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extract_timestack.py
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extract_timestack.py
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# ------------------------------------------------------------------------
# ------------------------------------------------------------------------
#
#
# SCRIPT : extract_timestack.py
# POURPOSE : Extract a timestack considering pixel surroundings
# AUTHOR : Caio Eadi Stringari
# EMAIL : [email protected]
#
# v1.0 : 06/09/2016 [Caio Stringari]
# v2.0 : 02/12/2016 [Caio Stringari]
#
# EXAMPLES:
#
# For usage help call: python extract_timestack.py -h
#
#
# ------------------------------------------------------------------------
# ------------------------------------------------------------------------
from __future__ import print_function, division
# system
import os
import sys
import subprocess
# warnings
import warnings
# Files
from glob import glob
# Arguments
import argparse
# Dates
import datetime
# numpy
import numpy as np
import pandas as pd
# scipy
import scipy.spatial
from scipy.stats import mode
# multiprocess
import multiprocessing as mp
from multiprocessing import Queue
# random numbers
import random
# strings
import string
# netcdf IO
import xarray
# computer vision
import cv2
import skimage.io
# local tools
import pywavelearn.image as ipwl
from pywavelearn.utils import chunkify
def rectify_worker(num, frames):
"""
Worker function passed to multiprocessing.Process(). This function:
- reads a list of frames, loop over its elements;
- undistort each frame using the camera matrix;
- crop the edges;
- rectify (project) the geometry to real-world coordinates;
- calculate the horizon;
- rotate the matrix;
- extract the red, green and blue pixel arrays;
- save the data in netcdf4 format.
----------
Args:
num [Mandatory (int)]: Number of the process being called.
frames [Mandatory (list)]: List of frames to process.
----------
Returns:
"""
name = mp.current_process().name
# print ("Worker",num)
print(" + Worker ", num, ' starting')
N = len(frames)
# If
# Loop over the files in the chunk
k = 0
for frame in frames:
percent = round(((k * 100) / N), 2)
print(
" --> Processing frame {} of {} ({} %) [Worker {}]".format(
k +
1,
N,
percent,
num),
" <--")
# time
fmt = "%Y%m%d_%H%M%S_%f"
now = datetime.datetime.strptime(
frame.split("/")[-1].strip(".jpg"), fmt)
# Read image
Ir = skimage.io.imread(frame)
h, w = Ir.shape[:2]
# undistort image
Kn, roi = cv2.getOptimalNewCameraMatrix(K, DC, (w, h), 1, (w, h))
Ir = cv2.undistort(Ir, K, DC, None, Kn)
# homography
H = ipwl.find_homography(UV, XYZ, K, z=z, distortion=0)
# rectify coordinates
X, Y = ipwl.rectify_image(Ir, H)
# find the horizon limits
horizon = ipwl.find_horizon_offset(X, Y, max_distance=hor)
# rotate and translate
Xr, Yr = ipwl.rotate_translate(
X, Y, rotation=theta, translation=[xt, yt])
# final arrays
if hor == -999:
Xc = Xr
Yc = Yr
Ic = Ir
else:
Xc = Xr[horizon:, :]
Yc = Yr[horizon:, :]
Ic = Ir[horizon:, :, :]
# new image dimensions
hc, wc = Ic.shape[:2]
# flattened coordinates
XYc = np.dstack([Xc.flatten(), Yc.flatten()])[0]
# interp = False
# interpolate to a regular grid
# do KDTree in the cliped array
IDXc = kdtree(XYc, points)
# points in metric coordinates - cliped array
xc = XYc[IDXc, 0]
yc = XYc[IDXc, 1]
# points in pixel coordinates - cliped array
ic = np.unravel_index(IDXc, Xr.shape)[0]
jc = np.unravel_index(IDXc, Yr.shape)[1]
# create a line of pixel centers
i_stack_center = np.linspace(
ic[0], ic[1], np.int(
args.stackpoints[0])).astype(
np.int).tolist()
j_stack_center = np.linspace(
jc[0], jc[1], np.int(
args.stackpoints[0])).astype(
np.int).tolist()
# pixel centes in metric coordinates
x_stack_center = Xc[i_stack_center, j_stack_center]
y_stack_center = Yc[i_stack_center, j_stack_center]
R = []
G = []
B = []
# loop over pixel centers
for i, j in zip(i_stack_center, j_stack_center):
# find surrounding pixels
isurrounding, jsurrounding = pixel_window(Ic, i, j, pxwin)
# final pixel arrays
iall = np.hstack([isurrounding, i])
jall = np.hstack([jsurrounding, j])
# extract pixel stats
if pxstats == "mean":
