API : Python SDK

Chloros Python SDK giver programmatisk adgang til Chloros-billedbehandlingsmotoren, hvilket muliggør automatisering, tilpassede arbejdsgange og problemfri integration med dine Python-applikationer og forskningspipelines.

Nøglefunktioner

• 🐍 Native Python - Ren, Pythonic API til billedbehandling

• 🔧 Fuld API-adgang - Fuld kontrol over Chloros-behandlingen

• 🚀 Automatisering - Opbyg brugerdefinerede arbejdsgange til batchbehandling

• 🔗 Integration - Integrer Chloros i eksisterende Python-applikationer

• 📊 Klar til forskning - Perfekt til videnskabelige analysepipelines

• ⚡ Parallel behandling - Skalerer efter dine CPU-kerner (Chloros+)

Krav

Krav	Detaljer
Chloros installeret	Windows: Desktop-installationsprogram; Linux: .deb-pakke
Licens	Chloros+ (betalt abonnement kræves)
Operativsystem	Windows 10/11 (64-bit), Linux x86_64 (amd64), Linux arm64 (NVIDIA Jetson JetPack 6)
Python	Python 3.7 eller nyere
Hukommelse	Minimum 8 GB RAM (16 GB anbefales)
Internet	Kræves til aktivering af licens

Licenskrav: Python SDK kræver et betalt Chloros+-abonnement for at få adgang til API. Standardabonnementer (gratis) har ikke adgang til API/SDK. Besøg https://cloud.mapir.camera/pricing for at opgradere.

Hurtigstart

Installation

Installer via pip:

pip install chloros-sdk

Første opsætning: Før du bruger SDK, skal du aktivere din Chloros+-licens ved at åbne Chloros, Chloros (Browser) eller Chloros CLI og logge ind med dine loginoplysninger. Dette behøver kun at gøres én gang. På Linux (ingen GUI) skal du bruge: chloros-cli login [e-mail] 'password'

Grundlæggende brug

Behandl en mappe med blot et par linjer:

from chloros_sdk import process_folder

# One-line processing (Windows)
results = process_folder("C:\\DroneImages\\Flight001")

# One-line processing (Linux)
results = process_folder("/home/user/drone_images/flight001")

Platformsuafhængige stier: Kodeeksemplerne på denne side bruger stier i stil med Windows (f.eks. C:\\DroneImages\\Flight001). På Linux skal du i stedet bruge Linux-stier (f.eks. /home/user/drone_images/flight001 eller ~/drone_images/flight001). SDK fungerer på samme måde på begge platforme.

Fuld kontrol

Til avancerede arbejdsgange:

from chloros_sdk import ChlorosLocal

# Initialize SDK
chloros = ChlorosLocal()

# Create project
chloros.create_project("MyProject", camera="Survey3N_RGN")

# Import images
chloros.import_images("C:\\DroneImages\\Flight001")  # Windows
# chloros.import_images("/home/user/drone_images/flight001")  # Linux

# Configure settings
chloros.configure(
    vignette_correction=True,
    reflectance_calibration=True,
    indices=["NDVI", "NDRE", "GNDVI"]
)

# Process images
chloros.process(mode="parallel", wait=True)

---

Installationsvejledning

Forudsætninger

Før du installerer SDK, skal du sikre dig, at du har:

1. Chloros installeret — Windows: Desktop-installationsprogram (download); Linux: .deb-pakke (Linux Installation)

2. Python 3.7+ installeret (python.org)

3. Aktiv Chloros+-licens (opgradering)

Installation via pip

Standardinstallation:

pip install chloros-sdk

Med understøttelse af fremskridtsovervågning:

pip install chloros-sdk[progress]

Udviklingsinstallation:

pip install chloros-sdk[dev]

Bekræft installation

Test, at SDK er installeret korrekt:

import chloros_sdk
print(f"Chloros SDK version: {chloros_sdk.__version__}")

---

Første opsætning

Licensaktivering

SDK bruger den samme licens som Chloros, Chloros (Browser) og Chloros CLI. Aktivér én gang via GUI'en eller CLI:Windows:ÅbnChloros eller Chloros (Browser) og log ind på fanen Bruger eller brug CLI.Linux: Brug CLI (ingen GUI tilgængelig):

chloros-cli login [e-mail] 'your_password'

Licensen gemmes lokalt og bevares ved genstart.

