Using NVIDIA Isaac for Perception
Overview​
NVIDIA Isaac provides powerful tools for robot perception tasks, enabling the development of AI-powered computer vision applications. This lesson explores how to leverage Isaac's capabilities for perception tasks including object detection, segmentation, and AI model integration. We'll cover Isaac's synthetic data generation capabilities, perception pipelines, and how to integrate trained AI models into robotic perception systems.
Isaac Perception Architecture​
Perception Components in Isaac​
Isaac provides several perception-focused components:
class IsaacPerceptionSystem:
def __init__(self):
self.components = {
'synthetic_data_generation': {
'name': 'Synthetic Data Generation',
'description': 'Generate labeled training data using Isaac Sim',
'features': [
'Photorealistic rendering',
'Automatic annotation',
'Domain randomization',
'Multi-sensor simulation'
]
},
'ai_acceleration': {
'name': 'AI Acceleration',
'description': 'Hardware-accelerated AI inference',
'features': [
'TensorRT optimization',
'CUDA acceleration',
'Multi-GPU support',
'Real-time inference'
]
},
'sensor_simulation': {
'name': 'Sensor Simulation',
'description': 'Realistic sensor simulation',
'features': [
'Camera simulation',
'LiDAR simulation',
'Depth sensors',
'Multi-modal sensors'
]
},
'perception_algorithms': {
'name': 'Perception Algorithms',
'description': 'Pre-built perception algorithms',
'features': [
'Object detection',
'Semantic segmentation',
'Instance segmentation',
'Pose estimation'
]
}
}
def get_perception_pipeline(self):
"""Get the complete Isaac perception pipeline"""
pipeline = {
'data_generation': self.components['synthetic_data_generation'],
'model_training': 'Use generated synthetic data for training',
'model_optimization': self.components['ai_acceleration'],
'inference': 'Deploy optimized models for real-time inference',
'sensor_integration': self.components['sensor_simulation']
}
return pipeline
# Example of Isaac perception system
perception_system = IsaacPerceptionSystem()
pipeline = perception_system.get_perception_pipeline()
print(f"Isaac Perception Pipeline: {list(pipeline.keys())}")
Isaac ROS Perception Packages​
Isaac ROS provides hardware-accelerated perception packages:
class IsaacROSPipeline:
def __init__(self):
self.perception_nodes = [
'isaac_ros_detectnet', # Object detection
'isaac_ros_segmentation', # Semantic segmentation
'isaac_ros_pointcloud', # Point cloud processing
'isaac_ros_stereo', # Stereo vision
'isaac_ros_visual_slam', # Visual SLAM
'isaac_ros_pose_estimation' # Pose estimation
]
self.hardware_acceleration = {
'tensor_rt': True,
'cuda': True,
'dla': False, # Deep Learning Accelerator
'nvmedia': True
}
def create_perception_pipeline(self, tasks):
"""Create a perception pipeline for specific tasks"""
pipeline = {
'nodes': [],
'connections': [],
'acceleration': self.hardware_acceleration
}
for task in tasks:
if task == 'object_detection':
pipeline['nodes'].append({
'name': 'detectnet_node',
'type': 'isaac_ros_detectnet',
'inputs': ['/camera/image_raw'],
'outputs': ['/detections'],
'parameters': {
'model_type': 'detectnet_v2',
'threshold': 0.5,
'max_objects': 100
}
})
elif task == 'segmentation':
pipeline['nodes'].append({
'name': 'segmentation_node',
'type': 'isaac_ros_segmentation',
'inputs': ['/camera/image_raw'],
'outputs': ['/segmentation/masks'],
'parameters': {
'model_type': 'unet',
'num_classes': 21
}
})
elif task == 'depth_estimation':
pipeline['nodes'].append({
'name': 'depth_estimation_node',
'type': 'isaac_ros_stereo',
'inputs': ['/left/image_rect', '/right/image_rect'],
'outputs': ['/depth/disparity', '/depth/points'],
'parameters': {
'stereo_algorithm': 'sgbm',
'min_disparity': 0,
'max_disparity': 64
}
})
return pipeline
# Example usage
isaac_ros = IsaacROSPipeline()
perception_pipeline = isaac_ros.create_perception_pipeline([
'object_detection', 'segmentation', 'depth_estimation'
])
print(f"Created perception pipeline with {len(perception_pipeline['nodes'])} nodes")
Object Detection with Isaac​
Isaac DetectNet for Object Detection​
import numpy as np
import cv2
import torch
from PIL import Image
class IsaacDetectNet:
def __init__(self, model_path=None, confidence_threshold=0.5):
self.model_path = model_path
self.confidence_threshold = confidence_threshold
# Isaac-specific detection model
self.model = self.load_model()
# COCO class names for detection
self.coco_classes = [
'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train',
'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign',
