add resizing logic to preparation script

api/scripts/prepare-training.py

@@ -1,6 +1,6 @@
 from argparse import ArgumentParser
 from typing import Any, List, Tuple
-from PIL.Image import Image, open as pil_open
+from PIL.Image import Image, open as pil_open, merge, Resampling
 from torchvision.transforms import RandomCrop, Resize, Normalize, ToTensor
 from os import environ, path
 from logging import getLogger
@@ -37,6 +37,7 @@ def parse_args():
     parser.add_argument("--crops", type=int)
     parser.add_argument("--height", type=int, default=512)
     parser.add_argument("--width", type=int, default=512)
+    parser.add_argument("--scale", type=float, default=1.5)
     parser.add_argument("--threshold", type=float, default=0.75)
 
     return parser.parse_args()
@@ -51,9 +52,17 @@ def load_images(root: str) -> List[Tuple[str, Image]]:
         prefix, _ext = path.splitext(name)
         prefix = path.basename(prefix)
 
+        try:
             image = pil_open(name)
             image = ImageOps.exif_transpose(image)
+
+            if image.mode == "L":
+                image = merge("RGB", (image, image, image))
+
+            logger.info("adding %s to sources", name)
             images.append((prefix, image))
+        except:
+            logger.exception("error loading image")
 
     return images
 
@@ -66,10 +75,18 @@ def save_images(root: str, images: List[Tuple[str, Image]]):
     logger.info("saved %s images to %s", len(images), root)
 
 
-def resize_images(images: List[Tuple[str, Image]], size: Tuple[int, int]) -> List[Tuple[str, Image]]:
+def resize_images(images: List[Tuple[str, Image]], size: Tuple[int, int], min_scale: float) -> List[Tuple[str, Image]]:
     results = []
     for name, image in images:
-        results.append((name, ImageOps.contain(image, size)))
+        scale = min(image.width / size[0], image.height / size[1])
+        resize = (int(image.width / scale), int(image.height / scale))
+        logger.info("resize %s from %s to %s (%s scale)", name, image.size, resize, scale)
+
+        if scale < min_scale:
+            logger.warning("image %s is too small: %s", name, resize)
+            continue
+
+        results.append((name, image.resize(resize, Resampling.LANCZOS)))
 
     return results
 
@@ -97,25 +114,13 @@ def remove_duplicates(sources: List[Tuple[str, Image]], threshold: float, vector
             score = similarity(source_vector, cache_vector)
             logger.debug("similarity score for %s: %s", name, score)
 
-            if score > threshold:
+            if score.max() > threshold:
                 cached = True
 
         if cached == False:
             vector_cache.append(source_vector)
             results.append((name, source))
 
-
-    # count = len(sources)
-    # for i in range(count):
-    #     if i not in duplicates:
-    #         for j in range(i + 1, count):
-    #           if j not in duplicates and i != j:
-    #             score = similarity(vectors[i], vectors[j])
-    #             logger.info("similarity score between %s and %s: %s", i, j, score)
-
-    #             if score > threshold:
-    #                 duplicates.add(j)
-
     logger.info("keeping %s of %s images", len(results), len(sources))
 
     return results
@@ -126,6 +131,12 @@ def crop_images(sources: List[Tuple[str, Image]], size: Tuple[int, int], crops:
     results = []
 
     for name, source in sources:
+        logger.info("cropping %s", name)
+
+        if source.width < size[0] or source.height < size[1]:
+            logger.info("a small image leaked into the set: %s", name)
+            continue
+
         for i in range(crops):
             results.append((f"{name}_{i}", transform(source)))
 
@@ -134,11 +145,15 @@ def crop_images(sources: List[Tuple[str, Image]], size: Tuple[int, int], crops:
 
 if __name__ == "__main__":
     args = parse_args()
+    size = (int(args.width * args.scale), int(args.height * args.scale))
 
     # load unique sources
     sources = load_images(args.src)
-    sources = resize_images(sources, (args.width * 2, args.height * 2))
+    logger.info("loaded %s source images, resizing", len(sources))
+    sources = resize_images(sources, size, 0.5)
+    logger.info("resized images, removing duplicates")
     sources = remove_duplicates(sources, args.threshold, [])
+    logger.info("removed duplicated, kept %s source images", len(sources))
 
     # randomly crop
     cache = []