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Dataset - DeepFashion2 数据集
论文:DeepFashion2: A Versatile Benchmark for Detection, Pos...
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2019/06

Dataset - DeepFashion2 数据集

论文:DeepFashion2: A Versatile Benchmark for Detection, Pose Estimation, Segmentation and Re-Identification of Clothing Images - CVPR2019

作者:Yuying Ge, Ruimao Zhang, Xiaogang Wang, Xiaoou Tang, Ping Luo

团队:The Chinese University of Hong Kong

开源:Github - witchablenorms/DeepFashion2

DeepFashion2 数据集是一个更加综合的服装数据集,其包含了适用于服装检测、服装姿态估计、服装分割以及服装检索等场景的图片与标注数据.

1. DeepFashion2 数据集

1.1. DeepFashion2 数据集概况

DeepFashion2 共包含 491K 张图片,13 个常见服装类目,收集自电商和用户.

DeepFashion2 共包含 801K 个服装主体,图片中每个服装主体的标注信息包括:

[1] - scale 尺度:

根据服装主体相对于图像尺寸的比例,包含三种尺度:small(< 10%), moderate(10% ∼ 40%), large(> 40%) .

[2] - occlusion 遮挡

遮挡表示的是,如果服装主体的区域被头发、肢体、配饰或者其它物体所遮挡,导致的服装主体有一定的不完整.

每个服装主体根据其关键点缺失的数量进行归类为:partial occlusion(< 20% occluded keypoints), heavy
occlusion(> 50% occluded keypoints), medium occlusion(otherwise).

注:服装主体超出图片不适于 occlusion.

[3] - zoom-in 放大

服装主体被标注为 zoom-in,表示其区域超出了图片. 根据超出图片的关键点数量进行归类为:no, large(> 30%, medium.

[4] - viewpoint 视角

数据集中的服装主体被归类为 4 中视角:7% clothes that are not on people, 78% clothes on people from frontal viewpoint, 15% clothes on people from side, back viewpoint.

[5] - category 类目

根据对 DeepFashion 的 50 个类目进行归组,得到 13 个类目:short sleeve top, long sleeve top, short sleeve outwear, long sleeve outwear, vest, sling(吊带), shorts, trousers, skirt, short sleeve dress, long sleeve dress, vest dress, sling dress.

[6] - bounding box 边界框

[7] - dense landmarks 关键点

[8] - per-pixel mask 像素级 mask

[9] - style 风格

DeepFashion2 还包含了 873K 组 Commercial-Consumer 服装搭配组.

DeepFashion2 中,training 数据集 391K 张图片,validation 数据集 34K 张图片,test 数据集 67K 张图片.

例图,如:

1.2. DeepFashion2 数据集下载

谷歌Drive:https://drive.google.com/drive/folders/125F48fsMBz2EF0Cpqk6aaHet5VH399Ok?usp=sharing

解压密码:需要填写表格申请,链接 - https://docs.google.com/forms/d/e/1FAIpQLSeIoGaFfCQILrtIZPykkr8q_h9qQ5BoTYbjvf95aXbid0v2Bw/viewform?usp=sf_link

Second Workshop on Computer Vision for Fashion, Art and Design

1.3. DeepFashion2 数据集统计

TrainValidationTestOverall
images390,88433,66967,342491,895
bboxes636,62454,910109,198800,732
landmarks636,62454,910109,198800,732
masks636,62454,910109,198800,732
pairs685,584query: 12,550 gallery: 37183query: 24,402 gallery: 75,347873,234

2. DeepFashion2 数据组织形式

每张图片的图片名是由六位数字组成,如 000001.jpg. 其对应的 json 标注文件为 000001.json.

每个 json 标注数据的组织形式为:

|---- source,string,表示图片是来自于电商(shop)还是用户(user).

|---- pair_id,number. 同一家 shop 的图片和对应的用户所购买的图片,具有相同的 pair id.

|-------- item 1

|------------ category_name,string,服装类目名

|------------ category_id,number,对应与服装类目名.

|------------ style,number,用于区分具有相同 pair id 的图片的服装主体.具有相同 pair id 的图片的服装主体 的 style numbers 不同时,其 style 是不同的,如 color, printing, logo 等.

|------------ bounding_box,[x1, y1, x2, y2],依次为边界框的左下(lower left) 和右上(upper right) 点的坐标值.

|------------ landmarks,[x1, y1, v1, ..., xn, yn, vn],其中 v 表示可见性:v=2 visible; v=1 occlusion; v=0 not labeled.

|------------ segmentation,[[x1, y1, ..., xn, yn], []],其中,[x1, y1, xn, yn] 表示多边形标注,单一服装主体可能包含多个多边形(polygon)标注.

|------------ scale,number,1-small; 2-modest; 3-large.

|------------ occlusion,number,1-slight occlusion(no occlusion); 2-medium occlusion; 3-heavy occlusion.

|------------ zoom_in,number,1-no zoom-in; 2-medium zoom-in; 3-large zoom-in.

|------------ viewpoint,number,1-no wear; 2-front viewpoint; 3-side of back viewpoint.

|-------- item 2

|-------- item n

注:pair_idsource 是图片级的标注. 同一张图片的所有服装主体具有相同的 pair_idsource.

