Advanced Image Segmentation System
Enterprise-grade image segmentation platform implementing deep learning models, real-time processing, and automated training pipelines for computer vision
System Architecture
A comprehensive image segmentation system that combines state-of-the-art deep learning models with efficient processing and deployment pipelines.
Core Components
1. Image Processing Pipeline
class ImageProcessor:
def __init__(self, config: Dict[str, Any]):
self.transforms = A.Compose([
A.Resize(
height=config['height'],
width=config['width']
),
A.Normalize(
mean=config['mean'],
std=config['std']
),
A.ColorJitter(
brightness=0.2,
contrast=0.2,
saturation=0.2
)
])
self.augmenter = ImageAugmenter(config['augmentation'])
async def process_image(
self,
image: np.ndarray,
augment: bool = False
) -> ProcessedImage:
# Apply base transforms
processed = self.transforms(image=image)['image']
if augment:
# Apply augmentations
augmented = await self.augmenter.augment(processed)
return ProcessedImage(
original=processed,
augmented=augmented
)
return ProcessedImage(
original=processed,
augmented=None
)
class ImageAugmenter:
def __init__(self, config: Dict[str, Any]):
self.spatial_transforms = A.Compose([
A.RandomRotate90(p=0.5),
A.Flip(p=0.5),
A.ShiftScaleRotate(p=0.5),
A.ElasticTransform(p=0.3)
])
self.intensity_transforms = A.Compose([
A.RandomBrightnessContrast(p=0.5),
A.RandomGamma(p=0.5),
A.GaussNoise(p=0.3)
])2. Segmentation Model
class SegmentationModel(nn.Module):
def __init__(self, config: Dict[str, Any]):
super().__init__()
self.encoder = self._build_encoder(
config['encoder_name'],
config['encoder_weights']
)
self.decoder = UNetDecoder(
encoder_channels=self.encoder.channels,
decoder_channels=config['decoder_channels']
)
self.segmentation_head = SegmentationHead(
in_channels=config['decoder_channels'][-1],
num_classes=config['num_classes']
)
def forward(
self,
x: torch.Tensor
) -> Dict[str, torch.Tensor]:
# Extract features
features = self.encoder(x)
# Decode features
decoder_output = self.decoder(*features)
# Generate masks
masks = self.segmentation_head(decoder_output)
return {
'masks': masks,
'features': features[-1]
}
class UNetDecoder(nn.Module):
def __init__(
self,
encoder_channels: List[int],
decoder_channels: List[int]
):
super().__init__()
# Create decoder blocks
self.blocks = nn.ModuleList([
DecoderBlock(
in_channels=in_ch,
skip_channels=skip_ch,
out_channels=out_ch
)
for in_ch, skip_ch, out_ch in zip(
encoder_channels[:-1],
encoder_channels[1:],
decoder_channels
)
])3. Training System
class SegmentationTrainer:
def __init__(self, config: Dict[str, Any]):
self.model = SegmentationModel(config['model_config'])
self.criterion = self._setup_loss(config['loss_config'])
self.optimizer = self._setup_optimizer(config['optimizer_config'])
self.scheduler = self._setup_scheduler(config['scheduler_config'])
self.metrics = SegmentationMetrics(config['metrics_config'])
async def train(
self,
train_loader: DataLoader,
val_loader: DataLoader
) -> TrainingResults:
best_model = None
best_iou = 0.0
for epoch in range(self.config['epochs']):
# Training phase
train_metrics = await self._train_epoch(train_loader)
# Validation phase
val_metrics = await self._validate_epoch(val_loader)
# Update best model
if val_metrics['mean_iou'] > best_iou:
best_iou = val_metrics['mean_iou']
best_model = copy.deepcopy(self.model)
# Update learning rate
self.scheduler.step(val_metrics['loss'])
return TrainingResults(
model=best_model,
metrics=val_metrics
)
async def _train_epoch(
self,
dataloader: DataLoader
) -> Dict[str, float]:
self.model.train()
epoch_metrics = defaultdict(float)
for batch in dataloader:
images, masks = batch['image'], batch['mask']
# Forward pass
outputs = self.model(images)
loss = self.criterion(outputs['masks'], masks)
# Backward pass
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
# Update metrics
batch_metrics = self.metrics.calculate(
outputs['masks'],
masks
)
for k, v in batch_metrics.items():
epoch_metrics[k] += v
return {k: v / len(dataloader) for k, v in epoch_metrics.items()}4. Inference Pipeline
class SegmentationInference:
def __init__(self, config: Dict[str, Any]):
self.processor = ImageProcessor(config['processor_config'])
self.model = self._load_model(config['model_path'])
self.post_processor = SegmentationPostProcessor(
config['post_processing']
)
async def segment_image(
self,
image: np.ndarray
) -> SegmentationResult:
# Process image
processed = await self.processor.process_image(image)
# Generate predictions
with torch.no_grad():
outputs = self.model(processed.original)
# Post-process predictions
masks = await self.post_processor.process(
outputs['masks'],
original_size=(image.shape[0], image.shape[1])
)
return SegmentationResult(
masks=masks,
confidence=outputs.get('confidence', None)
)
class SegmentationPostProcessor:
def __init__(self, config: Dict[str, Any]):
self.threshold = config['confidence_threshold']
self.min_size = config['min_object_size']
self.refinement = MaskRefinement(config['refinement_config'])
async def process(
self,
masks: torch.Tensor,
original_size: Tuple[int, int]
) -> np.ndarray:
# Apply confidence threshold
binary_masks = (masks > self.threshold).float()
# Remove small objects
filtered_masks = self._filter_small_objects(binary_masks)
# Refine mask boundaries
refined_masks = await self.refinement.refine(filtered_masks)
# Resize to original dimensions
return F.interpolate(
refined_masks,
size=original_size,
mode='bilinear',
align_corners=False
)Usage Example
# Initialize segmentation system
config = {
'model_config': {
'encoder_name': 'resnet50',
'encoder_weights': 'imagenet',
'decoder_channels': [256, 128, 64, 32, 16],
'num_classes': 3
},
'training_config': {
'batch_size': 16,
'learning_rate': 1e-4,
'epochs': 50
},
'inference_config': {
'confidence_threshold': 0.5,
'min_object_size': 100
}
}
segmentation = ImageSegmentationSystem(config)
# Train model
training_results = await segmentation.train(train_data, val_data)
# Perform segmentation
image = cv2.imread('example.jpg')
result = await segmentation.segment_image(image)
# Visualize results
visualization = segmentation.visualize_results(
image,
result.masks,
alpha=0.5
)