Johnny Fernandes
352 lines
12 KiB
Plaintext
352 lines
12 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "eda-00",
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"metadata": {},
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"source": [
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"# 01 — EDA\n",
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"\n",
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"Explore DeepFakeFace (DFF) data quality before training: composition, source distribution, image properties, and split safety.\n",
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"\n",
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"**Sections:**\n",
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"1. Dataset composition and label balance\n",
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"2. Visual sanity-check samples\n",
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"3. Image dimension profile\n",
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"4. Per-source color statistics\n",
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"5. CV split and leakage sanity check\n",
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"6. Observations"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "eda-01",
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"metadata": {},
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"outputs": [],
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"source": [
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"import sys\n",
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"sys.path.insert(0, '..')\n",
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"\n",
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"import random\n",
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"from collections import Counter\n",
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"from pathlib import Path\n",
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"\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"from PIL import Image\n",
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"\n",
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"from src.data import DFFDataset, SOURCES, get_splits\n",
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"\n",
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"DATA_DIR = Path('../../data')\n",
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"FIG_DIR = Path('../outputs/figures')\n",
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"FIG_DIR.mkdir(parents=True, exist_ok=True)\n",
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"\n",
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"SEED = 42\n",
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"random.seed(SEED)\n",
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"np.random.seed(SEED)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "eda-02",
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"metadata": {},
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"source": [
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"## 1. Dataset composition and label balance"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "eda-03",
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"metadata": {},
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"outputs": [],
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"source": [
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"full_ds = DFFDataset(DATA_DIR)\n",
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"label_counts = full_ds.label_counts()\n",
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"\n",
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"print(f\"Total images : {len(full_ds):,}\")\n",
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"print(f\" Real (label=0) : {label_counts[0]:,}\")\n",
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"print(f\" Fake (label=1) : {label_counts[1]:,}\")\n",
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"print(f\" Fake:real ratio : {label_counts[1] / label_counts[0]:.2f}x\\n\")\n",
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"\n",
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"source_info = []\n",
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"for source, label in SOURCES.items():\n",
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" ds = DFFDataset(DATA_DIR, sources=[source])\n",
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" source_info.append((source, len(ds), label))\n",
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" tag = 'real' if label == 0 else 'fake'\n",
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" print(f\" {source:12s} n={len(ds):6,} label={label} ({tag})\")\n",
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"\n",
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"# Identity-level sanity check: each basename should appear in every source.\n",
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"basename_counts = Counter(path.name for path, _ in full_ds.samples)\n",
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"presence_hist = Counter(basename_counts.values())\n",
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"\n",
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"print(\"\\nIdentity (basename) presence across sources:\")\n",
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"for n_sources, count in sorted(presence_hist.items()):\n",
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" print(f\" present in {n_sources} source(s): {count:,} identities\")\n",
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"\n",
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"incomplete = sum(v for k, v in presence_hist.items() if k < len(SOURCES))\n",
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"print(f\" complete in all {len(SOURCES)} sources: {presence_hist.get(len(SOURCES), 0):,}\")\n",
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"print(f\" incomplete identities : {incomplete:,}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "eda-04",
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"metadata": {},
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"outputs": [],
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"source": [
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"fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))\n",
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"\n",
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"# Overall class balance\n",
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"class_names = ['Real (wiki)', 'Fake (all 3)']\n",
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"class_counts = [label_counts[0], label_counts[1]]\n",
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"bars = ax1.bar(class_names, class_counts, color=['#2196F3', '#F44336'], width=0.5)\n",
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"ax1.set_title('Overall Class Balance', fontsize=13)\n",
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"ax1.set_ylabel('Images')\n",
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"ax1.set_ylim(0, max(class_counts) * 1.15)\n",
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"for bar, v in zip(bars, class_counts):\n",
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" ax1.text(bar.get_x() + bar.get_width() / 2, v + 300,\n",
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" f'{v:,}', ha='center', fontsize=11)\n",
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"\n",
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"# Per-source breakdown\n",
