# -*- coding: utf-8 -*-
import sys, getopt
import os
import json
import cv2
import random
import numpy as np
np.random.seed(41)
import glob
import shutil
import os
# -*- coding: utf-8 -*-
import os
import sys, getopt
from pycocotools.coco import COCO, maskUtils
import cv2
import numpy as np
def mkdir_os(path):
if not os.path.exists(path):
os.makedirs(path)
def groberect(points, ww, hh):
x1 = points[0]
y1 = points[1]
w = points[2]
h = points[3]
x2 = x1 + w - 1
y2 = y1 + h - 1
px = float(x1 + x2) / 2
py = float(y1 + y2) / 2
w = w * 2
h = h * 2
l = max(0, px - w / 2)
r = min(ww - 1, px + w / 2) #x2
t = max(0, py - h / 2)
b = min(hh - 1, py + h / 2) #y2
w = r - l + 1
h = b - t + 1
# x1y1 x2y2
return [int(l), int(t), int(w), int(h)]
def main(argv):
# json_file = './data/coco/annotations/instances_val2017.json'
# dataset_dir = './data/coco/val2017/'
# save_dir = './data/coco/vis/'
inputfile = ''
jsonfile = ''
outputfile = ''
try:
opts, args = getopt.getopt(argv, "hi:j:o:", ["ifile=", "jfile=", "ofile="])
except getopt.GetoptError:
print('test.py -i -j -o ')
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
print('test.py -i -j -o ')
sys.exit()
elif opt in ("-i", "--ifile"):
inputfile = arg
elif opt in ("-j", "--jfile"):
jsonfile = arg
elif opt in ("-o", "--ofile"):
outputfile = arg
print('\n输入的文件为：', inputfile)
print('\n输入的json为：', jsonfile)
print('\n输出的文件为：', outputfile)
images = []
annotations = []
mkdir_os(outputfile)
coco = COCO(jsonfile)
catIds = coco.getCatIds(catNms=['wires']) # catIds=1 表示人这一类
imgIds = coco.getImgIds(catIds=catIds) # 图片id，许多值
image_id = 0
annotations_id = 0
for i in range(len(imgIds)):
if i % 100 == 0:
print(i, "/", len(imgIds))
img_info = coco.loadImgs(imgIds[i])[0]
cvImage = cv2.imread(os.path.join(inputfile, img_info['file_name']), -1)
cvImage = cv2.cvtColor(cvImage, cv2.COLOR_BGR2GRAY)
cvImage = cv2.cvtColor(cvImage, cv2.COLOR_GRAY2BGR)
ori_H, ori_W = cvImage.shape[:2]
annIds = coco.getAnnIds(imgIds=img_info['id'], catIds=catIds, iscrowd=None)
anns = coco.loadAnns(annIds)
#原来一幅图可能拆成多个，需要保存多个图像，生成多个image信息，多个annotation信息
img_info_append = []
for index in range(len(anns)):
ann = anns[index]
img_temp = img_info.copy()
if 'segmentation' in ann: #只处理存在annotation的情况
if type(ann['segmentation']) == list: #只处理points这种形式的mask标注的情况
bbox = ann['bbox']
new_bbox = groberect(bbox, ori_W, ori_H)
img_temp['width'] = new_bbox[2]
img_temp['height'] = new_bbox[3]
img_temp['file_name'] = img_temp['file_name'].split('.')[0] + "_" + str(index) + ".jpg"
img_temp['id'] = image_id
#cropImg = img[(y):(y + hh), (x):(x + ww)]
save_cvImage = cvImage[new_bbox[1]:(new_bbox[1] + new_bbox[3]), new_bbox[0]:(new_bbox[0] + new_bbox[2])]
cv2.imwrite(os.path.join(outputfile, img_temp['file_name']), save_cvImage)
img_info_append.append(img_temp)
ann['bbox'] = [bbox[0]-new_bbox[0], bbox[1]-new_bbox[1], bbox[2], bbox[3]]
for seg in range(len(ann['segmentation'])):
ori_seg = ann['segmentation'][seg]
for k in range(len(ori_seg)//2):
ori_seg[2 * k] = ori_seg[2 * k] - new_bbox[0]
ori_seg[2 * k + 1] = ori_seg[2 * k + 1] - new_bbox[1]
ann['segmentation'][seg] = ori_seg
ann['image_id'] = int(image_id)
#anns[index] = ann
else:
exit()
else:
exit()
image_id += 1
#annotations_id += 1
annotations.extend(anns)
images.extend(img_info_append)
instance = {}
instance['info'] = 'spytensor created'
instance['license'] = ['license']
instance['images'] = images
instance['annotations'] = annotations
instance['categories'] = coco.dataset['categories']
import io
#json.dump(instance, io.open("./result.json", 'w', encoding='utf-8'), ensure_ascii=False, indent=1) # indent=2 更加美观显示
with io.open("./new_json.json", 'w', encoding="utf-8") as outfile:
my_json_str = json.dumps(instance, ensure_ascii=False, indent=1)
#python3 无
# if isinstance(my_json_str, str):
# my_json_str = my_json_str.decode("utf-8")
outfile.write(my_json_str)
if __name__ == "__main__":
main(sys.argv[1:])
