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