move files

src/config.py

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+from pathlib import Path
+
+DATA = sorted(Path("/mnt/wsl/PHYSICALDRIVE1/data/unsplash").glob("*.jpg"))
+
+CACHE_PATH = Path("/mnt/wsl/PHYSICALDRIVE1/data/cache2")
+CACHE_PATH.mkdir(exist_ok=True, parents=True)
+
+MODELS_PATH = Path("models")
+MODELS_PATH.mkdir(exist_ok=True, parents=True)

src/editor/histogram_transfer/__init__.py

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+from .regrain import regrain
+from .pdf_transfer_1d import pdf_transfer_1d
+from .pdf_transfer_3d import pdf_transfer_3d

src/editor/histogram_transfer/pdf_transfer_1d.py

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+import numpy as np
+
+
+def pdf_transfer_1d(pX: np.ndarray, pY: np.ndarray) -> np.ndarray:
+    PX = np.cumsum(pX + np.finfo(float).eps)
+    PX /= PX[-1]
+
+    PY = np.cumsum(pY + np.finfo(float).eps)
+    PY /= PY[-1]
+
+    f = np.interp(PX, PY, np.arange(len(pX)))
+
+    return f

src/editor/histogram_transfer/pdf_transfer_3d.py

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+import numpy as np
+from editor.utils import generate_rotation_matrices
+from editor.histogram_transfer import pdf_transfer_1d
+from editor.histogram_transfer import regrain
+
+
+EPSILON = 1e-6
+
+
+def pdf_transfer_3d(
+    source: np.ndarray,
+    target_flattened: np.ndarray,
+    relaxation: float = 1,
+    bin_count: int = 1000,
+    iterations: int = 25,
+    smoothness: float = 1,
+):
+    [h, w, c] = source.shape
+    source_flattened = source.reshape(-1, c).transpose()
+
+    rotation_matrices = generate_rotation_matrices(iterations)
+    for i, rotation in enumerate(rotation_matrices, start=1):
+        D0R = rotation @ source_flattened
+        D1R = rotation @ target_flattened
+        D0R_ = np.zeros_like(source_flattened)
+
+        for i in range(rotation.shape[0]):
+            datamin = min(np.min(D0R[i, :]), np.min(D1R[i, :])) - EPSILON
+            datamax = max(np.max(D0R[i, :]), np.max(D1R[i, :])) + EPSILON
+            u = np.linspace(datamin, datamax, bin_count)
+
+            p0R, _ = np.histogram(D0R[i, :], bins=u, density=True)
+            p1R, _ = np.histogram(D1R[i, :], bins=u, density=True)
+
+            f = pdf_transfer_1d(p0R, p1R)
+            mapped_values = (
+                np.interp(D0R[i, :], u[:-1], f) * (datamax - datamin) / (bin_count - 1)
+                + datamin
+            )
+            D0R_[i, :] = mapped_values
+
+        source_flattened = source_flattened + relaxation * (rotation.T @ (D0R_ - D0R))
+        source_flattened.clip(0, 255, out=source_flattened)
+
+    result = source_flattened.astype(np.uint8).transpose().reshape(h, w, c)
+    return regrain(source, result, smoothness=smoothness)

