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import numpy as np
import torch
import shutil
import os
import matplotlib.pyplot as plt
import cv2
import json
from PIL import Image
import pickle
from skimage.transform import resize
def split_data(datasetname):
dir_image = "./dataset_demo/datasetname/images"
dir_label = "./dataset_demo/datasetname/labels"
dir_image = dir_image.replace("datasetname", datasetname)
dir_label = dir_label.replace("datasetname", datasetname)
path = "./dataset_demo"
filesplit = "./files_split"
file_data = os.path.join(filesplit, datasetname+".json")
path_data = os.path.join(path, datasetname)
with open(file_data, 'r') as openfile:
json_object = json.load(openfile)
for typ in json_object.keys():
print(typ)
file_type = json_object[typ]
path_type = os.path.join(path_data, typ)
if os.path.exists(path_type):
shutil.rmtree(path_type)
os.mkdir(path_type)
for img in file_type:
if not "labels" in typ:
shutil.copy(os.path.join(dir_image, img), os.path.join(path_type, img.replace(" ", "")))
else:
shutil.copy(os.path.join(dir_label, img), os.path.join(path_type, img.replace(" ", "")))
def save_fileLabel(datasetname):
file_image = "./dataset_demo/datasetname/type"
file_label = "./dataset_demo/datasetname/type_labels"
path = "./dataset_demo/datasetname"
# if datasetname == "BUID":
# B = ['train', 'valid']
# else:
B = ['train']
file_image = file_image.replace("datasetname", datasetname)
file_label = file_label.replace("datasetname", datasetname)
path = path.replace("datasetname", datasetname)
with open(os.path.join(path, 'have_label.txt'), 'w') as F:
for j in B:
dir_label = file_label.replace("type", j)
dir_train = file_image.replace("type", j)
for i in os.listdir(dir_label):
file = os.path.join(dir_label, i)
img = np.array(Image.open(file))
img[img < 50] = 0
img[img > 200] = 1
values, counts = np.unique(img, return_counts = True)
if (len(values) == 2):
a = i.replace("_segmentation.png", ".jpg")
F.write(os.path.join(dir_train, a))
F.write(" ")
F.write(file)
F.write(" ")
F.write("[0, 1]")
F.write("\n")
with open(os.path.join(path, 'non_label.txt'), 'w') as F:
for j in B:
dir_label = file_label.replace("type", j)
dir_train = file_image.replace("type", j)
for i in os.listdir(dir_label):
file = os.path.join(dir_label, i)
img = np.array(Image.open(file))
img[img < 50] = 0
img[img > 200] = 1
values, counts = np.unique(img, return_counts = True)
if (len(values) == 1):
a = i.replace("_segmentation.png", ".jpg")
F.write(os.path.join(dir_train, a))
F.write(" ")
F.write(file)
F.write(" ")
F.write("[0]")
F.write("\n")
def save_fileLabel_3D(datasetname):
file_image = "./dataset_demo/datasetname/type"
file_label = "./dataset_demo/datasetname/type_labels"
path = "./dataset_demo/datasetname"
# if datasetname == "BUID":
# B = ['train', 'valid']
# else:
B = ['train']
file_image = file_image.replace("datasetname", datasetname)
file_label = file_label.replace("datasetname", datasetname)
path = path.replace("datasetname", datasetname)
with open(os.path.join(path, 'have_label.txt'), 'w') as F:
for j in B:
dir_label = file_label.replace("type", j)
dir_train = file_image.replace("type", j)
for i in os.listdir(dir_label):
file = os.path.join(dir_label, i)
img = np.array(Image.open(file))
values, counts = np.unique(img, return_counts = True)
if 60 in values:
a = i.replace("label", "image")
F.write(os.path.join(dir_train, a))
F.write(" ")
F.write(file)
F.write(" ")
F.write("[0, 2]")
F.write("\n")
with open(os.path.join(path, 'non_label.txt'), 'w') as F:
for j in B:
dir_label = file_label.replace("type", j)
dir_train = file_image.replace("type", j)
for i in os.listdir(dir_label):
file = os.path.join(dir_label, i)
img = np.array(Image.open(file))
values, counts = np.unique(img, return_counts = True)
if not 60 in values:
a = i.replace("label", "image")
F.write(os.path.join(dir_train, a))
F.write(" ")
F.write(file)
F.write(" ")
F.write("[0]")
F.write("\n") |