Engangsopsætning: Efter login via GUI'en eller CLI bruger SDK automatisk den cachelagrede licens. Der kræves ingen yderligere godkendelse!

Logout: SDK-brugere kan programmatisk rydde cachelagrede legitimationsoplysninger ved hjælp af logout()-metoden. Se logout()-metoden i API-referencen.

Test forbindelse

Kontroller, at SDK kan oprette forbindelse til Chloros:

from chloros_sdk import ChlorosLocal

# Initialize SDK (auto-starts backend if needed)
chloros = ChlorosLocal()

# Check status
status = chloros.get_status()
print(f"Backend running: {status['running']}")

---

API-reference

ChlorosLocal-klasse

Hovedklasse til lokal Chloros-billedbehandling.

Konstruktor

ChlorosLocal(
    api_url="http://localhost:5000",     # Backend URL
    auto_start_backend=True,             # Auto-start backend if not running
    backend_exe=None,                    # Backend path (auto-detected)
    timeout=30,                          # Request timeout (seconds)
    backend_startup_timeout=60           # Backend startup timeout
)

Parametre:

Parameter	Type	Standard	Beskrivelse
api_url	str	"http://localhost:5000"	URL for lokal Chloros-backend
auto_start_backend	bool	True	Start backend automatisk, hvis nødvendigt
backend_exe	str	None (auto-detect)	Sti til backend-eksekverbar fil
timeout	int	30	Anmodningstimeout i sekunder
backend_startup_timeout	int	60	Timeout for opstart af backend (sekunder)

Eksempler:

# Default (auto-start backend, auto-detect path on Windows and Linux)
chloros = ChlorosLocal()

# Connect to running backend
chloros = ChlorosLocal(auto_start_backend=False)

# Custom backend path (Windows)
chloros = ChlorosLocal(backend_exe="C:/Custom/chloros-backend.exe")

# Custom backend path (Linux)
chloros = ChlorosLocal(backend_exe="/opt/mapir/chloros/backend/chloros-backend")

# Custom timeout with longer startup (e.g., for Jetson)
chloros = ChlorosLocal(timeout=60, backend_startup_timeout=120)

Automatisk platformdetektering: SDK prøver automatisk den korrekte backend-sti til din platform:

• Windows: C:\Program Files\MAPIR\Chloros\resources\backend\chloros-backend.exe

• Linux (.deb): /usr/lib/chloros/chloros-backend

• Linux (manuelt): /opt/mapir/chloros/backend/chloros-backend

---

Metoder

create_project(project_name, camera=None)

Opret et nyt Chloros-projekt.

Parametre:

Parameter	Type	Påkrævet	Beskrivelse
project_name	str	Ja	Navn på projektet
camera	str	Nej	Kameraskabelon (f.eks. "Survey3N_RGN", "Survey3W_OCN")

Returnerer: dict - Svar på oprettelse af projektEksempel:

# Basic project
chloros.create_project("DroneField_A")

# With camera template
chloros.create_project("DroneField_A", camera="Survey3N_RGN")

---

import_images(folder_path, recursive=False)

Importer billeder fra en mappe.

Parametre:

Parameter	Type	Påkrævet	Beskrivelse
folder_path	str/Path	Ja	Sti til mappe med billeder
recursive	bool	Nej	Søg i undermapper (standard: False)

Returnerer: dict - Importerede resultater med antal filerEksempel:

# Import from folder
chloros.import_images("C:\\DroneImages\\Flight001")

# Import recursively
chloros.import_images("C:\\DroneImages", recursive=True)

---

configure(**settings)

Konfigurer behandlingsindstillinger.

Parametre:

Parameter	Type	Standard	Beskrivelse
debayer	str	"Standard (Hurtig, Medium kvalitet)"	Debayer-metode
vignette_correction	bool	True	Aktiver vignetteringskorrektion
reflectance_calibration	bool	True	Aktiver reflektanskalibrering
indices	liste	None	Vegetationsindekser, der skal beregnes
export_format	str	"TIFF (16-bit)"	Outputformat
ppk	bool	False	Aktiver PPK-korrektioner
custom_settings	dict	None	Avancerede brugerdefinerede indstillinger

Eksportformater:

• "TIFF (16-bit)" - Anbefales til GIS/fotogrammetri

• "TIFF (32-bit, Percent)" - Videnskabelig analyse

• "PNG (8-bit)" - Visuel inspektion

• "JPG (8-bit)" - Komprimeret output

**Tilgængelige indekser:**NDVI, NDRE, GNDVI, OSAVI, CIG, EVI, SAVI, MSAVI, MTVI2 og flere.Eksempel:

# Basic configuration
chloros.configure(
    vignette_correction=True,
    reflectance_calibration=True,
    indices=["NDVI", "NDRE"]
)

# Advanced configuration
chloros.configure(
    debayer="Standard (Fast, Medium Quality)",
    vignette_correction=True,
    reflectance_calibration=True,
    ppk=True,
    export_format="TIFF (32-bit, Percent)",
    indices=["NDVI", "NDRE", "GNDVI", "OSAVI", "CIG"]
)

---

process(mode="parallel", wait=True, progress_callback=None)

Behandl projektbillederne.

Parametre:

Parameter	Type	Standard	Beskrivelse
mode	str	"parallel"	Behandlingsmodus: "parallel" eller "seriel"
wait	bool	True	Vent på afslutning
progress_callback	callable	None	Tilbagemeldingsfunktion for fremskridt (progress, msg)
poll_interval	float	2.0	Afstemningsinterval for fremskridt (sekunder)

Returnerer: dict - Behandlingsresultater

Parallel tilstand: Kræver Chloros+-licens. Skalerer automatisk til dine CPU-kerner (op til 16 arbejdsprocesser).

Eksempel:

# Simple processing
results = chloros.process()

# With progress monitoring
def show_progress(progress, message):
    print(f"[{progress}%] {message}")

chloros.process(
    mode="parallel",
    progress_callback=show_progress,
    wait=True
)

# Fire-and-forget (non-blocking)
chloros.process(wait=False)

---

get_config()

Hent den aktuelle projektkonfiguration.

Returnerer: dict - Aktuel projektkonfigurationEksempel:

config = chloros.get_config()
print(config['Project Settings'])

---

get_status()

Henter statusoplysninger om backend, herunder behandlingsstatus pr. tråd.

Returnerer: dict - Backend-status med følgende struktur:

{
    "running": True,
    "url": "http://localhost:5000",
    "processing": {
        "percent": 75.0,
        "phase": "processing"
    },
    "export": {
        "percent": 50.0,
        "phase": "exporting",
        "active": True
    }
}

Eksempel:

status = chloros.get_status()
print(f"Running: {status['running']}")
print(f"URL: {status['url']}")
print(f"Processing: {status['processing']['percent']}%")
print(f"Export: {status['export']['percent']}% - Active: {status['export']['active']}")

---

shutdown_backend()

Lukker backend (hvis startet af SDK).

Eksempel:

chloros.shutdown_backend()

---

logout()

Rydder cachelagrede legitimationsoplysninger fra det lokale system.

Beskrivelse:

Logger ud programmatisk ved at fjerne cachelagrede legitimationsoplysninger. Dette er nyttigt til:

• At skifte mellem forskellige Chloros+-konti

• At rydde legitimationsoplysninger i automatiserede miljøer

• Sikkerhedsformål (f.eks. fjernelse af legitimationsoplysninger før afinstallation)

Returnerer: dict - Resultat af logout-operationEksempel:

from chloros_sdk import ChlorosLocal

# Initialize SDK
chloros = ChlorosLocal()

# Clear cached credentials
result = chloros.logout()
print(f"Logout successful: {result}")

# After logout, login required via GUI/CLI/Browser before next SDK use

Genautentificering påkrævet: Efter at have kaldt logout() skal du logge ind igen via Chloros, Chloros (Browser) eller Chloros CLI, før du bruger SDK.

---

Hjælpefunktioner

process_folder(folder_path, **options)

Enkel hjælpefunktion til at behandle en mappe.

Parametre:

Parameter	Type	Standard	Beskrivelse
folder_path	str/Path	Påkrævet	Sti til mappe med billeder
project_name	str	Genereres automatisk	Projektnavn
camera	str	None	Kameraskabelon
indices	liste	["NDVI"]	Indekser, der skal beregnes
vignette_correction	bool	True	Aktiver vignetteringskorrektion
reflectance_calibration	bool	True	Aktiver reflektanskalibrering
export_format	str	"TIFF (16-bit)"	Outputformat
mode	str	"parallel"	Behandlingsmodus
progress_callback	callable	None	Status-callback

