數據集介紹：https://github.com/switchablenorms/DeepFashion2
鏈接：https://pan.baidu.com/s/1f9oIMEmWc3XtTn8LJViw7A?pwd=pnex
提取碼：pnex
解壓密碼2019Deepfashion2**
廢棄理由：轉COCO出一個很大的json並非我目的,直接下個章節.
參考：https://github.com/Manishsinghrajput98/deepfashion2coco_to_yolo_/tree/master/deepfashion2coco_to_yolo_
下面代碼有所改動,使用需要改寫annos_path路徑和image_path路徑即可.
# -*- coding: utf-8 -*-
""" Created on Sun Jul 21 21:15:50 2019 @author: loktarxiao """
import json
import os
import numpy as np
from PIL import Image
from tqdm import tqdm
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': []
})
annos_path = r"E:\06服飾\Deepfashion2\train\train\annos"
image_path = r"E:\06服飾\Deepfashion2\train\train\image"
num_images = len(os.listdir(annos_path))
sub_index = 0 # the index of ground truth instance
for num in tqdm(range(1, num_images + 1)):
json_name = os.path.join(annos_path, str(num).zfill(6) + '.json')
image_name = os.path.join(image_path, 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 = os.path.join(os.path.dirname(annos_path), 'result.json')
with open(json_name, 'w') as f:
json.dump(dataset, f)
一個json裡的內容,比如000001.json：
一個item是一個衣服對象.item裡面就是一些標簽信息：
官網解釋：
source: a string, where 'shop' indicates that the image is from commercial store while 'user' indicates that the image is taken by users.
pair_id: a number. Images from the same shop and their corresponding consumer-taken images have the same pair id.
item 1
category_name: a string which indicates the category of the item.
category_id: a number which corresponds to the category name. In category_id, 1 represents short sleeve top, 2 represents long sleeve top, 3 represents short sleeve outwear, 4 represents long sleeve outwear, 5 represents vest, 6 represents sling, 7 represents shorts, 8 represents trousers, 9 represents skirt, 10 represents short sleeve dress, 11 represents long sleeve dress, 12 represents vest dress and 13 represents sling dress.
style: a number to distinguish between clothing items from images with the same pair id. Clothing items with different style numbers from images with the same pair id have different styles such as color, printing, and logo. In this way, a clothing item from shop images and a clothing item from user image are positive commercial-consumer pair if they have the same style number greater than 0 and they are from images with the same pair id.(If you are confused with style, please refer to issue#10.)
bounding_box: [x1,y1,x2,y2],where x1 and y_1 represent the upper left point coordinate of bounding box, x_2 and y_2 represent the lower right point coordinate of bounding box. (width=x2-x1;height=y2-y1)
landmarks: [x1,y1,v1,...,xn,yn,vn], where v represents the visibility: v=2 visible; v=1 occlusion; v=0 not labeled. We have different definitions of landmarks for different categories. The orders of landmark annotations are listed in figure 2.
segmentation: [[x1,y1,...xn,yn],[ ]], where [x1,y1,xn,yn] represents a polygon and a single clothing item may contain more than one polygon.
scale: a number, where 1 represents small scale, 2 represents modest scale and 3 represents large scale.
occlusion: a number, where 1 represents slight occlusion(including no occlusion), 2 represents medium occlusion and 3 represents heavy occlusion.
zoom_in: a number, where 1 represents no zoom-in, 2 represents medium zoom-in and 3 represents lagre zoom-in.
viewpoint: a number, where 1 represents no wear, 2 represents frontal viewpoint and 3 represents side or back viewpoint.
item 2
...
item n
翻譯一下就是13個對象以category_id標識對象不同,bounding_box中存左上、右下兩個點,並且category_id會有：
1 represents short sleeve top,
2 represents long sleeve top,
3 represents short sleeve outwear,
4 represents long sleeve outwear,
5 represents vest,
6 represents sling,
7 represents shorts,
8 represents trousers,
9 represents skirt,
10 represents short sleeve dress,
11 represents long sleeve dress,
12 represents vest dress,
13 represents sling dress
把image改為images名稱,另外增加labels空文件.