'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep',
'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella',
'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard',
'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard',
'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork',
'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange',
'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair',
'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv',
'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave',
'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase',
'scissors', 'teddy bear', 'hair drier', 'toothbrush'
]
def load_model(self):
"""Load the DetectNet model"""
# In practice, this would load a TensorRT-optimized model
# For this example, we'll create a placeholder
if self.model_path:
print(f"Loading DetectNet model from {self.model_path}")
# model = torch.load(self.model_path)
else:
print("Initializing DetectNet model with default parameters")
# model = self.initialize_default_model()
return "detectnet_model_placeholder"
def detect_objects(self, image):
"""Detect objects in an image using Isaac DetectNet"""
# Convert image to required format
if isinstance(image, np.ndarray):
# Convert BGR to RGB if needed
if len(image.shape) == 3 and image.shape[2] == 3:
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Preprocess image
input_tensor = self.preprocess_image(image)
# Run inference (placeholder)
detections = self.run_inference(input_tensor)
# Post-process detections
results = self.postprocess_detections(detections)
return results
def preprocess_image(self, image):
"""Preprocess image for DetectNet"""
# Convert to tensor and normalize
if isinstance(image, Image.Image):
image = np.array(image)
# Resize to model input size (typically 224x224 or 512x512)
resized_image = cv2.resize(image, (512, 512))
# Normalize to [0, 1] range
normalized_image = resized_image.astype(np.float32) / 255.0
# Convert to CHW format (channel, height, width)
chw_image = np.transpose(normalized_image, (2, 0, 1))
# Add batch dimension
input_tensor = np.expand_dims(chw_image, axis=0)
return input_tensor
def run_inference(self, input_tensor):
"""Run inference on the input tensor"""
# In a real implementation, this would call the actual model
# For this example, we'll return placeholder detections
# Simulate inference delay
import time
time.sleep(0.01) # 10ms delay
# Return placeholder detections
detections = [
{
'bbox': [100, 100, 200, 150], # [x1, y1, x2, y2]
'class_id': 0,
'confidence': 0.9,
'class_name': 'person'
},
{
'bbox': [300, 200, 350, 250],
'class_id': 1,
'confidence': 0.85,
'class_name': 'bicycle'
}
]
return detections
def postprocess_detections(self, detections):
"""Post-process detections to filter by confidence threshold"""
filtered_detections = []
for detection in detections:
if detection['confidence'] >= self.confidence_threshold:
# Convert bbox from [x1, y1, x2, y2] to [x, y, width, height]
x1, y1, x2, y2 = detection['bbox']
bbox_xywh = [x1, y1, x2 - x1, y2 - y1]
detection['bbox'] = bbox_xywh
filtered_detections.append(detection)
return filtered_detections
def visualize_detections(self, image, detections, colors=None):
"""Visualize detections on image"""
if colors is None:
colors = [(255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 255, 0), (255, 0, 255)]
output_image = image.copy() if isinstance(image, np.ndarray) else np.array(image)
for i, detection in enumerate(detections):
x, y, w, h = detection['bbox']
color = colors[i % len(colors)]
# Draw bounding box
cv2.rectangle(output_image, (int(x), int(y)), (int(x+w), int(y+h)), color, 2)
# Draw label and confidence
label = f"{detection['class_name']}: {detection['confidence']:.2f}"
cv2.putText(output_image, label, (int(x), int(y)-10),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
return output_image
# Example usage
detectnet = IsaacDetectNet(confidence_threshold=0.7)
sample_image = np.random.randint(0, 255, (480, 640, 3), dtype=np.uint8)
detections = detectnet.detect_objects(sample_image)
print(f"Detected {len(detections)} objects with confidence > 0.7")
Isaac Segmentation for Scene Understanding​
class IsaacSegmentation:
def __init__(self, model_type='semantic', num_classes=21):
self.model_type = model_type # 'semantic' or 'instance'
self.num_classes = num_classes
self.model = self.load_segmentation_model()
def load_segmentation_model(self):
"""Load the segmentation model"""
if self.model_type == 'semantic':
print(f"Loading semantic segmentation model with {self.num_classes} classes")
# In practice: model = load_semantic_segmentation_model()
elif self.model_type == 'instance':