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"src_names = [s for s, _, _ in source_info]\n",
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"src_counts = [n for _, n, _ in source_info]\n",
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"colors = ['#2196F3', '#FF9800', '#9C27B0', '#4CAF50']\n",
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"bars2 = ax2.bar(src_names, src_counts, color=colors, width=0.5)\n",
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"ax2.set_title('Images per Source', fontsize=13)\n",
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"ax2.set_ylabel('Images')\n",
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"ax2.set_ylim(0, max(src_counts) * 1.15)\n",
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"for bar, v in zip(bars2, src_counts):\n",
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" ax2.text(bar.get_x() + bar.get_width() / 2, v + 300,\n",
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" f'{v:,}', ha='center', fontsize=11)\n",
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"\n",
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"fig.tight_layout()\n",
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"fig.savefig(FIG_DIR / 'class_balance.png', dpi=120, bbox_inches='tight')\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "eda-05",
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"metadata": {},
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"source": [
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"## 2. Visual sanity-check samples"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "eda-06",
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"metadata": {},
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"outputs": [],
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"source": [
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"N_COLS = 6\n",
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"fig, axes = plt.subplots(len(SOURCES), N_COLS, figsize=(18, 12))\n",
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"fig.suptitle('Sample images — 6 per source', fontsize=14)\n",
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"\n",
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"for row, (source, label) in enumerate(SOURCES.items()):\n",
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" ds_src = DFFDataset(DATA_DIR, sources=[source])\n",
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" indices = random.sample(range(len(ds_src)), N_COLS)\n",
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" for col, idx in enumerate(indices):\n",
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" path, _ = ds_src.samples[idx]\n",
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" img = Image.open(path).convert('RGB').resize((128, 128))\n",
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" axes[row, col].imshow(img)\n",
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" axes[row, col].axis('off')\n",
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" tag = 'real' if label == 0 else 'fake'\n",
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" axes[row, 0].set_ylabel(f'{source}\\n({tag})', fontsize=10)\n",
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"\n",
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"fig.tight_layout()\n",
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"fig.savefig(FIG_DIR / 'sample_images.png', dpi=100, bbox_inches='tight')\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "eda-07",
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"metadata": {},
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"source": [
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"## 3. Image dimension profile"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "eda-08",
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"metadata": {},
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"outputs": [],
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"source": [
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"sample_paths = [p for p, _ in random.sample(full_ds.samples, min(2000, len(full_ds)))]\n",
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"sizes = Counter(Image.open(p).size for p in sample_paths)\n",
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"\n",
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"print('Most common image dimensions (W x H):')\n",
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"for (w, h), count in sizes.most_common(10):\n",
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" pct = count / len(sample_paths)\n",
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" print(f' {w:4d} x {h:4d} — {count:4d} samples ({pct:.1%})')\n",
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"\n",
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"widths = [w for (w, _) in sizes.elements()]\n",
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"heights = [h for (_, h) in sizes.elements()]\n",
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"square = sum(1 for w, h in zip(widths, heights) if w == h)\n",
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"print(f'\\nWidth range: {min(widths)}–{max(widths)} mean={np.mean(widths):.0f}')\n",
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"print(f'Height range: {min(heights)}–{max(heights)} mean={np.mean(heights):.0f}')\n",
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"print(f'Square images: {square}/{len(widths)} ({square / len(widths):.1%})')"
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]
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},
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{
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"cell_type": "markdown",
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"id": "eda-11",
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"metadata": {},
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"source": [
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"## 4. Per-source color statistics"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "eda-12",
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"metadata": {},
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"outputs": [],
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"source": [
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"print('Sampling per-source colour statistics (sampling 300 images per source)...')\n",
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"N_SAMPLES = 300\n",
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"CH_NAMES = ['R', 'G', 'B']\n",
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"\n",
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"source_means, source_stds = {}, {}\n",
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"for source in SOURCES:\n",
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" ds_src = DFFDataset(DATA_DIR, sources=[source])\n",
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" idxs = random.sample(range(len(ds_src)), min(N_SAMPLES, len(ds_src)))\n",
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" arrays = [\n",
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" np.array(\n",
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" Image.open(ds_src.samples[i][0]).convert('RGB').resize((64, 64)),\n",
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" dtype=np.float32\n",
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" ) / 255.0\n",
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" for i in idxs\n",
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" ]\n",
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" stack = np.stack(arrays) # (N, 64, 64, 3)\n",
