Renamed project

README.md

@@ -1,4 +1,4 @@
-# TiledImage V3
+# TilImg V3
 This program composites many images into one big image using a set of images and a reference image
 ![results](https://user-images.githubusercontent.com/34125174/208235487-44f5e641-e6eb-453a-a9db-25d93a093782.png)
 [Generated from Photo by Pixabay from Pexels: https://www.pexels.com/photo/dock-under-cloudy-sky-in-front-of-mountain-206359/ )
@@ -19,7 +19,7 @@ Usage (Before v3.1.0): `timg REFERENCE_IMAGEPATH OUT_PATH`
 
 ##### Optional Arguments
 - --resize-factor: The factor by which to resize the reference image. Default is -1 (auto, resizes based on tile size. Final image resolution will stay mostly the same)
-- --process-type: TiledImage uses numba to speed up computation. This argument specifies the method used to do so. Default is guvectorize
+- --process-type: TilImg uses numba to speed up computation. This argument specifies the method used to do so. Default is guvectorize
   - guvectorize: Uses numba's guvectorize to speed up computation. This is the default method
   - njit: Uses numba's njit to speed up computation. This is known to be extremely slow
   - cuda: Also uses numba's guvectorize but targets CUDA-enabled GPUs. This is known to be slighly faster than guvectorize but requires a CUDA-enabled GPU AND has some overhead costs
@@ -39,7 +39,7 @@ Usage: `timg vid [OPTIONS] SOURCE_PATH SAVE_PATH TILESET_PATHS...`
 
 ##### Optional Arguments
 - --resize-factor: The factor by which to resize the reference image. Default is -1 (auto, resizes based on tile size. Final image resolution will stay mostly the same)
-- --process-type: TiledImage uses numba to speed up computation. This argument specifies the method used to do so. Default is guvectorize
+- --process-type: TilImg uses numba to speed up computation. This argument specifies the method used to do so. Default is guvectorize
   - guvectorize: Uses numba's guvectorize to speed up computation. This is the default method
   - ~~njit: Uses numba's njit to speed up computation. This is known to be extremely slow~~ Not available in video mode
   - cuda: Also uses numba's guvectorize but targets CUDA-enabled GPUs. This is known to be slighly faster than guvectorize but requires a CUDA-enabled GPU AND has some overhead costs

TiledImage/__init__.py → TilImg/__init__.py

@@ -4,8 +4,8 @@
 import numba as nb
 from rich.progress import Progress, SpinnerColumn, TextColumn
 
-from TiledImage.errors import UnexpectedImageShapeError
-from TiledImage.utils import ClockTimer, SpinnerProgress
+from TilImg.errors import UnexpectedImageShapeError
+from TilImg.utils import ClockTimer, SpinnerProgress
 
 nb.warnings.simplefilter('ignore', category=nb.NumbaDeprecationWarning)
 

TiledImage/__main__.py → TilImg/__main__.py

@@ -8,9 +8,9 @@
 from rich.progress import Progress, BarColumn, TextColumn, SpinnerColumn
 from rich.table import Table
 
-import TiledImage
-from TiledImage import video
-from TiledImage.utils import ProcessType
+import TilImg
+from TilImg import video
+from TilImg.utils import ProcessType
 
 
 
@@ -24,12 +24,12 @@ def tiledImage_cli(
 ):
     os.makedirs("./build/", exist_ok=True)
 
-    overall_progress = TiledImage.utils.getProgressBar()
+    overall_progress = TilImg.utils.getProgressBar()
     overall_task = overall_progress.add_task("Overall Progress", total=5)
 
     with overall_progress:
 
-        tiles, tile_shape = TiledImage.utils.load_imageset(Path(), "", tileset_paths, progress=overall_progress)
+        tiles, tile_shape = TilImg.utils.load_imageset(Path(), "", tileset_paths, progress=overall_progress)
         overall_progress.advance(overall_task)
 
         if resize_factor < 0:
@@ -39,18 +39,18 @@ def tiledImage_cli(
                 typer.echo(f"Invalid resize_factor: {resize_factor}")
                 return
 
-        referenceImage = TiledImage.utils.load_image(reference_imagepath, resize=resize_factor, progress=overall_progress)
+        referenceImage = TilImg.utils.load_image(reference_imagepath, resize=resize_factor, progress=overall_progress)
         overall_progress.advance(overall_task)
 
         if process_type == ProcessType.njit:
             overall_progress.print("Warning!!!. Using njit process type!!!!! This is EXTREMELY SLOW and should only be used for testing !!!")
-            image = TiledImage.generate_tiledimage(referenceImage, tiles, tile_shape)
+            image = TilImg.generate_tiledimage(referenceImage, tiles, tile_shape)
         elif process_type == ProcessType.cuda:
             overall_progress.print("Using cuda process type!!!!! This only works on CUDA enabled GPUS !!!")
-            image = TiledImage.generate_tiledimage_gu(referenceImage, tiles, tile_shape, useCuda=True,progress=overall_progress)
+            image = TilImg.generate_tiledimage_gu(referenceImage, tiles, tile_shape, useCuda=True, progress=overall_progress)
         else:
             overall_progress.print("Using default process type guvectorize...")
-            image = TiledImage.generate_tiledimage_gu(referenceImage, tiles, tile_shape, useCuda=False,progress=overall_progress)
+            image = TilImg.generate_tiledimage_gu(referenceImage, tiles, tile_shape, useCuda=False, progress=overall_progress)
         overall_progress.advance(overall_task)
 
