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read_data19.py
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read_data19.py
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# -*- coding: utf-8 -*-
"""
Spyder Editor
This is a temporary script file.
"""
import pandas as pd
import numpy as np
import shutil
import os
from pathlib import Path
from tqdm import tqdm
#import cv2
#%%
src_im_fd = 'D:/dataset/ISIC/ISIC_2019_Training_Input/'
tar_im_fd = '../data/train19/'
df = pd.read_csv('D:/dataset/ISIC/ISIC_2019_Training_GroundTruth.csv')
df_v = df.values
img_name = df_v[:,0]
label_np = df_v[:,1:]
#labels = np.zeros_like(img_name)
#
#for idx,v in enumerate(label_np):
# labels[idx] = np.where(label_np[1]==1)[0][0]
#
labels = [ np.where(v==1)[0][0] for v in label_np]
dict_label = dict()
for i in range(8):
dict_label[i] = df.columns[1:][i]
for val,key in dict_label.items():
os.makedirs(tar_im_fd +key, exist_ok =True)
for idx,fn in enumerate(tqdm(img_name)):
src_fn = Path(src_im_fd)/(fn + '.jpg')
tar_fn = Path(tar_im_fd)/ dict_label[labels[idx]]/(fn + '.jpg')
if os.path.exists(str(src_fn)):
shutil.copyfile(src_fn,tar_fn)
else:
print(f'filename {str(src_fn)} not exist')
# #%% write test
# df = pd.read_csv('./data/ISIC/ISIC2018_Task3_Testing_Score_imb.csv')
# tar_im_fd = './data/ISIC/test18/'
# src_im_fd = '/home/minjie/dataset/ISIC/ISIC2018_Task3_Test_Input/'
# for val,key in dict_label.items():
# os.makedirs(tar_im_fd +key, exist_ok =True)
# df_v = df.values
# img_name = df_v[:,0]
# label_np = df_v[:,1:]
# labels = [ np.where(v==v.max())[0][0] for v in label_np]
# for idx,fn in enumerate(tqdm(img_name)):
# src_fn = Path(src_im_fd)/(fn + '.jpg')
# tar_fn = Path(tar_im_fd)/ dict_label[labels[idx]]/(fn + '.jpg')
# if os.path.exists(str(src_fn)):
# shutil.copyfile(src_fn,tar_fn)
# else:
# print(f'filename {str(src_fn)} not exist')
# #%% read ISIC19 data
# src_im_fd = '/home/minjie/dataset/ISIC/ISIC_2019_Training_Input/'
# tar_im_fd = './data/ISIC/train19/'
# df = pd.read_csv('./data/ISIC/ISIC_2019_Training_GroundTruth.csv')
# df_v = df.values
# img_name = df_v[:,0]
# label_np = df_v[:,1:]
# #labels = np.zeros_like(img_name)
# #
# #for idx,v in enumerate(label_np):
# # labels[idx] = np.where(label_np[1]==1)[0][0]
# #
# labels = [ np.where(v==1)[0][0] for v in label_np]
# dict_label = dict()
# n_label = len(df.columns)-2
# for i in range(n_label):
# dict_label[i] = df.columns[1:][i]
# dict_label[3]= 'AKIEC'
# for val,key in dict_label.items():
# os.makedirs(tar_im_fd +key, exist_ok =True)
# for idx,fn in enumerate(tqdm(img_name)):
# src_fn = Path(src_im_fd)/(fn + '.jpg')
# tar_fn = Path(tar_im_fd)/ dict_label[labels[idx]]/(fn + '.jpg')
# if os.path.exists(str(src_fn)):
# #img = cv2.imread(str(src_fn))
# #img_resize = cv2.resize(img,(600,450))
# #cv2.imwrite(str(tar_fn),img_resize)
# shutil.copyfile(src_fn,tar_fn)
# else:
# print(f'filename {str(src_fn)} not exist')