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| -rw-r--r-- | crop-equirectangular-rotating.py | 119 |
1 files changed, 119 insertions, 0 deletions
diff --git a/crop-equirectangular-rotating.py b/crop-equirectangular-rotating.py new file mode 100644 index 0000000..4f4de4c --- /dev/null +++ b/crop-equirectangular-rotating.py @@ -0,0 +1,119 @@ +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 crops with a specific aspect ratio from a 360 video. +# It creates three sequences (identified by "--label") +# The default is a 24 degree overlap (equivalent to 1/6 of the 3:1 output image) +# Setting a higher overlap means you can have taller vertical FOV. + +parser = argparse.ArgumentParser() +parser.add_argument('--folder', default="./sequences/venice_360/equi") +parser.add_argument('--label', required=True) +parser.add_argument('--vertical_offset', type=int, default=983) +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 +src_one_half_width = int(src_width * 3/2) + +count = 3 +output_aspect = 3 + +overall_aspect = count * output_aspect +overlapped_aspect = overall_aspect - count * opt.overlap +crop_width = src_width * output_aspect / overlapped_aspect +crop_height = crop_width / output_aspect + +# c0 = 7/6 * src_width +c1 = 3/6 * src_width +# c2 = 5/6 * src_width + +# x0 = c0 - crop_width / 2 +x1 = c1 - crop_width / 2 +# x2 = c2 - crop_width / 2 + +y0 = opt.vertical_offset - crop_height / 2 + +# p0 = (x0, y0, x0 + crop_width, y0 + crop_height,) +# p1 = (x1, y0, x1 + crop_width, y0 + crop_height,) +# p2 = (x2, y0, x2 + crop_width, y0 + crop_height,) + +# label_0 = opt.label + '_a' +label_1 = opt.label +# label_2 = opt.label + '_c' + +# labels = [label_0, label_1, label_2] +labels = [label_1] + +# path_0 = os.path.join("sequences", label_0) +path_1 = os.path.join("sequences", label_1) +# path_2 = os.path.join("sequences", label_2) + +if opt.clobber: + # if os.path.exists(path_0): + # rmtree(path_0) + if os.path.exists(path_1): + rmtree(path_1) + # if os.path.exists(path_2): + # rmtree(path_2) + +# os.makedirs(path_0) +os.makedirs(path_1) +# os.makedirs(path_2) + +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)) + + canvas = Image.new('RGB', (src_one_half_width, src_height,)) + image = Image.open(fn) + canvas.paste(image, (0, 0)) + canvas.paste(image, (src_width, 0)) + + ii = i % src_width + p1 = (x1 + ii, y0, x1 + crop_width + ii, y0 + crop_height,) + + # canvas.crop(p0).resize((1024, 512,), Image.ANTIALIAS).save(os.path.join(path_0, out_fn)) + canvas.crop(p1).resize((1024, 512,), Image.ANTIALIAS).save(os.path.join(path_1, out_fn)) + # canvas.crop(p2).resize((1024, 512,), Image.ANTIALIAS).save(os.path.join(path_2, 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 + ]) + |