下面是訓練數據,val數據同樣操作.有空就等,沒空就把下面程序改成多進程.
# coding:utf-8
import json
import os
import os.path
from PIL import Image
from tqdm import tqdm
def listPathAllfiles(dirname):
result = []
for maindir, subdir, file_name_list in os.walk(dirname):
for filename in file_name_list:
apath = os.path.join(maindir, filename)
result.append(apath)
return result
if __name__ == '__main__':
annos_path = r"E:\06服飾\Deepfashion2\train\train\annos" # 改成需要路徑
image_path = r"E:\06服飾\Deepfashion2\train\train\images" # 改成需要路徑
labels_path = r"E:\06服飾\Deepfashion2\train\train\labels" # 改成需要路徑
num_images = len(os.listdir(annos_path))
for num in tqdm(range(1, num_images + 1)):
json_name = os.path.join(annos_path, str(num).zfill(6) + '.json')
image_name = os.path.join(image_path, str(num).zfill(6) + '.jpg')
txtfile = os.path.join(labels_path, str(num).zfill(6) + '.txt')
imag = Image.open(image_name)
width, height = imag.size
res = []
with open(json_name, 'r') as f:
temp = json.loads(f.read())
for i in temp:
if i == 'source' or i == 'pair_id':
continue
else:
box = temp[i]['bounding_box']
x_1 = round((box[0] + box[2]) / 2 / width, 6)
y_1 = round((box[1] + box[3]) / 2 / height, 6)
w = round((box[2] - box[0]) / width, 6)
h = round((box[3] - box[1]) / height, 6)
category_id = int(temp[i]['category_id'] - 1)
res.append(" ".join([str(category_id), str(x_1), str(y_1), str(w), str(h)]))
open(txtfile, "w").write("\n".join(res))
換了個英文存儲路徑,此外注意是13個類別.
path: E:\detection\13clothes\clothes\Deepfashion2 # dataset root dir
train: validation\validation
val: validation\validation
# Classes
nc: 13 # number of classes
names: [ 'short sleeve top', 'long sleeve top','short sleeve outwear','long sleeve outwear',
'vest','sling','shorts','trousers','skirt','short sleeve dress','long sleeve dress',
'vest dress','sling dress' ] # class names
python train.py --batch-size 4 --data fashion2.yaml --img 640 --epochs 10 --weight weights/yolov5m.pt
動起來就行了：
import os
import cv2
import matplotlib.pyplot as plt
import numpy as np
ASSETS_DIRECTORY = "assets"
plt.rcParams["savefig.bbox"] = "tight"
def listPathAllfiles(dirname):
result = []
for maindir, subdir, file_name_list in os.walk(dirname):
for filename in file_name_list:
apath = os.path.join(maindir, filename)
result.append(apath)
return result
if __name__ == '__main__':
labelspath = r'E:\WIIDERFACE\WIDER_VOC\train\labels'
imagespath = r'E:\WIIDERFACE\WIDER_VOC\train\images'
labelsFiles = listPathAllfiles(labelspath)
for lbf in labelsFiles:
labels = open(lbf, "r").readlines()
labels = list(map(lambda x: x.strip().split(" "), labels))
imgfileName = os.path.join(imagespath, os.path.basename(lbf)[:-4] + ".jpg")
img = cv2.imdecode(np.fromfile(imgfileName, dtype=np.uint8), 1) # img是矩陣
for lbs in labels:
lb = list(map(float, lbs))[1:]
x1 = int((lb[0] - lb[2] / 2) * img.shape[1])
y1 = int((lb[1] - lb[3] / 2) * img.shape[0])
x2 = int((lb[0] + lb[2] / 2) * img.shape[1])
y2 = int((lb[1] + lb[3] / 2) * img.shape[0])
cv2.rectangle(img, (x1, y1), (x2, y2), (0, 0, 255), 5)
cv2.imshow("1", img)
cv2.waitKey()
cv2.destroyAllWindows()
