Annotation files are exported as an Create ML JSON file.
Put training images and the JSON file into the same folder.
Do not put any other files in the folder and be sure that image file names do not contain spaces.
[{
"image": "sneakers-1.jpg",
"annotations": [
{
"label": "sneakers",
"coordinates":
{
"y": 838,
"x": 393,
"width": 62,
"height": 118
}
},
{
"label": "sneakers",
"coordinates":
{
"y": 881,
"x": 392,
"width": 51,
"height": 102
}
}]
}]
The Create ML JSON file is imported to annotation files in the current folder.
Before importing, be sure that you opened images/annotations folders.
RectLabel can import from "imagefilename" and "annotation" keys, too.
Annotation files are exported as an COCO JSON file.
For a box object, "segmentation" is exported as empty.
"annotations": [
{
"area": 254521,
"bbox": [2150, 419, 595, 428],
"category_id": 14,
"id": 1,
"image_id": 1,
"iscrowd": 0,
"segmentation": []
},
For a rotated box, polygon, line, and point object, "segmentation" is exported as polygons.
"annotations": [
{
"area": 164608,
"bbox": [132, 417, 594, 432],
"category_id": 14,
"id": 1,
"image_id": 1,
"iscrowd": 0,
"segmentation": [
[136, 557, 152, 532, 191, 509, 266, 482, 367, 456, 375, 428, 427, 417, 486, 443, 516, 481, 518, 499, 564, 522, 611, 518, 661, 536, 701, 557, 724, 574, 719, 597, 691, 645, 723, 654, 715, 678, 695, 719, 681, 759, 681, 791, 670, 801, 659, 789, 656, 765, 627, 756, 644, 778, 629, 832, 591, 842, 553, 834, 514, 809, 494, 781, 491, 767, 433, 769, 403, 761, 405, 794, 387, 823, 369, 840, 344, 847, 309, 837, 295, 810, 286, 776, 290, 755, 297, 741, 259, 723, 216, 693, 179, 658, 147, 629, 132, 601, 132, 577]
]
},
For a cubic bezier object and a pixels object, "segmentation" is exported as RLE.
RLE is encoding the mask image using the COCO Mask API.
"annotations": [
{
"area": 1022954,
"bbox": [212, 0, 4204, 2960],
"category_id": 26,
"id": 1,
"image_id": 1,
"iscrowd": 0,
"segmentation":
{
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"size": [3317, 4417]
}
},
For a keypoints object, "keypoints" and "num_keypoints" are exported.
You can export a keypoints object combined with a polygon object when you aligned the keypoints object at the row and the polygon object at the row + 1 on the label table.
"annotations": [
{
"area": 555429,
"bbox": [732, 1446, 864, 1309],
"category_id": 1,
"id": 1,
"image_id": 1,
"iscrowd": 0,
"keypoints": [1108, 1633, 2, 1104, 1603, 2, 1112, 1596, 1, 1149, 1593, 2, 1146, 1582, 1, 1108, 1730, 2, 1334, 1687, 2, 1061, 1890, 2, 1387, 1936, 2, 1017, 1665, 2, 1458, 2106, 2, 1160, 2060, 2, 1299, 2053, 2, 1174, 2291, 2, 1196, 2291, 2, 1321, 2609, 2, 1196, 2590, 2],
"num_keypoints": 17,
"segmentation": [
[986, 1654, 1030, 1613, 1087, 1596, 1105, 1511, 1168, 1485, 1183, 1449, 1216, 1446, 1239, 1496, 1260, 1557, 1236, 1619, 1267, 1662, 1359, 1678, 1411, 1864, 1477, 2065, 1494, 2157, 1594, 2367, 1454, 2488, 1330, 2578, 1359, 2618, 1409, 2659, 1384, 2695, 1319, 2754, 1303, 2741, 1300, 2710, 1250, 2702, 1099, 2720, 1048, 2705, 1042, 2673, 1105, 2655, 1159, 2601, 1165, 2485, 1054, 2414, 895, 2340, 789, 2304, 732, 2278, 794, 2135, 889, 2013, 970, 1913, 968, 1714]
]
},
You can export a keypoints object combined with a cubic bezier object or a pixels object when you aligned the keypoints object at the row and the cubic bezier object or the pixels object at the row + 1 on the label table.
