lcnn/post.py

#!/usr/bin/env python3
"""Post-processing the output of neural network
Usage:
    post.py [options] <input-dir> <output-dir>
    post.py ( -h | --help )

Examples:
    post.py logs/logname/npz/000336000  result/logname

Arguments:
   input-dir                         Directory that stores the npz
   output-dir                        Output directory

Options:
   -h --help                         Show this screen.
   --plot                            Generate images besides npz files
   --thresholds=<thresholds>         A comma-separated list for thresholding
                                     [default: 0.006,0.010,0.015]
"""

import os
import sys
import glob
import math
import os.path as osp

import cv2
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from docopt import docopt

from lcnn.utils import parmap

cmap = plt.get_cmap("jet")
norm = mpl.colors.Normalize(vmin=0.92, vmax=1.02)
sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
sm.set_array([])


def c(x):
    return sm.to_rgba(x)


def imshow(im):
    plt.close()
    sizes = im.shape
    height = float(sizes[0])
    width = float(sizes[1])

    fig = plt.figure()
    fig.set_size_inches(width / height, 1, forward=False)
    ax = plt.Axes(fig, [0.0, 0.0, 1.0, 1.0])
    ax.set_axis_off()
    fig.add_axes(ax)
    plt.xlim([-0.5, sizes[1] - 0.5])
    plt.ylim([sizes[0] - 0.5, -0.5])
    plt.imshow(im)


def pline(x1, y1, x2, y2, x, y):
    px = x2 - x1
    py = y2 - y1
    dd = px * px + py * py
    u = ((x - x1) * px + (y - y1) * py) / max(1e-9, float(dd))
    dx = x1 + u * px - x
    dy = y1 + u * py - y
    return dx * dx + dy * dy


def psegment(x1, y1, x2, y2, x, y):
    px = x2 - x1
    py = y2 - y1
    dd = px * px + py * py
    u = max(min(((x - x1) * px + (y - y1) * py) / float(dd), 1), 0)
    dx = x1 + u * px - x
    dy = y1 + u * py - y
    return dx * dx + dy * dy


def plambda(x1, y1, x2, y2, x, y):
    px = x2 - x1
    py = y2 - y1
    dd = px * px + py * py
    return ((x - x1) * px + (y - y1) * py) / max(1e-9, float(dd))


def process(lines, scores, threshold=0.01, tol=1e9, do_clip=False):
    nlines, nscores = [], []
    for (p, q), score in zip(lines, scores):
        start, end = 0, 1
        for a, b in nlines:
            if (
                min(
                    max(pline(*p, *q, *a), pline(*p, *q, *b)),
                    max(pline(*a, *b, *p), pline(*a, *b, *q)),
                )
                > threshold ** 2
            ):
                continue
            lambda_a = plambda(*p, *q, *a)
            lambda_b = plambda(*p, *q, *b)
            if lambda_a > lambda_b:
                lambda_a, lambda_b = lambda_b, lambda_a
            lambda_a -= tol
            lambda_b += tol

            # case 1: skip (if not do_clip)
            if start < lambda_a and lambda_b < end:
                continue

            # not intersect
            if lambda_b < start or lambda_a > end:
                continue

            # cover
            if lambda_a <= start and end <= lambda_b:
                start = 10
                break

            # case 2 & 3:
            if lambda_a <= start and start <= lambda_b:
                start = lambda_b
            if lambda_a <= end and end <= lambda_b:
                end = lambda_a

            if start >= end:
                break

        if start >= end:
            continue
        nlines.append(np.array([p + (q - p) * start, p + (q - p) * end]))
        nscores.append(score)
    return np.array(nlines), np.array(nscores)


def main():
    args = docopt(__doc__)

    files = sorted(glob.glob(osp.join(args["<input-dir>"], "*.npz")))
    inames = sorted(glob.glob("data/wireframe/valid-images/*.jpg"))
    gts = sorted(glob.glob("data/wireframe/valid/*.npz"))
    prefix = args["<output-dir>"]

    inputs = list(zip(files, inames, gts))
    thresholds = list(map(float, args["--thresholds"].split(",")))

    def handle(allname):
        fname, iname, gtname = allname
        print("Processing", fname)
        im = cv2.imread(iname)
        with np.load(fname) as f:
            lines = f["lines"]
            scores = f["score"]
        with np.load(gtname) as f:
            gtlines = f["lpos"][:, :, :2]
        gtlines[:, :, 0] *= im.shape[0] / 128
        gtlines[:, :, 1] *= im.shape[1] / 128
        for i in range(1, len(lines)):
            if (lines[i] == lines[0]).all():
                lines = lines[:i]
                scores = scores[:i]
                break

        lines[:, :, 0] *= im.shape[0] / 128
        lines[:, :, 1] *= im.shape[1] / 128
        diag = (im.shape[0] ** 2 + im.shape[1] ** 2) ** 0.5

        for threshold in thresholds:
            nlines, nscores = process(lines, scores, diag * threshold, 0, False)

            outdir = osp.join(prefix, f"{threshold:.3f}".replace(".", "_"))
            os.makedirs(outdir, exist_ok=True)
            npz_name = osp.join(outdir, osp.split(fname)[-1])

            PLTOPTS = {"color": "#33FFFF", "s": 1.2, "edgecolors": "none", "zorder": 5}
            if args["--plot"]:
                # plot gt
                imshow(im[:, :, ::-1])
                for (a, b) in gtlines:
                    plt.plot([a[1], b[1]], [a[0], b[0]], c="orange", linewidth=0.5)
                    plt.scatter(a[1], a[0], **PLTOPTS)
                    plt.scatter(b[1], b[0], **PLTOPTS)
                plt.savefig(npz_name.replace(".npz", ".png"), dpi=500, bbox_inches=0)

                thres = [0.96, 0.97, 0.98, 0.99]
                for i, t in enumerate(thres):
                    imshow(im[:, :, ::-1])
                    for (a, b), s in zip(nlines[nscores > t], nscores[nscores > t]):
                        plt.plot([a[1], b[1]], [a[0], b[0]], c=c(s), linewidth=0.5)
                        plt.scatter(a[1], a[0], **PLTOPTS)
                        plt.scatter(b[1], b[0], **PLTOPTS)
                    plt.savefig(
                        npz_name.replace(".npz", f"_{i}.png"), dpi=500, bbox_inches=0
                    )

            nlines[:, :, 0] *= 128 / im.shape[0]
            nlines[:, :, 1] *= 128 / im.shape[1]
            np.savez_compressed(npz_name, lines=nlines, score=nscores)

    parmap(handle, inputs, 12)


if __name__ == "__main__":
    main()