print(f"Loading instance segmentation model with {self.num_classes} classes")
# In practice: model = load_instance_segmentation_model()
return "segmentation_model_placeholder"
def segment_image(self, image):
"""Perform segmentation on image"""
# Preprocess image
input_tensor = self.preprocess_segmentation_input(image)
# Run segmentation inference
segmentation_mask = self.run_segmentation_inference(input_tensor)
# Post-process segmentation
results = self.postprocess_segmentation(segmentation_mask)
return results
def preprocess_segmentation_input(self, image):
"""Preprocess image for segmentation"""
# Resize to model input size
resized_image = cv2.resize(image, (512, 512))
# Normalize
normalized_image = resized_image.astype(np.float32) / 255.0
# Convert to tensor format
input_tensor = np.transpose(normalized_image, (2, 0, 1))
input_tensor = np.expand_dims(input_tensor, axis=0)
return input_tensor
def run_segmentation_inference(self, input_tensor):
"""Run segmentation inference"""
# In practice, this would call the actual segmentation model
# For this example, we'll return a placeholder segmentation mask
height, width = input_tensor.shape[2], input_tensor.shape[3]
# Create a simple segmentation mask with random classes
segmentation_mask = np.random.randint(0, self.num_classes, (height, width), dtype=np.uint8)
# Add some structure to make it look like real segmentation
# Create a few regions with the same class
for i in range(5):
center_x = np.random.randint(50, width-50)
center_y = np.random.randint(50, height-50)
radius = np.random.randint(20, 50)
class_id = np.random.randint(1, self.num_classes)
y, x = np.ogrid[:height, :width]
mask = (x - center_x)**2 + (y - center_y)**2 <= radius**2
segmentation_mask[mask] = class_id
return segmentation_mask
def postprocess_segmentation(self, segmentation_mask):
"""Post-process segmentation results"""
# Convert to class probabilities if needed
if len(segmentation_mask.shape) == 3:
# Mask is already in class format [H, W]
class_mask = segmentation_mask
else:
# Convert from logits to class predictions
class_mask = np.argmax(segmentation_mask, axis=0)
# Create color palette for visualization
color_palette = self.generate_color_palette()
# Convert class mask to RGB for visualization
colored_mask = self.class_mask_to_colored_image(class_mask, color_palette)
# Extract segmentation statistics
unique_classes, counts = np.unique(class_mask, return_counts=True)
class_statistics = dict(zip(unique_classes, counts))
results = {
'class_mask': class_mask,
'colored_mask': colored_mask,
'statistics': class_statistics,
'class_names': [f"class_{i}" for i in unique_classes]
}
return results
def generate_color_palette(self):
"""Generate a color palette for segmentation visualization"""
colors = []
for i in range(self.num_classes):
# Generate distinct colors using HSV color space
hue = i * 360.0 / self.num_classes
saturation = 0.7 + (i % 3) * 0.1 # Vary saturation slightly
value = 0.8 + (i % 2) * 0.2 # Vary brightness slightly
# Convert HSV to RGB
import colorsys
rgb = colorsys.hsv_to_rgb(hue/360.0, saturation, value)
rgb = tuple(int(c * 255) for c in rgb)
colors.append(rgb)
return colors
def class_mask_to_colored_image(self, class_mask, color_palette):
"""Convert class mask to colored image"""
height, width = class_mask.shape
colored_image = np.zeros((height, width, 3), dtype=np.uint8)
for class_id in range(len(color_palette)):
mask = class_mask == class_id
if np.any(mask):
colored_image[mask] = color_palette[class_id]
return colored_image
def visualize_segmentation(self, original_image, segmentation_result):
"""Overlay segmentation on original image"""
# Resize original image to match segmentation mask
seg_height, seg_width = segmentation_result['class_mask'].shape
resized_original = cv2.resize(original_image, (seg_width, seg_height))
# Blend original image with segmentation mask
alpha = 0.6 # Transparency for segmentation overlay
overlay = cv2.addWeighted(
resized_original, 1-alpha,
segmentation_result['colored_mask'], alpha,
0
)
return overlay
# Example usage
segmentation_module = IsaacSegmentation(model_type='semantic', num_classes=21)
sample_image = np.random.randint(0, 255, (480, 640, 3), dtype=np.uint8)
seg_result = segmentation_module.segment_image(sample_image)
print(f"Segmentation completed with {len(seg_result['statistics'])} unique classes detected")
Synthetic Data Generation​
Isaac Synthetic Data Tools​
class IsaacSyntheticDataGenerator:
def __init__(self):
self.recording_modes = {
'rgb': True,
'depth': True,
'segmentation': True,