Returnerer: dict - BehandlingsresultaterEksempel:

from chloros_sdk import process_folder

# Simple one-liner
results = process_folder("C:\\DroneImages\\Flight001")

# With custom settings
results = process_folder(
    "C:\\DroneImages\\Flight001",
    project_name="Field_A_Survey",
    camera="Survey3N_RGN",
    indices=["NDVI", "NDRE", "GNDVI"],
    mode="parallel"
)

# With progress monitoring
def show_progress(progress, message):
    print(f"[{progress}%] {message}")

results = process_folder(
    "C:\\DroneImages\\Flight001",
    progress_callback=show_progress
)

---

Understøttelse af konteksthåndtering

SDK understøtter konteksthåndtering til automatisk oprydning:

from chloros_sdk import ChlorosLocal

# Auto-cleanup when done
with ChlorosLocal() as chloros:
    chloros.create_project("MyProject")
    chloros.import_images("C:\\Images")
    chloros.configure(indices=["NDVI"])
    chloros.process()
# Backend automatically shut down here

---

Komplette eksempler

Linux-brugere: Alle eksempler nedenfor bruger Windows-stier. Erstat C:\\...-stier med dine Linux-stier (f.eks. /home/user/... eller ~/...). Alle SDK-funktioner er identiske på tværs af platforme.

Eksempel 1: Grundlæggende behandling

Behandl en mappe med standardindstillinger:

from chloros_sdk import process_folder

# Process with default settings
results = process_folder("C:\\Datasets\\Field_A_2025_01_15")

print(f"Processing complete: {results}")

---

Eksempel 2: Tilpasset arbejdsgang

Fuld kontrol over behandlingspipeline:

from chloros_sdk import ChlorosLocal

# Initialize SDK
chloros = ChlorosLocal()

# Create project with camera template
chloros.create_project("Research_Plot_A", camera="Survey3N_RGN")

# Import images
import_results = chloros.import_images("C:\\Research\\PlotA")
print(f"Imported {len(import_results.get('files', []))} images")

# Configure advanced settings
chloros.configure(
    debayer="Standard (Fast, Medium Quality)",
    vignette_correction=True,
    reflectance_calibration=True,
    ppk=False,
    export_format="TIFF (16-bit)",
    indices=["NDVI", "NDRE", "GNDVI", "OSAVI"]
)

# Process with progress monitoring
def show_progress(progress, message):
    print(f"Progress: {progress}% - {message}")

chloros.process(
    mode="parallel",
    progress_callback=show_progress,
    wait=True
)

print("Processing complete!")

---

Eksempel 3: Batchbehandling af flere mapper

Behandl flere flyvedatasæt:

from chloros_sdk import ChlorosLocal
from pathlib import Path

# Initialize SDK once
chloros = ChlorosLocal()

# List of flight folders
flights = [
    "C:\\Datasets\\Flight_001",
    "C:\\Datasets\\Flight_002",
    "C:\\Datasets\\Flight_003"
]

for flight_path in flights:
    flight_name = Path(flight_path).name
    print(f"\n{'='*60}")
    print(f"Processing: {flight_name}")
    print('='*60)
    
    try:
        # Create project
        chloros.create_project(flight_name, camera="Survey3N_RGN")
        
        # Import images
        chloros.import_images(flight_path)
        
        # Configure
        chloros.configure(
            vignette_correction=True,
            reflectance_calibration=True,
            indices=["NDVI", "NDRE", "GNDVI"]
        )
        
        # Process
        chloros.process(mode="parallel", wait=True)
        
        print(f"✓ {flight_name} completed successfully")
    
    except Exception as e:
        print(f"✗ {flight_name} failed: {e}")

print("\n" + "="*60)
print("All flights processed!")

---

Eksempel 4: Integration af forskningspipeline

Integrer Chloros med dataanalyse:

from chloros_sdk import ChlorosLocal
import pandas as pd
import matplotlib.pyplot as plt

# Initialize Chloros
chloros = ChlorosLocal()

# Field survey data
surveys = [
    {"name": "Plot_A", "folder": "C:\\Research\\PlotA", "biomass": 4500},
    {"name": "Plot_B", "folder": "C:\\Research\\PlotB", "biomass": 3800},
    {"name": "Plot_C", "folder": "C:\\Research\\PlotC", "biomass": 5200}
]

results = []

for survey in surveys:
    # Process with Chloros
    chloros.create_project(survey['name'])
    chloros.import_images(survey['folder'])
    chloros.configure(indices=["NDVI", "NDRE"])
    chloros.process(mode="parallel", wait=True)
    
