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aug2.py
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# -*- coding:utf-8 -*-
from pycocotools.coco import COCO
from tqdm import tqdm
import shutil
import os
import xml.etree.ElementTree as ET
import json
import random
import numpy as np
import argparse
import cv2
import sys
import PIL.Image
from shutil import copyfile
import redis
coco = dict()
coco['images'] = []
coco['type'] = 'instances'
coco['annotations'] = []
coco['categories'] = []
category_set = dict()
image_set = set()
category_item_id = -1
image_id = 20180000000
annotation_id = 0
CLASS_NAMES = ["background", "Person", "Car", "Bicycle", "ElectroMboile", "MotorBike", "Tricycle", "Bus", "Truck", "Head","Rider"]
MyDataPath = '/chenzhi_cxzyzx/dataset/fcos_nonveh_modify'
# --------------- param ---------------------
parser = argparse.ArgumentParser(description='PyTorch CSRNet')
r = redis.Redis(host='10.68.4.10', port=6379, password='root', db=0)
parser.add_argument('--train_data_dir', default=None, help="the path of train_data_dir", nargs="?")
parser.add_argument('--preprocess_output_dir', default=None, help="the path of preprocess_output_dir", nargs="?")
parser.add_argument('--train_label_dir', default='', help="the path of train_label_dir", nargs="?")
parser.add_argument('--redis_key', default='', help='redis key', nargs="?")
args = parser.parse_args()
r.set(args.redis_key, 1)
bg_imgs = []
xj_bg_imgs = []
cut_obs = []
cut_ids = [3, 6, 8]
#cut_ids = [6]
#the path you want to save your results for coco to voc
img_dir = os.path.join(MyDataPath, 'coco_images')
anno_dir = os.path.join(MyDataPath, 'coco_annotations')
ch_img_dir = os.path.join(MyDataPath, 'chinese_images')
ch_anno_dir = os.path.join(MyDataPath, 'chinese_annotations')
# datasets_list=['train2014', 'val2014']
datasets_list=['train2017']
coco_filenames = []
select_class = ['person']
select_class2 = ['bicycle']
classes_names = ['car', 'bicycle', 'person', 'motorcycle', 'bus', 'truck']
#Store annotations and train2014/val2014/... in this folder
dataDir= '/coco/2017/'
headstr = """\
<annotation>
<folder>VOC</folder>
<filename>%s</filename>
<source>
<database>My Database</database>
<annotation>COCO</annotation>
<image>flickr</image>
<flickrid>NULL</flickrid>
</source>
<owner>
<flickrid>NULL</flickrid>
<name>company</name>
</owner>
<size>
<width>%d</width>
<height>%d</height>
<depth>%d</depth>
</size>
<segmented>0</segmented>
"""
objstr = """\
<object>
<name>%s</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>%d</xmin>
<ymin>%d</ymin>
<xmax>%d</xmax>
<ymax>%d</ymax>
</bndbox>
</object>
"""
tailstr = '''\
</annotation>
'''
#if the dir is not exists,make it,else delete it
def mkr(path):
if os.path.exists(path):
shutil.rmtree(path)
os.mkdir(path)
else:
os.mkdir(path)
mkr(img_dir)
mkr(anno_dir)
mkr(ch_img_dir)
mkr(ch_anno_dir)
def id2name(coco):
classes=dict()
for cls in coco.dataset['categories']:
classes[cls['id']]=cls['name']
return classes
def write_xml(anno_path,head, objs, tail):
f = open(anno_path, "w")
f.write(head)
for obj in objs:
f.write(objstr%(obj[0],obj[1],obj[2],obj[3],obj[4]))
f.write(tail)
def save_annotations_and_imgs(coco,dataset,filename,objs):
#eg:COCO_train2014_000000196610.jpg-->COCO_train2014_000000196610.xml
anno_path=anno_dir+ '/' + filename[:-3]+'xml'