'bounding_boxes': True,
'poses': True
}
self.domain_randomization = {
'lighting': True,
'textures': True,
'object_placement': True,
'camera_parameters': True
}
self.annotations = {
'2d_bounding_boxes': True,
'3d_bounding_boxes': True,
'instance_masks': True,
'semantic_masks': True,
'object_poses': True,
'keypoints': False
}
def configure_synthetic_data_generation(self, config):
"""Configure synthetic data generation parameters"""
for key, value in config.items():
if hasattr(self, key):
setattr(self, key, value)
print(f"Synthetic data generation configured with: {config}")
return True
def generate_synthetic_dataset(self, scene_config, num_samples=1000):
"""Generate a synthetic dataset with annotations"""
dataset = {
'metadata': {
'generator': 'Isaac Synthetic Data Generator',
'num_samples': num_samples,
'scene_config': scene_config,
'annotations': list(self.annotations.keys()),
'date_created': str(time.time())
},
'samples': []
}
for i in range(num_samples):
# Apply domain randomization for this sample
randomized_scene = self.apply_domain_randomization(scene_config)
# Generate synthetic image and annotations
sample = self.generate_single_sample(randomized_scene)
# Add to dataset
dataset['samples'].append(sample)
if i % 100 == 0:
print(f"Generated {i}/{num_samples} synthetic samples")
return dataset
def apply_domain_randomization(self, scene_config):
"""Apply domain randomization to scene configuration"""
randomized_config = scene_config.copy()
# Randomize lighting
if self.domain_randomization['lighting']:
randomized_config['lighting'] = {
'intensity': np.random.uniform(0.5, 2.0),
'position': [
np.random.uniform(-5, 5),
np.random.uniform(-5, 5),
np.random.uniform(3, 10)
],
'color': np.random.uniform(0.8, 1.2, 3)
}
# Randomize textures
if self.domain_randomization['textures']:
randomized_config['textures'] = {
'floor_texture': np.random.choice(['wood', 'tile', 'carpet', 'metal']),
'wall_texture': np.random.choice(['paint', 'brick', 'stone', 'concrete']),
'object_textures': np.random.choice(['matte', 'glossy', 'metallic'])
}
# Randomize object placement
if self.domain_randomization['object_placement']:
if 'objects' in randomized_config:
for obj in randomized_config['objects']:
obj['position'] = [
np.random.uniform(-2, 2),
np.random.uniform(-2, 2),
np.random.uniform(0.1, 1.0)
]
obj['orientation'] = [
np.random.uniform(-np.pi, np.pi),
np.random.uniform(-np.pi, np.pi),
np.random.uniform(-np.pi, np.pi)
]
return randomized_config
def generate_single_sample(self, scene_config):
"""Generate a single synthetic sample with all annotations"""
# In a real implementation, this would render the scene in Isaac Sim
# For this example, we'll generate placeholder data
# Generate synthetic RGB image
rgb_image = self.generate_synthetic_rgb(scene_config)
# Generate synthetic depth image
depth_image = self.generate_synthetic_depth(scene_config)
# Generate segmentation mask
segmentation_mask = self.generate_synthetic_segmentation(scene_config)
# Generate bounding boxes
bounding_boxes = self.generate_synthetic_bboxes(scene_config)
# Generate object poses
object_poses = self.generate_synthetic_poses(scene_config)
sample = {
'rgb_image': rgb_image,
'depth_image': depth_image,
'segmentation_mask': segmentation_mask,
'bounding_boxes': bounding_boxes,
'object_poses': object_poses,
'scene_config': scene_config,
'sample_id': len(self.generated_samples) if hasattr(self, 'generated_samples') else 0
}
return sample
def generate_synthetic_rgb(self, scene_config):
"""Generate synthetic RGB image"""
# This would normally render the scene in Isaac Sim
# For this example, we'll create a placeholder image
height, width = scene_config.get('image_size', [480, 640])
rgb_image = np.random.randint(0, 255, (height, width, 3), dtype=np.uint8)
# Add some scene-specific patterns based on scene config
# This would normally render actual 3D scene
return rgb_image
def generate_synthetic_depth(self, scene_config):
"""Generate synthetic depth image"""
# This would normally render depth from 3D scene
height, width = scene_config.get('image_size', [480, 640])
depth_image = np.random.uniform(0.1, 10.0, (height, width)).astype(np.float32)
# Add depth patterns based on scene objects
return depth_image
def generate_synthetic_segmentation(self, scene_config):
"""Generate synthetic segmentation mask"""
height, width = scene_config.get('image_size', [480, 640])
num_classes = scene_config.get('num_classes', 10)
segmentation_mask = np.random.randint(0, num_classes, (height, width), dtype=np.uint8)
# Add object-specific segmentation based on scene config
return segmentation_mask
