat"))
        lines = np.array(mat["lines"]).reshape(-1, 2, 2)
        path = osp.join(data_output, prefix)
        save_heatmap(f"{path}", im[::, ::], lines)
        print(f"Finishing {path}")

    parmap(handle, dataset)


if __name__ == "__main__":
    main()