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" source_means[source] = stack.mean(axis=(0, 1, 2)) # per channel\n",
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" source_stds[source] = stack.std(axis=(0, 1, 2))\n",
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" print(f' {source}: mean={source_means[source].round(3)} std={source_stds[source].round(3)}')\n",
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"\n",
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"src_keys = list(SOURCES.keys())\n",
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"x = np.arange(len(src_keys))\n",
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"bar_w = 0.22\n",
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"ch_colors = ['#F44336', '#4CAF50', '#2196F3']\n",
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"\n",
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"fig, axes = plt.subplots(1, 2, figsize=(14, 4))\n",
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"for ci, ch in enumerate(CH_NAMES):\n",
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" offset = (ci - 1) * bar_w\n",
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" axes[0].bar(x + offset, [source_means[s][ci] for s in src_keys],\n",
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" bar_w, label=ch, color=ch_colors[ci], alpha=0.85)\n",
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" axes[1].bar(x + offset, [source_stds[s][ci] for s in src_keys],\n",
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" bar_w, label=ch, color=ch_colors[ci], alpha=0.85)\n",
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"\n",
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"for ax, title, ylabel in zip(\n",
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" axes,\n",
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" ['Mean pixel intensity per source', 'Pixel std dev per source'],\n",
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" ['Mean (0–1)', 'Std dev (0–1)'],\n",
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"):\n",
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" ax.set_xticks(x)\n",
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" ax.set_xticklabels(src_keys)\n",
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" ax.set_title(title, fontsize=12)\n",
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" ax.set_ylabel(ylabel)\n",
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" ax.legend(title='Channel')\n",
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" ax.grid(axis='y', alpha=0.3)\n",
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"\n",
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"fig.tight_layout()\n",
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"fig.savefig(FIG_DIR / 'color_stats.png', dpi=120, bbox_inches='tight')\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1c7d4660",
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"metadata": {},
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"source": [
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"## 5. CV split and leakage sanity check"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "89513a74",
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"metadata": {},
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"outputs": [],
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"source": [
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"cfg = {\n",
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" \"cv_folds\": 5,\n",
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" \"seed\": SEED,\n",
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" \"image_size\": 224,\n",
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" \"train_sources\": None,\n",
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" \"eval_sources\": None,\n",
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"}\n",
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"\n",
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"splits = get_splits(full_ds, cfg)\n",
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"print(f\"Generated {len(splits)} CV folds\")\n",
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"\n",
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"for fold_i, (train_idx, val_idx, test_idx) in enumerate(splits):\n",
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" train_ids = {full_ds.samples[i][0].name for i in train_idx}\n",
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" val_ids = {full_ds.samples[i][0].name for i in val_idx}\n",
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" test_ids = {full_ds.samples[i][0].name for i in test_idx}\n",
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"\n",
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" overlap = (train_ids & val_ids) | (train_ids & test_ids) | (val_ids & test_ids)\n",
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" print(\n",
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" f\"Fold {fold_i}: train={len(train_idx):6d} val={len(val_idx):6d} test={len(test_idx):6d} \"\n",
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" f\"identity_overlap={len(overlap)}\"\n",
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" )\n",
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"\n",
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"print(\"\\nExpected: identity_overlap should be 0 for every fold.\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "eda-13",
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"metadata": {},
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"source": [
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"## 6. Observations template\n",
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"\n",
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"Fill in after running the notebook:\n",
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"\n",
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"**Class balance**\n",
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"- Confirm fake:real ratio and whether sampler/reweighting is needed.\n",
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"\n",
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"**Identity completeness**\n",
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"- Note whether most basenames appear in all sources or if there are missing-source identities.\n",
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"\n",
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"**Dimensions**\n",
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"- Record dominant dimensions and whether extreme outliers appear.\n",
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"\n",
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"**Color stats**\n",
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"- Note clear mean/std shifts by source (if any).\n",
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"\n",
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"**Split sanity**\n",
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"- Confirm every fold reports `identity_overlap=0`.\n",
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"\n",
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"**Action items before training**\n",
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"- List any cleanup/filtering decisions (if required)."
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "drl",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.13"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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