def generate_synthetic_bboxes(self, scene_config):
"""Generate synthetic bounding boxes"""
bounding_boxes = []
if 'objects' in scene_config:
for i, obj in enumerate(scene_config['objects']):
# Generate bounding box based on object position and size
x = np.random.uniform(0, 600) # Random position in image
y = np.random.uniform(0, 400)
w = np.random.uniform(50, 200) # Random size
h = np.random.uniform(50, 200)
bbox = {
'class_id': i % 10, # Cycle through class IDs
'bbox': [x, y, x+w, y+h], # [x1, y1, x2, y2]
'object_name': obj.get('name', f'object_{i}')
}
bounding_boxes.append(bbox)
return bounding_boxes
def generate_synthetic_poses(self, scene_config):
"""Generate synthetic object poses"""
object_poses = []
if 'objects' in scene_config:
for i, obj in enumerate(scene_config['objects']):
pose = {
'object_id': i,
'position': obj.get('position', [0, 0, 0]),
'orientation': obj.get('orientation', [0, 0, 0, 1]), # quaternion
'class_name': obj.get('name', f'object_{i}')
}
object_poses.append(pose)
return object_poses
def export_dataset(self, dataset, export_path, format='coco'):
"""Export synthetic dataset in specified format"""
if format == 'coco':
self.export_coco_format(dataset, export_path)
elif format == 'kitti':
self.export_kitti_format(dataset, export_path)
elif format == 'custom':
self.export_custom_format(dataset, export_path)
print(f"Dataset exported to {export_path} in {format} format")
def export_coco_format(self, dataset, export_path):
"""Export dataset in COCO format"""
coco_format = {
'info': {
'description': 'Synthetic dataset generated with Isaac',
'version': '1.0',
'year': 2025,
'contributor': 'Isaac Synthetic Data Generator',
'date_created': dataset['metadata']['date_created']
},
'licenses': [{'id': 1, 'name': 'Synthetic Data License', 'url': 'http://example.com/license'}],
'categories': [],
'images': [],
'annotations': []
}
# Add categories
for i in range(dataset['metadata']['num_classes']):
coco_format['categories'].append({
'id': i,
'name': f'class_{i}',
'supercategory': 'object'
})
# Add images and annotations
annotation_id = 0
for i, sample in enumerate(dataset['samples']):
image_info = {
'id': i,
'file_name': f'synthetic_image_{i:06d}.jpg',
'height': sample['rgb_image'].shape[0],
'width': sample['rgb_image'].shape[1],
'date_captured': str(time.time()),
'license': 1,
'coco_url': f'http://example.com/images/synthetic_image_{i:06d}.jpg'
}
coco_format['images'].append(image_info)
# Add annotations for this image
for bbox in sample['bounding_boxes']:
annotation = {
'id': annotation_id,
'image_id': i,
'category_id': bbox['class_id'],
'bbox': [bbox['bbox'][0], bbox['bbox'][1],
bbox['bbox'][2]-bbox['bbox'][0], bbox['bbox'][3]-bbox['bbox'][1]], # Convert to [x, y, w, h]
'area': (bbox['bbox'][2]-bbox['bbox'][0]) * (bbox['bbox'][3]-bbox['bbox'][1]),
'iscrowd': 0
}
coco_format['annotations'].append(annotation)
annotation_id += 1
# Save to file
import json
with open(f"{export_path}/annotations.json", 'w') as f:
json.dump(coco_format, f, indent=2)
# Example usage
synthetic_generator = IsaacSyntheticDataGenerator()
scene_config = {
'image_size': [480, 640],
'num_classes': 5,
'objects': [
{'name': 'cube', 'type': 'primitive'},
{'name': 'cylinder', 'type': 'primitive'},
{'name': 'sphere', 'type': 'primitive'}
]
}
# Generate a small synthetic dataset
synthetic_dataset = synthetic_generator.generate_synthetic_dataset(scene_config, num_samples=10)
print(f"Generated synthetic dataset with {len(synthetic_dataset['samples'])} samples")
AI Model Integration​
Integrating AI Models with Isaac​
import torch
import torch.nn as nn
import tensorrt as trt
import numpy as np
class IsaacAIPipeline:
def __init__(self):
self.models = {}
self.tensorrt_engines = {}
self.acceleration_enabled = True
def load_model(self, model_name, model_path, accelerator='tensorrt'):
"""Load and optimize model for Isaac"""
if accelerator == 'tensorrt':
engine = self.load_tensorrt_model(model_path)
self.tensorrt_engines[model_name] = engine
else:
# Load with PyTorch
model = torch.load(model_path)
model.eval()
self.models[model_name] = model
print(f"Loaded {model_name} with {accelerator} acceleration")
return True
def load_tensorrt_model(self, model_path):
"""Load TensorRT optimized model"""
# In practice, this would load a serialized TensorRT engine
# For this example, we'll return a placeholder
print(f"Loading TensorRT model from {model_path}")
return "tensorrt_engine_placeholder"
def create_perception_node(self, node_config):
"""Create a perception node with AI model integration"""
node = {
'name': node_config['name'],
