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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import os
import tempfile
import subprocess
import tensorflow as tf
import numpy as np
import tfimage as im
import threading
import time
import multiprocessing
edge_pool = None
parser = argparse.ArgumentParser()
parser.add_argument("--input_dir", required=True, help="path to folder containing images")
parser.add_argument("--output_dir", required=True, help="output path")
parser.add_argument("--operation", required=True, choices=["grayscale", "resize", "blank", "combine", "edges"])
parser.add_argument("--workers", type=int, default=1, help="number of workers")
# resize
parser.add_argument("--pad", action="store_true", help="pad instead of crop for resize operation")
parser.add_argument("--size", type=int, default=256, help="size to use for resize operation")
# combine
parser.add_argument("--b_dir", type=str, help="path to folder containing B images for combine operation")
a = parser.parse_args()
def resize(src):
height, width, _ = src.shape
dst = src
if height != width:
if a.pad:
size = max(height, width)
# pad to correct ratio
oh = (size - height) // 2
ow = (size - width) // 2
dst = im.pad(image=dst, offset_height=oh, offset_width=ow, target_height=size, target_width=size)
else:
# crop to correct ratio
size = min(height, width)
oh = (height - size) // 2
ow = (width - size) // 2
dst = im.crop(image=dst, offset_height=oh, offset_width=ow, target_height=size, target_width=size)
assert(dst.shape[0] == dst.shape[1])
size, _, _ = dst.shape
if size > a.size:
dst = im.downscale(images=dst, size=[a.size, a.size])
elif size < a.size:
dst = im.upscale(images=dst, size=[a.size, a.size])
return dst
def blank(src):
height, width, _ = src.shape
if height != width:
raise Exception("non-square image")
image_size = width
size = int(image_size * 0.3)
offset = int(image_size / 2 - size / 2)
dst = src
dst[offset:offset + size,offset:offset + size,:] = np.ones([size, size, 3])
return dst
def combine(src, src_path):
if a.b_dir is None:
raise Exception("missing b_dir")
# find corresponding file in b_dir, could have a different extension
basename, _ = os.path.splitext(os.path.basename(src_path))
for ext in [".png", ".jpg"]:
sibling_path = os.path.join(a.b_dir, basename + ext)
if os.path.exists(sibling_path):
sibling = im.load(sibling_path)
break
else:
raise Exception("could not find sibling image for " + src_path)
# make sure that dimensions are correct
height, width, _ = src.shape
if height != sibling.shape[0] or width != sibling.shape[1]:
raise Exception("differing sizes")
# convert both images to RGB if necessary
if src.shape[2] == 1:
src = im.grayscale_to_rgb(images=src)
if sibling.shape[2] == 1:
sibling = im.grayscale_to_rgb(images=sibling)
# remove alpha channel
if src.shape[2] == 4:
src = src[:,:,:3]
if sibling.shape[2] == 4:
sibling = sibling[:,:,:3]
return np.concatenate([src, sibling], axis=1)
def grayscale(src):
return im.grayscale_to_rgb(images=im.rgb_to_grayscale(images=src))
net = None
def run_caffe(src):
# lazy load caffe and create net
global net
if net is None:
# don't require caffe unless we are doing edge detection
os.environ["GLOG_minloglevel"] = "2" # disable logging from caffe
import caffe
# using this requires using the docker image or assembling a bunch of dependencies
# and then changing these hardcoded paths
net = caffe.Net("/opt/caffe/examples/hed/deploy.prototxt", "/opt/caffe/hed_pretrained_bsds.caffemodel", caffe.TEST)
net.blobs["data"].reshape(1, *src.shape)
net.blobs["data"].data[...] = src
net.forward()
return net.blobs["sigmoid-fuse"].data[0][0,:,:]
def edges(src):
# based on https://github.com/phillipi/pix2pix/blob/master/scripts/edges/batch_hed.py
# and https://github.com/phillipi/pix2pix/blob/master/scripts/edges/PostprocessHED.m
import scipy.io
src = src * 255
border = 128 # put a padding around images since edge detection seems to detect edge of image
src = src[:,:,:3] # remove alpha channel if present
