crop equirectangular rotating

1 files changed, 119 insertions, 0 deletions

diff --git a/crop-equirectangular-rotating.py b/crop-equirectangular-rotating.py
new file mode 100644
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+++ b/crop-equirectangular-rotating.py
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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 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
+    ])
+