Refactor

config.py

@@ -0,0 +1,9 @@
+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)

editor/ploting/__init__.py

@@ -1,2 +0,0 @@
-from .display_images import display_images
-from .plot_histograms import plot_histograms

editor/training/histogram_dataset.py

@@ -1,5 +1,5 @@
 from torch.utils.data import Dataset
-from typing import List, Optional
+from typing import List, Optional, Tuple
 from editor.utils import compute_histogram
 from .random_edit import random_edit
 from PIL import Image
@@ -45,7 +45,19 @@ class HistogramDataset(Dataset):
     def __len__(self):
         return len(self._paths) * self._edit_count
 
-    def __getitem__(self, idx):
+    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():
@@ -55,10 +67,7 @@ class HistogramDataset(Dataset):
                     print(f"Failed to load {self._cached_data_path}, regenerating...")
 
         original_idx = idx // self._edit_count
-        original_path = self._paths[original_idx]
+        original = self.get_original_image(original_idx)
-        original = Image.open(original_path)
-        original.thumbnail(self._target_size, Image.Resampling.LANCZOS)
-
         edited = random_edit(original, seed=idx)
 
         edited_histogram = compute_histogram(

editor/visualisation/__init__.py

@@ -0,0 +1,3 @@
+from .display_images import display_images
+from .plot_histograms_in_3d import plot_histograms_in_3d
+from .plot_histograms_in_2d import plot_histograms_in_2d

editor/visualisation/display_images.py

@@ -0,0 +1,25 @@
+import matplotlib.pyplot as plt
+from typing import Dict
+from PIL.Image import Image
+from math import ceil
+
+
+def display_images(images: Dict[str, Image], images_per_row: int = 3):
+    fig, axes = plt.subplots(
+        nrows=ceil(len(images) / images_per_row),
+        ncols=min(images_per_row, len(images)),
+        figsize=(12, 8),
+    )
+
+    axes = axes.flatten()
+
+    for i, (title, image) in enumerate(images.items()):
+        axes[i].imshow(image)
+        axes[i].axis("off")
+        axes[i].set_title(title)
+
+    for i in range(len(images), len(axes)):
+        axes[i].axis("off")
+
+    plt.tight_layout()
+    plt.show()

editor/visualisation/plot_histograms_in_2d.py

@@ -0,0 +1,32 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import numpy as np
+from typing import Dict
+
+
+def plot_histograms_in_2d(histograms: Dict[str, np.ndarray]):
+    fig = plt.figure(figsize=(15, 5))
+
+    for i, (title, histogram) in enumerate(histograms.items(), 1):
+        ax = fig.add_subplot(1, 3, i, projection="3d")
+
+        size = histogram.shape[0]
+
+        x, y, z = np.indices(histogram.shape)
+        x = x.flatten()
+        y = y.flatten()
+        z = z.flatten()
+        values = histogram.flatten()
+
+        sizes = values * 5000
+
+        colors = np.vstack((x, y, z)).T / (size - 1)
+
+        sc = ax.scatter(x, y, z, c=colors, s=sizes, marker="o", alpha=0.5)
+
+        ax.set_xlim([0, (size - 1)])
+        ax.set_ylim([0, (size - 1)])
+        ax.set_zlim([0, (size - 1)])
+        ax.set_title(title)
+
+    return fig

editor/visualisation/plot_histograms_in_3d.py

@@ -0,0 +1,54 @@
+from plotly.subplots import make_subplots
+import plotly.graph_objects as go
+from math import ceil
+from typing import Dict
+import numpy as np
+
+
+def plot_histograms_in_3d(
+    histograms: Dict[str, np.ndarray], histogram_per_row: int = 3
+):
+    cols = min(histogram_per_row, len(histograms))
+    rows = ceil(len(histograms) / histogram_per_row)
+    fig = make_subplots(
+        rows=rows,
+        cols=cols,
+        specs=[[{"type": "scatter3d"} for _ in range(cols)] for _ in range(rows)],
+    )
+    for i, (title, histogram) in enumerate(histograms.items()):
+        fig.add_trace(
+            _get_3d_scatter_plot_from_histogram(title, histogram),
+            row=(i // (histogram_per_row + 1)) + 1,
+            col=(i % histogram_per_row) + 1,
+        )
+    fig.show()
+
+
+def _get_3d_scatter_plot_from_histogram(title, histogram):
+    x, y, z, marker_size = [], [], [], []
+    bins = len(histogram)
+
+    for i, row in enumerate(histogram):
+        for j, col in enumerate(row):
+            for k, value in enumerate(col):
+                if value > 0:
+                    x.append(i)
+                    y.append(j)
+                    z.append(k)
+                    marker_size.append(value)
+
+    return go.Scatter3d(
+        x=x,
+        y=y,
+        z=z,
+        mode="markers",
+        marker=dict(
+            size=[min(20, ms * 10000) for ms in marker_size],
+            color=[
+                f"rgb({xi*256/bins},{yi*256/bins},{zi*256/bins})"
+                for xi, yi, zi in zip(x, y, z)
+            ],
+            opacity=0.8,
+        ),
+        name=title,
+    )