Computer Vision with TuskLang Python SDK

Overview

TuskLang's Python SDK provides revolutionary computer vision capabilities that integrate seamlessly with the FUJSEN intelligence system. From basic image processing to advanced object detection and recognition, TuskLang makes computer vision accessible, powerful, and production-ready.

Installation & Setup

Core Vision Dependencies

Install TuskLang Python SDK with vision extensions
pip install tuskvision[full]
Or install specific vision components
pip install tuskvision[opencv]     # OpenCV integration
pip install tuskvision[torch]      # PyTorch vision models
pip install tuskvision[tensorflow] # TensorFlow vision models
pip install tuskvision[pillow]     # PIL/Pillow integration

Environment Configuration

peanu.tsk configuration for vision workloads
vision_config = {
    "image_processing": {
        "default_format": "RGB",
        "max_resolution": [1920, 1080],
        "gpu_enabled": true,
        "batch_size": 8
    },
    "model_cache": {
        "cache_dir": "/var/cache/tuskvision/models",
        "auto_download": true,
        "version_control": true
    },
    "fujsen_integration": {
        "enable_intelligence": true,
        "context_awareness": true,
        "real_time_processing": true
    }
}

Basic Image Processing

Image Loading & Preprocessing

from tuskvision import ImageProcessor, ImageLoader
from tuskvision.fujsen import @load_image, @preprocess_image
Load images using FUJSEN operators
@raw_image = @load_image("product_photo.jpg")
@image_batch = @load_image("images/*.jpg", batch=True)
TuskLang-native image processing
processor = ImageProcessor([
    "resize",
    "normalize",
    "color_correction",
    "noise_reduction",
    "enhancement"
])
@processed_image = processor.process(@raw_image)
Advanced image loading
loader = ImageLoader(
    target_size=(224, 224),
    color_mode="RGB",
    interpolation="bilinear"
)
@loaded_image = loader.load("@raw_image")

Image Analysis & Features

from tuskvision.analysis import ImageAnalyzer, FeatureExtractor
from tuskvision.fujsen import @analyze_image, @extract_features
Basic image analysis
analyzer = ImageAnalyzer()
@analysis = analyzer.analyze(@raw_image, features=[
    "brightness",
    "contrast",
    "sharpness",
    "color_distribution",
    "texture"
])
FUJSEN-powered image analysis
@image_analysis = @analyze_image(
    image="@raw_image",
    analysis_types=["quality", "composition", "aesthetics"],
    confidence_threshold=0.8
)
Feature extraction
extractor = FeatureExtractor(
    model="tusk-vision-features-v1",
    feature_dim=2048
)
@features = extractor.extract(@processed_image)

Advanced Vision Models

Object Detection & Recognition

from tuskvision.detection import ObjectDetector, RecognitionEngine
from tuskvision.fujsen import @detect_objects, @recognize_objects
Object detector
detector = ObjectDetector(
    model="tusk-detector-v1",
    confidence_threshold=0.5,
    nms_threshold=0.4
)
@detections = detector.detect(@raw_image)
FUJSEN-powered object detection
@objects = @detect_objects(
    image="@raw_image",
    classes=["person", "car", "building", "animal"],
    model="tusk-detector-advanced",
    include_bbox=True
)
Object recognition
recognizer = RecognitionEngine(
    model="tusk-recognizer-v1",
    top_k=5
)
@recognitions = recognizer.recognize(@detections)
FUJSEN object recognition
@recognized_objects = @recognize_objects(
    detections="@detections",
    model="tusk-recognizer-v2",
    include_confidence=True
)

Image Classification & Categorization

from tuskvision.classification import ImageClassifier, CategoryPredictor
from tuskvision.fujsen import @classify_image, @categorize_images
Image classifier
classifier = ImageClassifier(
    model="tusk-classifier-v1",
    categories=["nature", "urban", "portrait", "abstract"],
    confidence_threshold=0.7
)
@classification = classifier.classify(@raw_image)
FUJSEN-powered classification
@image_category = @classify_image(
    image="@raw_image",
    categories=["product", "scene", "object", "text"],
    model="tusk-intent-classifier"
)
Batch image categorization
@image_categories = @categorize_images(
    images="@image_batch",
    categories=["indoor", "outdoor", "day", "night"],
    batch_size=16
)