    # Get results
    config = chloros.get_config()
    
    # Extract NDVI values (example - adjust based on your needs)
    # In real implementation, you would read the processed TIFF files
    
    results.append({
        'plot': survey['name'],
        'biomass': survey['biomass'],
        # Add your NDVI extraction here
    })

# Statistical analysis
df = pd.DataFrame(results)
print("\nResults:")
print(df)

# Create correlation plot
# plt.scatter(df['ndvi'], df['biomass'])
# plt.xlabel('NDVI')
# plt.ylabel('Biomass (kg/ha)')
# plt.title('NDVI vs Biomass Correlation')
# plt.show()

---

Eksempel 5: Tilpasset statusovervågning

Avanceret statusovervågning med logning:

from chloros_sdk import ChlorosLocal
from datetime import datetime
import logging

# Setup logging
logging.basicConfig(
    filename=f'processing_{datetime.now():%Y%m%d_%H%M%S}.log',
    level=logging.INFO,
    format='%(asctime)s - %(message)s'
)

# Progress callback with logging
def log_progress(progress, message):
    log_msg = f"[{progress}%] {message}"
    logging.info(log_msg)
    print(log_msg)

# Process with logging
chloros = ChlorosLocal()
chloros.create_project("LoggedProcess")
chloros.import_images("C:\\DroneImages")
chloros.configure(indices=["NDVI", "NDRE"])

logging.info("Starting processing...")
chloros.process(
    mode="parallel",
    progress_callback=log_progress,
    wait=True
)
logging.info("Processing complete!")

---

Eksempel 6: Fejlhåndtering

Robust fejlhåndtering til produktionsbrug:

from chloros_sdk import ChlorosLocal
from chloros_sdk.exceptions import (
    ChlorosError,
    ChlorosBackendError,
    ChlorosLicenseError,
    ChlorosProcessingError
)

def process_safely(folder_path):
    """Process with comprehensive error handling"""
    try:
        with ChlorosLocal() as chloros:
            chloros.create_project("SafeProcess")
            chloros.import_images(folder_path)
            chloros.configure(indices=["NDVI"])
            chloros.process()
            
        return True, "Success"
    
    except ChlorosLicenseError as e:
        return False, f"License error: {e}. Upgrade to Chloros+ at cloud.mapir.camera/pricing"
    
    except ChlorosBackendError as e:
        return False, f"Backend error: {e}. Ensure Chloros is installed (Windows installer or Linux .deb package)."
    
    except ChlorosProcessingError as e:
        return False, f"Processing error: {e}"
    
    except FileNotFoundError as e:
        return False, f"Folder not found: {e}"
    
    except ChlorosError as e:
        return False, f"Chloros error: {e}"
    
    except Exception as e:
        return False, f"Unexpected error: {e}"

# Use the safe function
success, message = process_safely("C:\\DroneImages\\Flight001")
if success:
    print(f"✓ {message}")
else:
    print(f"✗ {message}")

---

Eksempel 7: Kontoadministration og logout

Administrer legitimationsoplysninger programmatisk:

from chloros_sdk import ChlorosLocal

def switch_account():
    """Clear credentials to switch to a different account"""
    try:
        chloros = ChlorosLocal()
        
        # Clear current credentials
        result = chloros.logout()
        print("✓ Credentials cleared successfully")
        print("Please log in with new account via Chloros, Chloros (Browser), or CLI")
        
        return True
    
    except Exception as e:
        print(f"✗ Logout failed: {e}")
        return False

def secure_cleanup():
    """Remove credentials for security purposes"""
    try:
        chloros = ChlorosLocal()
        chloros.logout()
        print("✓ Credentials removed for security")
        
    except Exception as e:
        print(f"Warning: Cleanup error: {e}")

# Switch accounts
if switch_account():
    print("\nRe-authenticate via Chloros GUI/CLI/Browser before next SDK use")

# Or perform secure cleanup
# secure_cleanup()

---

Eksempel 8: Kommandolinjeværktøj

Opret et brugerdefineret CLI-værktøj med SDK:

#!/usr/bin/env python
"""
Custom Chloros CLI Tool
Process multiple folders from command line
"""

import sys
import argparse
from pathlib import Path
from chloros_sdk import process_folder

def main():
    parser = argparse.ArgumentParser(description='Custom Chloros Processor')
    parser.add_argument('folders', nargs='+', help='Folders to process')
    parser.add_argument('--indices', nargs='+', default=['NDVI'],
                       help='Indices to calculate (default: NDVI)')
    parser.add_argument('--camera', default=None,
                       help='Camera template')
    parser.add_argument('--format', default='TIFF (16-bit)',
                       help='Export format')
    parser.add_argument('--logout', action='store_true',
                       help='Clear cached credentials before processing')
    
    args = parser.parse_args()
    
    # Handle logout if requested
    if args.logout:
        from chloros_sdk import ChlorosLocal
        chloros = ChlorosLocal()
        chloros.logout()
        print("Credentials cleared. Please re-login via Chloros GUI/CLI/Browser.")
        return 0
    
    successful = []
    failed = []
    
    for folder in args.folders:
        folder_path = Path(folder)
        
        if not folder_path.exists():
            print(f"✗ Skipping {folder}: not found")
            failed.append(folder)
            continue
        
        print(f"\nProcessing: {folder_path.name}...")
        
        try:
            process_folder(
                folder_path,
                camera=args.camera,
                indices=args.indices,
                export_format=args.format
            )
            print(f"✓ {folder_path.name} complete")
            successful.append(folder)
        
        except Exception as e:
            print(f"✗ {folder_path.name} failed: {e}")
            failed.append(folder)
    
    # Summary
    print(f"\n{'='*60}")
    print(f"Summary: {len(successful)} successful, {len(failed)} failed")
    
    return 0 if not failed else 1

if __name__ == '__main__':
    sys.exit(main())

Anvendelse:

# Process multiple folders
python my_processor.py "C:\Flight001" "C:\Flight002" --indices NDVI NDRE GNDVI

# Clear cached credentials
python my_processor.py --logout

---

Håndtering af undtagelser

SDK indeholder specifikke undtagelsesklasser for forskellige fejltyper:

Undtagelseshierarki

ChlorosError                    # Base exception
├── ChlorosBackendError        # Backend startup/connection issues
├── ChlorosLicenseError        # License validation issues
├── ChlorosConnectionError     # Network/connection failures
├── ChlorosProcessingError     # Image processing failures
├── ChlorosAuthenticationError # Authentication failures
└── ChlorosConfigurationError  # Configuration errors

Eksempler på undtagelser

from chloros_sdk import ChlorosLocal
from chloros_sdk.exceptions import *

try:
    chloros = ChlorosLocal()
    chloros.process()

except ChlorosLicenseError:
    print("Chloros+ license required. Upgrade at cloud.mapir.camera/pricing")

except ChlorosBackendError:
    print("Backend failed to start. Ensure Chloros is installed (Windows installer or Linux .deb package).")

except ChlorosProcessingError as e:
    print(f"Processing failed: {e}")

except ChlorosError as e:
    print(f"General Chloros error: {e}")

---

Avancerede emner

Brugerdefineret backend-konfiguration

Brug en brugerdefineret backend-placering eller -konfiguration:

chloros = ChlorosLocal(
    backend_exe="C:\\Custom\\chloros-backend.exe",
    api_url="http://localhost:5001",  # Custom port
    timeout=60,                        # Longer timeout
    backend_startup_timeout=120        # 2 minutes startup
)

Ikke-blokerende behandling

Start behandlingen og fortsæt med andre opgaver:

# Start processing (non-blocking)
chloros.process(wait=False)

# Do other work here...
print("Processing started in background...")

# Check status later
import time
while True:
    status = chloros.get_config()
    if status.get('processing_complete'):
        break
    time.sleep(5)

print("Processing complete!")

Hukommelsesstyring

Ved store datasæt skal du behandle i batches:

from pathlib import Path

base_folder = Path("C:\\LargeDataset")
batch_size = 100

# Get all image files
images = list(base_folder.glob("*.RAW"))

# Process in batches
for i in range(0, len(images), batch_size):
    batch = images[i:i+batch_size]
    batch_folder = base_folder / f"batch_{i//batch_size}"
    
    # Create batch folder and move images
    # ... (implementation details)
    
    # Process batch
    process_folder(batch_folder)

---

Fejlfinding

Backend starter ikke

Problem: SDK kan ikke starte backendLøsninger:

1. Kontroller, at Chloros er installeret:

import os
import platform

# Auto-detect backend path
if platform.system() == "Windows":
    backend_path = r"C:\Program Files\MAPIR\Chloros\resources\backend\chloros-backend.exe"
else:
    backend_path = "/usr/lib/chloros/chloros-backend"

print(f"Backend exists: {os.path.exists(backend_path)}")

1. Kontroller firewall (Windows) eller porttilgængelighed (Linux: lsof -i :5000)

2. Prøv manuel backend-sti:

# Windows
chloros = ChlorosLocal(backend_exe="C:\\Path\\To\\chloros-backend.exe")

# Linux
chloros = ChlorosLocal(backend_exe="/opt/mapir/chloros/backend/chloros-backend")

---

Licens ikke fundetProblem: SDK advarer om manglende licensLøsninger:

1. Åbn Chloros, Chloros (Browser) eller Chloros CLI og log ind.

2. Kontroller, at licensen er gemt i cachen:

from pathlib import Path
import os
import platform

# Check cache location
if platform.system() == "Windows":
    cache_path = Path(os.getenv('APPDATA')) / 'Chloros' / 'cache'
else:
    cache_path = Path.home() / '.cache' / 'chloros'

print(f"Cache exists: {cache_path.exists()}")

1. Hvis du oplever problemer med legitimationsoplysningerne, skal du rydde de cachelagrede legitimationsoplysninger og logge ind igen:

from chloros_sdk import ChlorosLocal

# Clear cached credentials
chloros = ChlorosLocal()
chloros.logout()

# Then login again via Chloros, Chloros (Browser), or Chloros CLI

1. Kontakt support: [e-mail]

---

ImportfejlProblem: ModuleNotFoundError: No module named 'chloros_sdk'Løsninger:

# Verify installation
pip show chloros-sdk

# Reinstall if needed
pip uninstall chloros-sdk
pip install chloros-sdk

# Check Python environment
python -c "import sys; print(sys.path)"

---

Behandlings-timeoutProblem: Behandlingen går i timeoutLøsninger:

1. Forøg timeout:

chloros = ChlorosLocal(timeout=120)  # 2 minutes

1. Behandl mindre batcher

2. Kontroller ledig diskplads

3. Overvåg systemressourcer

---

Port allerede i brugProblem: Backend-port 5000 optagetLøsninger:

# Use different port
chloros = ChlorosLocal(api_url="http://localhost:5001")

Eller find og luk den konfliktende proces:

# Windows PowerShell
Get-NetTCPConnection -LocalPort 5000

# Linux
lsof -i :5000
kill $(lsof -t -i :5000)

---

Tips til ydeevne

Optimér behandlingshastigheden

1. Brug parallel tilstand (kræver Chloros+)

chloros.process(mode="parallel")  # Up to 16 workers

1. Reducer outputopløsningen (hvis det er acceptabelt)

chloros.configure(export_format="PNG (8-bit)")  # Faster than TIFF

1. Deaktiver unødvendige indekser

# Only calculate needed indices
chloros.configure(indices=["NDVI"])  # Not all indices

1. Behandl på SSD (ikke HDD)***

Hukommelsesoptimering

For store datasæt:

# Process in batches instead of all at once
# See "Memory Management" in Advanced Topics

---

Baggrundsbehandling

Frigør Python til andre opgaver:

chloros.process(wait=False)  # Non-blocking

# Continue with other work
# ...

---

Integrationseksempler

Django-integration

# views.py
from django.http import JsonResponse
from chloros_sdk import process_folder

def process_images_view(request):
    if request.method == 'POST':
        folder_path = request.POST.get('folder_path')
        
        try:
            results = process_folder(folder_path)
            return JsonResponse({'success': True, 'results': results})
        except Exception as e:
            return JsonResponse({'success': False, 'error': str(e)})

Flask API

# app.py
from flask import Flask, request, jsonify
from chloros_sdk import process_folder

app = Flask(__name__)

@app.route('/api/process', methods=['POST'])
def process():
    data = request.get_json()
    folder_path = data.get('folder_path')
    
    try:
        results = process_folder(folder_path)
        return jsonify({'success': True, 'results': results})
    except Exception as e:
        return jsonify({'success': False, 'error': str(e)}), 500

if __name__ == '__main__':
    app.run()

Jupyter Notebook

# notebook.ipynb
from chloros_sdk import ChlorosLocal
import matplotlib.pyplot as plt

# Initialize
chloros = ChlorosLocal()

# Process
chloros.create_project("JupyterTest")
chloros.import_images("C:\\Data")
chloros.configure(indices=["NDVI"])

# Progress in notebook
from IPython.display import clear_output

def notebook_progress(progress, message):
    clear_output(wait=True)
    print(f"Progress: {progress}%")
    print(message)

chloros.process(progress_callback=notebook_progress)

# Visualize results
# ... (your visualization code)

---

Ofte stillede spørgsmål

Spørgsmål: Kræver SDK en internetforbindelse?

