image-metadata-tool

仓库: dkyazzentwatwa/chatgpt-skills
安装量: 50
排名: #14912

安装

npx skills add https://github.com/dkyazzentwatwa/chatgpt-skills --skill image-metadata-tool

Image Metadata Tool

Extract, analyze, and manage EXIF metadata from images with GPS mapping and privacy features.

Features EXIF Extraction: Camera, lens, settings, timestamps GPS Data: Extract coordinates, map locations Metadata Removal: Strip EXIF for privacy Batch Processing: Process multiple images Map Generation: Create location maps from photos Export: JSON, CSV, HTML reports Quick Start from image_metadata import ImageMetadata

meta = ImageMetadata() meta.load("photo.jpg")

Get all metadata

info = meta.extract() print(f"Camera: {info['camera']}") print(f"Date: {info['datetime']}")

Get GPS coordinates

gps = meta.get_gps() if gps: print(f"Location: {gps['latitude']}, {gps['longitude']}")

CLI Usage

Extract metadata

python image_metadata.py --input photo.jpg

Extract with GPS info

python image_metadata.py --input photo.jpg --gps

Batch extract from folder

python image_metadata.py --input ./photos/ --output metadata.csv

Generate location map

python image_metadata.py --input ./photos/ --map locations.html

Strip metadata (create clean copy)

python image_metadata.py --input photo.jpg --strip --output clean_photo.jpg

Batch strip metadata

python image_metadata.py --input ./photos/ --strip --output ./clean_photos/

JSON output

python image_metadata.py --input photo.jpg --json

Get specific fields

python image_metadata.py --input photo.jpg --fields camera,datetime,gps,dimensions

API Reference ImageMetadata Class class ImageMetadata: def init(self)

# Loading
def load(self, filepath: str) -> 'ImageMetadata'

# Extraction
def extract(self) -> dict
def get_camera_info(self) -> dict
def get_datetime(self) -> dict
def get_gps(self) -> dict
def get_dimensions(self) -> dict
def get_all_exif(self) -> dict

# Privacy
def strip_metadata(self, output: str, keep_orientation: bool = True) -> str
def has_location(self) -> bool

# Batch operations
def extract_batch(self, folder: str, recursive: bool = False) -> list
def strip_batch(self, input_folder: str, output_folder: str) -> list

# Maps
def generate_map(self, images: list, output: str) -> str

# Export
def to_json(self, output: str) -> str
def to_csv(self, output: str) -> str

Extracted Metadata Camera Information camera_info = meta.get_camera_info()

Returns:

{ "make": "Canon", "model": "EOS R5", "lens": "RF 24-70mm F2.8 L IS USM", "lens_id": "61", "software": "Adobe Photoshop 24.0", "serial_number": "012345678901" }

Capture Settings settings = meta.extract()["settings"]

Returns:

{ "exposure_time": "1/250", "f_number": 2.8, "iso": 400, "focal_length": 50, "focal_length_35mm": 50, "exposure_program": "Aperture priority", "metering_mode": "Pattern", "flash": "No flash", "white_balance": "Auto" }

GPS Data gps = meta.get_gps()

Returns:

{ "latitude": 37.7749, "longitude": -122.4194, "altitude": 10.5, "altitude_ref": "Above sea level", "timestamp": "2024-01-15 14:30:00", "direction": 180.5, "speed": 0, "maps_url": "https://maps.google.com/maps?q=37.7749,-122.4194" }

Timestamps datetime_info = meta.get_datetime()

Returns:

{ "original": "2024-01-15 14:30:00", "digitized": "2024-01-15 14:30:00", "modified": "2024-01-16 10:00:00", "timezone": "+00:00" }

Image Dimensions dims = meta.get_dimensions()

Returns:

{ "width": 8192, "height": 5464, "megapixels": 44.8, "orientation": "Horizontal", "resolution_x": 300, "resolution_y": 300, "resolution_unit": "inch" }

Full Output info = meta.extract()

Returns:

{ "file": { "name": "IMG_1234.jpg", "path": "/photos/IMG_1234.jpg", "size": 15234567, "format": "JPEG" }, "camera": { "make": "Canon", "model": "EOS R5", "lens": "RF 24-70mm F2.8 L IS USM" }, "settings": { "exposure_time": "1/250", "f_number": 2.8, "iso": 400, "focal_length": 50 }, "datetime": { "original": "2024-01-15 14:30:00" }, "gps": { "latitude": 37.7749, "longitude": -122.4194 }, "dimensions": { "width": 8192, "height": 5464 } }

Privacy Features Strip Metadata

Remove EXIF data for privacy:

Strip all metadata

meta.load("original.jpg") meta.strip_metadata("clean.jpg")

Keep orientation (prevents rotated images)

meta.strip_metadata("clean.jpg", keep_orientation=True)

Check for Location Data meta.load("photo.jpg") if meta.has_location(): print("Warning: Photo contains GPS coordinates!")

Batch Strip meta.strip_batch("./originals/", "./cleaned/")

GPS Mapping Generate Location Map

Create an interactive map from geotagged photos:

meta = ImageMetadata()

Batch extract with GPS

images = meta.extract_batch("./vacation_photos/")

Filter to only geotagged images

geotagged = [img for img in images if img.get("gps")]

Generate map

meta.generate_map(geotagged, "photo_map.html")

The map includes:

Markers for each photo location Popup with photo thumbnail and metadata Clustering for dense areas Batch Processing Extract from Folder meta = ImageMetadata()

All images in folder

results = meta.extract_batch("./photos/")

Recursive (include subfolders)

results = meta.extract_batch("./photos/", recursive=True)

Export to CSV

df = pd.DataFrame(results) df.to_csv("metadata.csv", index=False)

Filter by Criteria results = meta.extract_batch("./photos/")

Find high ISO photos

high_iso = [r for r in results if r.get("settings", {}).get("iso", 0) > 3200]

Find photos from specific camera

canon_photos = [r for r in results if "Canon" in r.get("camera", {}).get("make", "")]

Find photos with GPS

geotagged = [r for r in results if r.get("gps")]

Example Workflows Photo Organization meta = ImageMetadata() results = meta.extract_batch("./camera_import/")

for photo in results: date = photo.get("datetime", {}).get("original", "unknown") camera = photo.get("camera", {}).get("model", "unknown") print(f"{photo['file']['name']}: {date} - {camera}")

Privacy Audit meta = ImageMetadata() results = meta.extract_batch("./to_share/")

risky = [] for photo in results: if photo.get("gps"): risky.append({ "file": photo["file"]["name"], "location": f"{photo['gps']['latitude']}, {photo['gps']['longitude']}" })

if risky: print(f"Warning: {len(risky)} photos contain location data!") for r in risky: print(f" - {r['file']}: {r['location']}")

Travel Photo Map meta = ImageMetadata() results = meta.extract_batch("./trip_photos/", recursive=True)

Generate interactive map

geotagged = [r for r in results if r.get("gps")] print(f"Found {len(geotagged)} geotagged photos")

meta.generate_map(geotagged, "trip_map.html")

Supported Formats JPEG/JPG (full EXIF support) TIFF (full EXIF support) PNG (limited metadata) HEIC/HEIF (iOS photos) WebP (limited metadata) RAW formats (CR2, NEF, ARW, etc.) Dependencies pillow>=10.0.0 folium>=0.14.0 (for map generation)

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