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import visiontech_ai as vt
# Initialize Smart Glasses SDK
glasses = vt.SmartGlasses()
# Enable AI vision processing
vision = glasses.vision.enable_ai_detection()
# Create AR overlay
overlay = glasses.ar.create_overlay()
# Real-time object detection
@glasses.on_frame
def process_frame(frame):
objects = vision.detect_objects(frame)
overlay.render_annotations(objects)
return overlay.composite(frame)
# Start the application
glasses.run()Everything you need to build next-generation smart glasses applications
Complete development kit for building AI-powered smart glasses applications with computer vision, AR rendering, and edge AI processing.
Advanced computer vision capabilities including object detection, scene understanding, and real-time image processing.
/api/v1/vision/detect
/api/v1/vision/classify
/api/v1/vision/track
Comprehensive augmented reality framework for creating immersive 3D experiences and spatial computing applications.
High-performance AI processing engine optimized for edge computing with neural network acceleration and real-time inference.
Enterprise-grade security framework with end-to-end encryption, secure enclaves, and privacy-preserving AI processing.
Complete suite of development tools including simulators, debuggers, performance profilers, and deployment utilities.
Get started quickly with practical examples and tutorials
Implement real-time object detection with AI-powered computer vision
import visiontech_ai as vt
import numpy as np
# Initialize the Smart Glasses SDK
glasses = vt.SmartGlasses()
vision = glasses.vision
# Configure object detection model
detector = vision.ObjectDetector(
model='yolo-v8-optimized',
confidence_threshold=0.7,
nms_threshold=0.4
)
# Real-time detection callback
@glasses.on_frame
def detect_objects(frame):
# Run AI inference
detections = detector.detect(frame)
# Filter and process results
for detection in detections:
if detection.confidence > 0.8:
# Draw bounding box
glasses.ar.draw_bbox(
detection.bbox,
label=detection.class_name,
confidence=detection.confidence
)
# Trigger haptic feedback for important objects
if detection.class_name in ['person', 'vehicle']:
glasses.haptic.pulse(intensity=0.5)
return frame
# Start processing
glasses.start_camera()
glasses.run()Create contextual AR overlays with 3D graphics and animations
import visiontech_ai as vt
from visiontech_ai.ar import Scene3D, Text3D, Model3D
# Initialize AR system
glasses = vt.SmartGlasses()
ar = glasses.ar
# Create 3D scene
scene = Scene3D()
# Add contextual information overlay
info_panel = ar.InfoPanel(
position=(0, 0, -2), # 2 meters in front
size=(0.5, 0.3),
background_color=(0, 0, 0, 0.8)
)
# Add dynamic text
status_text = Text3D(
text="Smart Glasses Active",
font_size=0.05,
color=(0, 1, 1, 1), # Cyan
position=(0, 0.1, -1.5)
)
# Add 3D model for navigation
arrow = Model3D(
model_path="assets/arrow.obj",
position=(0, -0.1, -1.5),
scale=(0.1, 0.1, 0.1)
)
# Animation for attention
@ar.animate(duration=2.0, loop=True)
def pulse_arrow():
arrow.scale = (0.1, 0.1, 0.1)
yield ar.tween_to(arrow, scale=(0.15, 0.15, 0.15), duration=1.0)
yield ar.tween_to(arrow, scale=(0.1, 0.1, 0.1), duration=1.0)
# Add elements to scene
scene.add(info_panel)
scene.add(status_text)
scene.add(arrow)
# Render scene
ar.render_scene(scene)
glasses.run()Implement natural language processing for voice-controlled interactions
import visiontech_ai as vt
from visiontech_ai.nlp import VoiceProcessor
# Initialize voice processing
glasses = vt.SmartGlasses()
voice = VoiceProcessor(
language='en-US',
wake_word='hey_vision',
continuous_listening=True
)
# Define command handlers
@voice.command("take a photo")
def take_photo():
photo = glasses.camera.capture()
glasses.storage.save(photo, "photos/")
glasses.audio.play_sound("camera_click.wav")
glasses.display.show_notification("Photo saved!")