2.1. 服装类目名与对应id

category_namecategory_id 的对应关系如下:

1 - short sleeve top
2 - long sleeve top
3 - short sleeve outwear
4 - long sleeve outwear
5 - vest
6 - sling
7 - shorts
8 - trousers
9 - skirt
10 - short sleeve dress
11 - long sleeve dress
12 - vest dress
13 -sling dress

2.2. 服装 landmarks 次序

13 种服装类目的 landmarks 和 skeletons 的表示如图.

图中的数字表示在标注文件中,每个服装类目的 landmarks 标注的词序.

13 种服装类目共定义了 294 个 landmarks.

2.3. 服装 pairs 说明

DeepFashion2 数据集中,图片是以连续的 paird_id 进行组织的,其同时包括用户和商店来源的图片. 例如:

000001.jpg(pair_id:1; from consumer), 
000002.jpg(pair_id:1; from shop),
000003.jpg(pair_id:2; from consumer),
000004.jpg(pair_id:2; from consumer),
000005.jpg(pair_id:2; from consumer), 
000006.jpg(pair_id:2; from consumer),
000007.jpg(pair_id:2; from shop),
000008.jpg(pair_id:2; from shop)
...

对于 shop 图片和 consumer 图片的两个服装主体,如果其具有相同的 style number(大于0),且其来自具有相同 pair_id 的图片,则这两个服装主体是 positive commerical-consumer pair;否则,这两个服装主体shi negative commerical-consumer pairs. 据此,可以构建实例级的 positive pairs 和 negative pairs,以用于训练.

例如下图中:前三张图片来自 consumers,最后两张来自于 shops. 这五张图片具有相同的 pair_id. 橙色框的服装主体具有相同的 style: 1;绿色框的服装主体具有相同的 style: 2. 其它未画出边界框的服装主体的 style 是 0,其不能用于构建 positive commerical-consumer pairs. 一组 positive commerical-consumer pair 是在第一张图片中标注的 short sleeve top 和最后一张图片中标注的 short sleeve top.

因此,DeepFashion2 数据集可以很灵活的用于构建实例级(instance-level)的pairs.

2.4. DeepFashion2 数据集划分

下载后的数据集组织形式为:

|---- train

|-------- image 训练图片数据

|-------- annos 训练标注数据

|---- validation

|-------- image 验证图片数据

|-------- annos 验证标注数据

|---- test

|-------- image 测试图片数据

[1] - DeepFashion2 数据集中,每张图片的图片名是由六位数字组成,如 000001.jpg;其对应的 json 标注文件为 000001.json.

[2] - DeepFashion2 数据集提供了将数据组织为 COCO 格式的脚本 - deepfashion2_to_coco.py.

[3] - DeepFashion2 的 validation 数据集提供了图片级的数据信息 - keypoints_val_information.jsonretrieval_val_consumer_information.jsonretrieval_val_shop_information.json(前 10844 张图片来自 consumers ,后 20681 张图片来自 shops). 对于服装检测任务和语义分割任务,也可以采用 keypoints_val_information.json.

[4] - DeepFashion2 数据集还提供了 keypoints_val_vis.json, keypoints_val_vis_and_occ.json, val_query.jsonval_gallery.json,以用于 validation 数据集的评测.

DeepFashion2 - Evaluation

[5] - DeepFashion2 的 test 数据集提供了图片集的数据信息 - keypoints_test_information.json, retrieval_test_consumer_information.jsonretrieval_test_shop_information.json(前 20681 张图片来自 consumers,后 41984 张图片来自于 shops).

