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Get started with artpipelines

Source: vignettes/artpipelines.qmd

Introduction

artpipelines is the artwork processing engine for the Artalytics platform. It orchestrates the complete workflow from raw artwork submission to fully populated database records, handling:

  • Procreate canvas validation and structure assessment
  • Asset upload to CDN storage (vault, public, data buckets)
  • Metadata extraction (creation dates, timestamps, image dimensions)
  • Timelapse video processing and frame extraction
  • AI-powered style classification and artwork profiling
  • Benchmark calculation against artist portfolios
  • Certificate of authenticity generation

This guide walks you through the core workflows.

Installation 

# Install from GitHub
pak::pkg_install("artalytics/artpipelines")

library(artpipelines)

Configuration

Before using artpipelines, configure environment variables for database, CDN, and AI services. See the README Environment Variables section for complete details.

Core Workflow: Processing an Artwork

The main entry point is run_pipeline(), which executes a complete artwork processing workflow in sequence.

Step 1: Validate the Procreate Canvas

Before committing to a full pipeline run, validate that the uploaded canvas contains genuine artwork:

canvas_path <- "path/to/artwork.procreate"

# Assess canvas structure and creative evidence
assessment <- assess_procreate_canvas(canvas_path)

# Check the classification
assessment$classification
# Possible values:
# - "DEFINITELY_CREATIVE" - Has timelapse AND sufficient tile content
# - "PROBABLY_CREATIVE"   - Has timelapse OR sufficient tiles
# - "INDETERMINATE"       - Valid structure but weak creative evidence
# - "NOT_CREATIVE"        - Appears empty or invalid

# Review the evidence
assessment$creative_evidence  # "definite", "probable", "weak", "none"
assessment$n_mp4_nonempty     # Count of timelapse video segments
assessment$n_tile_files       # Count of raster tile/chunk files

Step 2: Prepare Input Files

The pipeline expects these files:

File Description
file_image Main artwork PNG (high resolution)
file_canvas Original .procreate canvas file
file_video Timelapse MP4 video
file_stats Screenshot of Procreate stats panel
file_variants (Optional) Directory of PNG variants

Step 3: Run the Pipeline 

result <- run_pipeline(
  # UUIDs - obtain from your application or generate with artcore
  artist = "746b8207-72f5-4ab6-8d19-a91d03daec3d",
  artwork = "99a61148-1d3b-4340-8cf6-92ad26046b0f",
  collection = "77102117-xxxx-xxxx-xxxx-xxxxxxxxxxxx",

  # Metadata
  artist_name = "Bobby Fatemi",
  art_title = "Portrait of Jeezy",
  art_story = "Inspired by hip-hop culture and urban artistry",

  # Input files
  file_image = "/uploads/main.png",
  file_canvas = "/uploads/canvas.procreate",
  file_video = "/uploads/timelapse.mp4",
  zip_frames = NULL,  # Optional pre-extracted frames
  file_stats = "/uploads/stats-screenshot.png",
  file_variants = NULL  # Optional variants directory
)

Step 4: Understand the Results

The pipeline returns a list of data.tables ready for database insertion:

names(result)
# [1] "artist"                "artwork"
# [3] "artwork_colors"        "artwork_frame_analytics"
# [5] "artwork_hash"          "artwork_index"
# [7] "artwork_meta"          "artwork_paths"
# [9] "artwork_profiles"      "artwork_stats"
# [11] "artwork_styles"       "global_styles"
# [13] "artwork_opensea"

# The main artwork record
result$artwork_index
#>                          artist_uuid         collection_uuid
#> 1: 746b8207-72f5-4ab6-8d19-a91d03daec3d 77102117-xxxx-...
#>                            art_uuid   art_name      art_title
#> 1: 99a61148-1d3b-4340-8cf6-92ad26046b0f portrait_of_jeezy Portrait of Jeezy

# Processing metrics
result$artwork_stats
#>    drawing_hours brush_strokes n_unique_colors complexity_score
#> 1:         12.5         45000            1250            0.85

Extraction Functions

For finer control, use individual extraction functions:

Extract Creation Date 

create_date <- extract_create_date(artist, artwork, file_canvas)
# Returns POSIXct timestamp from the earliest MP4 segment

Extract Image Metadata 

meta <- extract_image_meta(artist, artwork, file_image)
# Returns list with:
# - meta_1: width, height, filesize
# - meta_2: Full EXIF data as JSON

Extract Canvas Statistics 

stats <- extract_stats_procreate(artist, artwork, fp = file_canvas, new_utc)
# Returns data.table with drawing_hours, brush_strokes, n_unique_colors

Verify Creation Period

For provenance verification, compute a defensible creation window:

window <- verify_creation_period(file_canvas, verbose = TRUE)

window$start_utc        # Earliest MP4 creation time
window$stop_utc         # Latest tile write time
window$interval_utc     # lubridate::interval
window$tz_offset_hours  # Inferred timezone offset
window$start_confidence # "high" (from MP4) or "low"
window$stop_confidence  # "moderate" (from filtered tiles)

Job Management

Track pipeline progress with the job management functions:

# Add a new job (called automatically by run_pipeline)
job_id <- job_add(artist, artwork, art_title)

# Update job status during processing
job_update(job_id, msg = "Processing frames", progress = 50)

# Check job status
job <- job_get(job_id)
job$status  # "Running", "Success", "Error"

Next Steps