img_path=dataDir+dataset+'/'+filename
# print(img_path)
dst_imgpath=img_dir+ '/' + filename
img=cv2.imread(img_path)
if (img.shape[2] == 1):
print(filename + " not a RGB image")
return
shutil.copy(img_path, dst_imgpath)
head=headstr % (filename, img.shape[1], img.shape[0], img.shape[2])
tail = tailstr
write_xml(anno_path,head, objs, tail)
def showimg_dog(coco,dataset,img,classes,cls_id,show=True):
global dataDir
has_class = False
I=PIL.Image.open('%s/%s/%s'%(dataDir,dataset,img['file_name']))
#通过id，得到注释的信息
annIds = coco.getAnnIds(imgIds=img['id'], catIds=cls_id, iscrowd=None)
# print(annIds)
anns = coco.loadAnns(annIds)
# print(anns)
# coco.showAnns(anns)
objs = []
for ann in anns:
class_name=classes[ann['category_id']]
if class_name in classes_names:
has_class = True
# print(class_name)
if 'bbox' in ann:
bbox=ann['bbox']
xmin = int(bbox[0])
ymin = int(bbox[1])
xmax = int(bbox[2] + bbox[0])
ymax = int(bbox[3] + bbox[1])
obj = [class_name, xmin, ymin, xmax, ymax]
objs.append(obj)
# draw = ImageDraw.Draw(I)
# draw.rectangle([xmin, ymin, xmax, ymax])
# if show:
# plt.figure()
# plt.axis('off')
# plt.imshow(I)
# plt.show()
return objs, has_class
def parse_coco_dog():
for dataset in datasets_list:
#./COCO/annotations/instances_train2014.json
annFile='{}/annotations/instances_{}.json'.format(dataDir,dataset)
#COCO API for initializing annotated data
coco = COCO(annFile)
'''
COCO 对象创建完毕后会输出如下信息:
loading annotations into memory...
Done (t=0.81s)
creating index...
index created!
至此, json 脚本解析完毕, 并且将图片和对应的标注数据关联起来.
'''
#show all classes in coco
classes = id2name(coco)
# print(classes)
#[1, 2, 3, 4, 6, 8]
classes_ids = coco.getCatIds(catNms=classes_names)
# print(classes_ids)
for cls in ['dog']:
#Get ID number of this class
cls_id=coco.getCatIds(catNms=[cls])
img_ids=coco.getImgIds(catIds=cls_id)
# print(cls,len(img_ids))
# imgIds=img_ids[0:30000]
for imgId in tqdm(img_ids):
img = coco.loadImgs(imgId)[0]
filename = img['file_name']
# print(filename)
objs, has_class=showimg_dog(coco, dataset, img, classes,classes_ids,show=False)
# print(objs)
if has_class and filename not in coco_filenames:
save_annotations_and_imgs(coco, dataset, filename, objs)
def showimg(coco,dataset,img,classes,cls_id,show=True):
global dataDir
I=PIL.Image.open('%s/%s/%s'%(dataDir,dataset,img['file_name']))
#通过id，得到注释的信息
annIds = coco.getAnnIds(imgIds=img['id'], catIds=cls_id, iscrowd=None)
# print(annIds)
anns = coco.loadAnns(annIds)
# print(anns)
# coco.showAnns(anns)
objs = []
for ann in anns:
class_name=classes[ann['category_id']]
if class_name in classes_names:
# print(class_name)
if 'bbox' in ann:
bbox=ann['bbox']
xmin = int(bbox[0])
ymin = int(bbox[1])
xmax = int(bbox[2] + bbox[0])
ymax = int(bbox[3] + bbox[1])
obj = [class_name, xmin, ymin, xmax, ymax]
objs.append(obj)
# draw = ImageDraw.Draw(I)
# draw.rectangle([xmin, ymin, xmax, ymax])
# if show:
# plt.figure()
# plt.axis('off')
# plt.imshow(I)
# plt.show()
return objs
def parse_coco():
for dataset in datasets_list:
#./COCO/annotations/instances_train2014.json
annFile='{}/annotations/instances_{}.json'.format(dataDir,dataset)
#COCO API for initializing annotated data
coco = COCO(annFile)
'''
COCO 对象创建完毕后会输出如下信息:
loading annotations into memory...
Done (t=0.81s)
creating index...
index created!
至此, json 脚本解析完毕, 并且将图片和对应的标注数据关联起来.