'type': 'perception',
'model': node_config['model'],
'input_topics': node_config.get('input_topics', []),
'output_topics': node_config.get('output_topics', []),
'parameters': node_config.get('parameters', {}),
'acceleration': node_config.get('acceleration', 'tensorrt'),
'enabled': True
}
# Load the specified model
self.load_model(
node_config['model'],
node_config['model_path'],
node_config.get('acceleration', 'tensorrt')
)
return node
def run_inference(self, model_name, input_data):
"""Run inference using loaded model"""
if model_name in self.tensorrt_engines:
# Run inference with TensorRT engine
result = self.run_tensorrt_inference(model_name, input_data)
elif model_name in self.models:
# Run inference with PyTorch model
result = self.run_pytorch_inference(model_name, input_data)
else:
raise ValueError(f"Model {model_name} not found")
return result
def run_tensorrt_inference(self, model_name, input_data):
"""Run inference with TensorRT engine"""
# This would normally execute the TensorRT engine
# For this example, we'll return placeholder results
print(f"Running TensorRT inference for {model_name}")
# Simulate inference time
import time
time.sleep(0.005) # 5ms inference time
# Return placeholder results
if 'detect' in model_name.lower():
return [
{'bbox': [100, 100, 200, 150], 'class': 0, 'confidence': 0.9},
{'bbox': [300, 200, 350, 250], 'class': 1, 'confidence': 0.85}
]
elif 'segment' in model_name.lower():
height, width = input_data.shape[2], input_data.shape[3]
return np.random.randint(0, 21, (height, width), dtype=np.uint8)
else:
return np.random.random((10,)) # Generic output
def run_pytorch_inference(self, model_name, input_data):
"""Run inference with PyTorch model"""
# Convert numpy array to PyTorch tensor
tensor_input = torch.from_numpy(input_data).float()
# Run model inference
with torch.no_grad():
model = self.models[model_name]
output = model(tensor_input)
# Convert output back to numpy
result = output.numpy()
return result
def optimize_model_for_isaac(self, model, precision='fp16'):
"""Optimize model for Isaac deployment"""
if self.acceleration_enabled:
# Convert to TensorRT for acceleration
trt_model = self.convert_to_tensorrt(model, precision)
return trt_model
else:
return model
def convert_to_tensorrt(self, model, precision='fp16'):
"""Convert PyTorch model to TensorRT engine"""
# This would use TensorRT's Python API to optimize the model
# For this example, we'll return a placeholder
print(f"Converting model to TensorRT with {precision} precision")
return "optimized_tensorrt_model"
def create_inference_pipeline(self, pipeline_config):
"""Create a complete inference pipeline"""
pipeline = {
'name': pipeline_config['name'],
'nodes': [],
'connections': [],
'input_topic': pipeline_config['input_topic'],
'output_topic': pipeline_config['output_topic']
}
for node_config in pipeline_config['nodes']:
node = self.create_perception_node(node_config)
pipeline['nodes'].append(node)
# Create connections between nodes
for i in range(len(pipeline['nodes']) - 1):
connection = {
'from': pipeline['nodes'][i]['name'],
'to': pipeline['nodes'][i+1]['name'],
'topic': f"/internal/{pipeline['nodes'][i]['name']}_to_{pipeline['nodes'][i+1]['name']}"
}
pipeline['connections'].append(connection)
return pipeline
# Example of creating an AI perception pipeline
ai_pipeline = IsaacAIPipeline()
pipeline_config = {
'name': 'object_detection_pipeline',
'input_topic': '/camera/image_raw',
'output_topic': '/detections/objects',
'nodes': [
{
'name': 'preprocessing_node',
'model': 'preprocessing_model',
'model_path': '/path/to/preprocessing.engine',
'input_topics': ['/camera/image_raw'],
'output_topics': ['/preprocessed/image'],
'acceleration': 'tensorrt'
},
{
'name': 'detection_node',
'model': 'detectnet_model',
'model_path': '/path/to/detectnet.engine',
'input_topics': ['/preprocessed/image'],
'output_topics': ['/detections/objects'],
'acceleration': 'tensorrt',
'parameters': {'confidence_threshold': 0.7}
},
{
'name': 'postprocessing_node',
'model': 'postprocessing_model',
'model_path': '/path/to/postprocessing.engine',
'input_topics': ['/detections/objects'],
'output_topics': ['/final/detections'],
'acceleration': 'tensorrt'
}
]
}
perception_pipeline = ai_pipeline.create_inference_pipeline(pipeline_config)
print(f"Created perception pipeline with {len(perception_pipeline['nodes'])} nodes")
Isaac Perception Best Practices​
Performance Optimization​
class IsaacPerceptionOptimizer:
def __init__(self):
self.optimization_strategies = {
'tensor_parallelism': True,
'model_quantization': True,
'dynamic_batching': True,