"annotations": [
{
"area": 2977340,
"bbox": [1435, 1125, 1105, 3708],
"category_id": 1,
"id": 1,
"image_id": 1,
"iscrowd": 0,
"keypoints": [1904, 1495, 2, 1970, 1417, 2, 1822, 1433, 2, 2068, 1481, 2, 1730, 1533, 2, 2308, 1951, 2, 1595, 2075, 2, 2369, 2555, 2, 1561, 2614, 2, 2371, 3076, 2, 1508, 3136, 2, 2172, 3035, 2, 1753, 3101, 2, 2280, 4034, 2, 1806, 4124, 2, 2367, 4833, 1, 1842, 4833, 1],
"num_keypoints": 17,
"segmentation":
{
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"size": [4834, 3648]
}
},
In "categories", "keypoints" and "skeleton" are exported.
"categories": [
{
"id": 1,
"keypoints": ["nose", "leftEye", "rightEye", "leftEar", "rightEar", "leftShoulder", "rightShoulder", "leftElbow", "rightElbow", "leftWrist", "rightWrist", "leftHip", "rightHip", "leftKnee", "rightKnee", "leftAnkle", "rightAnkle"],
"name": "person",
"skeleton": [
[9, 11],
[6, 12],
[14, 16],
[7, 13],
[15, 17],
[12, 13],
[14, 12],
[8, 6],
[10, 8],
[6, 7],
[9, 7],
[15, 13],
[5, 3],
[3, 1],
[1, 2],
[2, 4]
]
},
The COCO JSON file is imported to annotation files in the current folder.
Before importing, be sure that you opened images/annotations folders.
Annotation files are exported as Labelme JSON files.
{
"flags":
{},
"imageData": null,
"imageHeight": 3022,
"imagePath": "wembley-S3Vq97p3gSk-unsplash.jpg",
"imageWidth": 4666,
"shapes": [
{
"flags":
{},
"group_id": null,
"label": "anemonefish",
"points": [
[2152.53857421875, 556.815673828125],
[2149.539306640625, 586.8057861328125],
[2156.53759765625, 613.79681396484375],
[2245.5185546875, 698.7686767578125],
[2314.50390625, 737.75579833984375],
[2308.505126953125, 782.74090576171875],
[2315.503662109375, 814.73028564453125],
[2331.500244140625, 835.723388671875],
[2383.489013671875, 836.7230224609375],
[2420.481201171875, 800.73492431640625],
[2427.479736328125, 785.73992919921875],
[2424.480224609375, 764.746826171875],
[2511.461669921875, 775.74322509765625],
[2524.458740234375, 795.736572265625],
[2580.44677734375, 830.72503662109375],
[2647.432373046875, 827.72601318359375],
[2661.429443359375, 795.736572265625],
[2661.429443359375, 772.74420166015625],
[2653.43115234375, 757.7491455078125],
[2675.426513671875, 762.74749755859375],
[2681.42529296875, 800.73492431640625],
[2692.4228515625, 797.7359619140625],
[2701.4208984375, 737.75579833984375],
[2723.416259765625, 687.7723388671875],
[2744.41162109375, 662.78057861328125],
[2735.41357421875, 647.78558349609375],
[2722.41650390625, 650.78460693359375],
[2697.421875, 641.78753662109375],
[2737.4130859375, 599.80145263671875],
[2737.4130859375, 569.8114013671875],
[2668.427978515625, 529.82464599609375],
[2580.44677734375, 522.82696533203125],
[2535.45654296875, 494.83621215820312],
[2533.45703125, 482.84017944335938],
[2508.46240234375, 447.85174560546875],
[2452.474365234375, 422.86001586914062],
[2432.478515625, 420.86068725585938],
[2393.48681640625, 430.85739135742188],
[2375.49072265625, 457.84844970703125],
[2284.51025390625, 479.84115600585938],
[2215.525146484375, 506.83224487304688],
[2160.536865234375, 543.82000732421875]
],
"shape_type": "polygon"
}],
"version": "4.0.0"
}
The Labelme JSON files are imported to annotation files in the current folder.
Before importing, be sure that you opened images/annotations folders.
Annotation files are exported in the YOLO text format.
├── datasets
│ └── sneakers
│ ├── images
│ └── labels
└── yolov5
└── data
└── sneakers.yaml
A YOLO text file is saved per an image.
For a box object, the bounding box is saved.
Where center_x, center_y, width, and height are float values relative to width and height of the image.
class_index center_x center_y width height 0 0.464615 0.594724 0.680000 0.769784
For a rotated box, polygon, cubic bezier, line, point, and pixels object, the points coordinates are saved.
This format is for YOLOv5 Instance Segmentation.
class_index x1 y1 x2 y2 x3 y3 ... 0 0.180027 0.287930 0.181324 0.280698 0.183726 0.270573 ...
For a keypoints object, the bounding box and the points coordinates are saved.
This format is for YOLO-Pose.
class_index center_x center_y width height x1 y1 v1 x2 v2 y2 x3 y3 v3 ... 0 0.545230 0.616880 0.298794 0.766239 0.522073 0.309332 2 0.540170 0.293193 2 0.499589 0.296503 2 ...