'''
#show all classes in coco
classes = id2name(coco)
# print(classes)
#[1, 2, 3, 4, 6, 8]
classes_ids = coco.getCatIds(catNms=classes_names)
# print(classes_ids)
for cls in select_class:
#Get ID number of this class
cls_id=coco.getCatIds(catNms=[cls])
img_ids=coco.getImgIds(catIds=cls_id)
# print(cls,len(img_ids))
imgIds=img_ids[0:20000]
for imgId in tqdm(imgIds):
img = coco.loadImgs(imgId)[0]
filename = img['file_name']
# print(filename)
objs=showimg(coco, dataset, img, classes,classes_ids,show=False)
# print(objs)
save_annotations_and_imgs(coco, dataset, filename, objs)
coco_filenames.append(filename)
for cls in select_class2:
#Get ID number of this class
cls_id=coco.getCatIds(catNms=[cls])
img_ids=coco.getImgIds(catIds=cls_id)
# print(cls,len(img_ids))
imgIds=img_ids[0:5000]
for imgId in tqdm(imgIds):
img = coco.loadImgs(imgId)[0]
filename = img['file_name']
# print(filename)
objs=showimg(coco, dataset, img, classes,classes_ids,show=False)
# print(objs)
save_annotations_and_imgs(coco, dataset, filename, objs)
coco_filenames.append(filename)
def cutmix(imga, imgb):
#imga : 背景图片； imgb：提取的目标图片
h_a, w_a, c_a = imga.shape
h_b, w_b, c_b = imgb.shape
# print(imga.shape)
# print(imgb.shape)
new_box = [] ##cut图片在背景图中的位置
# if b.size > a.size, reduce b.size
scale = 0
if h_b >= h_a or w_b >= w_a:
scale = max(float(h_b) / float(h_a), float(w_b) / float(w_a))
scale = int(scale) + 1
# print(w_b//scale, h_b//scale)
imgb = cv2.resize(imgb, (w_b//scale, h_b//scale))
h_b, w_b, c_b = imgb.shape
x = random.randint(0, w_a - w_b)
y = random.randint(0, h_a - h_b)
imgc = imga * 0.5
imgc[y:y+h_b, x:x+w_b,:] = imgc[y:y+h_b, x:x+w_b,:] + imgb * 0.5
new_box.append(x)
new_box.append(y)
new_box.append(w_b)
new_box.append(h_b)
return imgc, new_box
#对比度和亮度
def Contrast_and_Brightness(alpha, beta, img):
blank = np.zeros(img.shape, img.dtype)
# dst = alpha * img + beta * blank
dst = cv2.addWeighted(img, alpha, blank, 1-alpha, beta)
return dst
def add_gasuss_noise(image, mean=0, var=0.001):
'''
添加高斯噪声
mean : 均值
var : 方差
'''
image = np.array(image/255, dtype=float)
noise = np.random.normal(mean, var ** 0.5, image.shape)
out = image + noise
if out.min() < 0:
low_clip = -1.
else:
low_clip = 0.
out = np.clip(out, low_clip, 1.0)
out = np.uint8(out*255)
return out
## 数据增强：对比度亮度调整，高斯模糊，高斯噪声,cutmix
def data_aug(img_path):
try:
img = cv2.imread(img_path)
except UnicodeEncodeError:
return None
aug_ind = random.randint(0, 1) ##随机生成一个数（前闭后闭），每个数代表一个数据增强方式
isBlur = random.randint(0, 1) ##是否进行高斯模糊，0 = 进行 ，1 = 不进行
if aug_ind == 0: ## 降低对比度和亮度
_alpha = random.uniform(0.5,1)
_beta = random.uniform(-50,0)
c_img = Contrast_and_Brightness(_alpha , _beta, img)
if isBlur == 0:
img_aug = cv2.GaussianBlur(c_img, (7,7), 2)
else:
img_aug = c_img
elif aug_ind == 1:## ## 调高对比度和亮度
_alpha = random.uniform(1, 1.5)
_beta = random.uniform(0, 50)
c_img = Contrast_and_Brightness(_alpha , _beta, img)
if isBlur == 0:
img_aug = cv2.GaussianBlur(c_img, (7,7), 2)
else:
img_aug = c_img
# else: ## 添加高斯噪声
# g_nosie = add_gasuss_noise(img, 0, 0.001)
# if isBlur == 0:
# img_aug = cv2.GaussianBlur(g_nosie, (7,7), 2)
# else:
# img_aug = g_nosie
return img_aug
def addCatItem(name):
global category_item_id
category_item = dict()
category_item['supercategory'] = 'none'
category_item_id += 1
category_item['id'] = category_item_id
category_item['name'] = name
coco['categories'].append(category_item)
category_set[name] = category_item_id
return category_item_id
def addImgItem(file_name, size):
global image_id
if file_name is None:
raise Exception('Could not find filename tag in xml file.')
