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import os
import glob
import argparse
from shutil import rmtree
from PIL import Image
from multiprocessing import Pool, cpu_count
from dotenv import load_dotenv, find_dotenv
import subprocess
load_dotenv(find_dotenv())
# This script generates frames from a 360 equirectangular video and crops out the bottom third.
parser = argparse.ArgumentParser()
parser.add_argument('--folder', default="./sequences/venice_360/equi_4096/")
parser.add_argument('--label', default="venice_360")
parser.add_argument('--folder_id', type=int, required=True)
parser.add_argument('--overlap', type=float, default=0.5)
parser.add_argument('--clobber', action='store_false')
opt = parser.parse_args()
src_width = 4096
src_height = 2048
crop_width = 4096
crop_height = 1670
crop_dim = (0, 0, crop_width, crop_height,)
label_0 = opt.label + "_equi_2048"
label_1 = opt.label + "_equi_1024"
labels = [label_0, label_1]
path_0 = os.path.join("sequences", label_0)
path_1 = os.path.join("sequences", label_1)
if opt.clobber:
if os.path.exists(path_0):
rmtree(path_0)
if os.path.exists(path_1):
rmtree(path_1)
os.makedirs(path_0)
os.makedirs(path_1)
dataset = []
for i, fn in enumerate(sorted(glob.glob(os.path.join(opt.folder, '*.png')))):
out_fn = "frame_{:05d}.png".format(i + 1)
if not opt.clobber and os.path.exists(os.path.join(path_1, out_fn)):
continue
dataset.append((i, fn,))
def build_thumbnail(i, fn):
out_fn = "frame_{:05d}.png".format(i + 1)
if (i % 100) == 0:
print("{}...".format(i))
image = Image.open(fn).crop(crop_dim)
image.resize((2048, 1024,), Image.ANTIALIAS).save(os.path.join(path_0, out_fn))
image.resize((1024, 512,), Image.ANTIALIAS).save(os.path.join(path_1, out_fn))
chunksize = 3
with Pool(processes=cpu_count()) as pool:
pool.starmap(build_thumbnail, dataset, chunksize)
if opt.folder_id > 0:
endpoint = os.getenv('API_REMOTE') + '/api/file/'
for label in labels:
subprocess.call([
"curl",
"-X", "POST",
"-d", "folder_id={}".format(opt.folder_id),
"-d", "module=pix2pixhd",
"-d", "name={}.mov".format(label),
"-d", "url=https://s3.amazonaws.com/i.asdf.us/cortex/lens/data/{}/{}.mov".format(opt.folder_id, label),
"-d", "dataset={}".format(label),
"-d", "activity=splice",
"-d", "generated=0",
"-d", "processed=1",
"-d", "datatype=video",
endpoint
])
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