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make_coco_inst_mask_label.py
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make_coco_inst_mask_label.py
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#!/usr/bin/python
# pip install lxml
import sys
import os
import json
import xml.etree.ElementTree as ET
from pycocotools.coco import COCO
from pycocotools import mask
import glob
import numpy as np
from skimage import measure
from PIL import Image
START_BOUNDING_BOX_ID = 1
PRE_DEFINE_CATEGORIES = None
# If necessary, pre-define category and its id
# PRE_DEFINE_CATEGORIES = {"aeroplane": 1, "bicycle": 2, "bird": 3, "boat": 4,
# "bottle":5, "bus": 6, "car": 7, "cat": 8, "chair": 9,
# "cow": 10, "diningtable": 11, "dog": 12, "horse": 13,
# "motorbike": 14, "person": 15, "pottedplant": 16,
# "sheep": 17, "sofa": 18, "train": 19, "tvmonitor": 20}
def get(root, name):
vars = root.findall(name)
return vars
def get_and_check(root, name, length):
vars = root.findall(name)
if len(vars) == 0:
raise ValueError("Can not find %s in %s." % (name, root.tag))
if length > 0 and len(vars) != length:
raise ValueError(
"The size of %s is supposed to be %d, but is %d."
% (name, length, len(vars))
)
if length == 1:
vars = vars[0]
return vars
def close_contour(contour):
if not np.array_equal(contour[0], contour[-1]):
contour = np.vstack((contour, contour[0]))
return contour
def binary_mask_to_polygon(binary_mask, tolerance=0):
"""Converts a binary mask to COCO polygon representation
Args:
binary_mask: a 2D binary numpy array where '1's represent the object
tolerance: Maximum distance from original points of polygon to approximated
polygonal chain. If tolerance is 0, the original coordinate array is returned.
"""
polygons = []
# pad mask to close contours of shapes which start and end at an edge
padded_binary_mask = np.pad(binary_mask, pad_width=1, mode='constant', constant_values=0)
contours = measure.find_contours(padded_binary_mask, 0.5)
contours = np.subtract(contours, 1)
for contour in contours:
contour = close_contour(contour)
contour = measure.approximate_polygon(contour, tolerance)
if len(contour) < 3:
continue
contour = np.flip(contour, axis=1)
segmentation = contour.ravel().tolist()
# after padding and subtracting 1 we may get -0.5 points in our segmentation
segmentation = [0 if i < 0 else i for i in segmentation]
polygons.append(segmentation)
return polygons
def get_filename_as_int(filename):
try:
filename = filename.replace("\\", "/")
filename = os.path.splitext(os.path.basename(filename))[0]
return int(filename)
except:
raise ValueError("Filename %s is supposed to be an integer." % (filename))
def get_categories(xml_files):
"""Generate category name to id mapping from a list of xml files.
Arguments:
xml_files {list} -- A list of xml file paths.
Returns:
dict -- category name to id mapping.
"""
classes_names = []
for xml_file in xml_files:
tree = ET.parse(xml_file)
root = tree.getroot()
for member in root.findall("object"):
classes_names.append(member[0].text)
classes_names = list(set(classes_names))
classes_names.sort()
return {name: (i+1) for i, name in enumerate(classes_names)}
def convert(xml_files, json_file, mask_dir, train_files, val=False, inst_dir=None):
json_dict = {"images": [], "type": "instances", "annotations": [], "categories": []}
if PRE_DEFINE_CATEGORIES is not None:
categories = PRE_DEFINE_CATEGORIES
else:
categories = get_categories(xml_files)
bnd_id = START_BOUNDING_BOX_ID
now=0
for xml_file in xml_files:
tree = ET.parse(xml_file)
root = tree.getroot()
path = get(root, "path")
if len(path) == 1:
filename = os.path.basename(path[0].text)
elif len(path) == 0:
filename = get_and_check(root, "filename", 1).text
else:
raise ValueError("%d paths found in %s" % (len(path), xml_file))
mask_path = os.path.join(mask_dir, filename[:-4] + '.png')
if not (filename[:-4] in train_files):
print ('skip this %s'%filename)
continue
now+=1
#The filename must be a number
image_id = get_filename_as_int(filename)
size = get_and_check(root, "size", 1)
width = int(get_and_check(size, "width", 1).text)
height = int(get_and_check(size, "height", 1).text)
image = {
"file_name": filename,
"height": height,
"width": width,
"id": image_id,
}
json_dict["images"].append(image)
mask_cls = np.asarray(Image.open(mask_path), dtype=np.int32)
if val:
inst_path = os.path.join(inst_dir, filename[:-4] + '.png')
inst_mask = np.asarray(Image.open(inst_path), dtype=np.int32)
this_mask = inst_mask.copy()
inst_id_list = np.unique(inst_mask)
sum_pix = 0
max_id = 0
for inst in inst_id_list:
if inst==0 or inst==255:
continue
this_mask = this_mask*0.0
this_mask[inst_mask==inst]=1
category_id = np.unique(mask_cls[this_mask==1])[0]
this_mask = np.array(this_mask).astype(np.uint8)
segmentation = binary_mask_to_polygon(this_mask, tolerance=2)
binary_mask_encoded = mask.encode(np.asfortranarray(this_mask.astype(np.uint8)))
area = mask.area(binary_mask_encoded)
bounding_box = mask.toBbox(binary_mask_encoded)
if segmentation ==[]:
this_mask = inst_mask.copy()
this_mask = this_mask*0.0
xmin = int(bounding_box[0])
xmax = int(bounding_box[0]+bounding_box[2])
ymin = int(bounding_box[1])
ymax = int(bounding_box[1]+bounding_box[3])
this_mask[ymin:ymax,xmin:xmax]=1
this_mask = np.array(this_mask).astype(np.uint8)
segmentation = binary_mask_to_polygon(this_mask, tolerance=2)
if segmentation==[]:
continue
ann = {
"area": area.tolist(),#o_width * o_height,
"iscrowd": 0,
"image_id": image_id,
"bbox": bounding_box.tolist(),
"category_id": int(category_id),
"id": bnd_id,
"ignore": 0,
"segmentation": segmentation,
}
json_dict["annotations"].append(ann)
bnd_id = bnd_id + 1
else:
for obj in get(root, "object"):
category = get_and_check(obj, "name", 1).text
if category not in categories:
new_id = len(categories)
categories[category] = new_id
category_id = categories[category]
bndbox = get_and_check(obj, "bndbox", 1)
xmin = int(get_and_check(bndbox, "xmin", 1).text) - 1
ymin = int(get_and_check(bndbox, "ymin", 1).text) - 1
xmax = int(get_and_check(bndbox, "xmax", 1).text)
ymax = int(get_and_check(bndbox, "ymax", 1).text)
assert xmax > xmin
assert ymax > ymin
o_width = abs(xmax - xmin)
o_height = abs(ymax - ymin)
this_mask = mask_cls.copy()
this_mask = this_mask*0.0
this_mask[ymin:ymax,xmin:xmax][mask_cls[ymin:ymax,xmin:xmax]==category_id]=1
this_mask = np.array(this_mask).astype(np.uint8)
segmentation = binary_mask_to_polygon(this_mask, tolerance=2)
binary_mask_encoded = mask.encode(np.asfortranarray(this_mask.astype(np.uint8)))
area = mask.area(binary_mask_encoded)
if segmentation ==[]:
this_mask = mask_cls.copy()
this_mask = this_mask*0.0
this_mask[ymin:ymax,xmin:xmax]=1
this_mask = np.array(this_mask).astype(np.uint8)
segmentation = binary_mask_to_polygon(this_mask, tolerance=2)
if segmentation==[]:
continue
ann = {
"area": o_width * o_height,
"iscrowd": 0,
"image_id": image_id,
"bbox": [xmin, ymin, o_width, o_height],
"category_id": category_id,
"id": bnd_id,
"ignore": 0,
"segmentation": segmentation,
}
json_dict["annotations"].append(ann)
bnd_id = bnd_id + 1
for cate, cid in categories.items():
cat = {"supercategory": "none", "id": cid, "name": cate}
json_dict["categories"].append(cat)
os.makedirs(os.path.dirname(json_file), exist_ok=True)
json_fp = open(json_file, "w")
json_str = json.dumps(json_dict)
json_fp.write(json_str)
json_fp.close()
print ('-->%d'%now)
if __name__ == "__main__":
source_dir = './data/VOCdevkit/VOC2012/' #Edit this to your own dataset path.
xml_dir = source_dir + 'Annotations' #Path of xml data directory.
train_or_val= 'trainaug' #trainaug|val
if train_or_val=='val':
val = True #With GT objects and Masks.
inst_dir = source_dir +'SegmentationObject'
mask_dir = source_dir +'SegmentationClass'
else:
val = False
inst_dir = None
mask_dir = './data/gen_labels/FR_95/mask' #Path of generated pseudo label directory.
train_list = os.path.join(source_dir+'ImageSets/Segmentation', train_or_val + ".txt") #Path of data list directory.
train_files = [i.strip() for i in open(train_list) if not i.strip() == ' ']
json_file = './voc_inst_%s.json'%train_or_val #Save to current dir.
xml_files = glob.glob(os.path.join(xml_dir, "*.xml"))
# If you want to do train/test split, you can pass a subset of xml files to convert function.
print("Number of xml files: {}".format(len(xml_files)))
convert(xml_files, json_file, mask_dir, train_files, val=val, inst_dir=inst_dir)
print("Success: {}".format(json_file))