'memory_pooling': True
}
self.performance_metrics = {
'inference_time': [],
'throughput': [],
'memory_usage': [],
'accuracy': []
}
def optimize_inference_pipeline(self, pipeline):
"""Optimize the perception pipeline for performance"""
optimized_pipeline = pipeline.copy()
# Apply TensorRT optimization
for node in optimized_pipeline['nodes']:
if node['acceleration'] == 'tensorrt':
node['optimized_model'] = self.optimize_tensorrt_node(node)
# Apply batching optimization
optimized_pipeline['batching_enabled'] = True
optimized_pipeline['batch_size'] = self.determine_optimal_batch_size(pipeline)
# Apply memory optimization
optimized_pipeline['memory_optimized'] = True
return optimized_pipeline
def optimize_tensorrt_node(self, node):
"""Optimize individual TensorRT node"""
# This would perform TensorRT-specific optimizations
optimization_config = {
'precision': 'fp16', # Use FP16 for speed
'max_batch_size': 8, # Optimal batch size for throughput
'workspace_size': 1 << 30, # 1GB workspace
'min_timing_iters': 10,
'avg_timing_iters': 10
}
print(f"Optimized {node['name']} with TensorRT settings: {optimization_config}")
return optimization_config
def determine_optimal_batch_size(self, pipeline):
"""Determine optimal batch size based on available GPU memory"""
# In practice, this would query GPU memory and model requirements
# For this example, we'll return a sensible default
gpu_memory_gb = 8 # Assume 8GB GPU
model_memory_mb = 500 # Assume 500MB per model instance
# Calculate max batch size based on memory
max_batch_by_memory = int((gpu_memory_gb * 1024 * 0.7) / model_memory_mb) # 70% of GPU memory
optimal_batch_size = min(max_batch_by_memory, 8) # Cap at 8 for stability
return max(optimal_batch_size, 1) # At least batch size 1
def benchmark_pipeline(self, pipeline, test_data):
"""Benchmark the perception pipeline"""
import time
benchmark_results = {
'average_inference_time': 0.0,
'frames_per_second': 0.0,
'memory_peak_usage': 0.0,
'accuracy': 0.0
}
inference_times = []
start_time = time.time()
for i in range(100): # Run 100 test inferences
frame_start = time.time()
# Run pipeline inference
result = self.run_pipeline_inference(pipeline, test_data[i % len(test_data)])
frame_time = time.time() - frame_start
inference_times.append(frame_time)
total_time = time.time() - start_time
benchmark_results['average_inference_time'] = np.mean(inference_times)
benchmark_results['frames_per_second'] = 1.0 / np.mean(inference_times)
benchmark_results['memory_peak_usage'] = self.get_gpu_memory_usage()
benchmark_results['accuracy'] = self.estimate_accuracy(result)
return benchmark_results
def run_pipeline_inference(self, pipeline, input_data):
"""Run inference through the entire pipeline"""
# This would execute the complete pipeline
# For this example, return placeholder result
return np.random.random((10, 5)) # 10 detections with 5 attributes each
def get_gpu_memory_usage(self):
"""Get current GPU memory usage"""
# In practice, this would query GPU memory using pynvml or similar
# For this example, return placeholder
return 2.5 # GB
def estimate_accuracy(self, result):
"""Estimate accuracy of perception results"""
# This would compare results to ground truth
# For this example, return placeholder accuracy
return 0.85 # 85% accuracy
def apply_model_quantization(self, model, method='int8'):
"""Apply quantization to reduce model size and increase speed"""
if method == 'int8':
print("Applying INT8 quantization to model")
# This would use TensorRT's INT8 calibration
return "quantized_int8_model"
elif method == 'fp16':
print("Applying FP16 quantization to model")
# This would use TensorRT's FP16 optimization
return "quantized_fp16_model"
else:
return model
def enable_dynamic_batching(self, pipeline):
"""Enable dynamic batching for variable input sizes"""
for node in pipeline['nodes']:
node['dynamic_batching'] = True
node['max_batch_size'] = 8
print("Enabled dynamic batching for all nodes")
return pipeline
Practical Implementation Example​
Complete Perception System​
class IsaacPerceptionSystem:
def __init__(self):
self.ai_pipeline = IsaacAIPipeline()
self.synth_data_gen = IsaacSyntheticDataGenerator()
self.optimizer = IsaacPerceptionOptimizer()
self.segmentation_module = IsaacSegmentation()
self.detectnet_module = IsaacDetectNet()
# System configuration
self.config = {
'input_resolution': (640, 480),
'inference_frequency': 30, # Hz
'confidence_threshold': 0.7,
'max_objects': 50,
'gpu_id': 0
}
def setup_perception_system(self):
"""Setup the complete perception system"""
print("Setting up Isaac perception system...")