2.5. deepfashion2_to_coco.py

deepfashion2_to_coco.py

import json
from PIL import Image
import numpy as np


dataset = {
    "info": {},
    "licenses": [],
    "images": [],
    "annotations": [],
    "categories": []
}

dataset['categories'].append({
    'id': 1,
    'name': "short_sleeved_shirt",
    'supercategory': "clothes",
    'keypoints': ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96', '97', '98', '99', '100', '101', '102', '103', '104', '105', '106', '107', '108', '109', '110', '111', '112', '113', '114', '115', '116', '117', '118', '119', '120', '121', '122', '123', '124', '125', '126', '127', '128', '129', '130', '131', '132', '133', '134', '135', '136', '137', '138', '139', '140', '141', '142', '143', '144', '145', '146', '147', '148', '149', '150', '151', '152', '153', '154', '155', '156', '157', '158', '159', '160', '161', '162', '163', '164', '165', '166', '167', '168', '169', '170', '171', '172', '173', '174', '175', '176', '177', '178', '179', '180', '181', '182', '183', '184', '185', '186', '187', '188', '189', '190', '191', '192', '193', '194', '195', '196', '197', '198', '199', '200', '201', '202', '203', '204', '205', '206', '207', '208', '209', '210', '211', '212', '213', '214', '215', '216', '217', '218', '219', '220', '221', '222', '223', '224', '225', '226', '227', '228', '229', '230', '231', '232', '233', '234', '235', '236', '237', '238', '239', '240', '241', '242', '243', '244', '245', '246', '247', '248', '249', '250', '251', '252', '253', '254', '255', '256', '257', '258', '259', '260', '261', '262', '263', '264', '265', '266', '267', '268', '269', '270', '271', '272', '273', '274', '275', '276', '277', '278', '279', '280', '281', '282', '283', '284', '285', '286', '287', '288', '289', '290', '291', '292', '293', '294'],
    'skeleton': []
})
dataset['categories'].append({
    'id': 2,
    'name': "long_sleeved_shirt",
    'supercategory': "clothes",
    'keypoints': ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96', '97', '98', '99', '100', '101', '102', '103', '104', '105', '106', '107', '108', '109', '110', '111', '112', '113', '114', '115', '116', '117', '118', '119', '120', '121', '122', '123', '124', '125', '126', '127', '128', '129', '130', '131', '132', '133', '134', '135', '136', '137', '138', '139', '140', '141', '142', '143', '144', '145', '146', '147', '148', '149', '150', '151', '152', '153', '154', '155', '156', '157', '158', '159', '160', '161', '162', '163', '164', '165', '166', '167', '168', '169', '170', '171', '172', '173', '174', '175', '176', '177', '178', '179', '180', '181', '182', '183', '184', '185', '186', '187', '188', '189', '190', '191', '192', '193', '194', '195', '196', '197', '198', '199', '200', '201', '202', '203', '204', '205', '206', '207', '208', '209', '210', '211', '212', '213', '214', '215', '216', '217', '218', '219', '220', '221', '222', '223', '224', '225', '226', '227', '228', '229', '230', '231', '232', '233', '234', '235', '236', '237', '238', '239', '240', '241', '242', '243', '244', '245', '246', '247', '248', '249', '250', '251', '252', '253', '254', '255', '256', '257', '258', '259', '260', '261', '262', '263', '264', '265', '266', '267', '268', '269', '270', '271', '272', '273', '274', '275', '276', '277', '278', '279', '280', '281', '282', '283', '284', '285', '286', '287', '288', '289', '290', '291', '292', '293', '294'],
    'skeleton': []
})
dataset['categories'].append({
    'id': 3,
    'name': "short_sleeved_outwear",
    'supercategory': "clothes",
    'keypoints': ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96', '97', '98', '99', '100', '101', '102', '103', '104', '105', '106', '107', '108', '109', '110', '111', '112', '113', '114', '115', '116', '117', '118', '119', '120', '121', '122', '123', '124', '125', '126', '127', '128', '129', '130', '131', '132', '133', '134', '135', '136', '137', '138', '139', '140', '141', '142', '143', '144', '145', '146', '147', '148', '149', '150', '151', '152', '153', '154', '155', '156', '157', '158', '159', '160', '161', '162', '163', '164', '165', '166', '167', '168', '169', '170', '171', '172', '173', '174', '175', '176', '177', '178', '179', '180', '181', '182', '183', '184', '185', '186', '187', '188', '189', '190', '191', '192', '193', '194', '195', '196', '197', '198', '199', '200', '201', '202', '203', '204', '205', '206', '207', '208', '209', '210', '211', '212', '213', '214', '215', '216', '217', '218', '219', '220', '221', '222', '223', '224', '225', '226', '227', '228', '229', '230', '231', '232', '233', '234', '235', '236', '237', '238', '239', '240', '241', '242', '243', '244', '245', '246', '247', '248', '249', '250', '251', '252', '253', '254', '255', '256', '257', '258', '259', '260', '261', '262', '263', '264', '265', '266', '267', '268', '269', '270', '271', '272', '273', '274', '275', '276', '277', '278', '279', '280', '281', '282', '283', '284', '285', '286', '287', '288', '289', '290', '291', '292', '293', '294'],
    'skeleton': []
})
dataset['categories'].append({
    'id': 4,
    'name': "long_sleeved_outwear",
    'supercategory': "clothes",