@voice.command("start recording")
def start_recording():
glasses.camera.start_video_recording()
glasses.display.show_indicator("REC", color="red")
@voice.command("stop recording")
def stop_recording():
video = glasses.camera.stop_video_recording()
glasses.storage.save(video, "videos/")
glasses.display.hide_indicator()
@voice.command("translate {text} to {language}")
def translate_text(text, language):
translation = glasses.ai.translate(text, target_language=language)
glasses.ar.display_text(
translation,
position=(0, 0, -1),
duration=5.0
)
@voice.command("what do you see")
def describe_scene():
frame = glasses.camera.get_current_frame()
description = glasses.ai.describe_scene(frame)
glasses.audio.speak(description)
# Start voice processing
voice.start_listening()
glasses.run()Implement intuitive hand gesture controls for seamless interaction
import visiontech_ai as vt
from visiontech_ai.gestures import HandTracker, GestureRecognizer
# Initialize gesture recognition
glasses = vt.SmartGlasses()
hand_tracker = HandTracker()
gesture_recognizer = GestureRecognizer()
# Define gesture handlers
@gesture_recognizer.on_gesture("thumbs_up")
def thumbs_up_handler():
glasses.audio.play_sound("success.wav")
glasses.ar.show_emoji("👍", duration=2.0)
@gesture_recognizer.on_gesture("peace_sign")
def peace_sign_handler():
glasses.camera.capture_selfie()
glasses.ar.show_emoji("✌️", duration=1.5)
@gesture_recognizer.on_gesture("point")
def point_handler(direction):
# Get pointing direction
target = glasses.ar.raycast(direction)
if target:
# Highlight pointed object
glasses.ar.highlight_object(target, color="yellow")
# Get object information
info = glasses.ai.identify_object(target)
glasses.ar.display_info_popup(info, position=target.position)
@gesture_recognizer.on_gesture("swipe_left")
def swipe_left_handler():
glasses.ui.navigate_previous()
@gesture_recognizer.on_gesture("swipe_right")
def swipe_right_handler():
glasses.ui.navigate_next()
@gesture_recognizer.on_gesture("pinch_zoom")
def pinch_zoom_handler(scale_factor):
glasses.ar.zoom_view(scale_factor)
# Process hand tracking
@glasses.on_frame
def process_gestures(frame):
# Track hand landmarks
hands = hand_tracker.detect_hands(frame)
for hand in hands:
# Recognize gestures
gesture = gesture_recognizer.recognize(hand)
if gesture:
# Trigger gesture handler
gesture_recognizer.handle_gesture(gesture)
# Visual feedback
glasses.ar.draw_hand_skeleton(hand)
return frame
# Start gesture recognition
glasses.run()Create detailed 3D maps of environments for precise AR object placement
import visiontech_ai as vt
from visiontech_ai.spatial import SpatialMapper, MeshGenerator
# Initialize spatial mapping system
glasses = vt.SmartGlasses()
spatial_mapper = SpatialMapper(
resolution=0.01, # 1cm resolution
max_range=10.0, # 10 meter range
update_frequency=30 # 30Hz updates
)
# Configure mesh generation
mesh_generator = MeshGenerator(
smoothing_factor=0.8,
decimation_ratio=0.3,
normal_estimation=True
)
# Real-time mapping callback
@glasses.on_depth_frame
def update_spatial_map(depth_frame, rgb_frame):
# Process depth data
point_cloud = spatial_mapper.process_depth(depth_frame)
# Update 3D mesh
mesh = mesh_generator.update_mesh(point_cloud)
# Detect surfaces and planes
surfaces = spatial_mapper.detect_surfaces(mesh)
# Visualize mapping progress
glasses.ar.render_mesh(mesh, wireframe=True)
glasses.ar.highlight_surfaces(surfaces)
return mesh
# Place AR objects on detected surfaces
@spatial_mapper.on_surface_detected
def place_ar_content(surface):
if surface.type == 'horizontal' and surface.area > 0.5:
# Place virtual furniture on tables/floors
furniture = glasses.ar.load_model('chair.obj')
glasses.ar.place_object(
furniture,
position=surface.center,
orientation=surface.normal
)
elif surface.type == 'vertical' and surface.area > 1.0:
# Place UI panels on walls
ui_panel = glasses.ar.create_ui_panel(
size=(0.4, 0.3),
content='Welcome to AR Space'
)
glasses.ar.attach_to_surface(ui_panel, surface)
# Export spatial map
def export_map():
mesh = spatial_mapper.get_current_mesh()
glasses.storage.save_mesh(mesh, 'spatial_maps/room_scan.ply')
# Generate occlusion map for realistic AR
occlusion_map = spatial_mapper.generate_occlusion_map()
glasses.ar.set_occlusion_map(occlusion_map)
# Start spatial mapping
spatial_mapper.start_mapping()
glasses.run()Comprehensive API reference for all VisionTech AI services
Detect and classify objects in real-time from camera feed or uploaded images.
{
"image": "base64_encoded_image",
"model": "yolo-v8-optimized",
"confidence_threshold": 0.7,
"max_detections": 10,
"classes": ["person", "vehicle", "object"]
}{
"detections": [
{
"class_name": "person",
"confidence": 0.95,
"bbox": [100, 150, 200, 300],
"center": [150, 225]
}
],
"processing_time_ms": 12,
"model_version": "v2.1.0"
}Retrieve current AR scene state and 3D object positions.
Process voice commands and natural language queries with context awareness.
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Follow our quick start guide to build your first smart glasses application
Sign up for a free developer account and get instant access to our SDK, APIs, and development tools.
Install the VisionTech AI SDK for your preferred programming language and development environment.
pip install visiontech-ai-sdk
Generate your API key from the developer dashboard to authenticate your applications.
Follow our quick start tutorial to create a simple object detection application in under 10 minutes.
Join the VisionTech AI developer ecosystem and create revolutionary smart glasses applications that will change how people interact with the world.