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convert_xml2yolo.py
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convert_xml2yolo.py
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import json
import os
import glob
import pandas as pd
import argparse
import xml.etree.ElementTree as ET
import cv2
import numpy as np
from skimage.measure import label, regionprops
import csv
label_dir = 'labelled'
datasets = glob.glob(os.path.join(label_dir, '*'))
print(datasets)
if not os.path.exists('images'):
os.mkdir('images')
if not os.path.exists('labels'):
os.mkdir('labels')
classnames = ['ST','GT', 'HG', 'TC', 'Pink', 'BB','KB', 'FB', 'PF', 'CPF']
overlay_flag = False
def write_txt(file, data):
with open(file, 'a') as f:
writer = csv.writer(f, delimiter = ' ')
writer.writerow(data)
for dataset in datasets:
xml_files = glob.glob(os.path.join(dataset, 'quads_images/*.xml'))
if True:#dataset=='labelled/310_16B_GPS':
for xml_file in xml_files:
img_path = xml_file.replace('xml', 'jpg')
mask_path = img_path.replace('quads_images', 'mask_quads')
img = cv2.imread(img_path)
mask = cv2.imread(mask_path, 0)
ret, thresh = cv2.threshold(mask, 10, 255, cv2.THRESH_BINARY)
label_img = label(thresh)
regions = regionprops(label_img)[0]
minr,minc,maxr,maxc = regions.bbox
quads_images = img[minr:maxr, minc:maxc, :]
quad_h, quad_w = quads_images.shape[0], quads_images.shape[1]
new_name = os.path.basename(dataset) + '_' +os.path.basename(img_path)[:-3]+'png'
print(new_name, quad_h, quad_w)
tree = ET.parse(xml_file)
root = tree.getroot()
annotations = []
for member in root.findall('object'):
value = (root.find('filename').text,
int(root.find('size')[0].text),
int(root.find('size')[1].text), member[0].text,
int(member[4][0].text), int(member[4][1].text),
int(member[4][2].text), int(member[4][3].text))
class_name = member[0].text
class_num = classnames.index(class_name)
x_tl, y_tl, x_br, y_br = float(member[4][0].text), float(member[4][1].text),float(member[4][2].text), float(member[4][3].text)
coords = [x_tl-minc, y_tl-minr, x_br-minc, y_br-minr]
if all(i<0 for i in coords):
print(new_name)
continue
else:
#qx_tl, qy_tl, qx_br, qy_br
newcoords = []
for i in coords:
if i < 0:
i = 0
newcoords.append(i)
x_min = newcoords[0]
y_min = newcoords[1]
x_max = newcoords[2]
y_max = newcoords[3]
if x_max > quads_images.shape[1]:
x_max = quads_images.shape[1]
if y_max > quads_images.shape[0]:
y_max = quads_images.shape[0]
if x_min > quads_images.shape[1] or y_min > quads_images.shape[0]:
continue
else:
ww, hh = x_max-x_min, y_max-y_min
xc = x_min + ww/2
yc = y_min + hh/2
xc /= quads_images.shape[1]
yc /= quads_images.shape[0]
ww /= quads_images.shape[1]
hh /= quads_images.shape[0]
write_txt("labels/{}.txt".format(new_name[:-4]), [class_num, xc, yc, ww, hh]) #xc/quad_w, yc/quad_h, ww/quad_w, hh/quad_h])
if overlay_flag:
pt1 = (int(xc*quads_images.shape[1]-ww*quads_images.shape[1]/2), int(yc*quads_images.shape[0]-hh*quads_images.shape[0]/2))
pt2 = (pt1[0]+int(ww*quads_images.shape[1]), pt1[1]+int(hh*quads_images.shape[0]))
quads_images = cv2.rectangle(quads_images, pt1, pt2, (0, 255, 0), 1)
cv2.imwrite(os.path.join('images', new_name), quads_images)
else:
cv2.imwrite(os.path.join('images', new_name), quads_images)