    'keypoints': ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96', '97', '98', '99', '100', '101', '102', '103', '104', '105', '106', '107', '108', '109', '110', '111', '112', '113', '114', '115', '116', '117', '118', '119', '120', '121', '122', '123', '124', '125', '126', '127', '128', '129', '130', '131', '132', '133', '134', '135', '136', '137', '138', '139', '140', '141', '142', '143', '144', '145', '146', '147', '148', '149', '150', '151', '152', '153', '154', '155', '156', '157', '158', '159', '160', '161', '162', '163', '164', '165', '166', '167', '168', '169', '170', '171', '172', '173', '174', '175', '176', '177', '178', '179', '180', '181', '182', '183', '184', '185', '186', '187', '188', '189', '190', '191', '192', '193', '194', '195', '196', '197', '198', '199', '200', '201', '202', '203', '204', '205', '206', '207', '208', '209', '210', '211', '212', '213', '214', '215', '216', '217', '218', '219', '220', '221', '222', '223', '224', '225', '226', '227', '228', '229', '230', '231', '232', '233', '234', '235', '236', '237', '238', '239', '240', '241', '242', '243', '244', '245', '246', '247', '248', '249', '250', '251', '252', '253', '254', '255', '256', '257', '258', '259', '260', '261', '262', '263', '264', '265', '266', '267', '268', '269', '270', '271', '272', '273', '274', '275', '276', '277', '278', '279', '280', '281', '282', '283', '284', '285', '286', '287', '288', '289', '290', '291', '292', '293', '294'],
    'skeleton': []
})
dataset['categories'].append({
    'id': 5,
    'name': "vest",
    'supercategory': "clothes",
    'keypoints': ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96', '97', '98', '99', '100', '101', '102', '103', '104', '105', '106', '107', '108', '109', '110', '111', '112', '113', '114', '115', '116', '117', '118', '119', '120', '121', '122', '123', '124', '125', '126', '127', '128', '129', '130', '131', '132', '133', '134', '135', '136', '137', '138', '139', '140', '141', '142', '143', '144', '145', '146', '147', '148', '149', '150', '151', '152', '153', '154', '155', '156', '157', '158', '159', '160', '161', '162', '163', '164', '165', '166', '167', '168', '169', '170', '171', '172', '173', '174', '175', '176', '177', '178', '179', '180', '181', '182', '183', '184', '185', '186', '187', '188', '189', '190', '191', '192', '193', '194', '195', '196', '197', '198', '199', '200', '201', '202', '203', '204', '205', '206', '207', '208', '209', '210', '211', '212', '213', '214', '215', '216', '217', '218', '219', '220', '221', '222', '223', '224', '225', '226', '227', '228', '229', '230', '231', '232', '233', '234', '235', '236', '237', '238', '239', '240', '241', '242', '243', '244', '245', '246', '247', '248', '249', '250', '251', '252', '253', '254', '255', '256', '257', '258', '259', '260', '261', '262', '263', '264', '265', '266', '267', '268', '269', '270', '271', '272', '273', '274', '275', '276', '277', '278', '279', '280', '281', '282', '283', '284', '285', '286', '287', '288', '289', '290', '291', '292', '293', '294'],
    'skeleton': []
})
dataset['categories'].append({
    'id': 6,
    'name': "sling",
    'supercategory': "clothes",
    'keypoints': ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96', '97', '98', '99', '100', '101', '102', '103', '104', '105', '106', '107', '108', '109', '110', '111', '112', '113', '114', '115', '116', '117', '118', '119', '120', '121', '122', '123', '124', '125', '126', '127', '128', '129', '130', '131', '132', '133', '134', '135', '136', '137', '138', '139', '140', '141', '142', '143', '144', '145', '146', '147', '148', '149', '150', '151', '152', '153', '154', '155', '156', '157', '158', '159', '160', '161', '162', '163', '164', '165', '166', '167', '168', '169', '170', '171', '172', '173', '174', '175', '176', '177', '178', '179', '180', '181', '182', '183', '184', '185', '186', '187', '188', '189', '190', '191', '192', '193', '194', '195', '196', '197', '198', '199', '200', '201', '202', '203', '204', '205', '206', '207', '208', '209', '210', '211', '212', '213', '214', '215', '216', '217', '218', '219', '220', '221', '222', '223', '224', '225', '226', '227', '228', '229', '230', '231', '232', '233', '234', '235', '236', '237', '238', '239', '240', '241', '242', '243', '244', '245', '246', '247', '248', '249', '250', '251', '252', '253', '254', '255', '256', '257', '258', '259', '260', '261', '262', '263', '264', '265', '266', '267', '268', '269', '270', '271', '272', '273', '274', '275', '276', '277', '278', '279', '280', '281', '282', '283', '284', '285', '286', '287', '288', '289', '290', '291', '292', '293', '294'],
    'skeleton': []
})
dataset['categories'].append({
    'id': 7,
    'name': "shorts",
    'supercategory': "clothes",
