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takane revised this on 8 Jul 2021

No changes. e89f171

takane revised this on 8 Jul 2021

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turiAnnotation.py
import turicreate as tc import os import inspect IMAGES_DIR = 'Images' # Change if applicable csv_path = 'Images' # assumes CSV column format is image,id,name,xMin,xMax,yMin,yMax csv_sf = tc.SFrame.read_csv(csv_path) def row_to_bbox_coordinates(row): """ Takes a row and returns a dictionary representing bounding box coordinates: (center_x, center_y, width, height) e.g. {'x': 100, 'y': 120, 'width': 80, 'height': 120} """ return {'x': row['xMin'] + (row['xMax'] - row['xMin'])/2, 'width': (row['xMax'] - row['xMin']), 'y': row['yMin'] + (row['yMax'] - row['yMin'])/2, 'height': (row['yMax'] - row['yMin'])} csv_sf['coordinates'] = csv_sf.apply(row_to_bbox_coordinates) # delete no longer needed columns del csv_sf['id'], csv_sf['xMin'], csv_sf['xMax'], csv_sf['yMin'], csv_sf['yMax'] # rename columns csv_sf = csv_sf.rename({'name': 'label', 'image': 'name'}) # Load all images in random order sf_images = tc.image_analysis.load_images(IMAGES_DIR, recursive=True, random_order=True) # Split path to get filename # info = sf_images['path'].apply(lambda path: os.path.basename(path).split('/')[:1]) info = sf_images['path'].apply(lambda path: ['/'.join(path.split('/')[-2:])]) # Rename columns to 'name' info = info.unpack().rename({'X.0': 'name'}) # Add to our main SFrame sf_images = sf_images.add_columns(info) # Original path no longer needed del sf_images['path'] # Combine label and coordinates into a bounding box dictionary csv_sf = csv_sf.pack_columns(['label', 'coordinates'], new_column_name='bbox', dtype=dict) # Combine bounding boxes of the same 'name' into lists sf_annotations = csv_sf.groupby('name', {'annotations': tc.aggregate.CONCAT('bbox')}) # Join annotations with the images. Note, some images do not have annotations, # but we still want to keep them in the dataset. This is why it is important to # a LEFT join. sf = sf_images.join(sf_annotations, on='name', how='left') # The LEFT join fills missing matches with None, so we replace these with empty # lists instead using fillna. sf['annotations'] = sf['annotations'].fillna([]) # Save SFrame sf.save('test.sframe') sf.explore() input('Pause...')

turiAnnotation.py

import turicreate as tc
import os
import inspect

IMAGES_DIR = 'Images' # Change if applicable
csv_path = 'Images'  # assumes CSV column format is image,id,name,xMin,xMax,yMin,yMax

csv_sf = tc.SFrame.read_csv(csv_path)

def row_to_bbox_coordinates(row):
    """
    Takes a row and returns a dictionary representing bounding
    box coordinates:  (center_x, center_y, width, height)  e.g. {'x': 100, 'y': 120, 'width': 80, 'height': 120}
    """
    return {'x': row['xMin'] + (row['xMax'] - row['xMin'])/2, 
            'width': (row['xMax'] - row['xMin']),
            'y': row['yMin'] + (row['yMax'] - row['yMin'])/2, 
            'height': (row['yMax'] - row['yMin'])}

csv_sf['coordinates'] = csv_sf.apply(row_to_bbox_coordinates)

# delete no longer needed columns
del csv_sf['id'], csv_sf['xMin'], csv_sf['xMax'], csv_sf['yMin'], csv_sf['yMax']
# rename columns
csv_sf = csv_sf.rename({'name': 'label', 'image': 'name'})

# Load all images in random order
sf_images = tc.image_analysis.load_images(IMAGES_DIR, recursive=True, random_order=True)

# Split path to get filename
# info = sf_images['path'].apply(lambda path: os.path.basename(path).split('/')[:1])
info = sf_images['path'].apply(lambda path: ['/'.join(path.split('/')[-2:])])

# Rename columns to 'name'
info = info.unpack().rename({'X.0': 'name'})

# Add to our main SFrame
sf_images = sf_images.add_columns(info)

# Original path no longer needed
del sf_images['path']

# Combine label and coordinates into a bounding box dictionary
csv_sf = csv_sf.pack_columns(['label', 'coordinates'], new_column_name='bbox', dtype=dict)

# Combine bounding boxes of the same 'name' into lists
sf_annotations = csv_sf.groupby('name', {'annotations': tc.aggregate.CONCAT('bbox')})

# Join annotations with the images. Note, some images do not have annotations,
# but we still want to keep them in the dataset. This is why it is important to
# a LEFT join.
sf = sf_images.join(sf_annotations, on='name', how='left')

# The LEFT join fills missing matches with None, so we replace these with empty
# lists instead using fillna.
sf['annotations'] = sf['annotations'].fillna([])
# Save SFrame
sf.save('test.sframe')
sf.explore()
input('Pause...')