#!/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 glob
import math
import os
import os.path as osp
import sys
import cv2
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
from docopt import docopt
from lcnn.postprocess import postprocess
from lcnn.utils import parmap
PLTOPTS = {"color": "#33FFFF", "s": 1.2, "edgecolors": "none", "zorder": 5}
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 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 = postprocess(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])
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()