Svar: Kun til den første aktivering af licensen. Efter at have logget ind via Chloros, Chloros (Browser) eller Chloros CLI gemmes licensen lokalt og fungerer offline i 30 dage.***

Spørgsmål: Kan jeg bruge SDK på en server uden GUI?Svar: Ja! SDK fungerer headless på både Windows- og Linux-servere.Linux (anbefales til headless):

• Installer via .deb-pakken

• Aktiver licens: chloros-cli login [e-mail] 'password'

Windows-server:

• Windows Server 2016 eller nyere

• Chloros installeret (engangs)

• Licens aktiveret via CLI eller på en hvilken som helst maskine

---

Spørgsmål: Hvad er forskellen mellem Desktop, CLI og SDK?

Funktion	Desktop GUI	CLI Kommandolinje	Python SDK
Grænseflade	Peg-og-klik	Kommando	Python API
Bedst egnet til	Visuelt arbejde	Scripting	Integration
Automatisering	Begrænset	God	Fremragende
Fleksibilitet	Grundlæggende	God	Maksimal
Licens	Chloros+	Chloros+	Chloros+

Spørgsmål: Kan jeg distribuere apps, der er bygget med SDK?Svar: SDK-kode kan integreres i dine applikationer, men:

• Slutbrugere skal have Chloros installeret

• Slutbrugere skal have aktive Chloros+-licenser

• Kommerciel distribution kræver OEM-licens

Kontakt [e-mail] for spørgsmål vedrørende OEM.

---

Spørgsmål: Hvordan opdaterer jeg SDK?

pip install --upgrade chloros-sdk

---

Spørgsmål: Hvor gemmes de behandlede billeder?

Som standard i projektstien:

Project_Path/
└── MyProject/
    └── Survey3N_RGN/          # Processed outputs

---

Spørgsmål: Kan jeg behandle billeder fra Python-scripts, der kører efter en tidsplan?Svar: Ja! Brug din OS-planlægger med Python-scripts:

# scheduled_processing.py
from chloros_sdk import process_folder

# Process today's flights
results = process_folder("/data/flights/today")  # Linux
# results = process_folder("C:\\Flights\\Today")  # Windows

Windows: Planlæg via Opgavestyring til at køre dagligt.Linux: Planlæg via cron:

# Run at 2 AM daily
0 2 * ** /usr/bin/python3 /home/user/scheduled_processing.py >> /var/log/chloros.log 2>&1

---

Spørgsmål: Understøtter SDK async/await?Svar: Den aktuelle version er synkron. For asynkron adfærd skal du bruge wait=False eller køre i en separat tråd:

import threading

def process_thread():
    chloros.process()

thread = threading.Thread(target=process_thread)
thread.start()

# Continue with other work...

---

Spørgsmål: Hvordan skifter jeg mellem forskellige Chloros+-konti?Svar: Brug metoden logout() til at rydde cachelagrede legitimationsoplysninger, og log derefter ind igen med den nye konto:

from chloros_sdk import ChlorosLocal

# Clear current credentials
chloros = ChlorosLocal()
chloros.logout()

# Re-login via Chloros, Chloros (Browser), or Chloros CLI with new account

Efter udlogning skal du godkende med den nye konto via GUI, browser eller CLI, før du bruger SDK igen.

---

Få hjælp

Dokumentation

• API-reference: Denne side

Supportkanaler

• E-mail: [e-mail]

• Websted: https://www.mapir.camera/community/contact

• Priser: https://cloud.mapir.camera/pricing

Eksempelkode

Alle eksempler, der er angivet her, er testet og klar til brug. Kopier og tilpas dem til dit brugsscenarie.

---

LicensProprietær software - Copyright (c) 2025 MAPIR Inc.

SDK kræver et aktivt Chloros+-abonnement. Uautoriseret brug, distribution eller ændring er forbudt.