# Load AI models
self.ai_pipeline.load_model(
'object_detection_model',
'/path/to/detectnet.engine',
'tensorrt'
)
self.ai_pipeline.load_model(
'segmentation_model',
'/path/to/segmentation.engine',
'tensorrt'
)
# Configure synthetic data generation for training
synth_config = {
'domain_randomization': {
'lighting': True,
'textures': True,
'object_placement': True
},
'annotations': {
'bounding_boxes': True,
'segmentation_masks': True,
'object_poses': True
}
}
self.synth_data_gen.configure_synthetic_data_generation(synth_config)
print("Isaac perception system setup complete")
return True
def process_camera_input(self, image):
"""Process camera input through the perception pipeline"""
results = {}
# Object detection
detections = self.detectnet_module.detect_objects(image)
results['detections'] = detections
# Segmentation
segmentation_result = self.segmentation_module.segment_image(image)
results['segmentation'] = segmentation_result
# Depth estimation (if depth camera is available)
# results['depth'] = self.estimate_depth(image)
# Pose estimation (if applicable)
# results['poses'] = self.estimate_poses(image, detections)
return results
def generate_training_data(self, scene_configs, num_samples_per_scene=1000):
"""Generate synthetic training data for perception models"""
all_datasets = []
for i, scene_config in enumerate(scene_configs):
print(f"Generating synthetic data for scene {i+1}/{len(scene_configs)}")
dataset = self.synth_data_gen.generate_synthetic_dataset(
scene_config,
num_samples=num_samples_per_scene
)
all_datasets.append(dataset)
# Export dataset
export_path = f"./synthetic_data/scene_{i+1}"
self.synth_data_gen.export_dataset(dataset, export_path, format='coco')
print(f"Generated {len(all_datasets)} synthetic datasets with {num_samples_per_scene} samples each")
return all_datasets
def optimize_for_deployment(self, pipeline):
"""Optimize perception pipeline for deployment"""
# Apply all optimizations
optimized_pipeline = self.optimizer.optimize_inference_pipeline(pipeline)
# Apply model quantization
for node in optimized_pipeline['nodes']:
if 'model' in node:
node['quantized_model'] = self.optimizer.apply_model_quantization(
node['model'], method='fp16'
)
# Enable dynamic batching
optimized_pipeline = self.optimizer.enable_dynamic_batching(optimized_pipeline)
return optimized_pipeline
def run_perception_benchmark(self, test_images):
"""Run benchmark on perception system"""
pipeline_config = {
'name': 'benchmark_pipeline',
'input_topic': '/camera/image_raw',
'output_topic': '/perception/results',
'nodes': [
{
'name': 'detection_node',
'model': 'object_detection_model',
'model_path': '/path/to/detectnet.engine',
'input_topics': ['/camera/image_raw'],
'output_topics': ['/detections'],
'acceleration': 'tensorrt'
},
{
'name': 'segmentation_node',
'model': 'segmentation_model',
'model_path': '/path/to/segmentation.engine',
'input_topics': ['/camera/image_raw'],
'output_topics': ['/segmentation'],
'acceleration': 'tensorrt'
}
]
}
pipeline = self.ai_pipeline.create_inference_pipeline(pipeline_config)
optimized_pipeline = self.optimize_for_deployment(pipeline)
benchmark_results = self.optimizer.benchmark_pipeline(optimized_pipeline, test_images)
print(f"Benchmark Results: {benchmark_results}")
return benchmark_results
def visualize_perception_results(self, original_image, perception_results):
"""Visualize perception results on original image"""
# Visualize detections
if 'detections' in perception_results:
image_with_detections = self.detectnet_module.visualize_detections(
original_image,
perception_results['detections']
)
else:
image_with_detections = original_image
# Overlay segmentation
if 'segmentation' in perception_results:
segmented_overlay = self.segmentation_module.visualize_segmentation(
original_image,
perception_results['segmentation']
)
else:
segmented_overlay = original_image
# Combine visualizations
combined_visualization = cv2.addWeighted(
image_with_detections, 0.7,
segmented_overlay, 0.3,
0
)
return combined_visualization
# Example usage of the complete perception system
def run_isaac_perception_demo():
"""Run a complete Isaac perception demo"""
perception_system = IsaacPerceptionSystem()
# Setup the system
perception_system.setup_perception_system()
# Create sample scene configurations for synthetic data generation
scene_configs = [
{
'name': 'indoor_office',
'image_size': [480, 640],
'num_classes': 10,
'objects': [
{'name': 'desk', 'type': 'furniture'},
{'name': 'chair', 'type': 'furniture'},
{'name': 'monitor', 'type': 'electronics'},
{'name': 'plant', 'type': 'decoration'}
]
},
{
'name': 'warehouse',
'image_size': [480, 640],
'num_classes': 8,
'objects': [
{'name': 'pallet', 'type': 'storage'},
{'name': 'shelf', 'type': 'storage'},
{'name': 'box_large', 'type': 'obstacle'},
{'name': 'box_small', 'type': 'obstacle'}
]
}
]
# Generate synthetic training data
training_datasets = perception_system.generate_training_data(scene_configs, num_samples_per_scene=500)
# Process a sample image
sample_image = np.random.randint(0, 255, (480, 640, 3), dtype=np.uint8)
results = perception_system.process_camera_input(sample_image)
print(f"Perception results: {len(results['detections'])} detections, "
f"{len(results['segmentation']['statistics'])} segmentation classes")
# Visualize results
visualization = perception_system.visualize_perception_results(sample_image, results)
print("Perception visualization created")
return perception_system, results, visualization
# Run the demo
perception_system, results, visualization = run_isaac_perception_demo()
print("Isaac perception system demo completed successfully!")
Learning Objectives​
By the end of this lesson, you should be able to:
- Understand the NVIDIA Isaac platform architecture and its perception components
- Implement object detection using Isaac's DetectNet framework
- Apply segmentation techniques using Isaac's segmentation tools
- Generate synthetic training data using Isaac's data generation capabilities
- Integrate AI models with Isaac using TensorRT optimization
- Create perception pipelines using Isaac ROS packages
- Optimize perception systems for real-time performance
- Configure and deploy perception systems on robotics platforms
- Evaluate perception system performance and accuracy
- Apply domain randomization for robust perception models