if size['width'] is None:
raise Exception('Could not find width tag in xml file.')
if size['height'] is None:
raise Exception('Could not find height tag in xml file.')
image_id += 1
image_item = dict()
image_item['id'] = image_id
image_item['file_name'] = file_name
image_item['width'] = size['width']
image_item['height'] = size['height']
coco['images'].append(image_item)
image_set.add(file_name)
return image_id
def addAnnoItem(object_name, image_id, category_id, bbox):
global annotation_id
annotation_item = dict()
annotation_item['segmentation'] = []
seg = []
#bbox[] is x,y,w,h
#left_top
seg.append(bbox[0])
seg.append(bbox[1])
#left_bottom
seg.append(bbox[0])
seg.append(bbox[1] + bbox[3])
#right_bottom
seg.append(bbox[0] + bbox[2])
seg.append(bbox[1] + bbox[3])
#right_top
seg.append(bbox[0] + bbox[2])
seg.append(bbox[1])
annotation_item['segmentation'].append(seg)
annotation_item['area'] = bbox[2] * bbox[3]
annotation_item['iscrowd'] = 0
annotation_item['ignore'] = 0
annotation_item['image_id'] = image_id
annotation_item['bbox'] = bbox
annotation_item['category_id'] = category_id
annotation_id += 1
annotation_item['id'] = annotation_id
coco['annotations'].append(annotation_item)
def parseXmlFiles(xml_path, image_path, image_file_name, is_xj):
# parse image
image = PIL.Image.open(image_path)
# image = PIL.Image.open(image_path)
if image.format != 'JPEG':
# raise ValueError('Image format not JPEG')
print('Image format not JPEG')
num_objs = 0 ##用于记录每个图片中的目标个数，小于定值则作为背景图片
bndbox = dict()
size = dict()
current_image_id = None
current_category_id = None
file_name = None
size['width'] = None
size['height'] = None
size['depth'] = None
width, height = image.size
size['width'] = width
size['height'] = height
size['depth'] = 3
file_name = image_file_name
current_image_id = addImgItem(file_name, size)
# parse xml
tree = ET.parse(xml_path)
root = tree.getroot()
if root.tag != 'annotation':
raise Exception('pascal voc xml root element should be annotation, rather than {}'.format(root.tag))
#elem is <folder>, <filename>, <size>, <object>
for elem in root:
current_parent = elem.tag
current_sub = None
objName = None
if elem.tag != 'object':
continue
objID = -1
for subelem in elem:
bndbox ['xmin'] = None
bndbox ['xmax'] = None
bndbox ['ymin'] = None
bndbox ['ymax'] = None
current_sub = subelem.tag
if current_parent == 'object' and subelem.tag == 'name':
objName = subelem.text
if objName == u"行人" or objName == u"人":
objID = 1
elif objName == u"骑手":
objID = 1
elif objName == u"轿车" or objName == u"汽车" or objName == u"小轿车":
objID = 2
elif objName == u"SUV":
objID = 2
elif objName == u"MPV":
objID = 2
elif objName == u"跑车":
objID = 2
elif objName == u"皮卡":
objID = 2
elif objName == u"面包车":
objID = 2
elif objName == u"自行车":
objID = 3
elif objName == u"电瓶车":
objID = 4
elif objName == u"摩托车":
objID = 5
elif objName == u"三轮车":
objID = 6
elif objName == u"小客":
objID = 7
elif objName == u"中客":
objID = 7
elif objName == u"大客" or objName == u"客车":
objID = 7
elif objName == u"微型卡车":
objID = 8
elif objName == u"轻型卡车":
objID = 8
elif objName == u"中型卡车":
objID = 8
elif objName == u"重型卡车" or objName == u"卡车":
objID = 8
elif objName == u"头肩" or objName == u"头肩人脸" or objName == u"头肩脸":
objID = 9
elif objName == 'mask':
objID = 0
elif objName == 'person' or objName == 'Person':
objID = 1
elif objName == 'car' or objName == 'Car':
objID = 2
elif objName == 'bicycle' or objName == 'Bicycle':
objID = 3