r = Ic[iall, jall, 0].mean()
g = Ic[iall, jall, 1].mean()
b = Ic[iall, jall, 2].mean()
elif pxstats == "max":
r = Ic[iall, jall, 0].max()
g = Ic[iall, jall, 1].max()
b = Ic[iall, jall, 2].max()
elif pxstats == "min":
r = Ic[iall, jall, 0].min()
g = Ic[iall, jall, 1].min()
b = Ic[iall, jall, 2].min()
elif pxstats == "mode":
r = mode(Ic[iall, jall, 0])[0][0]
g = mode(Ic[iall, jall, 1])[0][0]
b = mode(Ic[iall, jall, 2])[0][0]
# Append to output
R.append(r)
G.append(g)
B.append(b)
# Final RGB array
RGB = np.vstack([R, G, B]).astype(np.int).T
# build the output data model
ds = xarray.Dataset()
# write rgb variable
ds['rgb'] = (('points', 'bands'), RGB) # All bandas
# write positional cooridinates
ds['x'] = x_stack_center # x-coordinate
ds['y'] = y_stack_center # y-coordinate
ds["i"] = i_stack_center # central i-coordinate
ds["j"] = j_stack_center # central j-coordinate
# write coordinates
ds.coords['time'] = now # camera time
ds.coords["points"] = np.arange(0, len(i_stack_center), 1)
# auxiliary variables
ds["bands"] = ["red", "green", "blue"]
# write to file
units = 'days since 2000-01-01 00:00:00'
calendar = 'gregorian'
encoding = dict(time=dict(units=units, calendar=calendar))
ds.to_netcdf("{}/{}.nc".format(tmpfolder, now.strftime(fmt)),
encoding=encoding)
k += 1
if fbreak and k == fbreak:
print("++> Breaking loop")
break
print(" - Worker", num, ' finishing')
return
def mergestacks(files, ncout='pts.nc', ramcut=3.0):
"""
Merge extracted pixel lines in a timestack. Only will be called
if parameter --timestack is True. Will always merge files in the
temporal "time" dimension.
----------
Args:
files [Mandatory (list, np.ndarray)]: Sorted list of files to merge.
ncout [Optional (str)]: Output filename. Defaul is timestack.nc
ramcut [Optinal (float)]: Max fraction of memory to use. If the size of
the expected merged file exceds this fraction will raise a MemoryError.
----------
Returns:
"""
def process_one_path(path):
# use a context manager, to ensure the file gets closed after use
with xarray.open_dataset(path) as ds:
# load dataset into memory
ds.load()
return ds
# Memory check
f_bytes = sum(os.path.getsize(f) for f in glob("tmp/*.nc"))
m_bytes = os.sysconf('SC_PAGE_SIZE') * os.sysconf('SC_PHYS_PAGES')
if f_bytes >= m_bytes / ramcut:
raise MemoryError("Not enough memory available to merge all files.")
print("\n + Merging timestacks, please wait...")
# # progressbar
# widgets = [ progressbar.FormatLabel(''),
# progressbar.Percentage(),
# ' ', progressbar.Bar(marker='#', left='[', right=']'),
# ' ', progressbar.ETA()]
# bar = progressbar.ProgressBar(widgets=widgets,max_value=len(files))
# bar.start()
# Loop over files
datasets = []
for k, fname in enumerate(files):
datasets.append(process_one_path(fname))
# widgets[0] = progressbar.FormatLabel(' Stack {} of {}'.format(k+1,len(files)))
# bar.update(k)
# if k>100:break
merged = xarray.concat(datasets, "time")
merged.to_netcdf(ncout)
merged.close()
subprocess.call("rm -rf tmp/", shell=True)
print("\n - Stacks sucessfully merged")
def kdtree(A, pt):
_, indexes = scipy.spatial.KDTree(A).query(pt)
return indexes
def pixel_window(a, i, j, s=8):
# compute domain
i = np.arange(i - s, i + s + 1, 1)
j = np.arange(j - s, j + s + 1, 1)
# all pixels inside the domain
I, J = np.meshgrid(i, j)
# Remove pixels outside the borders
# i-dimension
I = I.flatten()
I[I < 0] = -999
I[I > a.shape[0]] = -999
idx = np.where(I == -999)
# j-dimension
J = J.flatten()
J[J < 0] = -999
J[J > a.shape[1]] = -999
jdx = np.where(J == -999)
Ifinal = np.delete(I, np.hstack([idx, jdx]))
Jfinal = np.delete(J, np.hstack([idx, jdx]))
return Ifinal, Jfinal
def random_string(n):
return ''.join(random.choice(string.ascii_lowercase) for i in range(n))
if __name__ == '__main__':
# Argument parser
parser = argparse.ArgumentParser()
# Number of cores to use
parser.add_argument(
'--nproc',
'-n',
nargs=1,
action='store',
default=[1],
dest='nproc',
required=False,
help="Number of processors to use. Default is to use one.",)
# Input frame folder
parser.add_argument(
'--input',
'-i',
nargs=1,
action='store',
dest='input',
required=True,
help="Folder with the extracted frames."
