Johnny Fernandes
363 lines
12 KiB
Plaintext
363 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": "pp-00",
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"metadata": {},
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"source": [
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"# 02 — Preprocessing\n",
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"\n",
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"Inspect what images look like right before model input.\n",
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"\n",
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"Face cropping is an offline step — run `tools/precrop.py` once to produce `data_cropped/`, then point configs at that directory. The sections below show the standard pipeline on already-cropped or uncropped images. `facenet_pytorch` is only needed to visualize the offline cropper.\n"
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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": "pp-01",
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"metadata": {},
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"outputs": [],
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"source": [
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"import random\n",
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"import sys\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 matplotlib.patches as patches\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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"sys.path.insert(0, '..')\n",
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"from src.data import DFFDataset, SOURCES\n",
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"from src.preprocessing.pipeline import DFFImagePipeline\n",
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"\n",
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"DATA_DIR = Path('../../data')\n",
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"SEED = 7\n",
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"random.seed(SEED)\n",
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"np.random.seed(SEED)\n",
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"\n",
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"full_ds = DFFDataset(DATA_DIR)\n",
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"\n",
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"print(f\"Dataset root: {DATA_DIR.resolve()}\")\n",
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"print(f\"Total samples: {len(full_ds):,}\")\n",
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"source_counts = Counter(path.parent.parent.name for path, _ in full_ds.samples)\n",
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"print(\"Per-source counts:\")\n",
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"for src in SOURCES:\n",
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" print(f\" {src:12s} {source_counts[src]:6,}\")\n",
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"\n",
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"def denorm(tensor):\n",
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" mean = np.array([0.485, 0.456, 0.406])\n",
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" std = np.array([0.229, 0.224, 0.225])\n",
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" arr = tensor.permute(1, 2, 0).numpy()\n",
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" return np.clip(arr * std + mean, 0, 1)\n",
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"\n",
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"def pick_samples(n=4, sources=None):\n",
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" ds = DFFDataset(DATA_DIR, sources=sources) if sources else full_ds\n",
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" idxs = random.sample(range(len(ds)), n)\n",
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" return [Image.open(ds.samples[i][0]).convert('RGB') for i in idxs]\n",
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"\n",
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"# Runtime face-crop helper from tools (kept for notebook visualization only).\n",
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"try:\n",
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" from facenet_pytorch import MTCNN\n",
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" from tools.precrop import FaceCropper\n",
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" FACE_CROP_AVAILABLE = True\n",
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" _detector = MTCNN(keep_all=False, select_largest=True, device='cpu', post_process=False)\n",
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" _cropper = FaceCropper(margin=0.6, size=224, device='cpu')\n",
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" print('facenet_pytorch available — crop helper enabled.')\n",
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"except ImportError:\n",
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" FACE_CROP_AVAILABLE = False\n",
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" _cropper = None\n",
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" print('WARNING: facenet_pytorch not installed — crop sections will be skipped.')\n",
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" print(' Install with: pip install facenet-pytorch')\n",
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"\n",
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"pipe_eval = DFFImagePipeline(image_size=224, train=False)\n",
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"pipe_aug = DFFImagePipeline(image_size=224, train=True)\n",
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"\n",
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"crop_note = 'offline face-crop preview -> ' if FACE_CROP_AVAILABLE else '(no face crop) -> '\n",
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"print('Pipelines ready.')"
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]
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},
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{
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"cell_type": "markdown",
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"id": "pp-02",
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"metadata": {},
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"source": [
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"---\n",
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"## 1. Crop preview\n",
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"\n",
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"Visualizes what `tools/precrop.py` does: MTCNN detects the largest face, crops a square with a 60% margin, and falls back to center-crop when no face is found.\n"
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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": "pp-03",
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"metadata": {},
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"outputs": [],
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"source": [
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"if not FACE_CROP_AVAILABLE:\n",
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" print('Skipped — facenet_pytorch not installed.')\n",
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"else:\n",
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" src_images = {src: pick_samples(1, sources=[src])[0] for src in SOURCES}\n",
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"\n",
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" fig, axes = plt.subplots(len(SOURCES), 3, figsize=(10, 14))\n",
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" fig.suptitle(\n",
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" 'Face crop helper | col 1: original + detection box | col 2: cropped | col 3: cropped + eval pipeline',\n",
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" fontsize=10\n",
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" )\n",
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"\n",
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" for row, (src, img) in enumerate(src_images.items()):\n",
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" label = 'real' if SOURCES[src] == 0 else 'fake'\n",
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"\n",
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" # col 0: original with bounding box\n",
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" boxes, probs = _detector.detect(img)\n",
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" axes[row, 0].imshow(img)\n",
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" if boxes is not None and len(boxes) > 0:\n",
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" x1, y1, x2, y2 = boxes[0]\n",
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" rect = patches.Rectangle(\n",
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" (x1, y1), x2 - x1, y2 - y1,\n",
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" linewidth=2, edgecolor='lime', facecolor='none'\n",
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" )\n",
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" axes[row, 0].add_patch(rect)\n",
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" axes[row, 0].set_title(f'detected p={probs[0]:.2f}', fontsize=8, color='green')\n",
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" else:\n",
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" axes[row, 0].set_title('no face — centre crop fallback', fontsize=8, color='red')\n",
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" axes[row, 0].set_ylabel(f'{src}\\n({label})', fontsize=9)\n",
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"\n",
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" # col 1: cropped result from tools.precrop.FaceCropper\n",
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" cropped = _cropper(img)\n",
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" axes[row, 1].imshow(cropped)\n",
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" axes[row, 1].set_title('cropped (224px)', fontsize=8)\n",
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"\n",
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" # col 2: cropped image through eval pipeline\n",
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" axes[row, 2].imshow(denorm(pipe_eval(cropped)))\n",
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" axes[row, 2].set_title('crop + eval pipeline', fontsize=8)\n",
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"\n",
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" for ax in axes.flat:\n",
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" ax.axis('off')\n",
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"\n",
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" plt.tight_layout()\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": "pp-04",
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"metadata": {},
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"source": [
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"---\n",
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"## 2. Eval path vs Train path\n",
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"\n",
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"Compare the deterministic eval transform and the stochastic train transform.\n"
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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": "pp-05",
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"metadata": {},
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"outputs": [],
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"source": [
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"src_images = {src: pick_samples(1, sources=[src])[0] for src in SOURCES}\n",
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"\n",
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"fig, axes = plt.subplots(len(SOURCES), 3, figsize=(10, 14))\n",
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"fig.suptitle(\n",
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" f'original | {crop_note}eval (no aug) | {crop_note}train aug',\n",
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" fontsize=11\n",
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")\n",
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"\n",
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"for row, (src, img) in enumerate(src_images.items()):\n",
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" label = 'real' if SOURCES[src] == 0 else 'fake'\n",
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" proc_img = _cropper(img) if FACE_CROP_AVAILABLE else img\n",
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"\n",
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" axes[row, 0].imshow(img.resize((224, 224)))\n",
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" axes[row, 0].set_title('original', fontsize=8)\n",
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" axes[row, 0].set_ylabel(f'{src}\\n({label})', fontsize=9)\n",
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"\n",
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" axes[row, 1].imshow(denorm(pipe_eval(proc_img)))\n",
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" axes[row, 1].set_title(f'{crop_note}eval (no aug)', fontsize=8)\n",
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"\n",
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" axes[row, 2].imshow(denorm(pipe_aug(proc_img)))\n",
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" axes[row, 2].set_title(f'{crop_note}train aug', fontsize=8)\n",
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"\n",
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"for ax in axes.flat:\n",
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" ax.axis('off')\n",
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"\n",
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"plt.tight_layout()\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": "pp-06",
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"metadata": {},
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"source": [
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"---\n",
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"## 3. Augmentation variety\n",
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"\n",
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"Use the same source image with multiple independent stochastic draws.\n",
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"This shows the realistic variation the model sees during training."
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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": "pp-07",
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"metadata": {},
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"outputs": [],
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"source": [
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"N_DRAWS = 8\n",
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"imgs_to_show = pick_samples(2)\n",
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"\n",
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"fig, axes = plt.subplots(2, N_DRAWS + 1, figsize=(20, 5))\n",
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"fig.suptitle(\n",
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" f'{N_DRAWS} independent draws — {crop_note}aug — each column is a different random sample',\n",
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" fontsize=11\n",
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")\n",
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"\n",
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"for row, img in enumerate(imgs_to_show):\n",
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" axes[row, 0].imshow(img.resize((224, 224)))\n",
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" axes[row, 0].set_title('original', fontsize=8)\n",
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" axes[row, 0].set_ylabel(f'image {row + 1}', fontsize=9)\n",
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"\n",
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" for col in range(N_DRAWS):\n",
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" axes[row, col + 1].imshow(denorm(pipe_aug(img)))\n",
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" axes[row, col + 1].set_title(f'#{col + 1}', fontsize=8)\n",
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"\n",
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"for ax in axes.flat:\n",
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" ax.axis('off')\n",
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"\n",
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"plt.tight_layout()\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": "pp-09",
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"metadata": {},
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"source": [
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"---\n",
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"## 3. Full pipeline comparison\n",
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"\n",
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"All combinations in one grid. Crop columns appear only when `facenet_pytorch` is installed.\n"
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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": "pp-10",
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"metadata": {},
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"outputs": [],
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"source": [
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"samples = {src: pick_samples(1, sources=[src])[0] for src in SOURCES}\n",
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"\n",
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"cols = [\n",
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" ('original', False, False),\n",
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" ('no crop\\nno aug', False, False),\n",
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" ('no crop\\naug', False, True),\n",
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"]\n",
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"if FACE_CROP_AVAILABLE:\n",
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" cols += [\n",
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" ('crop\\nno aug', True, False),\n",
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" ('crop\\naug', True, True),\n",
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" ]\n",
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"\n",
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"n_cols = len(cols)\n",
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"fig, axes = plt.subplots(len(SOURCES), n_cols, figsize=(n_cols * 2.8, 14))\n",
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"fig.suptitle('Full pipeline comparison — pipeline order: (optional) face crop helper -> augmentation -> normalize', fontsize=11)\n",
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"\n",
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"for row, (src, img) in enumerate(samples.items()):\n",
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" label = 'real' if SOURCES[src] == 0 else 'fake'\n",
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" axes[row, 0].set_ylabel(f'{src}\\n({label})', fontsize=9)\n",
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"\n",
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" for col, (title, use_crop, train_mode) in enumerate(cols):\n",
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" ax = axes[row, col]\n",
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" if col == 0:\n",
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" ax.imshow(img.resize((224, 224)))\n",
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" else:\n",
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" proc_img = _cropper(img) if (use_crop and FACE_CROP_AVAILABLE) else img\n",
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" pipe = DFFImagePipeline(image_size=224, train=train_mode)\n",
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" ax.imshow(denorm(pipe(proc_img)))\n",
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" if row == 0:\n",
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" ax.set_title(title, fontsize=8)\n",
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" ax.axis('off')\n",
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"\n",
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"plt.tight_layout()\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": "19187059",
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"metadata": {},
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"source": [
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"---\n",
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"## 4. Tensor sanity checks\n",
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"\n",
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"Validate preprocessing outputs: shape, finite values, normalized value ranges.\n"
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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": "7e5697c4",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"\n",
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"check_imgs = pick_samples(n=12)\n",
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"issues = []\n",
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"\n",
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"for i, img in enumerate(check_imgs):\n",
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" t_eval = pipe_eval(img)\n",
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" t_aug = pipe_aug(img)\n",
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"\n",
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" for tag, t in [(\"eval\", t_eval), (\"aug\", t_aug)]:\n",
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" if tuple(t.shape) != (3, 224, 224):\n",
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" issues.append(f\"sample {i} ({tag}) shape={tuple(t.shape)}\")\n",
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" if not np.isfinite(t.numpy()).all():\n",
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" issues.append(f\"sample {i} ({tag}) has non-finite values\")\n",
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"\n",
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"print(f\"Checked {len(check_imgs)} images through eval+aug pipelines.\")\n",
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"if issues:\n",
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" print(\"Issues found:\")\n",
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" for msg in issues[:10]:\n",
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" print(f\" - {msg}\")\n",
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"else:\n",
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" print(\"No shape/finite-value issues found.\")\n",
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"\n",
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"stack_eval = np.stack([pipe_eval(img).numpy() for img in check_imgs])\n",
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"stack_aug = np.stack([pipe_aug(img).numpy() for img in check_imgs])\n",
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"\n",
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"print(\"\\nValue summary (normalized tensors):\")\n",
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"print(f\" eval: min={stack_eval.min():.3f} max={stack_eval.max():.3f} mean={stack_eval.mean():.3f} std={stack_eval.std():.3f}\")\n",
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"print(f\" aug : min={stack_aug.min():.3f} max={stack_aug.max():.3f} mean={stack_aug.mean():.3f} std={stack_aug.std():.3f}\")"
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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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