elif objName == 'ElectroMboile':
objID = 4
elif objName == 'motorcycle' or objName == 'MotorBike':
objID = 5
elif objName == 'Tricycle':
objID = 6
elif objName == 'bus' or objName == 'Bus':
objID = 7
elif objName == 'truck' or objName == 'Truck':
objID = 8
elif objName == 'Head':
objID = 9
else:
print(image_path)
num_objs += 1
if objID == 0:
objID = -1
break
if current_sub == 'bndbox':
for option in subelem:
bndbox[option.tag] = int(option.text)
bbox = []
bbox.append(bndbox ['xmin'])
bbox.append(bndbox ['ymin'])
bbox.append(bndbox['xmax'] - bndbox['xmin'])
bbox.append(bndbox['ymax'] - bndbox['ymin'])
object_name = CLASS_NAMES[objID]
current_category_id = objID
##剔除掉小的目标 ,规定面积在1024(32*32)以下为小目标
#if bbox[2] * bbox[3] >= 1024:
#if bbox[3] > 32:
if bbox[3] > 32 and objID != 9:
if objID in cut_ids:
cut_ob = [image_path, bbox, objID]
cut_obs.append(cut_ob)
addAnnoItem(object_name, current_image_id, current_category_id, bbox )
if objID == 9:
addAnnoItem(object_name, current_image_id, current_category_id, bbox )
#addAnnoItem(object_name, current_image_id, current_category_id, bbox )
# if current_sub == 'sub_number':
# num_sub = int(current_sub.text)
if 'subbox' in current_sub:
for option in subelem:
bndbox[option.tag] = int(option.text)
bbox = []
bbox.append(bndbox ['xmin'])
bbox.append(bndbox ['ymin'])
bbox.append(bndbox['xmax'] - bndbox['xmin'])
bbox.append(bndbox['ymax'] - bndbox['ymin'])
object_name = CLASS_NAMES[objID]
current_category_id = objID
##剔除掉小的目标 ,规定面积在1024(32*32)以下为小目标
#if bbox[2] * bbox[3] >= 1024:
if bbox[3] > 32 and objID != 9:
if objID in cut_ids:
cut_ob = [image_path, bbox, objID]
cut_obs.append(cut_ob)
addAnnoItem(object_name, current_image_id, current_category_id, bbox )
if objID == 9:
addAnnoItem(object_name, current_image_id, current_category_id, bbox )
#addAnnoItem(object_name, current_image_id, current_category_id, bbox )
#目标个数小于定值则作为背景图片
if num_objs < 10:
bg_img = [xml_path, image_path]
bg_imgs.append(bg_img)
if is_xj == 1 and num_objs < 20:
bg_img = [xml_path, image_path]
xj_bg_imgs.append(bg_img)
def parseXmlFiles_cut(xml_path, image_path, cut_obj, image_file_name):
# parse image
image = PIL.Image.open(image_path)
# image = PIL.Image.open(image_path)
if image.format != 'JPEG':
# print(image_file_name)
# f_bad.write(image_file_name+ '\n')
raise ValueError('Image format not JPEG')
bndbox = dict()
size = dict()
current_image_id = None
current_category_id = None
file_name = None
size['width'] = None
size['height'] = None
size['depth'] = None
width, height = image.size
size['width'] = width
size['height'] = height
size['depth'] = 3
file_name = image_file_name
current_image_id = addImgItem(file_name, size)
# parse xml
tree = ET.parse(xml_path)
root = tree.getroot()
if root.tag != 'annotation':
raise Exception('pascal voc xml root element should be annotation, rather than {}'.format(root.tag))
#elem is <folder>, <filename>, <size>, <object>
for elem in root:
current_parent = elem.tag
current_sub = None
objName = None
if elem.tag != 'object':
continue
objID = -1
for subelem in elem:
bndbox ['xmin'] = None
bndbox ['xmax'] = None
bndbox ['ymin'] = None
bndbox ['ymax'] = None
current_sub = subelem.tag
if current_parent == 'object' and subelem.tag == 'name':
objName = subelem.text
if objName == u"行人" or objName == u"人":
objID = 1
elif objName == u"骑手":
objID = 1
elif objName == u"轿车" or objName == u"汽车" or objName == u"小轿车":
objID = 2