"Please use extrac_frames.py before running this script.")
# output
parser.add_argument(
'--output', '-o',
nargs=1,
action='store',
default=['output.nc'],
dest='output',
required=False,
help="Output file name.",)
# Geometry
parser.add_argument(
'--gcpuv',
'-gcpuv',
'--uv',
'-uv',
nargs=1,
action='store',
dest='uvfile',
required=True,
help="GCP UV file. Use get_gcp_uvcoords.py to generate a valid file.",)
parser.add_argument(
'--gcpxyz',
'-gcpxyz',
'--xyz',
'-xyz',
nargs=1,
action='store',
dest='xyzfile',
required=True,
help="GCP XYZ file."
"Similar to UV file, but with real-world coordinates instead",)
# Horizon
parser.add_argument(
'--horizon',
'--hor',
'-horizon',
'-hor',
nargs=1,
action='store',
dest='horizon',
required=False,
default=[-999],
help="Maximum distance from origin to be included in the plot.",
)
# Theta
parser.add_argument(
'--theta', '-theta',
nargs=1,
action='store',
dest='theta',
required=False,
default=[0],
help="Rotation angle. Default is 0.0.",)
# Translation
parser.add_argument(
'--X', '--x', '-X', '-x',
nargs=1,
action='store',
dest='X',
required=False,
default=[0],
help="Translation in the x-direction",)
parser.add_argument(
'--Y', '--y', '-Y', '-y',
nargs=1,
action='store',
dest='Y',
required=False,
default=[0],
help="Translation in the x-direction",)
# Projeciton height
parser.add_argument(
'--Z',
'--z',
'-Z',
'-z',
nargs=1,
action='store',
dest='Z',
required=False,
default=[0],
help="Real-world elevation on which the image should be projected",)
# Camera matrix
parser.add_argument(
'--camera-matrix',
'-cm',
nargs=1,
action='store',
dest='camera',
required=True,
help="Camera matrix file."
"Please use calibrate.py to generate a valid file.",)
# pixel line coordinates
parser.add_argument(
'-x1',
nargs=1,
action='store',
dest='x1',
required=False,
help="First x-coordinate for the timestack."
"If passed WILL NOT use matplotlib GUI.",)
parser.add_argument(
'-x2',
nargs=1,
action='store',
dest='x2',
required=False,
help="Final x-coordinate for the timestack."
"If passed WILL NOT use matplotlib GUI.")
parser.add_argument(
'-y1',
nargs=1,
action='store',
dest='y1',
required=False,
help="Start y-coordinate for the timestack."
"If passed WILL NOT use matplotlib GUI.",
)
parser.add_argument(
'-y2',
nargs=1,
action='store',
dest='y2',
required=False,
help="Final y-coordinate for the timestack."
"If passed WILL NOT use matplotlib GUI..",
)
# Number of points in the stack
parser.add_argument('--stack-points', '-stkp',
nargs=1,
action='store',
default=[np.int(256)],
dest='stackpoints',
required=False,
help="Number of points in the timestack.",)
# Pixel window size
parser.add_argument(
'--pixel-window', '--pxwin', '-pxwin', '-win',
nargs=1,
action='store',
dest='pxwin',
required=False,
default=[0],
help="Pixel window size. Default is 2.",)
# Statistic
parser.add_argument(
'--pixel-statistic',
'--statistic',
'-stats',
nargs=1,
action='store',
dest='pxstats',
required=False,
default=["mean"],
help="Pixel statistics to use. Default is the average.",
)
# Whether to save frames or not
parser.add_argument(
'--save-frames',
action='store_true',
dest='save_frames',
required=False,
help="Save rectified frames in netcdf format. Default is false",
)
# Frame output path
parser.add_argument(
'--frame-output',
nargs=1,
action='store',
default=['rectified/'],
dest='frame_output',
required=False,
help="Output writing folder. Default is rectified/.",)
# Compress
parser.add_argument(
'--compress',
action='store_true',
dest='compress',
help="Compress the output folder to save disk space.")
