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csv2coco.py
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csv2coco.py
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# -*- coding: utf-8 -*-
'''
@time: 2019/01/11 11:28
spytensor
'''
import os
import json
import numpy as np
import pandas as pd
import glob
import cv2
import os
import shutil
from IPython import embed
from sklearn.model_selection import train_test_split
np.random.seed(41)
#0为背景
classname_to_id = {"person": 1}
class Csv2CoCo:
def __init__(self,image_dir,total_annos):
self.images = []
self.annotations = []
self.categories = []
self.img_id = 0
self.ann_id = 0
self.image_dir = image_dir
self.total_annos = total_annos
def save_coco_json(self, instance, save_path):
json.dump(instance, open(save_path, 'w'), ensure_ascii=False, indent=2) # indent=2 更加美观显示
# 由txt文件构建COCO
def to_coco(self, keys):
self._init_categories()
for key in keys:
self.images.append(self._image(key))
shapes = self.total_annos[key]
for shape in shapes:
bboxi = []
for cor in shape[:-1]:
bboxi.append(int(cor))
label = shape[-1]
annotation = self._annotation(bboxi,label)
self.annotations.append(annotation)
self.ann_id += 1
self.img_id += 1
instance = {}
instance['info'] = 'spytensor created'
instance['license'] = ['license']
instance['images'] = self.images
instance['annotations'] = self.annotations
instance['categories'] = self.categories
return instance
# 构建类别
def _init_categories(self):
for k, v in classname_to_id.items():
category = {}
category['id'] = v
category['name'] = k
self.categories.append(category)
# 构建COCO的image字段
def _image(self, path):
image = {}
print(path)
img = cv2.imread(self.image_dir + path)
image['height'] = img.shape[0]
image['width'] = img.shape[1]
image['id'] = self.img_id
image['file_name'] = path
return image
# 构建COCO的annotation字段
def _annotation(self, shape,label):
# label = shape[-1]
points = shape[:4]
annotation = {}
annotation['id'] = self.ann_id
annotation['image_id'] = self.img_id
annotation['category_id'] = int(classname_to_id[label])
annotation['segmentation'] = self._get_seg(points)
annotation['bbox'] = self._get_box(points)
annotation['iscrowd'] = 0
annotation['area'] = self._get_area(points)
return annotation
# COCO的格式: [x1,y1,w,h] 对应COCO的bbox格式
def _get_box(self, points):
min_x = points[0]
min_y = points[1]
max_x = points[2]
max_y = points[3]
return [min_x, min_y, max_x - min_x, max_y - min_y]
# 计算面积
def _get_area(self, points):
min_x = points[0]
min_y = points[1]
max_x = points[2]
max_y = points[3]
return (max_x - min_x+1) * (max_y - min_y+1)
# segmentation
def _get_seg(self, points):
min_x = points[0]
min_y = points[1]
max_x = points[2]
max_y = points[3]
h = max_y - min_y
w = max_x - min_x
a = []
a.append([min_x,min_y, min_x,min_y+0.5*h, min_x,max_y, min_x+0.5*w,max_y, max_x,max_y, max_x,max_y-0.5*h, max_x,min_y, max_x-0.5*w,min_y])
return a
if __name__ == '__main__':
csv_file = "train.csv"
image_dir = "images/"
saved_coco_path = "./"
# 整合csv格式标注文件
total_csv_annotations = {}
annotations = pd.read_csv(csv_file,header=None).values
for annotation in annotations:
key = annotation[0].split(os.sep)[-1]
value = np.array([annotation[1:]])
if key in total_csv_annotations.keys():
total_csv_annotations[key] = np.concatenate((total_csv_annotations[key],value),axis=0)
else:
total_csv_annotations[key] = value
# 按照键值划分数据
total_keys = list(total_csv_annotations.keys())
train_keys, val_keys = train_test_split(total_keys, test_size=0.2)
print("train_n:", len(train_keys), 'val_n:', len(val_keys))
# 创建必须的文件夹
if not os.path.exists('%scoco/annotations/'%saved_coco_path):
os.makedirs('%scoco/annotations/'%saved_coco_path)
if not os.path.exists('%scoco/images/train2017/'%saved_coco_path):
os.makedirs('%scoco/images/train2017/'%saved_coco_path)
if not os.path.exists('%scoco/images/val2017/'%saved_coco_path):
os.makedirs('%scoco/images/val2017/'%saved_coco_path)
# 把训练集转化为COCO的json格式
l2c_train = Csv2CoCo(image_dir=image_dir,total_annos=total_csv_annotations)
train_instance = l2c_train.to_coco(train_keys)
l2c_train.save_coco_json(train_instance, '%scoco/annotations/instances_train2017.json'%saved_coco_path)
for file in train_keys:
shutil.copy(image_dir+file,"%scoco/images/train2017/"%saved_coco_path)
for file in val_keys:
shutil.copy(image_dir+file,"%scoco/images/val2017/"%saved_coco_path)
# 把验证集转化为COCO的json格式
l2c_val = Csv2CoCo(image_dir=image_dir,total_annos=total_csv_annotations)
val_instance = l2c_val.to_coco(val_keys)
l2c_val.save_coco_json(val_instance, '%scoco/annotations/instances_val2017.json'%saved_coco_path)