elif objName == u"SUV":
objID = 2
elif objName == u"MPV":
objID = 2
elif objName == u"跑车":
objID = 2
elif objName == u"皮卡":
objID = 2
elif objName == u"面包车":
objID = 2
elif objName == u"自行车":
objID = 3
elif objName == u"电瓶车":
objID = 4
elif objName == u"摩托车":
objID = 5
elif objName == u"三轮车":
objID = 6
elif objName == u"小客":
objID = 7
elif objName == u"中客":
objID = 7
elif objName == u"大客" or objName == u"客车":
objID = 7
elif objName == u"微型卡车":
objID = 8
elif objName == u"轻型卡车":
objID = 8
elif objName == u"中型卡车":
objID = 8
elif objName == u"重型卡车" or objName == u"卡车":
objID = 8
elif objName == u"头肩" or objName == u"头肩人脸" or objName == u"头肩脸":
objID = 9
elif objName == 'mask':
objID = 0
elif objName == 'person' or objName == 'Person':
objID = 1
elif objName == 'car' or objName == 'Car':
objID = 2
elif objName == 'bicycle' or objName == 'Bicycle':
objID = 3
elif objName == 'ElectroMboile':
objID = 4
elif objName == 'motorcycle' or objName == 'MotorBike':
objID = 5
elif objName == 'Tricycle':
objID = 6
elif objName == 'bus' or objName == 'Bus':
objID = 7
elif objName == 'truck' or objName == 'Truck':
objID = 8
elif objName == 'Head':
objID = 9
else:
print(image_path)
if objID == 0:
objID = -1
break
if current_sub == 'bndbox':
for option in subelem:
bndbox[option.tag] = int(option.text)
bbox = []
bbox.append(bndbox ['xmin'])
bbox.append(bndbox ['ymin'])
bbox.append(bndbox['xmax'] - bndbox['xmin'])
bbox.append(bndbox['ymax'] - bndbox['ymin'])
object_name = CLASS_NAMES[objID]
current_category_id = objID
##剔除掉小的目标 ,规定面积在1024(32*32)以下为小目标
if bbox[3] > 32:
if objID in cut_ids:
cut_ob = [image_path, bbox, objID]
cut_obs.append(cut_ob)
addAnnoItem(object_name, current_image_id, current_category_id, bbox )
# if current_sub == 'sub_number':
# num_sub = int(current_sub.text)
if 'subbox' in current_sub:
for option in subelem:
bndbox[option.tag] = int(option.text)
bbox = []
bbox.append(bndbox ['xmin'])
bbox.append(bndbox ['ymin'])
bbox.append(bndbox['xmax'] - bndbox['xmin'])
bbox.append(bndbox['ymax'] - bndbox['ymin'])
object_name = CLASS_NAMES[objID]
current_category_id = objID
##剔除掉小的目标 ,规定面积在1024(32*32)以下为小目标
#if bbox[2] * bbox[3] >= 1024:
if bbox[3] > 32:
# if objID in cut_ids:
# cut_ob = [image_path, bbox, objID]
# cut_obs.append(cut_ob)
addAnnoItem(object_name, current_image_id, current_category_id, bbox )
#addAnnoItem(object_name, current_image_id, current_category_id, bbox )
## add cut img
_object_name = CLASS_NAMES[cut_obj[2]]
_current_category_id = cut_obj[2]
_bbox = cut_obj[1]
addAnnoItem(_object_name, current_image_id, _current_category_id, _bbox )
## 处理含有中文的图片和xml
def copy_chinese():
num = 0
chinese_num = 0
image_root_path = '/XinShizongAlreadyTargetDetectionCheck/Image' + '/zhengchang/'
xml_root_ptah = '/XinShizongAlreadyTargetDetectionCheck/Label' + '/zhengchang/'
imgs = os.listdir(image_root_path)
for img_path in imgs:
print(num)
num+=1
img_full_path = os.path.join(image_root_path, img_path)
xml_file_name = os.path.splitext(img_path)[0] + '.xml'
xml_full_path = os.path.join(xml_root_ptah, xml_file_name)
try:
_img = cv2.imread(img_full_path)
except UnicodeEncodeError:
copyfile(img_full_path, ch_img_dir + '/ch_' + str(chinese_num) + '.jpg')
copyfile(xml_full_path, ch_anno_dir + '/ch_' + str(chinese_num) + '.xml')
chinese_num += 1
print('chinese_num', chinese_num, img_path)
return chinese_num
if __name__ == '__main__':
#chin_num = copy_chinese()
#print(chin_num)
parse_coco()