src = np.pad(src, ((border, border), (border, border), (0,0)), "reflect")
src = src[:,:,::-1]
src -= np.array((104.00698793,116.66876762,122.67891434))
src = src.transpose((2, 0, 1))
# [height, width, channels] => [batch, channel, height, width]
fuse = edge_pool.apply(run_caffe, [src])
fuse = fuse[border:-border, border:-border]
with tempfile.NamedTemporaryFile(suffix=".png") as png_file, tempfile.NamedTemporaryFile(suffix=".mat") as mat_file:
scipy.io.savemat(mat_file.name, {"input": fuse})
octave_code = r"""
E = 1-load(input_path).input;
E = imresize(E, [image_width,image_width]);
E = 1 - E;
E = single(E);
[Ox, Oy] = gradient(convTri(E, 4), 1);
[Oxx, ~] = gradient(Ox, 1);
[Oxy, Oyy] = gradient(Oy, 1);
O = mod(atan(Oyy .* sign(-Oxy) ./ (Oxx + 1e-5)), pi);
E = edgesNmsMex(E, O, 1, 5, 1.01, 1);
E = double(E >= max(eps, threshold));
E = bwmorph(E, 'thin', inf);
E = bwareaopen(E, small_edge);
E = 1 - E;
E = uint8(E * 255);
imwrite(E, output_path);
"""
config = dict(
input_path="'%s'" % mat_file.name,
output_path="'%s'" % png_file.name,
image_width=256,
threshold=25.0/255.0,
small_edge=5,
)
args = ["octave"]
for k, v in config.items():
args.extend(["--eval", "%s=%s;" % (k, v)])
args.extend(["--eval", octave_code])
try:
subprocess.check_output(args, stderr=subprocess.STDOUT)
except subprocess.CalledProcessError as e:
print("octave failed")
print("returncode:", e.returncode)
print("output:", e.output)
raise
return im.load(png_file.name)
def process(src_path, dst_path):
src = im.load(src_path)
if a.operation == "grayscale":
dst = grayscale(src)
elif a.operation == "resize":
dst = resize(src)
elif a.operation == "blank":
dst = blank(src)
elif a.operation == "combine":
dst = combine(src, src_path)
elif a.operation == "edges":
dst = edges(src)
else:
raise Exception("invalid operation")
im.save(dst, dst_path)
complete_lock = threading.Lock()
start = None
num_complete = 0
total = 0
def complete():
global num_complete, rate, last_complete
with complete_lock:
num_complete += 1
now = time.time()
elapsed = now - start
rate = num_complete / elapsed
if rate > 0:
remaining = (total - num_complete) / rate
else:
remaining = 0
print("%d/%d complete %0.2f images/sec %dm%ds elapsed %dm%ds remaining" % (num_complete, total, rate, elapsed // 60, elapsed % 60, remaining // 60, remaining % 60))
last_complete = now
def main():
if not os.path.exists(a.output_dir):
os.makedirs(a.output_dir)
src_paths = []
dst_paths = []
skipped = 0
for src_path in im.find(a.input_dir):
name, _ = os.path.splitext(os.path.basename(src_path))
dst_path = os.path.join(a.output_dir, name + ".png")
if os.path.exists(dst_path):
skipped += 1
else:
src_paths.append(src_path)
dst_paths.append(dst_path)
print("skipping %d files that already exist" % skipped)
global total
total = len(src_paths)
print("processing %d files" % total)
global start
start = time.time()
if a.operation == "edges":
# use a multiprocessing pool for this operation so it can use multiple CPUs
# create the pool before we launch processing threads
global edge_pool
edge_pool = multiprocessing.Pool(a.workers)
if a.workers == 1:
with tf.Session() as sess:
for src_path, dst_path in zip(src_paths, dst_paths):
process(src_path, dst_path)
complete()
else:
queue = tf.train.input_producer(zip(src_paths, dst_paths), shuffle=False, num_epochs=1)
dequeue_op = queue.dequeue()
def worker(coord):
with sess.as_default():
while not coord.should_stop():
try:
src_path, dst_path = sess.run(dequeue_op)
except tf.errors.OutOfRangeError:
coord.request_stop()
break
process(src_path, dst_path)
complete()
# init epoch counter for the queue
local_init_op = tf.local_variables_initializer()
with tf.Session() as sess:
sess.run(local_init_op)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
for i in range(a.workers):
t = threading.Thread(target=worker, args=(coord,))
t.start()
threads.append(t)
try:
coord.join(threads)
except KeyboardInterrupt:
coord.request_stop()
coord.join(threads)
main()