photo-import-setup

name: photo-import-setup description: Set up a new Metashape project from scratch — import photos, load GPS reference, configure sensors (fisheye, rolling shutter, multi-camera), import EXR alpha masks, run image quality analysis, and disable bad frames. The first skill to use on any new capture. Works through the Metashape MCP server. user-invocable: false

Photo Import & Project Setup

Overview

Every photogrammetry project starts with the same sequence: create project, import photos, load GPS, configure sensors, import masks, check image quality. This skill covers that entire setup phase, getting a project ready for alignment.

When to Use

• Starting a new project from raw photos
• Adding a new camera/capture to an existing project
• Re-importing after changing folder structure or GPS data
• Setting up sensors for the first time (fisheye, rolling shutter, etc.)

Prerequisites

• Photos on disk (EXR, JPEG, TIFF, PNG, DNG)
• GPS data (EXIF embedded or external CSV)
• Metashape MCP server running

Setup Pipeline

Step 1: Create or open project

create_project(path="E:\\Projects\\MyProject.psx")

Or for existing:

open_project(path="E:\\Projects\\MyProject.psx")

Step 2: Import photos

For a single folder:

add_photos(paths=["E:\\Captures\\Day1\\"])

For multiple folders (multi-day, multi-camera):

add_photos(paths=["E:\\Captures\\Day1\\", "E:\\Captures\\Day2\\"])

For glob patterns:

add_photos(paths=["E:\\Captures\\**\\*.exr"])

After import, verify:

list_chunks()

Check camera count matches expected photo count.

Step 3: Import GPS reference

If GPS is NOT in EXIF (common for vehicle-mounted captures):

import_reference(
    path="E:\\Captures\\gps_data.csv",
    columns="nxyz",        # n=label, x=longitude, y=latitude, z=altitude
    delimiter=","
)

Column format options:

• "nxyz" — label, lon, lat, alt (most common)
• "nxy" — label, lon, lat (no altitude)
• "nxyzabc" — label, lon, lat, alt, accuracy_x, accuracy_y, accuracy_z

After import, verify GPS loaded:

get_alignment_stats()

Check cameras_with_reference matches expected count.

Step 4: Configure sensors

This is the most critical setup step. Wrong sensor configuration is the #1 cause of total alignment failure.

Check current sensors:

list_sensors()

For fisheye lenses (vehicle-mounted, GoPro, 360 cameras):

set_sensor(
    sensor_index=0,
    sensor_type="fisheye",
    pixel_width=0.00488,      # Check camera spec sheet
    pixel_height=0.00488,
    focal_length=8.0           # Nominal focal length in mm
)

CRITICAL: If you have a fisheye lens and don't set sensor_type="fisheye", alignment WILL fail. This is the single most common setup error. The alignment-doctor agent exists largely because of this mistake.

For rolling shutter cameras (most consumer cameras, phones, GoPros): Rolling shutter compensation is set during match_photos, not sensor config. But knowing your camera uses electronic (rolling) shutter vs mechanical (global) shutter matters for choosing settings.

Multi-camera rigs: If you have multiple camera bodies, each needs its own sensor:

# Check if Metashape auto-detected separate sensors
list_sensors()
# If all cameras share one sensor but shouldn't:
# Separate cameras by body/lens in the Metashape GUI, then configure each sensor

Step 5: Import masks (for EXR with alpha)

For EXR files where the alpha channel masks the sky:

import_masks(
    method="alpha",
    path="{filename}"
)

The {filename} template tells Metashape to use each camera's own file as the mask source, reading the alpha channel.

Other mask sources:

After mask import, verify:

get_camera_metadata(label="your_first_camera_label")

Check that mask information is present.

Step 6: Analyze image quality

analyze_images()

This estimates sharpness quality for every camera. Review results:

select_cameras(max_quality=0.5)

Quality thresholds:

• >0.7: Good quality
• 0.5-0.7: Acceptable, may cause issues in sparse areas
• <0.5: Poor — likely blurry from motion, defocus, or obstruction

Disable low-quality cameras:

enable_cameras(labels=[...poor_quality_labels...], enable=False)

Or disable all below threshold in one step:

remove_cameras(quality_threshold=0.5)

WARNING: remove_cameras DELETES them. Use enable_cameras(enable=False) to keep them disabled but available for re-enabling later.

Step 7: Set reference accuracy

Tell Metashape how accurate your GPS is:

set_reference_settings(
    camera_accuracy_xy=3.0,     # Consumer GPS: 3-5m
    camera_accuracy_z=5.0       # Vertical always worse
)

GPS accuracy by type:

Step 8: Set GPU config for alignment

set_gpu_config(cpu_enable=True)

CPU is ON for alignment (match_photos + align_cameras). It will be turned OFF before dense reconstruction.

Setup Complete — Ready for Alignment

At this point your project is ready. Next steps:

• For corridor captures: use the corridor-alignment-pipeline skill
• For aerial/close-range: proceed with match_photos + align_cameras directly
• If alignment fails: invoke the alignment-doctor agent

Decision Tree

Photos imported?
├── NO → Step 2: add_photos
├── YES → GPS loaded?
    ├── NO → Step 3: import_reference
    ├── YES → Sensors configured?
        ├── NO → Step 4: set_sensor (CHECK FISHEYE!)
        ├── YES → Masks needed?
            ├── YES (EXR) → Step 5: import_masks
            ├── NO → Quality checked?
                ├── NO → Step 6: analyze_images
                ├── YES → Ready for alignment

Common Setup Mistakes

Road Corridor Specifics

For Decker Canyon-style vehicle-mounted fisheye captures:

• Camera: full-frame with fisheye lens → set_sensor(sensor_type="fisheye")
• Format: EXR with alpha → import_masks(method="alpha", path="{filename}")
• GPS: CSV from logger → import_reference(path="gps.csv", columns="nxyz")
• Quality: motion blur on turns → analyze_images(), disable <0.5
• Batch size: ~200 cameras per alignment batch
• Reference accuracy: consumer GPS → camera_accuracy_xy=3.0