File size: 9,943 Bytes
b68205e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 |
from __future__ import annotations
import numpy as np
from PIL import Image
from typing import Dict, List, Tuple, Optional
import time
from .config import Config, Implementation
from .mosaic import MosaicGenerator
from .metrics import calculate_comprehensive_metrics, interpret_metrics
from .utils import pil_to_np, np_to_pil
class MosaicPipeline:
"""Complete pipeline for mosaic generation with performance analysis."""
def __init__(self, config: Config):
self.config = config
self.mosaic_generator = MosaicGenerator(config)
self.results = {}
def run_full_pipeline(self, image: Image.Image) -> Dict:
"""
Run the complete mosaic generation pipeline.
Args:
image: Input PIL Image
Returns:
Dictionary with all results and metrics
"""
self.config.validate()
results = {
'input_image': image,
'config': self.config.__dict__.copy(),
'timing': {},
'metrics': {},
'outputs': {}
}
# Generate mosaic
start_time = time.time()
mosaic_img, stats = self.mosaic_generator.generate_mosaic(image)
results['timing'] = stats['processing_time']
results['outputs']['mosaic'] = mosaic_img
# Calculate similarity metrics
metrics_start = time.time()
metrics = calculate_comprehensive_metrics(image, mosaic_img)
results['metrics'] = metrics
results['metrics_interpretation'] = interpret_metrics(metrics)
results['timing']['metrics_calculation'] = time.time() - metrics_start
# Store additional information
results['outputs']['processed_image'] = self.mosaic_generator.preprocess_image(image)
results['grid_info'] = {
'grid_size': self.config.grid,
'tile_size': self.config.tile_size,
'total_tiles': self.config.grid ** 2
}
self.results = results
return results
def benchmark_implementations(self, image: Image.Image) -> Dict:
"""
Compare vectorized vs loop-based implementations.
Args:
image: Input PIL Image
Returns:
Dictionary with performance comparison
"""
original_impl = self.config.impl
results = {
'vectorized': {},
'loop_based': {},
'comparison': {}
}
# Test vectorized implementation
self.config.impl = Implementation.VECT
start_time = time.time()
vec_results = self.run_full_pipeline(image)
vec_time = time.time() - start_time
results['vectorized'] = {
'processing_time': vec_time,
'metrics': vec_results['metrics'],
'mosaic': vec_results['outputs']['mosaic']
}
# Test loop-based implementation
self.config.impl = Implementation.LOOPS
start_time = time.time()
loop_results = self.run_full_pipeline(image)
loop_time = time.time() - start_time
results['loop_based'] = {
'processing_time': loop_time,
'metrics': loop_results['metrics'],
'mosaic': loop_results['outputs']['mosaic']
}
# Calculate comparison
speedup = loop_time / vec_time if vec_time > 0 else 0
results['comparison'] = {
'speedup_factor': speedup,
'time_difference': loop_time - vec_time,
'vectorized_faster': vec_time < loop_time
}
# Restore original implementation
self.config.impl = original_impl
return results
def benchmark_grid_sizes(self, image: Image.Image, grid_sizes: List[int]) -> Dict:
"""
Benchmark performance for different grid sizes.
Args:
image: Input PIL Image
grid_sizes: List of grid sizes to test
Returns:
Dictionary with grid size performance results
"""
results = {}
original_grid = self.config.grid
original_out_w = self.config.out_w
original_out_h = self.config.out_h
for grid_size in grid_sizes:
self.config.grid = grid_size
# Calculate appropriate output dimensions
aspect_ratio = image.width / image.height
if aspect_ratio > 1:
# Landscape
self.config.out_w = (image.width // grid_size) * grid_size
self.config.out_h = int(self.config.out_w / aspect_ratio // grid_size) * grid_size
else:
# Portrait
self.config.out_h = (image.height // grid_size) * grid_size
self.config.out_w = int(self.config.out_h * aspect_ratio // grid_size) * grid_size
# Time the generation
start_time = time.time()
pipeline_results = self.run_full_pipeline(image)
total_time = time.time() - start_time
results[grid_size] = {
'processing_time': total_time,
'output_resolution': f"{pipeline_results['outputs']['mosaic'].width}x{pipeline_results['outputs']['mosaic'].height}",
'total_tiles': grid_size * grid_size,
'tiles_per_second': (grid_size * grid_size) / total_time if total_time > 0 else 0,
'metrics': pipeline_results['metrics']
}
# Restore original configuration
self.config.grid = original_grid
self.config.out_w = original_out_w
self.config.out_h = original_out_h
return results
def analyze_performance_scaling(self, benchmark_results: Dict) -> Dict:
"""
Analyze how performance scales with grid size.