# Remove input frames to save space
parser.add_argument(
'--remove-frames',
action='store_true',
dest='remove_frames',
help="Delete input frames folder.")
# Force video duration
parser.add_argument(
'--force-break',
nargs=1,
action='store',
dest='force_break',
required=False,
help="Force break after N frames.",)
# parse all the arguments
args = parser.parse_args()
# ...I am not going to mess with this script moving things to main() ###
# starting things up
start = datetime.datetime.now()
print("\nProcessing starting at : {} ###\n".format(
datetime.datetime.now()))
# number of processors
nprocs = int(args.nproc[0])
# force breake
if args.force_break:
fbreak = np.int(args.force_break[0])
else:
fbreak = False
# input and output
# input
ipath = os.path.abspath(args.input[0])
if os.path.isdir(ipath):
pass
else:
raise IOError("Path {} not found !".format(ipath))
# all frames
files = np.sort(glob(ipath + "/*.jpg"))
# output
nc = args.output[0]
# output
if args.save_frames:
keep = True
opath = os.path.abspath(args.frame_output[0])
subprocess.call("rm -rf {}".format(opath), shell=True)
os.makedirs(opath)
else:
keep = False
# temporary folder
tmpfolder = 'tmp_' + random_string(8) + '/'
subprocess.call("mkdir {}".format(tmpfolder), shell=True)
# camera matrix
K, DC = ipwl.camera_parser(args.camera[0])
# read XYZ coords
dfxyz = pd.read_csv(os.path.realpath(args.xyzfile[0]))
XYZ = dfxyz[["x", "y", "z"]].values
# read UV coords
dfuv = pd.read_csv(os.path.realpath(args.uvfile[0]))
UV = dfuv[["u", "v"]].values
# horizon
hor = float(args.horizon[0])
# rotation Angle
theta = float(args.theta[0])
# projection height
z = float(args.Z[0])
# translation
xt = float(args.X[0])
yt = float(args.Y[0])
# pixel window size
pxwin = np.int(args.pxwin[0])
# pixel stats
pxstats = args.pxstats[0]
# homography
H = ipwl.find_homography(UV, XYZ, K, z=z)
# reference homography
# if args.
# hhunkify
fchunks = chunkify(files, nprocs)
# create timestack line
if args.x1 and args.x2 and args.y1 and args.y2:
xstack = [np.float(args.x1[0]), np.float(args.x2[0])]
ystack = [np.float(args.y1[0]), np.float(args.y2[0])]
points = np.vstack([xstack, ystack]).T
npoints = np.int(args.stackpoints[0])
else:
raise ValueError("Sorry, GUI was removed...")
# loop over the number of processors
Q = mp.Queue()
procs = []
for i, frames in zip(range(nprocs), fchunks):
p = mp.Process(target=rectify_worker, args=(i + 1, frames))
procs.append(p)
p.start()
# wait for all worker processes to finish
for p in procs:
p.join()
# merge all extracted stacks to one netcdf
mergestacks(np.sort(glob("{}*.nc".format(tmpfolder))), args.output[0])
# remove temporary folder
subprocess.call("rm -rf {}".format(tmpfolder), shell=True)
# compress
if keep:
if args.compress:
print("\nCompressing files, please wait...")
tar = opath.split("/")[-1] + ".tar.gz"
cmd = "tar -zcvf {} {}/*.nc > /dev/null 2>&1".format(
tar, os.path.relpath(opath))
subprocess.call(cmd, shell=True)
subprocess.call("rm -rf {}".format(opath), shell=True)
# delete input files
if args.remove_frames:
subprocess.call("rm -rf {}".format(ipath), shell=True)
end = datetime.datetime.now()
elapsed = (end - start).total_seconds()
print("\nElapsed time: {} seconds [{} minutes] ({} hours) \n".format(
elapsed, round(elapsed / 60, 2), round(elapsed / 3600., 2)))
print("\nRectification finished at : {} \n".format(
datetime.datetime.now()))