Vision Pipelines

End-to-End Vision Pipeline

from tuskvision.pipeline import VisionPipeline
from tuskvision.fujsen import @process_vision_pipeline
Complete vision pipeline
pipeline = VisionPipeline([
    "image_loading",
    "preprocessing",
    "feature_extraction",
    "detection",
    "classification",
    "post_processing",
    "output_generation"
])
Execute pipeline
@pipeline_result = pipeline.execute(
    config={
        "input": "@raw_image",
        "tasks": ["detection", "classification", "segmentation"],
        "output_format": "json"
    }
)
FUJSEN-powered pipeline
@vision_result = @process_vision_pipeline(
    image="@raw_image",
    pipeline="comprehensive",
    include_metadata=True
)

Real-time Vision Processing

from tuskvision.streaming import StreamingVisionPipeline
from tuskvision.fujsen import @stream_vision_analysis
Streaming vision pipeline
stream_pipeline = StreamingVisionPipeline(
    model="@model",
    frame_rate=30,
    processing_window=10,
    real_time=True
)
Process streaming video
@stream_analysis = stream_pipeline.process_stream(
    @video_stream,
    output_format="json"
)
Real-time vision analysis
@real_time_analysis = @stream_vision_analysis(
    video_stream="@video_stream",
    analysis_types=["detection", "tracking", "recognition"],
    latency_threshold=50  # ms
)

Advanced Vision Features

Image Segmentation

from tuskvision.segmentation import ImageSegmenter, MaskGenerator
from tuskvision.fujsen import @segment_image, @generate_masks
Image segmentation
segmenter = ImageSegmenter(
    model="tusk-segmenter-v1",
    segmentation_type="semantic",
    num_classes=21
)
@segmentation = segmenter.segment(@raw_image)
FUJSEN-powered segmentation
@segments = @segment_image(
    image="@raw_image",
    type="instance",
    model="tusk-segmenter-advanced",
    include_masks=True
)
Mask generation
mask_generator = MaskGenerator()
@masks = mask_generator.generate(@segmentation)
FUJSEN mask generation
@generated_masks = @generate_masks(
    segmentation="@segmentation",
    format="binary",
    include_overlay=True
)

Face Detection & Recognition

from tuskvision.faces import FaceDetector, FaceRecognizer
from tuskvision.fujsen import @detect_faces, @recognize_faces
Face detection
face_detector = FaceDetector(
    model="tusk-face-detector-v1",
    min_face_size=20,
    confidence_threshold=0.8
)
@faces = face_detector.detect(@raw_image)
FUJSEN face detection
@detected_faces = @detect_faces(
    image="@raw_image",
    model="tusk-face-detector-advanced",
    include_landmarks=True,
    include_attributes=True
)
Face recognition
face_recognizer = FaceRecognizer(
    model="tusk-face-recognizer-v1",
    database="@face_database",
    similarity_threshold=0.8
)
@recognized_faces = face_recognizer.recognize(@faces)
FUJSEN face recognition
@face_recognition = @recognize_faces(
    faces="@detected_faces",
    database="@face_database",
    model="tusk-face-recognizer-v2"
)

Optical Character Recognition (OCR)

from tuskvision.ocr import TextDetector, TextRecognizer
from tuskvision.fujsen import @detect_text, @recognize_text
Text detection
text_detector = TextDetector(
    model="tusk-text-detector-v1",
    min_text_size=10,
    confidence_threshold=0.7
)
@text_regions = text_detector.detect(@raw_image)
FUJSEN text detection
@detected_text = @detect_text(
    image="@raw_image",
    model="tusk-text-detector-advanced",
    languages=["en", "es", "fr"],
    include_bbox=True
)
Text recognition
text_recognizer = TextRecognizer(
    model="tusk-text-recognizer-v1",
    languages=["en", "es", "fr"],
    post_process=True
)
@recognized_text = text_recognizer.recognize(@text_regions)
FUJSEN text recognition
@ocr_result = @recognize_text(
    text_regions="@detected_text",
    model="tusk-text-recognizer-v2",
    include_confidence=True
)

Vision AI & Intelligence

Scene Understanding

from tuskvision.scene import SceneAnalyzer, ContextUnderstanding
from tuskvision.fujsen import @analyze_scene, @understand_context
Scene analysis
scene_analyzer = SceneAnalyzer(
    model="tusk-scene-v1",
    include_objects=True,
    include_relationships=True
)
@scene_analysis = scene_analyzer.analyze(@raw_image)
FUJSEN scene analysis
@scene_understanding = @analyze_scene(
    image="@raw_image",
    model="tusk-scene-advanced",
    include_context=True,
    include_actions=True
)
Context understanding
context_analyzer = ContextUnderstanding()
@context = context_analyzer.understand(@scene_analysis)
FUJSEN context understanding
@context_result = @understand_context(
    scene="@scene_analysis",
    model="tusk-context-v1",
    include_reasoning=True
)

Visual Question Answering

from tuskvision.vqa import VisualQA, QuestionAnswerer
from tuskvision.fujsen import @answer_visual_question
Visual question answering
vqa_system = VisualQA(
    model="tusk-vqa-v1",
    max_answer_length=20,
    confidence_threshold=0.7
)
@answer = vqa_system.answer(
    image="@raw_image",
    question="@question"
)
FUJSEN VQA
@vqa_result = @answer_visual_question(
    image="@raw_image",
    question="@question",
    model="tusk-vqa-advanced",
    include_confidence=True
)

Vision with TuskLang Ecosystem

Integration with TuskDB

from tuskvision.storage import TuskDBStorage
from tuskvision.fujsen import @store_vision_result, @load_vision_model
Store vision results in TuskDB
@vision_storage = TuskDBStorage(
    database="vision_results",
    collection="image_analysis"
)
@store_vision_result = @store_vision_result(
    image="@raw_image",
    analysis="@analysis",
    metadata={
        "timestamp": "@timestamp",
        "model_version": "tusk-vision-v1.2",
        "processing_time": 0.8
    }
)
Load vision models from TuskDB
@loaded_model = @load_vision_model(
    model_name="tusk-detector-v2",
    version="latest"
)

Vision with FUJSEN Intelligence

from tuskvision.fujsen import @vision_intelligence, @smart_image_processing
FUJSEN-powered vision intelligence
@intelligent_analysis = @vision_intelligence(
    image="@raw_image",
    context="@context",
    intelligence_level="advanced",
    include_reasoning=True
)
Smart image processing
@smart_processing = @smart_image_processing(
    image="@raw_image",
    tasks=["detection", "classification", "segmentation"],
    adaptive_processing=True,
    quality_threshold=0.9
)

Best Practices

Performance Optimization

GPU acceleration
import torch
if torch.cuda.is_available():
    model = model.cuda()
    image = image.cuda()
Batch processing
from tuskvision.optimization import BatchProcessor
batch_processor = BatchProcessor(
    model="@model",
    batch_size=16,
    max_workers=4
)
@batch_results = batch_processor.process_batch(@images)
Model quantization
from tuskvision.optimization import ModelQuantizer
quantizer = ModelQuantizer(
    model="@trained_model",
    quantization="int8"
)
@quantized_model = quantizer.quantize()

Quality Assurance

from tuskvision.quality import VisionQualityChecker
from tuskvision.validation import ImageValidator
Quality checking
quality_checker = VisionQualityChecker(
    min_confidence=0.8,
    max_processing_time=1.0,
    quality_metrics=["accuracy", "precision", "recall"]
)
@quality_report = quality_checker.check(@vision_result)
Image validation
validator = ImageValidator()
@validation_result = validator.validate(
    image="@raw_image",
    checks=["format", "size", "quality", "content"]
)

Example: Product Recognition System

Complete product recognition system
from tuskvision import *
Load and preprocess product images
@product_images = @load_image("products/*.jpg")
@processed_images = @preprocess_image(@product_images)
Detect and classify products
@product_detections = @detect_objects(
    image="@processed_images",
    classes=["product", "package", "label"],
    model="tusk-product-detector"
)
@product_classifications = @classify_image(
    image="@processed_images",
    categories=["electronics", "clothing", "food", "books"],
    model="tusk-product-classifier"
)
Extract product features
@product_features = @extract_features(
    image="@processed_images",
    model="tusk-product-features"
)
Store results in TuskDB
@store_product_analysis = @store_vision_result(
    images="@processed_images",
    analysis={
        "detections": "@product_detections",
        "classifications": "@product_classifications",
        "features": "@product_features"
    }
)

Conclusion

TuskLang's Python SDK provides a comprehensive computer vision ecosystem that integrates seamlessly with the revolutionary FUJSEN intelligence system. From basic image processing to advanced object detection and recognition, TuskLang makes computer vision accessible, powerful, and production-ready.

The integration with TuskDB, FlexEquil distributed computing, and the FUJSEN intelligence system creates a unique vision platform that scales from simple image analysis to enterprise-grade computer vision systems. Whether you're building product recognition systems, surveillance applications, or autonomous vehicles, TuskLang provides the tools and infrastructure you need to succeed.

Embrace the future of computer vision with TuskLang - where vision meets revolutionary technology.