src/editor/histogram_transfer/regrain.py

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+from scipy.ndimage import zoom
+import numpy as np
+
+
+NBITS = [4, 16, 32, 64, 64, 64]
+
+
+def regrain(img_arr_in, img_arr_col, smoothness: float = 1):
+    """keep gradient of img_arr_in and color of img_arr_col."""
+
+    img_arr_in = img_arr_in / 255.0
+    img_arr_col = img_arr_col / 255.0
+    img_arr_out = np.array(img_arr_in)
+    img_arr_out = _regrain_rec(
+        img_arr_out, img_arr_in, img_arr_col, NBITS, 0, smoothness
+    )
+    img_arr_out[img_arr_out < 0] = 0
+    img_arr_out[img_arr_out > 1] = 1
+    img_arr_out = (255.0 * img_arr_out).astype("uint8")
+    return img_arr_out
+
+
+def _regrain_rec(img_arr_out, img_arr_in, img_arr_col, nbits, level, smoothness):
+    [h, w, _] = img_arr_in.shape
+    h2 = (h + 1) // 2
+    w2 = (w + 1) // 2
+    if len(nbits) > 1 and h2 > 20 and w2 > 20:
+        resize_arr_in = _resize_image(img_arr_in, w2, h2)
+        resize_arr_col = _resize_image(img_arr_col, w2, h2)
+        resize_arr_out = _resize_image(img_arr_out, w2, h2)
+        resize_arr_out = _regrain_rec(
+            resize_arr_out,
+            resize_arr_in,
+            resize_arr_col,
+            nbits[1:],
+            level + 1,
+            smoothness,
+        )
+        img_arr_out = _resize_image(resize_arr_out, w, h)
+    img_arr_out = _solve(
+        img_arr_out, img_arr_in, img_arr_col, nbits[0], level, smoothness
+    )
+    return img_arr_out
+
+
+def _solve(img_arr_out, img_arr_in, img_arr_col, nbit, level, smoothness):
+    [width, height, c] = img_arr_in.shape
+    first_pad_0 = lambda arr: np.concatenate((arr[:1, :], arr[:-1, :]), axis=0)
+    first_pad_1 = lambda arr: np.concatenate((arr[:, :1], arr[:, :-1]), axis=1)
+    last_pad_0 = lambda arr: np.concatenate((arr[1:, :], arr[-1:, :]), axis=0)
+    last_pad_1 = lambda arr: np.concatenate((arr[:, 1:], arr[:, -1:]), axis=1)
+
+    delta_x = last_pad_1(img_arr_in) - first_pad_1(img_arr_in)
+    delta_y = last_pad_0(img_arr_in) - first_pad_0(img_arr_in)
+    delta = np.sqrt((delta_x**2 + delta_y**2).sum(axis=2, keepdims=True))
+
+    psi = 256 * delta / 5
+    psi[psi > 1] = 1
+    phi = 30 * 2 ** (-level) / (1 + 10 * delta / smoothness)
+
+    phi1 = (last_pad_1(phi) + phi) / 2
+    phi2 = (last_pad_0(phi) + phi) / 2
+    phi3 = (first_pad_1(phi) + phi) / 2
+    phi4 = (first_pad_0(phi) + phi) / 2
+
+    rho = 1 / 5.0
+    for i in range(nbit):
+        den = psi + phi1 + phi2 + phi3 + phi4
+        num = (
+            np.tile(psi, [1, 1, c]) * img_arr_col
+            + np.tile(phi1, [1, 1, c])
+            * (last_pad_1(img_arr_out) - last_pad_1(img_arr_in) + img_arr_in)
+            + np.tile(phi2, [1, 1, c])
+            * (last_pad_0(img_arr_out) - last_pad_0(img_arr_in) + img_arr_in)
+            + np.tile(phi3, [1, 1, c])
+            * (first_pad_1(img_arr_out) - first_pad_1(img_arr_in) + img_arr_in)
+            + np.tile(phi4, [1, 1, c])
+            * (first_pad_0(img_arr_out) - first_pad_0(img_arr_in) + img_arr_in)
+        )
+        img_arr_out = (
+            num / np.tile(den + 1e-6, [1, 1, c]) * (1 - rho) + rho * img_arr_out
+        )
+    return img_arr_out
+
+
+def _resize_image(data, target_width, target_height):
+    return zoom(data, (target_height / data.shape[0], target_width / data.shape[1], 1))

src/editor/operations/__init__.py

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+from .add_noise import add_noise
+from .change_temperature import change_temperature
+from .add_random_colour_spill import add_random_colour_spill

src/editor/operations/add_noise.py

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+import numpy as np
+from PIL import Image
+
+
+def add_noise(img: Image, alpha: float) -> Image:
+    img = img.convert("RGB")
+    width, height = img.size
+    random_colors = np.random.randint(0, 256, (height, width, 3), dtype=np.uint8)
+    random_img = Image.fromarray(random_colors, mode="RGB")
+    result = Image.blend(img, random_img, alpha)
+    return result

src/editor/operations/add_random_colour_spill.py

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+from PIL import Image
+from ..utils import random
+
+
+def add_random_colour_spill(image: Image, range: float) -> Image:
+    matrix = (
+        random(1 / range, range),
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        random(1 / range, range),
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        random(1 / range, range),
+        0.0,
+    )
+    return image.convert("RGB", matrix)

src/editor/operations/change_temperature.py

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+from PIL import Image
+
+kelvin_table = {
+    1000: (255, 56, 0),
+    1500: (255, 109, 0),
+    2000: (255, 137, 18),
+    2500: (255, 161, 72),
+    3000: (255, 180, 107),
+    3500: (255, 196, 137),
+    4000: (255, 209, 163),
+    4500: (255, 219, 186),
+    5000: (255, 228, 206),
+    5500: (255, 236, 224),
+    6000: (255, 243, 239),
+    6500: (255, 249, 253),
+    7000: (245, 243, 255),
+    7500: (235, 238, 255),
+    8000: (227, 233, 255),
+    8500: (220, 229, 255),
+    9000: (214, 225, 255),
+    9500: (208, 222, 255),
+    10000: (204, 219, 255),
+}
+
+
+def change_temperature(image: Image, temperature: float) -> Image:
+    r, g, b = kelvin_table[temperature]
+    matrix = (
+        r / 255.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        g / 255.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        b / 255.0,
+        0.0,
+    )
+    return image.convert("RGB", matrix)

src/editor/training/__init__.py

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+from .histogram_dataset import HistogramDataset
+from .random_edit import random_edit
+from .progressive_pooling_loss import ProgressivePoolingLoss

