Python computer vision drone von sloane eleanor (11 Ergebnisse)
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PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.
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PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.
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Paperback. Zustand: new. Paperback. Teach Your Drone to See-One Practical Project at a TimeA drone camera can capture thousands of images and video frames, but recording a scene is not the same as understanding it.A person can look at aerial footage and recognise a landing marker, moving vehicle, damaged surface, or important gr…ound feature. A computer needs a repeatable process for finding those details, measuring them, following them across frames, and recording the results.Drone Computer Vision with Python provides a practical, beginner-friendly path into aerial image processing, live video analysis, object detection, and visual tracking using Python and OpenCV.Instead of beginning with complicated mathematics or advanced machine-learning theory, this book starts with individual images and recorded footage. You will gradually progress to live cameras, landing-target recognition, motion analysis, feature matching, pretrained detection models, persistent tracking, flight telemetry, and companion-computer deployment.Inside this hands-on guide, you will learn how to: Understand classification, detection, segmentation, and trackingTurn camera frames into measurable pixel dataDesign a complete vision-processing pipelineSeparate image processing from flight-critical aircraft controlChoose between onboard, ground-based, and hybrid processingUnderstand the roles of the camera, flight controller, companion computer, telemetry link, storage device, and ground stationSet up an organised and reproducible development workspaceCreate and manage isolated Python virtual environmentsInstall NumPy, OpenCV, Matplotlib, PyYAML, MAVSDK, and supporting toolsAvoid conflicts between standard, contributed, and headless OpenCV packagesRecord package versions and rebuild a project environment from its dependency fileSelect cameras for aerial inspection, landing assistance, and ground-object observationCompare USB, CSI, action, network, rolling-shutter, and global-shutter camerasBalance resolution, frame rate, image detail, processing speed, storage, and latencyUnderstand exposure, gain, focus, compression, field of view, and motion blurIdentify propeller vibration, rolling-shutter distortion, buffering, and lighting problemsRead, display, resize, crop, convert, and save imagesWork with pixels, colour channels, masks, thresholds, and regions of interestThese projects follow a deliberate progression from repeatable offline analysis to live-camera work and, finally, controlled UAV integration. The book keeps flight stabilisation on the flight controller while assigning experimental image analysis to a companion or ground computer.Recorded footage is used first because it can be replayed and examined safely. Live-camera and aircraft-connected projects are introduced only after the underlying processing pipeline behaves predictably.Whether you are a drone pilot, Python learner, robotics student, inspection professional, researcher, maker, Raspberry Pi user, or developer exploring aerial imagery, this guide will help you move from raw footage to practical visual observations.Capture the frame. Validate the data. Find the target. Track the result. Keep experimental software separate from safe flight control. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
- Softcover
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Paperback. Zustand: new. Paperback. Transform Drone Photos and Videos into Useful Visual Data with PythonDrone flights can produce hundreds of aerial photographs and thousands of video frames. Reviewing every file manually is slow, inconsistent, and often overwhelming. With computer vision, you can automate parts of the process,… identify visible features, detect objects, analyse movement, measure coverage, and produce organised results for inspection, mapping, agriculture, construction, and research projects.Python Computer Vision for Drone Projects is a practical, beginner-friendly guide to processing aerial images and videos with Python. Rather than overwhelming you with mathematical theory, it teaches computer vision through clear explanations, working scripts, troubleshooting guidance, and realistic drone-related projects.You will begin by creating a clean Python workspace before learning how digital images, pixels, colour channels, metadata, resolution, and video frames work. From there, you will progress through image enhancement, edge detection, motion analysis, object detection, custom model training, geospatial data, measurement, and automated reporting.Inside this hands-on guide, you will learn how to: Set up an isolated Python computer-vision workspaceOrganise source photographs, videos, scripts, outputs, and resultsLoad, display, crop, resize, convert, and save aerial imagesProtect original drone files while creating processed copiesUnderstand RGB, BGR, grayscale, alpha channels, pixels, and resolutionRead drone-image metadata and recognise its limitationsImprove brightness, contrast, sharpness, and low-contrast imageryReduce noise without destroying useful visual detailApply thresholding, Canny edge detection, and contour analysisIdentify visible rooflines, roads, field boundaries, and land featuresRead drone video files frame by frameExtract useful still images from long inspection footageSplit image data into training, validation, and testing groupsTrain and evaluate a small custom object detectorRead GeoTIFF files using PythonWork with coordinates, raster data, masks, and geospatial layersEstimate visual coverage and approximate areas responsiblyExport counts, measurements, and detection results to CSVBatch-process multiple images with a repeatable workflowGenerate processed images, tables, charts, and project summariesPractical projects include an aerial-image viewer, field-boundary detector, video-frame extractor, movement detector, aerial vehicle detector, custom training dataset, GeoTIFF inspector, automated image-analysis report generator, and a complete integrated drone-vision workflow.The book uses practical tools such as OpenCV, NumPy, Matplotlib, Pillow, Ultralytics YOLO, Rasterio, and GeoPandas. Each project includes a defined objective, setup instructions, implementation steps, testing procedures, expected results, and troubleshooting guidance.Whether you are a drone pilot, student, inspection professional, agricultural worker, survey assistant, mapping technician, researcher, hobbyist, or developer, this guide will help you move beyond simply collecting aerial imagery.Process the footage. Detect useful features. Validate the results. Turn aerial data into information people can review and use. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Anbieter: California Books, Miami, FL, USACalifornia Books
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Zustand: New. Print on Demand.
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Paperback. Zustand: new. Paperback. Teach Your Drone to See-One Practical Project at a TimeA drone camera can capture thousands of images and video frames, but recording a scene is not the same as understanding it.A person can look at aerial footage and recognise a landing marker, moving vehicle, damaged surface, or important gr…ound feature. A computer needs a repeatable process for finding those details, measuring them, following them across frames, and recording the results.Drone Computer Vision with Python provides a practical, beginner-friendly path into aerial image processing, live video analysis, object detection, and visual tracking using Python and OpenCV.Instead of beginning with complicated mathematics or advanced machine-learning theory, this book starts with individual images and recorded footage. You will gradually progress to live cameras, landing-target recognition, motion analysis, feature matching, pretrained detection models, persistent tracking, flight telemetry, and companion-computer deployment.Inside this hands-on guide, you will learn how to: Understand classification, detection, segmentation, and trackingTurn camera frames into measurable pixel dataDesign a complete vision-processing pipelineSeparate image processing from flight-critical aircraft controlChoose between onboard, ground-based, and hybrid processingUnderstand the roles of the camera, flight controller, companion computer, telemetry link, storage device, and ground stationSet up an organised and reproducible development workspaceCreate and manage isolated Python virtual environmentsInstall NumPy, OpenCV, Matplotlib, PyYAML, MAVSDK, and supporting toolsAvoid conflicts between standard, contributed, and headless OpenCV packagesRecord package versions and rebuild a project environment from its dependency fileSelect cameras for aerial inspection, landing assistance, and ground-object observationCompare USB, CSI, action, network, rolling-shutter, and global-shutter camerasBalance resolution, frame rate, image detail, processing speed, storage, and latencyUnderstand exposure, gain, focus, compression, field of view, and motion blurIdentify propeller vibration, rolling-shutter distortion, buffering, and lighting problemsRead, display, resize, crop, convert, and save imagesWork with pixels, colour channels, masks, thresholds, and regions of interestThese projects follow a deliberate progression from repeatable offline analysis to live-camera work and, finally, controlled UAV integration. The book keeps flight stabilisation on the flight controller while assigning experimental image analysis to a companion or ground computer.Recorded footage is used first because it can be replayed and examined safely. Live-camera and aircraft-connected projects are introduced only after the underlying processing pipeline behaves predictably.Whether you are a drone pilot, Python learner, robotics student, inspection professional, researcher, maker, Raspberry Pi user, or developer exploring aerial imagery, this guide will help you move from raw footage to practical visual observations.Capture the frame. Validate the data. Find the target. Track the result. Keep experimental software separate from safe flight control. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
- Softcover
- Print-on-Demand
Anbieter: CitiRetail, Stevenage, Vereinigtes KönigreichCitiRetail
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 24,65
EUR 43,23 VersandVersand von Vereinigtes Königreich nach USAAnzahl: 1 verfügbar
Paperback. Zustand: new. Paperback. Transform Drone Photos and Videos into Useful Visual Data with PythonDrone flights can produce hundreds of aerial photographs and thousands of video frames. Reviewing every file manually is slow, inconsistent, and often overwhelming. With computer vision, you can automate parts of the process,… identify visible features, detect objects, analyse movement, measure coverage, and produce organised results for inspection, mapping, agriculture, construction, and research projects.Python Computer Vision for Drone Projects is a practical, beginner-friendly guide to processing aerial images and videos with Python. Rather than overwhelming you with mathematical theory, it teaches computer vision through clear explanations, working scripts, troubleshooting guidance, and realistic drone-related projects.You will begin by creating a clean Python workspace before learning how digital images, pixels, colour channels, metadata, resolution, and video frames work. From there, you will progress through image enhancement, edge detection, motion analysis, object detection, custom model training, geospatial data, measurement, and automated reporting.Inside this hands-on guide, you will learn how to: Set up an isolated Python computer-vision workspaceOrganise source photographs, videos, scripts, outputs, and resultsLoad, display, crop, resize, convert, and save aerial imagesProtect original drone files while creating processed copiesUnderstand RGB, BGR, grayscale, alpha channels, pixels, and resolutionRead drone-image metadata and recognise its limitationsImprove brightness, contrast, sharpness, and low-contrast imageryReduce noise without destroying useful visual detailApply thresholding, Canny edge detection, and contour analysisIdentify visible rooflines, roads, field boundaries, and land featuresRead drone video files frame by frameExtract useful still images from long inspection footageSplit image data into training, validation, and testing groupsTrain and evaluate a small custom object detectorRead GeoTIFF files using PythonWork with coordinates, raster data, masks, and geospatial layersEstimate visual coverage and approximate areas responsiblyExport counts, measurements, and detection results to CSVBatch-process multiple images with a repeatable workflowGenerate processed images, tables, charts, and project summariesPractical projects include an aerial-image viewer, field-boundary detector, video-frame extractor, movement detector, aerial vehicle detector, custom training dataset, GeoTIFF inspector, automated image-analysis report generator, and a complete integrated drone-vision workflow.The book uses practical tools such as OpenCV, NumPy, Matplotlib, Pillow, Ultralytics YOLO, Rasterio, and GeoPandas. Each project includes a defined objective, setup instructions, implementation steps, testing procedures, expected results, and troubleshooting guidance.Whether you are a drone pilot, student, inspection professional, agricultural worker, survey assistant, mapping technician, researcher, hobbyist, or developer, this guide will help you move beyond simply collecting aerial imagery.Process the footage. Detect useful features. Validate the results. Turn aerial data into information people can review and use. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
