upload bytesio object to cortex

2 files changed, 48 insertions, 0 deletions

diff --git a/cli/app/commands/cortex/upload.py b/cli/app/commands/cortex/upload.py
new file mode 100644
index 0000000..c1b0a46
--- /dev/null
+++ b/cli/app/commands/cortex/upload.py
@@ -0,0 +1,15 @@
+import click
+
+from app.utils.cortex_utils import upload_to_cortex
+
+@click.command('')
+@click.option('-i', '--input', 'opt_fp_in', required=True, 
+  help='Path to input image')
+@click.option('-f', '--folder_id', 'opt_folder_id', required=True, 
+  help='ID of folder on Cortex')
+@click.pass_context
+def cli(ctx, opt_fp_in, opt_folder_id):
+  """
+  Test uploading a file to Cortex
+  """
+  upload_to_cortex(opt_folder_id, opt_fp_in)
diff --git a/cli/app/commands/cortex/upload_bytes.py b/cli/app/commands/cortex/upload_bytes.py
new file mode 100644
index 0000000..5260b8e
--- /dev/null
+++ b/cli/app/commands/cortex/upload_bytes.py
@@ -0,0 +1,33 @@
+import click
+
+import numpy as np
+from PIL import Image
+from io import BytesIO
+from app.utils.cortex_utils import upload_bytes_to_cortex
+
+@click.command('')
+@click.option('-f', '--folder_id', 'opt_folder_id', required=True, 
+  help='ID of folder on Cortex')
+@click.pass_context
+def cli(ctx, opt_folder_id):
+  """
+  Test uploading a BytesIO file to Cortex
+  """
+  arr = get_gradation_3d(256, 256, (0, 0, 192), (255, 255, 64), (True, False, False))
+  arr = arr.astype(np.uint8)
+  image = Image.fromarray(arr)
+  fp = BytesIO()
+  image.save(fp, format='png')
+  upload_bytes_to_cortex(opt_folder_id, "test.png", fp, "image/png")
+
+def get_gradation_2d(start, stop, width, height, is_horizontal):
+  if is_horizontal:
+    return np.tile(np.linspace(start, stop, width), (height, 1))
+  else:
+    return np.tile(np.linspace(start, stop, height), (width, 1)).T
+
+def get_gradation_3d(width, height, start_list, stop_list, is_horizontal_list):
+  result = np.zeros((height, width, len(start_list)), dtype=np.float)
+  for i, (start, stop, is_horizontal) in enumerate(zip(start_list, stop_list, is_horizontal_list)):
+    result[:, :, i] = get_gradation_2d(start, stop, width, height, is_horizontal)
+  return result