    'keypoints': ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96', '97', '98', '99', '100', '101', '102', '103', '104', '105', '106', '107', '108', '109', '110', '111', '112', '113', '114', '115', '116', '117', '118', '119', '120', '121', '122', '123', '124', '125', '126', '127', '128', '129', '130', '131', '132', '133', '134', '135', '136', '137', '138', '139', '140', '141', '142', '143', '144', '145', '146', '147', '148', '149', '150', '151', '152', '153', '154', '155', '156', '157', '158', '159', '160', '161', '162', '163', '164', '165', '166', '167', '168', '169', '170', '171', '172', '173', '174', '175', '176', '177', '178', '179', '180', '181', '182', '183', '184', '185', '186', '187', '188', '189', '190', '191', '192', '193', '194', '195', '196', '197', '198', '199', '200', '201', '202', '203', '204', '205', '206', '207', '208', '209', '210', '211', '212', '213', '214', '215', '216', '217', '218', '219', '220', '221', '222', '223', '224', '225', '226', '227', '228', '229', '230', '231', '232', '233', '234', '235', '236', '237', '238', '239', '240', '241', '242', '243', '244', '245', '246', '247', '248', '249', '250', '251', '252', '253', '254', '255', '256', '257', '258', '259', '260', '261', '262', '263', '264', '265', '266', '267', '268', '269', '270', '271', '272', '273', '274', '275', '276', '277', '278', '279', '280', '281', '282', '283', '284', '285', '286', '287', '288', '289', '290', '291', '292', '293', '294'],
    'skeleton': []
})
dataset['categories'].append({
    'id': 8,
    'name': "trousers",
    'supercategory': "clothes",
    'keypoints': ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96', '97', '98', '99', '100', '101', '102', '103', '104', '105', '106', '107', '108', '109', '110', '111', '112', '113', '114', '115', '116', '117', '118', '119', '120', '121', '122', '123', '124', '125', '126', '127', '128', '129', '130', '131', '132', '133', '134', '135', '136', '137', '138', '139', '140', '141', '142', '143', '144', '145', '146', '147', '148', '149', '150', '151', '152', '153', '154', '155', '156', '157', '158', '159', '160', '161', '162', '163', '164', '165', '166', '167', '168', '169', '170', '171', '172', '173', '174', '175', '176', '177', '178', '179', '180', '181', '182', '183', '184', '185', '186', '187', '188', '189', '190', '191', '192', '193', '194', '195', '196', '197', '198', '199', '200', '201', '202', '203', '204', '205', '206', '207', '208', '209', '210', '211', '212', '213', '214', '215', '216', '217', '218', '219', '220', '221', '222', '223', '224', '225', '226', '227', '228', '229', '230', '231', '232', '233', '234', '235', '236', '237', '238', '239', '240', '241', '242', '243', '244', '245', '246', '247', '248', '249', '250', '251', '252', '253', '254', '255', '256', '257', '258', '259', '260', '261', '262', '263', '264', '265', '266', '267', '268', '269', '270', '271', '272', '273', '274', '275', '276', '277', '278', '279', '280', '281', '282', '283', '284', '285', '286', '287', '288', '289', '290', '291', '292', '293', '294'],
    'skeleton': []
})
dataset['categories'].append({
    'id': 9,
    'name': "skirt",
    'supercategory': "clothes",
    'keypoints': ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96', '97', '98', '99', '100', '101', '102', '103', '104', '105', '106', '107', '108', '109', '110', '111', '112', '113', '114', '115', '116', '117', '118', '119', '120', '121', '122', '123', '124', '125', '126', '127', '128', '129', '130', '131', '132', '133', '134', '135', '136', '137', '138', '139', '140', '141', '142', '143', '144', '145', '146', '147', '148', '149', '150', '151', '152', '153', '154', '155', '156', '157', '158', '159', '160', '161', '162', '163', '164', '165', '166', '167', '168', '169', '170', '171', '172', '173', '174', '175', '176', '177', '178', '179', '180', '181', '182', '183', '184', '185', '186', '187', '188', '189', '190', '191', '192', '193', '194', '195', '196', '197', '198', '199', '200', '201', '202', '203', '204', '205', '206', '207', '208', '209', '210', '211', '212', '213', '214', '215', '216', '217', '218', '219', '220', '221', '222', '223', '224', '225', '226', '227', '228', '229', '230', '231', '232', '233', '234', '235', '236', '237', '238', '239', '240', '241', '242', '243', '244', '245', '246', '247', '248', '249', '250', '251', '252', '253', '254', '255', '256', '257', '258', '259', '260', '261', '262', '263', '264', '265', '266', '267', '268', '269', '270', '271', '272', '273', '274', '275', '276', '277', '278', '279', '280', '281', '282', '283', '284', '285', '286', '287', '288', '289', '290', '291', '292', '293', '294'],
    'skeleton': []
})
dataset['categories'].append({
    'id': 10,
    'name': "short_sleeved_dress",
    'supercategory': "clothes",
    'keypoints': ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96', '97', '98', '99', '100', '101', '102', '103', '104', '105', '106', '107', '108', '109', '110', '111', '112', '113', '114', '115', '116', '117', '118', '119', '120', '121', '122', '123', '124', '125', '126', '127', '128', '129', '130', '131', '132', '133', '134', '135', '136', '137', '138', '139', '140', '141', '142', '143', '144', '145', '146', '147', '148', '149', '150', '151', '152', '153', '154', '155', '156', '157', '158', '159', '160', '161', '162', '163', '164', '165', '166', '167', '168', '169', '170', '171', '172', '173', '174', '175', '176', '177', '178', '179', '180', '181', '182', '183', '184', '185', '186', '187', '188', '189', '190', '191', '192', '193', '194', '195', '196', '197', '198', '199', '200', '201', '202', '203', '204', '205', '206', '207', '208', '209', '210', '211', '212', '213', '214', '215', '216', '217', '218', '219', '220', '221', '222', '223', '224', '225', '226', '227', '228', '229', '230', '231', '232', '233', '234', '235', '236', '237', '238', '239', '240', '241', '242', '243', '244', '245', '246', '247', '248', '249', '250', '251', '252', '253', '254', '255', '256', '257', '258', '259', '260', '261', '262', '263', '264', '265', '266', '267', '268', '269', '270', '271', '272', '273', '274', '275', '276', '277', '278', '279', '280', '281', '282', '283', '284', '285', '286', '287', '288', '289', '290', '291', '292', '293', '294'],
    'skeleton': []
})
dataset['categories'].append({
    'id': 11,
    'name': "long_sleeved_dress",
    'supercategory': "clothes",
    'keypoints': ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96', '97', '98', '99', '100', '101', '102', '103', '104', '105', '106', '107', '108', '109', '110', '111', '112', '113', '114', '115', '116', '117', '118', '119', '120', '121', '122', '123', '124', '125', '126', '127', '128', '129', '130', '131', '132', '133', '134', '135', '136', '137', '138', '139', '140', '141', '142', '143', '144', '145', '146', '147', '148', '149', '150', '151', '152', '153', '154', '155', '156', '157', '158', '159', '160', '161', '162', '163', '164', '165', '166', '167', '168', '169', '170', '171', '172', '173', '174', '175', '176', '177', '178', '179', '180', '181', '182', '183', '184', '185', '186', '187', '188', '189', '190', '191', '192', '193', '194', '195', '196', '197', '198', '199', '200', '201', '202', '203', '204', '205', '206', '207', '208', '209', '210', '211', '212', '213', '214', '215', '216', '217', '218', '219', '220', '221', '222', '223', '224', '225', '226', '227', '228', '229', '230', '231', '232', '233', '234', '235', '236', '237', '238', '239', '240', '241', '242', '243', '244', '245', '246', '247', '248', '249', '250', '251', '252', '253', '254', '255', '256', '257', '258', '259', '260', '261', '262', '263', '264', '265', '266', '267', '268', '269', '270', '271', '272', '273', '274', '275', '276', '277', '278', '279', '280', '281', '282', '283', '284', '285', '286', '287', '288', '289', '290', '291', '292', '293', '294'],
    'skeleton': []
})
dataset['categories'].append({
    'id': 12,
    'name': "vest_dress",
    'supercategory': "clothes",
    'keypoints': ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96', '97', '98', '99', '100', '101', '102', '103', '104', '105', '106', '107', '108', '109', '110', '111', '112', '113', '114', '115', '116', '117', '118', '119', '120', '121', '122', '123', '124', '125', '126', '127', '128', '129', '130', '131', '132', '133', '134', '135', '136', '137', '138', '139', '140', '141', '142', '143', '144', '145', '146', '147', '148', '149', '150', '151', '152', '153', '154', '155', '156', '157', '158', '159', '160', '161', '162', '163', '164', '165', '166', '167', '168', '169', '170', '171', '172', '173', '174', '175', '176', '177', '178', '179', '180', '181', '182', '183', '184', '185', '186', '187', '188', '189', '190', '191', '192', '193', '194', '195', '196', '197', '198', '199', '200', '201', '202', '203', '204', '205', '206', '207', '208', '209', '210', '211', '212', '213', '214', '215', '216', '217', '218', '219', '220', '221', '222', '223', '224', '225', '226', '227', '228', '229', '230', '231', '232', '233', '234', '235', '236', '237', '238', '239', '240', '241', '242', '243', '244', '245', '246', '247', '248', '249', '250', '251', '252', '253', '254', '255', '256', '257', '258', '259', '260', '261', '262', '263', '264', '265', '266', '267', '268', '269', '270', '271', '272', '273', '274', '275', '276', '277', '278', '279', '280', '281', '282', '283', '284', '285', '286', '287', '288', '289', '290', '291', '292', '293', '294'],
    'skeleton': []
})
dataset['categories'].append({
    'id': 13,
    'name': "sling_dress",
    'supercategory': "clothes",
    'keypoints': ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96', '97', '98', '99', '100', '101', '102', '103', '104', '105', '106', '107', '108', '109', '110', '111', '112', '113', '114', '115', '116', '117', '118', '119', '120', '121', '122', '123', '124', '125', '126', '127', '128', '129', '130', '131', '132', '133', '134', '135', '136', '137', '138', '139', '140', '141', '142', '143', '144', '145', '146', '147', '148', '149', '150', '151', '152', '153', '154', '155', '156', '157', '158', '159', '160', '161', '162', '163', '164', '165', '166', '167', '168', '169', '170', '171', '172', '173', '174', '175', '176', '177', '178', '179', '180', '181', '182', '183', '184', '185', '186', '187', '188', '189', '190', '191', '192', '193', '194', '195', '196', '197', '198', '199', '200', '201', '202', '203', '204', '205', '206', '207', '208', '209', '210', '211', '212', '213', '214', '215', '216', '217', '218', '219', '220', '221', '222', '223', '224', '225', '226', '227', '228', '229', '230', '231', '232', '233', '234', '235', '236', '237', '238', '239', '240', '241', '242', '243', '244', '245', '246', '247', '248', '249', '250', '251', '252', '253', '254', '255', '256', '257', '258', '259', '260', '261', '262', '263', '264', '265', '266', '267', '268', '269', '270', '271', '272', '273', '274', '275', '276', '277', '278', '279', '280', '281', '282', '283', '284', '285', '286', '287', '288', '289', '290', '291', '292', '293', '294'],
    'skeleton': []
})

sub_index = 0 # the index of ground truth instance
for num in range(1,num_images+1):
    json_name = '/path/to/val_annos/' + str(num).zfill(6)+'.json'
    image_name = '/path/to/val/' + str(num).zfill(6)+'.jpg'

    if (num>=0):
        imag = Image.open(image_name)
        width, height = imag.size
        with open(json_name, 'r') as f:
            temp = json.loads(f.read())
            pair_id = temp['pair_id']

            dataset['images'].append({
                'coco_url': '',
                'date_captured': '',
                'file_name': str(num).zfill(6) + '.jpg',
                'flickr_url': '',
                'id': num,
                'license': 0,
                'width': width,
                'height': height
            })
            for i in temp:
                if i == 'source' or i=='pair_id':
                    continue
                else:
                    points = np.zeros(294 * 3)
                    sub_index = sub_index + 1
                    box = temp[i]['bounding_box']
                    w = box[2]-box[0]
                    h = box[3]-box[1]
                    x_1 = box[0]
                    y_1 = box[1]
                    bbox=[x_1,y_1,w,h]
                    cat = temp[i]['category_id']
                    style = temp[i]['style']
                    seg = temp[i]['segmentation']
                    landmarks = temp[i]['landmarks']

                    points_x = landmarks[0::3]
                    points_y = landmarks[1::3]
                    points_v = landmarks[2::3]
                    points_x = np.array(points_x)
                    points_y = np.array(points_y)
                    points_v = np.array(points_v)

