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