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#!/usr/bin/env python
import sys, os, os.path, glob, math, cv2, sft
from datetime import datetime
from optparse import OptionParser
import re
import numpy as np
def extractPositive(f, path, opath, octave, min_possible):
newobj = re.compile("^lbl=\'(\w+)\'\s+str=(\d+)\s+end=(\d+)\s+hide=0$")
pos = re.compile("^pos\s=(\[[((\d+\.+\d*)|\s+|\;)]*\])$")
occl = re.compile("^occl\s*=(\[[0-1|\s]*\])$")
whole_mod_w = int(64 * octave) + 2 * int(20 * octave)
whole_mod_h = int(128 * octave) + 2 * int(20 * octave)
goNext = 0
start = 0
end = 0
person_id = -1;
boxes = []
occls = []
for l in f:
m = newobj.match(l)
if m is not None:
if m.group(1) == "person":
goNext = 1
start = int(m.group(2))
end = int(m.group(3))
person_id = person_id + 1
print m.group(1), person_id, start, end
else:
goNext = 0
else:
m = pos.match(l)
if m is not None:
if not goNext:
continue
strarr = re.sub(r"\s", ", ", re.sub(r"\;\s+(?=\])", "]", re.sub(r"\;\s+(?!\])", "],[", re.sub(r"(\[)(\d)", "\\1[\\2", m.group(1)))))
boxes = eval(strarr)
else:
m = occl.match(l)
if m is not None:
occls = eval(re.sub(r"\s+(?!\])", ",", m.group(1)))
if len(boxes) > 0 and len(boxes) == len(occls):
for idx, box in enumerate(boxes):
if occls[idx] == 1:
continue
x = box[0]
y = box[1]
w = box[2]
h = box[3]
id = int(start) - 1 + idx
file = os.path.join(path, "I0%04d.jpg" % id)
if (start + id) >= end or w < 10 or h < min_possible:
continue
mat = cv2.imread(file)
mat_h, mat_w, _ = mat.shape
# let default height of person be 96.
scale = h / float(96)
rel_scale = scale / octave
d_w = whole_mod_w * rel_scale
d_h = whole_mod_h * rel_scale
tb = (d_h - h) / 2.0
lr = (d_w - w) / 2.0
x = int(round(x - lr))
y = int(round(y - tb))
w = int(round(w + lr * 2.0))
h = int(round(h + tb * 2.0))
inner = [max(5, x), max(5, y), min(mat_w - 5, x + w), min(mat_h - 5, y + h) ]
cropped = mat[inner[1]:inner[3], inner[0]:inner[2], :]
top = int(max(0, 0 - y))
bottom = int(max(0, y + h - mat_h))
left = int(max(0, 0 - x))
right = int(max(0, x + w - mat_w))
if top < -d_h / 4.0 or bottom > d_h / 4.0 or left < -d_w / 4.0 or right > d_w / 4.0:
continue
cropped = cv2.copyMakeBorder(cropped, top, bottom, left, right, cv2.BORDER_REPLICATE)
resized = sft.resize_sample(cropped, whole_mod_w, whole_mod_h)
flipped = cv2.flip(resized, 1)
cv2.imshow("resized", resized)
c = cv2.waitKey(20)
if c == 27:
exit(0)
fname = re.sub(r"^.*\/(set[0-1]\d)\/(V0\d\d)\.(seq)/(I\d+).jpg$", "\\1_\\2_\\4", file)
fname = os.path.join(opath, fname + "_%04d." % person_id + "png")
fname_fl = os.path.join(opath, fname + "_mirror_%04d." % person_id + "png")
try:
cv2.imwrite(fname, resized)
cv2.imwrite(fname_fl, flipped)
except:
print "something wrong... go next."
pass
if __name__ == "__main__":
parser = OptionParser()
parser.add_option("-i", "--input", dest="input", metavar="DIRECTORY", type="string",
help="Path to the Caltech dataset folder.")
parser.add_option("-d", "--output-dir", dest="output", metavar="DIRECTORY", type="string",
help="Path to store data", default=".")
parser.add_option("-o", "--octave", dest="octave", type="float",
help="Octave for a dataset to be scaled", default="0.5")
parser.add_option("-m", "--min-possible", dest="min_possible", type="int",
help="Minimum possible height for positive.", default="64")
(options, args) = parser.parse_args()
if not options.input:
parser.error("Caltech dataset folder is required.")
opath = os.path.join(options.output, datetime.now().strftime("raw_ge64_cr_mirr_ts" + "-%Y-%m-%d-%H-%M-%S"))
os.mkdir(opath)
gl = glob.iglob( os.path.join(options.input, "set[0][0]/V0[0-9][0-9].txt"))
for each in gl:
path, ext = os.path.splitext(each)
path = path + ".seq"
print path
extractPositive(open(each), path, opath, options.octave, options.min_possible)