The YOLO text files are imported to annotation files in the current folder.
Before importing, be sure that you opened images/annotations folders.
Annotation files are exported in the DOTA oriented bounding box (OBB) text format.
The first point is drawn with fill-color to show the orientation, the first point is assumed that it is the top-left corner of the object, and 4 points are arranged in a clockwise order when save.
This format is for Yolov5 for Oriented Object Detection.
x1 y1 x2 y2 x3 y3 x4 y4 category difficult 1300.536987 1413.503784 1192.848755 1535.568848 530.876038 951.562073 638.564270 829.497009 truck 0
The DOTA text files are imported to annotation files in the current folder.
Before importing, be sure that you opened images/annotations folders.
Annotation files are exported as an CSV file.
To train a Turi Create Object Detection model, select "image" for each line and check on the "Convert to boxes" checkbox.
(x, y) means the center of the box where (0, 0) is the top-left corner.
path,annotations
/Users/ryo/rcam/test_annotations/sneakers/images/sneakers-1.jpg,[{"label":"sneakers","coordinates":{"x":302,"y":248,"width":442,"height":321}}]
When you select "image" for each line and check off the "Convert to boxes" checkbox.
path,annotations
/Users/ryo/rcam/test_annotations/sneakers/images/sneakers-1.jpg,[{"label":"sneakers","type":"rectangle","coordinates":{"x":302,"y":248,"width":442,"height":321}}]
When you select "label" for each line and check on the "Convert to boxes" checkbox.
filename,width,height,label,xmin,ymin,xmax,ymax /Users/ryo/rcam/test_annotations/sneakers/images/sneakers-1.jpg,650,417,sneakers,81,88,522,408
When you select "label" for each line and check off the "Convert to boxes" checkbox.
filename,width,height,label,type,annotations /Users/ryo/rcam/test_annotations/sneakers/images/sneakers-1.jpg,650,417,sneakers,rectangle,81,88,522,408
The CSV file is imported to annotation files in the current folder.
Before importing, be sure that you opened images/annotations folders.
The object names file is created from the objects table on the settings dialog.
YOLOv5 yaml file as dictionary.
path: ../datasets/sneakers train: images val: images names: 0: sneakers 1: ignore
YOLOv5 yaml file as array.
path: ../datasets/sneakers train: images val: images nc: 2 names: ['sneakers', 'ignore']
Object names text file.
sneakers ignore
Tensorflow Object Detection API label map file.
item {
id: 1
name: 'sneakers'
}
item {
id: 2
name: 'ignore'
}
You can import an object names file or import object names from xml files.
The mask images are exported in the PNG format.
Run an instance segmentation model on Tensorflow Object Detection API.
You can specify which mask image to export.
For the indexed color image, overlaps of objects are based on the layer order on the label table.
Pixel values are set based on the object index on the objects table and 0 is set for the background.
The indexed color table is created from object colors on the objects table.
For grayscale images, pixel values are set 255 for the foreground and 0 for the background.
You can export images and annotations as jpg images.
It exports labels when showing labels on boxes and exports coordinates when showing coordinates on boxes.
Images and annotations are augmented using "Flip", "Crop", "Contrast", and "Rotate".
For "Flip", each image is flipped horizontally with 0.5 probability.
For "Crop", each image is cropped to [100% - value, 100%] of the original size.
For "Contrast", each image contrast is changed to [100% - value, 100% + value].
For "Rotate", each image is rotated to [-value, value] degrees.
For "Number of augmented images", the number of generated images from an image through the augmentation.
If the object is cut out so that the bounding box size is less than 0.01 of the original size, the object is removed.
To flip keypoints horizontally, use "left" and "right" prefix or suffix for each keypoint name.
Images and annotations are sliced horizontally and vertically.
For "Horizontal slices", each image is sliced horizontally by the number of horizontal slices.
For "Vertical slices", each image is sliced vertically by the number of vertical slices.
All images are exported into object-named subfolders.
Creating an Image Classifier Model on Create ML.
└── saved_folder
├── object0
├── object1
└── object2
Specify the split ratio "80/10/10" so that all images are split into train, validation, and test sets.
In the specified folder, train.txt, val.txt, and test.txt are saved.
sneakers-1.jpg sneakers-2.jpg ...
Using "Full path" option, you can save full paths. Or you can add prefix to file names.
/Users/ryo/Desktop/test_annotations/sneakers/images/sneakers-1.jpg /Users/ryo/Desktop/test_annotations/sneakers/images/sneakers-2.jpg ...
The number of used objects is saved as objects_stats.txt file.
The number of used attributes is saved as attributes_stats.txt file.