Args:
benchmark_results: Results from benchmark_grid_sizes
Returns:
Dictionary with scaling analysis
"""
grid_sizes = sorted(benchmark_results.keys())
processing_times = [benchmark_results[gs]['processing_time'] for gs in grid_sizes]
total_tiles = [benchmark_results[gs]['total_tiles'] for gs in grid_sizes]
tiles_per_second = [benchmark_results[gs]['tiles_per_second'] for gs in grid_sizes]
# Calculate scaling factors
scaling_analysis = {
'grid_sizes': grid_sizes,
'processing_times': processing_times,
'total_tiles': total_tiles,
'tiles_per_second': tiles_per_second,
'scaling_factors': {}
}
if len(grid_sizes) >= 2:
# Calculate how processing time scales with number of tiles
tile_ratio = total_tiles[-1] / total_tiles[0]
time_ratio = processing_times[-1] / processing_times[0]
scaling_analysis['scaling_factors'] = {
'tile_increase_ratio': tile_ratio,
'time_increase_ratio': time_ratio,
'scaling_efficiency': tile_ratio / time_ratio if time_ratio > 0 else 0,
'is_linear_scaling': abs(time_ratio - tile_ratio) / tile_ratio < 0.1
}
return scaling_analysis
def generate_report(self, image: Image.Image, benchmark_results: Optional[Dict] = None) -> str:
"""
Generate a comprehensive report of the mosaic generation process.
Args:
image: Input PIL Image
benchmark_results: Optional benchmark results
Returns:
Formatted report string
"""
# Run full pipeline if not already done
if not self.results:
self.run_full_pipeline(image)
report = []
report.append("=" * 60)
report.append("MOSAIC GENERATION REPORT")
report.append("=" * 60)
# Configuration
report.append("\nCONFIGURATION:")
report.append(f"Grid Size: {self.config.grid}x{self.config.grid}")
report.append(f"Tile Size: {self.config.tile_size}x{self.config.tile_size}")
report.append(f"Output Resolution: {self.config.out_w}x{self.config.out_h}")
report.append(f"Implementation: {self.config.impl.value}")
report.append(f"Color Matching: {self.config.match_space.value}")
report.append(f"Total Tiles: {self.config.grid ** 2}")
# Processing Time
report.append("\nPROCESSING TIME:")
for stage, time_val in self.results['timing'].items():
report.append(f"{stage.replace('_', ' ').title()}: {time_val:.3f} seconds")
# Quality Metrics
report.append("\nQUALITY METRICS:")
metrics = self.results['metrics']
interpretations = self.results['metrics_interpretation']
report.append(f"MSE: {metrics['mse']:.6f} ({interpretations['mse']})")
report.append(f"PSNR: {metrics['psnr']:.2f} dB ({interpretations['psnr']})")
report.append(f"SSIM: {metrics['ssim']:.4f} ({interpretations['ssim']})")
report.append(f"RMSE: {metrics['rmse']:.6f}")
report.append(f"MAE: {metrics['mae']:.6f}")
# Benchmark Results
if benchmark_results:
report.append("\nBENCHMARK RESULTS:")
for grid_size, result in benchmark_results.items():
report.append(f"Grid {grid_size}x{grid_size}:")
report.append(f" Processing Time: {result['processing_time']:.3f}s")
report.append(f" Tiles per Second: {result['tiles_per_second']:.1f}")
report.append(f" Output Resolution: {result['output_resolution']}")
report.append("\n" + "=" * 60)
return "\n".join(report)
|