src/editor/training/histogram_dataset.py

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+from torch.utils.data import Dataset
+from typing import List, Optional, Tuple
+from editor.utils import compute_histogram
+from .random_edit import random_edit
+from PIL import Image
+from tqdm import tqdm
+import torch
+from pathlib import Path
+
+import PIL.Image
+
+PIL.Image.MAX_IMAGE_PIXELS = None
+
+
+class HistogramDataset(Dataset):
+    def __init__(
+        self,
+        paths: List[Path],
+        edit_count: int = 5,
+        bin_count: int = 32,
+        target_size=(480, 480),
+        delete_corrupt_images: bool = False,
+        cache_path: Optional[Path] = None,
+    ):
+        self._paths = sorted(paths)
+        self._edit_count = edit_count
+        self._bin_count = bin_count
+        self._target_size = target_size
+        self._cache_path = cache_path
+
+        if delete_corrupt_images:
+            self._delete_corrupt_images()
+
+    def _delete_corrupt_images(self) -> None:
+        deleted_count = 0
+        for path in tqdm(self._paths):
+            try:
+                Image.open(path)
+            except:
+                print(f"Failed to open {path}, deleting...")
+                deleted_count += 1
+                path.unlink()
+        print(f"Deleted {deleted_count} corrupt images")
+
+    def __len__(self):
+        return len(self._paths) * self._edit_count
+
+    def get_original_image(self, original_idx: int) -> Image.Image:
+        original_path = self._paths[original_idx]
+        original = Image.open(original_path)
+        original.thumbnail(
+            self._target_size, Image.Resampling.LANCZOS
+        )  # size will be at most target_size, the aspect ratio is preserved
+        return original
+
+    def get_edited_image(self, original_idx: int, edit_idx: int) -> Image.Image:
+        original_image = self.get_original_image(original_idx)
+        return random_edit(original_image, seed=edit_idx)
+
+    def __getitem__(self, idx: int) -> Tuple[torch.Tensor, torch.Tensor]:
+        if self._cache_path is not None:
+            self._cached_data_path = self._cache_path / f"{idx}.pt"
+            if self._cached_data_path.exists():
+                try:
+                    return torch.load(self._cached_data_path)
+                except:
+                    print(f"Failed to load {self._cached_data_path}, regenerating...")
+
+        original_idx = idx // self._edit_count
+        original = self.get_original_image(original_idx)
+        edited = random_edit(original, seed=idx)
+
+        edited_histogram = compute_histogram(
+            edited, bins=self._bin_count, normalize=True
+        )
+
+        original_histogram = compute_histogram(
+            original, bins=self._bin_count, normalize=True
+        )
+
+        result = (
+            torch.tensor(edited_histogram, dtype=torch.float).unsqueeze(0),
+            torch.tensor(original_histogram, dtype=torch.float).unsqueeze(0),
+        )
+
+        if self._cache_path is not None:
+            torch.save(result, self._cached_data_path)
+
+        return result

src/editor/training/progressive_pooling_loss.py

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+from typing import List
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class ProgressivePoolingLoss(nn.Module):
+    def __init__(self, target_sizes: List[int], damping: float):
+        super(ProgressivePoolingLoss, self).__init__()
+        self._target_sizes = target_sizes
+        self._damping = damping
+
+    def forward(self, tensor_a, tensor_b):
+        assert (
+            tensor_a.size() == tensor_b.size()
+        ), f"Input tensors must have the same size, got {tensor_a.size()} and {tensor_b.size()}"
+
+        assert (
+            len(tensor_a.size()) == 5
+        ), f"Input tensors must have 5 dimensions, got {tensor_a.size()}"
+
+        _minibatch_size, _channels, depth, height, width = tensor_a.size()
+        assert depth == height == width, "Input tensors must be cubes."
+
+        loss = 0.0
+        weight = 1
+
+        for target_size in self._target_sizes:
+            pool_size = depth // target_size
+            pooled_a = F.avg_pool3d(tensor_a, pool_size) * (pool_size**3)
+            pooled_b = F.avg_pool3d(tensor_b, pool_size) * (pool_size**3)
+
+            diff = torch.abs(pooled_a - pooled_b)
+
+            loss += diff.mean() * weight
+            weight *= self._damping
+
+        return loss

src/editor/training/random_edit.py

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+from PIL import Image, ImageEnhance
+from ..utils import random, get_colour_lut, apply_pixel_shader
+from ..operations import add_noise, add_random_colour_spill
+import numpy as np
+
+
+def random_edit(img: Image, seed: int = 42) -> Image:
+    np.random.seed(seed)
+    img = add_noise(img, random(0, 0.2))
+    img = ImageEnhance.Contrast(img).enhance(random(0.5, 2))
+    img = add_random_colour_spill(img, 1.3)
+    img = img.convert("HSV")
+    saturation_lut = get_colour_lut(variance=0.3, count=5, type="linear")
+    brightness_lut = get_colour_lut(variance=0.3, count=5, type="cubic")
+    img = apply_pixel_shader(
+        img, lambda h, s, v: (h, saturation_lut[s], brightness_lut[v])
+    )
+    img = img.convert("RGB")
+    return img

src/editor/utils/__init__.py

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+from .interpolate import interpolate
+from .random import random
+from .apply_pixel_shader import apply_pixel_shader
+from .get_colour_lut import get_colour_lut
+from .compute_histogram import compute_histogram
+from .kldiv import kldiv
+from .generate_rotation_matrices import generate_rotation_matrices