 
@@ -64,7 +64,7 @@ def tiledImage_cli(
 def main():
     app = typer.Typer()
     vidTyper = typer.Typer()
-    print("# TiledImage version:", TiledImage.__version__)
+    print("# TilImg version:", TilImg.__version__)
     app.command(name="img",help="Generates a tiled image using a reference image and a set of images as tiles")(tiledImage_cli)
 
     app.command(name="vid",help="Generates a tiled image video by converting all of its frames into a tiled image.")(video.video_cli)

TiledImage/utils.py → TilImg/utils.py

@@ -11,7 +11,7 @@
 from rich.progress import Progress, SpinnerColumn, TextColumn, TimeRemainingColumn, BarColumn, MofNCompleteColumn, \
     TimeElapsedColumn
 
-from TiledImage import UnexpectedImageShapeError
+from TilImg import UnexpectedImageShapeError
 
 
 class ClockTimer:

TiledImage/video.py → TilImg/video.py

@@ -8,7 +8,7 @@
     SpinnerColumn
 from rich.table import Table
 
-import TiledImage
+import TilImg
 import colorama
 import numpy
 import numpy as np
@@ -18,8 +18,8 @@
 import typer
 from PIL import Image
 
-from TiledImage import ClockTimer, generate_tiledimage_gu, utils
-from TiledImage.utils import ProcessType
+from TilImg import ClockTimer, generate_tiledimage_gu, utils
+from TilImg.utils import ProcessType
 
 
 class Video:
@@ -127,7 +127,7 @@ def video_cli(
         process_type: ProcessType = typer.Option(ProcessType.guvectorize,
                                                  help="Type of processing to use. Default: guvectorize. njit IS not available for video")
 ):
-    overall_progress = TiledImage.utils.getProgressBar()
+    overall_progress = TilImg.utils.getProgressBar()
     overall_progress_task = overall_progress.add_task(f"Overall Progress", total=4)
 
 
@@ -140,11 +140,11 @@ def video_cli(
     with overall_progress:
         useCuda = process_type == ProcessType.cuda
 
-        tiles, tile_shape = TiledImage.utils.load_imageset(Path(), "", tileset_paths, progress=overall_progress)
+        tiles, tile_shape = TilImg.utils.load_imageset(Path(), "", tileset_paths, progress=overall_progress)
         overall_progress.advance(overall_progress_task, 1)
-        video = TiledImage.video.Video(source_path)
-        TiledImage.video.generate_tiledimage_video(video, tiles, tile_shape, useCuda=useCuda,
-                                                   resize_factor=resize_factor, progress=overall_progress)
+        video = TilImg.video.Video(source_path)
+        TilImg.video.generate_tiledimage_video(video, tiles, tile_shape, useCuda=useCuda,
+                                               resize_factor=resize_factor, progress=overall_progress)
         overall_progress.advance(overall_progress_task, 1)
         utils.test_for_ffmpeg()
         overall_progress.update(overall_progress_task, description="Overall Progress - waiting for ffmpeg...",advance=1)

setup.cfg

@@ -1,5 +1,5 @@
 [metadata]
-name = TiledImage
+name = TilImg
 version = attr: TiledImage.__version__
 author = ultraflame42
 author_email = [email protected]

test.py

@@ -1,16 +1,16 @@
 import os
 from pathlib import Path
 from PIL import Image
-import TiledImage as tm
-import TiledImage.utils
+import TilImg as tm
+import TilImg.utils
 
 os.makedirs("./build/",exist_ok=True)
-tiles,tile_shape = TiledImage.utils.load_imageset(Path(), "./assets/tiles/*.png")
+tiles,tile_shape = TilImg.utils.load_imageset(Path(), "./assets/tiles/*.png")
 
 # atlas = tm.create_tiles_atlas(tiles,tile_shape)
 # Image.fromarray(atlas).save("./build/atlas.png")
 
-referenceImage = TiledImage.utils.load_image(Path("./assets/blackhole1.jpg"), resize=1 / max(tile_shape), silent=False)
+referenceImage = TilImg.utils.load_image(Path("./assets/blackhole1.jpg"), resize=1 / max(tile_shape), silent=False)
 # referenceImage = tm.load_image(Path("./assets/blackhole1.jpg"),silent=False)
 
 

test_video.py

@@ -2,10 +2,10 @@
 
 import cv2
 
-import TiledImage.video
-import TiledImage
+import TilImg.video
+import TilImg
 
-print(TiledImage.__version__)
-tiles,tile_shape = TiledImage.utils.load_imageset(Path(), "./assets/tiles/*.png")
-video = TiledImage.video.Video(Path("./assets/ref_vid.mp4"))
-TiledImage.video.generate_tiledimage_video(video, tiles, tile_shape,useCuda=True)
+print(TilImg.__version__)
+tiles,tile_shape = TilImg.utils.load_imageset(Path(), "./assets/tiles/*.png")
+video = TilImg.video.Video(Path("./assets/ref_vid.mp4"))
+TilImg.video.generate_tiledimage_video(video, tiles, tile_shape, useCuda=True)