parse_coco_dog()
anno_path = os.path.join(MyDataPath, 'annotations')
if not os.path.exists(anno_path):
os.makedirs(anno_path)
images_path = os.path.join(MyDataPath, 'images')
if not os.path.exists(images_path):
os.makedirs(images_path)
json_file_train = MyDataPath + '/annotations/instances_9_train.json'
# json_file_test = args.preprocess_output_dir + '/annotations/instances_8_test.json'
for cat in CLASS_NAMES:
addCatItem(cat)
print('==========category ==============')
for k, v in category_set.items():
print('category : (key : vale)', k, v)
print('==========category ==============')
ch_num = 0
idx = 0
# all_images = []
data_aug_num = 0 ##用来设定多少次进行一次data_aug操作
image_root_path = '/XinShiZong_TargetDetectionNon-motorVehiclesModification/Image' + '/youfeijidongche/'
xml_root_ptah = '/XinShiZong_TargetDetectionNon-motorVehiclesModification/Label' + '/youfeijidongche/'
xmls = os.listdir(xml_root_ptah)
for one_xml in xmls:
xml_full_path = os.path.join(xml_root_ptah, one_xml)
# print(xml_full_path)
image_file_name = os.path.splitext(one_xml)[0] + '.Jpeg'
if not os.path.exists(image_root_path + image_file_name):
image_file_name = os.path.splitext(one_xml)[0] + '.jpg'
if not os.path.exists(image_root_path + image_file_name):
image_file_name = os.path.splitext(one_xml)[0] + '.jpeg'
aug_image_name = os.path.splitext(one_xml)[0] + '_aug.jpg'
image_full_path = os.path.join(image_root_path, image_file_name)
# print(image_full_path)
copyfile(image_full_path, MyDataPath + '/images/' + image_file_name)
parseXmlFiles(xml_full_path, image_full_path, image_file_name, 0)
idx += 1
print(idx)
## data augmentation
data_aug_num += 1
aug_img = data_aug(image_full_path)
aug_img_path = MyDataPath + '/images/' + aug_image_name
if data_aug_num % 3 == 0: ##1/3的概率
try:
cv2.imwrite(aug_img_path, aug_img)
except UnicodeEncodeError:
ch_num += 1
continue
parseXmlFiles(xml_full_path, image_full_path, aug_image_name, 0)
idx += 1
print(idx)
image_root_path = '/XinShiZong_TargetDetectionNon-motorVehiclesModification/Image' + '/noNonVeh/'
xml_root_ptah = '/XinShiZong_TargetDetectionNon-motorVehiclesModification/Label' + '/noNonVeh/'
xmls = os.listdir(xml_root_ptah)
for one_xml in xmls:
xml_full_path = os.path.join(xml_root_ptah, one_xml)
# print(xml_full_path)
image_file_name = os.path.splitext(one_xml)[0] + '.Jpeg'
if not os.path.exists(image_root_path + image_file_name):
image_file_name = os.path.splitext(one_xml)[0] + '.jpg'
if not os.path.exists(image_root_path + image_file_name):
image_file_name = os.path.splitext(one_xml)[0] + '.jpeg'
aug_image_name = os.path.splitext(one_xml)[0] + '_aug.jpg'
image_full_path = os.path.join(image_root_path, image_file_name)
# print(image_full_path)
copyfile(image_full_path, MyDataPath + '/images/' + image_file_name)
parseXmlFiles(xml_full_path, image_full_path, image_file_name, 0)
idx += 1
print(idx)
## data augmentation
data_aug_num += 1
aug_img = data_aug(image_full_path)
aug_img_path = MyDataPath + '/images/' + aug_image_name
if data_aug_num % 3 == 0: ##1/3的概率
try:
cv2.imwrite(aug_img_path, aug_img)
except UnicodeEncodeError:
ch_num += 1
continue
parseXmlFiles(xml_full_path, image_full_path, aug_image_name, 0)
idx += 1
print(idx)
image_root_path = '/XinShiZong_TargetDetectionNon-motorVehicleDataCollection/Image/'
xml_root_ptah = '/XinShiZong_TargetDetectionNon-motorVehicleDataCollection/Label/'
xmls = os.listdir(xml_root_ptah)
for one_xml in xmls:
xml_full_path = os.path.join(xml_root_ptah, one_xml)
# print(xml_full_path)
image_file_name = os.path.splitext(one_xml)[0] + '.Jpeg'
if not os.path.exists(image_root_path + image_file_name):
image_file_name = os.path.splitext(one_xml)[0] + '.jpg'
if not os.path.exists(image_root_path + image_file_name):
image_file_name = os.path.splitext(one_xml)[0] + '.jpeg'
aug_image_name = os.path.splitext(one_xml)[0] + '_aug.jpg'
image_full_path = os.path.join(image_root_path, image_file_name)
# print(image_full_path)
copyfile(image_full_path, MyDataPath + '/images/' + image_file_name)
parseXmlFiles(xml_full_path, image_full_path, image_file_name, 0)
idx += 1
print(idx)
## data augmentation
data_aug_num += 1
aug_img = data_aug(image_full_path)
aug_img_path = MyDataPath + '/images/' + aug_image_name
if data_aug_num % 3 == 0: ##1/3的概率
try:
cv2.imwrite(aug_img_path, aug_img)
except UnicodeEncodeError:
ch_num += 1
continue
parseXmlFiles(xml_full_path, image_full_path, aug_image_name, 0)
idx += 1
print(idx)
image_root_path = '/XinShiZong_TargetDetectionNon-motorVehiclesModification/Image/' + '/ID1318932/detect/shinei/ditie/'
xml_root_ptah = '/XinShiZong_TargetDetectionNon-motorVehiclesModification/Label/' + '/ID1318932/detect/shinei/ditie/'
xmls = os.listdir(xml_root_ptah)
for one_xml in xmls:
xml_full_path = os.path.join(xml_root_ptah, one_xml)
# print(xml_full_path)
image_file_name = os.path.splitext(one_xml)[0] + '.Jpeg'
if not os.path.exists(image_root_path + image_file_name):
image_file_name = os.path.splitext(one_xml)[0] + '.jpg'
if not os.path.exists(image_root_path + image_file_name):
image_file_name = os.path.splitext(one_xml)[0] + '.jpeg'
aug_image_name = os.path.splitext(one_xml)[0] + '_aug.jpg'
image_full_path = os.path.join(image_root_path, image_file_name)
# print(image_full_path)
copyfile(image_full_path, MyDataPath + '/images/' + image_file_name)
parseXmlFiles(xml_full_path, image_full_path, image_file_name, 0)
idx += 1
print(idx)
## data augmentation
data_aug_num += 1
aug_img = data_aug(image_full_path)
aug_img_path = MyDataPath + '/images/' + aug_image_name
if data_aug_num % 3 == 0: ##1/3的概率
try:
cv2.imwrite(aug_img_path, aug_img)
except UnicodeEncodeError:
ch_num += 1
continue
parseXmlFiles(xml_full_path, image_full_path, aug_image_name, 0)
idx += 1
print(idx)
image_root_path = '/XinShiZong_TargetDetectionNon-motorVehiclesModification/Image/' + '/ID1318932/detect/shinei/gaotie/'
xml_root_ptah = '/XinShiZong_TargetDetectionNon-motorVehiclesModification/Label/' + '/ID1318932/detect/shinei/gaotie/'
xmls = os.listdir(xml_root_ptah)
for one_xml in xmls:
xml_full_path = os.path.join(xml_root_ptah, one_xml)
# print(xml_full_path)
image_file_name = os.path.splitext(one_xml)[0] + '.Jpeg'
if not os.path.exists(image_root_path + image_file_name):
image_file_name = os.path.splitext(one_xml)[0] + '.jpg'
if not os.path.exists(image_root_path + image_file_name):
image_file_name = os.path.splitext(one_xml)[0] + '.jpeg'
aug_image_name = os.path.splitext(one_xml)[0] + '_aug.jpg'
image_full_path = os.path.join(image_root_path, image_file_name)
# print(image_full_path)
copyfile(image_full_path, MyDataPath + '/images/' + image_file_name)
parseXmlFiles(xml_full_path, image_full_path, image_file_name, 0)
idx += 1
print(idx)
## data augmentation
data_aug_num += 1
aug_img = data_aug(image_full_path)
aug_img_path = MyDataPath + '/images/' + aug_image_name
if data_aug_num % 3 == 0: ##1/3的概率
try: