conjunto de datos de carril curvo (IMG: imagen (tamaño (660 * 1570), (720 * 1280), (1440 * 2560)), GT: json)
Propósito: procesar en un tamaño uniforme, lo cual es conveniente para el modelo de tren
(cambiar el tamaño a 800 * 320 después de recortar según el punto de fuga de la línea del carril, el formato GT incluye máscara, line_txt en formato de punto) Paso 1: convertir
json a línea_txt
import json
import tqdm
import os
import glob
path="/CurveLanes/Curvelanes"
def prepare_labels(src, dst):
label_files = glob.glob(f"{src}/*.json")
for label_file in tqdm(label_files):
with open(label_file, 'r') as json_file:
json_dict = json.load(json_file)
file_name = os.path.splitext(os.path.basename(label_file))[0]
save_name = '{}.txt'.format(file_name)
lines = json_dict['Lines']
coord_lines = []
for line in lines:
coord_line = []
for coord in line:
coord_x = coord['x']
coord_y = coord['y']
coord_line.append(coord_x)
coord_line.append(coord_y)
coord_lines.append(coord_line)
save_dir = os.path.join(dst, save_name)
with open(save_dir, 'w') as write_f:
for idx, coord_line in enumerate(coord_lines):
for coord in coord_line:
print(coord, file=write_f, end=' ')
if idx != len(coord_lines) - 1:
print(file=write_f)
def main():
train_src = os.path.join(path, 'train/labels')
train_dst = os.path.join(path, 'train/images')
valid_src = os.path.join(path, 'valid/labels')
valid_dst = os.path.join(path, 'valid/images')
prepare_labels(train_src, train_dst)
prepare_labels(valid_src, valid_dst)
if __name__ == '__main__':
main()
Paso 2: recortar y cambiar el tamaño
import os
import glob
import shutil
import cv2
import numpy as np
path="CurveLanes/Curvelanes/valid"
file_path="CurveLanes/Curvelanes/valid/valid.txt"
mask_path="/merge/curvelane/mask"
img_path="/merge/curvelane/image"
txt_path="/merge/curvelane/line_txt"
n_0 =0
with open(file_path,'r') as f:
for line in f:
ori_image = cv2.imread(path + line.split()[0])
h = ori_image.shape[0]
w = ori_image.shape[1]
fileHandler_txt = path + line.split()[0][:-4] + ".lines.txt"
fileHandler = open(fileHandler_txt, "r")
listOfLines = fileHandler.readlines()
fileHandler.close()
if len(listOfLines)==0:
continue
jamp = []
for line_yuan in listOfLines:
dataline = line_yuan.strip().split(" ")
jamp0 = []
for n in range(0, int(len(dataline) / 2)):
jamp1 = []
jamp1.append(float(dataline[n * 2]))
jamp1.append(float(dataline[n * 2 + 1]))
jamp0.append(jamp1)
jamp.append(jamp0)
y = []
for e in jamp:
for coord in e:
y.append(coord[1])
crop_y = min(y)
print(crop_y)
line_ = []
for e in jamp:
lines_ = []
for coord in e:
lines_.append(str(round(coord[0] / w * 800, 2)))
lines_.append(str(round((coord[1] - crop_y) / (h - crop_y) * 320, 2)))
line_.append(lines_)
image_name_old = line.split()[0].split('/')[-1]
mask_name_old = line.split()[1].split('/')[-1]
print(image_name_old)
mask = cv2.imread(path+line.split()[1])
Num= np.amax(mask[:,:,0],axis=0)
dice=np.argmax(mask[:,:,0],axis=0)
used_dice=[]
for i in range(0,len(dice)):
if dice[i]!=0:
used_dice.append(dice[i])
if len(used_dice)==0:
used_dice.append(0)
crop_y=np.amin(used_dice)
img = np.zeros((mask.shape[0]-crop_y, mask.shape[1], 3), np.uint8)
img[:mask.shape[0]-crop_y, :, :] = ori_image[crop_y:, ...]
mas = np.zeros((mask.shape[0] - crop_y, mask.shape[1], 3), np.uint8)
mas[:mask.shape[0] - crop_y, :, :] = mask[crop_y:, ...]
img_new = cv2.resize(img, (800, 320), interpolation=1)
mas_new = cv2.resize(mas, (800, 320), interpolation=1)
mas_new =np.where(mas_new>0 ,255,0)
len_num = len(str(n_0))
split_0 = 8 - len_num
Public_name = "0000000000000000000000000000000000000000000000000000000000000000"
split_name = Public_name[0:split_0]
image_name = 'curvelane' + "_" + split_name + str(n_0)+".png"
mask_name = 'curvelane' + "_" + split_name + str(n_0)+".png"
file_name='curvelane' + "_" + split_name + str(n_0)
with open(os.path.join(txt_path, file_name + ".lines.txt"), "w", encoding="utf-8") as linetxt:
linetxt.write('\n'.join([' '.join(i) for i in line_]))
linetxt.close()
cv2.imwrite(os.path.join(img_path, image_name), img_new)
cv2.imwrite(os.path.join(mask_path, mask_name), mas_new)
n_0 = n_0 + 1