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【深度学习】【Python】【Deepfashion2】 YOLOv5训练Deepfashion2数据集
Deepfashion2数据
数据集介绍：https://github.com/switchablenorms/DeepFashion2
链接：https://pan.baidu.com/s/1f9oIMEmWc3XtTn8LJViw7A?pwd=pnex 提取码：pnex 解压密码2019Deepfashion2**- 1
- 2
- 3
格式转到COCO(废弃章节)废弃理由：转COCO出一个很大的json并非我目的，直接下个章节。
参考：https://github.com/Manishsinghrajput98/deepfashion2coco_to_yolo_/tree/master/deepfashion2coco_to_yolo_
下面代码有所改动，使用需要改写
annos_path路径和image_path路径即可。# -*- 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', 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'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', 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'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', 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'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服饰\Deepfashion2\train\train\annos" image_path = r"E:\06服饰\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)- 1
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把Deepfashion2的label直接转到YOLO格式
基本介绍
一个json里的内容，比如000001.json：
一个item是一个衣服对象。item里面就是一些标签信息：
官网解释：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- 1
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翻译一下就是13个对象以category_id标识对象不同，bounding_box中存左上、右下两个点，并且category_id会有：
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修改文件名和增加空文件
把image改为images名称，另外增加labels空文件。
执行程序转换
下面是训练数据，val数据同样操作。有空就等，没空就把下面程序改成多进程。
# 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服饰\Deepfashion2\train\train\annos" # 改成需要路径 image_path = r"E:\06服饰\Deepfashion2\train\train\images" # 改成需要路径 labels_path = r"E:\06服饰\Deepfashion2\train\train\labels" # 改成需要路径 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))- 1
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训练YOLOv5
写data yaml
换了个英文存储路径，此外注意是13个类别。
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- 1
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训练
python train.py --batch-size 4 --data fashion2.yaml --img 640 --epochs 10 --weight weights/yolov5m.pt- 1
动起来就行了：
检查yolo labels对不对
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是矩阵 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()- 1
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- 原文地址：https://blog.csdn.net/x1131230123/article/details/125894663
