smartdownsample 1.8.4

Smart image downsampling for image classification datasets

smartdownsample

Fast, good-enough image downsampling designed for camera trap animal crops

SmartDownsample is specifically designed for camera trap images of animals, particularly cropped animal images where the subject is centered. It uses multi-dimensional visual features (DHash, AHash, color analysis) optimized for animal detection and camera trap scenarios.

Honest trade-offs: This tool prioritizes speed over perfection. It will do a pretty good job in minutes on 100k+ image datasets, rather than perfect results in hours or days. If you need mathematically optimal diversity, use specialized research tools. If you need fast, good-enough sampling for camera trap workflows, this is for you.

Other use cases: While designed for camera trap animal crops, it may work reasonably well for other centered-subject image collections (portraits, product photos, etc.).

Installation

pip install smartdownsample

Features

• 🐾 Camera trap focused - Designed for animal crops with center-focused detection
• 🎯 Multi-dimensional features - DHash (structure), AHash (brightness), color variance, color themes
• 🎨 Environment aware - Separates blue snow, green forest, brown desert scenes
• 💡 Lighting distinction - Groups grayscale IR vs color daylight images
• ⚡ Fast at scale - Minutes for 100k+ images, not hours
• 📊 Smart bucketing - 16-128 meaningful groups based on actual visual content
• 📁 Camera trap friendly - Natural sorting preserves folder structure and time sequences
• 📈 Built-in visualization - 5x5 thumbnail grids and distribution charts
• 🎲 Reproducible - Set seed for consistent results

Usage

from smartdownsample import sample_diverse

# Basic usage - intelligent visual diversity
selected = sample_diverse(
    image_paths=my_image_list,
    target_count=1000
)

# Full feature usage with visualization
selected = sample_diverse(
    image_paths=my_camera_trap_images,
    target_count=1000,
    hash_size=8,                # Perceptual hash size (8 recommended)
    n_workers=4,                # Parallel workers
    show_progress=True,         # Progress bars
    random_seed=42,             # Reproducible results
    show_summary=True,          # Text statistics  
    show_distribution=True,     # Bucket distribution chart
    show_thumbnails=True        # 10x10 thumbnail grids per bucket
)

print(f"Selected {len(selected)} images from {len(buckets)} visual similarity groups")

Visualization Options

The algorithm includes three built-in visualization modes to understand bucket quality:

# 1. Text summary (show_summary=True) - Default
selected = sample_diverse(paths, target_count=1000, show_summary=True)
# Prints: bucket sizes, distribution stats, diversity metrics

# 2. Distribution chart (show_distribution=True) 
selected = sample_diverse(paths, target_count=1000, show_distribution=True)  
# Shows: vertical bar chart of kept vs excluded per bucket

# 3. Thumbnail grids (show_thumbnails=True)
selected = sample_diverse(paths, target_count=1000, show_thumbnails=True)
# Shows: 10x10 grids of first 100 images from each bucket in square layout

# All visualizations together
selected = sample_diverse(paths, target_count=1000, 
                         show_summary=True, 
                         show_distribution=True, 
                         show_thumbnails=True)

How It Works

Multi-dimensional visual similarity algorithm optimized for camera trap data:

1. Multi-Feature Extraction

Each image is analyzed using 4 complementary visual features:

# For each image, compute:
1. DHash (8x8) → Structural patterns, edges, shapes
2. AHash (4x4) → Brightness distribution, contrast  
3. Color Variance → Separates grayscale from colorful images
4. Overall Brightness → Separates dark from bright scenes

2. Center-Focused Animal Detection

For camera trap data where animals are typically centered:

# From 8x8 DHash (64 bits), strategically sample center positions:
center_indices = [27, 36]  # Center-left and center-right positions
# Bit 27: Detects vertical edges (animal body/legs)  
# Bit 36: Detects horizontal edges (animal head/back)

3. Smart Bucket Key Creation

Combine features into meaningful visual groups (max 32 buckets):

bucket_key = (
    structure_bit_27,     # Center-left animal features (0 or 1)
    structure_bit_36,     # Center-right animal features (0 or 1)  
    brightness_pattern,   # AHash brightness pattern (0 or 1)
    color_type,          # Grayscale=0, Color=1
    brightness_level     # Dark=0, Bright=1
)
# Results in 2×2×2×2×2 = 32 maximum buckets

4. Diversity-Preserving Selection

# Phase 1: Ensure diversity - sample from every bucket
# Phase 2: Fill remaining quota proportionally from largest buckets
# Within buckets: Natural sort preserves camera/folder structure

Example output buckets for camera trap data:
• Bucket 1: Dark grayscale deer (vertical edges)
• Bucket 2: Bright color birds (horizontal patterns) 
• Bucket 3: Grayscale empty frames (low structure)
• Bucket 4: Color daytime mammals (mixed patterns)

Algorithm Benefits

Visual Similarity Improvements

• Better separation: Color vs grayscale images grouped separately
• Animal-focused: Center-positioned features detect different animal poses/species
• Brightness aware: Day vs night scenes properly distinguished
• Structure sensitive: Different animal orientations and camera angles detected
• Manageable buckets: 16-32 meaningful groups instead of random mixing

Performance Characteristics

• Still fast: Multi-feature extraction adds minimal overhead (~20% slower)
• Linear scaling: O(n) complexity maintained across all features
• Memory efficient: Features computed on-the-fly, not stored
• Parallel processing: Hash computation parallelized across workers
• Smart bucket counts: Never creates excessive micro-buckets

Camera Trap Optimizations

• Natural sorting: Preserves camera/folder structure (CAM01_IMG_001.jpg → CAM01_IMG_010.jpg)
• Center detection: Focus on image center where animals appear
• Scene variety: Separates empty frames, single animals, multiple animals
• Lighting diversity: Day/night scenes properly represented
• Color preservation: IR grayscale vs color daylight images distinguished

Comparison with Other Methods

Method	Bucket Quality	Speed	Animal Detection	Color Separation
Random sampling	None	Fastest	No	No
Single DHash	Poor mixing	Fast	No	No
smartdownsample v1.6+	Excellent	Fast+	Yes	Yes
Complex ML clustering	Perfect	Very Slow	Depends	Yes

Real Results: Camera Trap Dataset

Before (v1.5): 495 buckets, color/grayscale mixed randomly in each bucket
After (v1.6+): 32 buckets, clear separation:

• Bucket 1: Grayscale deer images (IR night camera)
• Bucket 2: Color bird images (daylight camera)
• Bucket 3: Dark empty frames (nighttime)
• Bucket 4: Bright color mammals (sunny daytime)

Performance: Only ~20% slower than single-hash method, dramatically better visual grouping.

Performance

Task	Time
100 from 1,000	<5 sec
900 from 1,000	<5 sec
1,000 from 24,000	~30 sec
23,000 from 24,000	~30 sec
Any ratio	Fast ✓

Parameters

Parameter	Default	Description
image_paths	Required	List of image file paths (str or Path objects)
target_count	Required	Exact number of images to select
hash_size	8	Perceptual hash size - 8 recommended for good speed/quality balance
n_workers	4	Number of parallel workers for hash computation
show_progress	True	Display progress bars during processing
random_seed	42	Random seed for reproducible bucket selection
show_summary	True	Print bucket statistics and distribution summary
show_distribution	False	Show bucket distribution bar chart (requires matplotlib)
show_thumbnails	False	Show 10x10 thumbnail grids for each bucket (requires matplotlib)

Parameter Recommendations

For camera trap animal crops (recommended use case):

• hash_size=8: Optimal balance of speed and center-focused animal detection
• show_thumbnails=True: Essential for validating visual similarity quality
• show_summary=True: Understand bucket distribution and diversity

For other centered-subject images:

• hash_size=8: Still works well for portraits, product photos, etc.
• May work less effectively for landscapes, random compositions, or non-centered subjects

Performance tuning:

• hash_size=6: Faster processing, may reduce detection quality
• hash_size=10: Slower but more detailed structural analysis

Technical Details

Hash Features Explained

DHash (Difference Hash)

• Detects structural patterns, edges, object boundaries
• 8×8 hash = 64 bits representing horizontal gradients
• Center bits (positions 27, 36) focus on animal detection
• Fast computation: resize → grayscale → compare adjacent pixels

AHash (Average Hash)

• Detects brightness patterns and contrast distribution
• 4×4 hash = 16 bits representing above/below average brightness
• Used for distinguishing lighting conditions
• Complements DHash with tonal information

Color Variance

• Separates grayscale (IR cameras) from color (daylight cameras)
• Computed as variance of RGB channel means
• Threshold: variance < 100 = grayscale, ≥ 100 = color

Overall Brightness

• Separates dark (nighttime) from bright (daytime) scenes
• Computed as mean pixel value across all channels
• Threshold: brightness < 128 = dark, ≥ 128 = bright

Performance Notes

• Multi-feature extraction adds ~20% processing time vs single hash
• Parallel hash computation scales linearly with worker count (up to CPU cores)
• Memory usage remains O(1) - features computed on-demand
• Bucket creation is O(n) - no expensive similarity comparisons

License

MIT License – see LICENSE file.