                    if cat == 1:
                        for n in range(0, 25):
                            points[3 * n] = points_x[n]
                            points[3 * n + 1] = points_y[n]
                            points[3 * n + 2] = points_v[n]
                    elif cat ==2:
                        for n in range(25, 58):
                            points[3 * n] = points_x[n - 25]
                            points[3 * n + 1] = points_y[n - 25]
                            points[3 * n + 2] = points_v[n - 25]
                    elif cat ==3:
                        for n in range(58, 89):
                            points[3 * n] = points_x[n - 58]
                            points[3 * n + 1] = points_y[n - 58]
                            points[3 * n + 2] = points_v[n - 58]
                    elif cat == 4:
                        for n in range(89, 128):
                            points[3 * n] = points_x[n - 89]
                            points[3 * n + 1] = points_y[n - 89]
                            points[3 * n + 2] = points_v[n - 89]
                    elif cat == 5:
                        for n in range(128, 143):
                            points[3 * n] = points_x[n - 128]
                            points[3 * n + 1] = points_y[n - 128]
                            points[3 * n + 2] = points_v[n - 128]
                    elif cat == 6:
                        for n in range(143, 158):
                            points[3 * n] = points_x[n - 143]
                            points[3 * n + 1] = points_y[n - 143]
                            points[3 * n + 2] = points_v[n - 143]
                    elif cat == 7:
                        for n in range(158, 168):
                            points[3 * n] = points_x[n - 158]
                            points[3 * n + 1] = points_y[n - 158]
                            points[3 * n + 2] = points_v[n - 158]
                    elif cat == 8:
                        for n in range(168, 182):
                            points[3 * n] = points_x[n - 168]
                            points[3 * n + 1] = points_y[n - 168]
                            points[3 * n + 2] = points_v[n - 168]
                    elif cat == 9:
                        for n in range(182, 190):
                            points[3 * n] = points_x[n - 182]
                            points[3 * n + 1] = points_y[n - 182]
                            points[3 * n + 2] = points_v[n - 182]
                    elif cat == 10:
                        for n in range(190, 219):
                            points[3 * n] = points_x[n - 190]
                            points[3 * n + 1] = points_y[n - 190]
                            points[3 * n + 2] = points_v[n - 190]
                    elif cat == 11:
                        for n in range(219, 256):
                            points[3 * n] = points_x[n - 219]
                            points[3 * n + 1] = points_y[n - 219]
                            points[3 * n + 2] = points_v[n - 219]
                    elif cat == 12:
                        for n in range(256, 275):
                            points[3 * n] = points_x[n - 256]
                            points[3 * n + 1] = points_y[n - 256]
                            points[3 * n + 2] = points_v[n - 256]
                    elif cat == 13:
                        for n in range(275, 294):
                            points[3 * n] = points_x[n - 275]
                            points[3 * n + 1] = points_y[n - 275]
                            points[3 * n + 2] = points_v[n - 275]
                    num_points = len(np.where(points_v > 0)[0])

                    dataset['annotations'].append({
                        'area': w*h,
                        'bbox': bbox,
                        'category_id': cat,
                        'id': sub_index,
                        'pair_id': pair_id,
                        'image_id': num,
                        'iscrowd': 0,
                        'style': style,
                        'num_keypoints':num_points,
                        'keypoints':points.tolist(),
                        'segmentation': seg,
                    })


json_name = '/path/to/deepfashion2.json'
with open(json_name, 'w') as f:
  json.dump(dataset, f)

3. DeepFashion2 数据集对比

[1] - Dataset - DeepFashion 服装数据集 - AIUAI

[2] - ModaNet 基于多边形标注的大规模街拍服装数据集 - AIUAI

[1] - 对比 DeepFashion 数据集:

图:(a) DeepFashion 数据集,每张图片只标注了单个服装主体,一般是 4-8 个关键点. 边界框是根据标注的关键点估计得到,噪声比较多. (b) DeepFashion2 数据集,每张图片最少标注一个服装主体,最多 7 个主体. 每个服装主体手工标注了边界框、mask、关键点(每个主体平均 20 个关键点) 以及 commercial-customer 图片对.

[2] - 对比多个服装相关数据集

DeepFashion2 数据集的标注至少是 DeepFashion 的 3.5X 倍,是ModaNet 的 6.7X 倍,是FashionAI 的 8X 倍.

Last modification:June 20th, 2019 at 11:06 am

One comment

  1. alex

    刚要转DL的初学者,求数据集密码,感谢

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