檔案夾周遊和檔案周遊
# 周遊檔案夾和檔案，傳回檔案的路徑和對應的檔案的名稱，同時可以根據自己的應用需要進行更改
def get_img_file(file_name):
imagelist = []
for parent, dirnames, filenames in os.walk(file_name):
for filename in filenames:
if filename.lower().endswith(('.bmp', '.dib', '.png', '.jpg', '.jpeg', '.pbm', '.pgm', '.ppm', '.tif', '.tiff')):
imagelist.append(os.path.join(parent, filename))
return imagelist,filenames
隻周遊目前檔案或者隻周遊目前檔案夾
def GetDirectory(path):
directory=os.listdir(path)
directory_list=[]
for dirs in directory:
if not os.path.isfile(os.path.join(path,dirs)):
directory_list.append(os.path.join(path,dirs))
return directory_list
def GetFile(path):
files=os.listdir(path)
file_list=[]
for file in files:
if os.path.isfile(os.path.join(path,file)):
file_list.append(os.path.join(path,file))
return file_list
和上段代碼的功能類似，周遊根目錄下的所有檔案夾和檔案，隻是這個是組個周遊，可以人為的調整和修改：
def preprocess(src_root, dst_root):
"""
:param src_root:
:param dst_root:
:return:
"""
if not os.path.isdir(src_root):
print("[Err]: invalid source root")
return
if not os.path.isdir(dst_root):
os.makedirs(dst_root)
print("{} made".format(dst_root))
# 建立用于訓練MOT的目錄結構
dst_img_dir_train = dst_root + '/images/train'
dst_img_dir_test = dst_root + '/images/test'
dst_labels_with_ids = dst_root + '/labels_with_ids'
if not os.path.isdir(dst_img_dir_train):
os.makedirs(dst_img_dir_train)
if not os.path.isdir(dst_img_dir_test):
os.makedirs(dst_img_dir_test)
if not os.path.isdir(dst_labels_with_ids):
os.makedirs(dst_labels_with_ids)
# 周遊src_root, 進一步完善訓練目錄并拷貝檔案
for x in os.listdir(src_root):
x_path = src_root + '/' + x
if os.path.isdir(x_path):
for y in os.listdir(x_path):
if y.endswith('.jpg'):
y_path = x_path + '/' + y
if os.path.isfile(y_path):
# 建立用于訓練的圖檔目标目錄
dst_img1_dir = dst_img_dir_train + '/' + x + '/img1'
if not os.path.isdir(dst_img1_dir):
os.makedirs(dst_img1_dir)
# copy image to train image dir
dst_f_path = dst_img1_dir + y
if os.path.isfile(dst_f_path):
shutil.copy(y_path, dst_img1_dir)
print('{} cp to {}'.format(y, dst_img1_dir))
else:
print('{} already exists.'.format(dst_f_path))
多張圖檔生成視訊代碼
import os
import cv2
def generate_video(path,size,fps=25):
# fps = 24 #幀率
# size = (640, 480)
videowriter = cv2.VideoWriter("./resultout/test.mp4",cv2.VideoWriter_fourcc(*'mp4v'),fps,size)
#path = r'F:/data/predict_landmark/'
for rootpath, dirs, names in os.walk(path):
names.sort()
for name in names:
img = cv2.imread(os.path.join(rootpath, name))
print("--", name)
videowriter.write(img)
videowriter.release()
generate_video('./resultout/frame/',(1920, 1080),25)
删除檔案和檔案夾
需要在執行某些代碼前清空指定的檔案夾，如果直接用os.remove()，可能出現因檔案夾中檔案被占用而無法删除，解決方法也很簡單，先強制删除檔案夾，再重建立同名檔案夾即可
import shutil
shutil.rmtree('要清空的檔案夾名')
os.mkdir('要清空的檔案夾名')
把一個檔案從一個檔案夾移動到另一個檔案夾，并同時重命名，用shutil也很簡單
shutil.move('原檔案夾/原檔案名','目标檔案夾/目标檔案名')
同理一個檔案夾的檔案複制到另一個檔案夾可以使用下面的代碼：
shutil.copy("源目錄/檔案名", "目的目錄/檔案名")
資料集元素分割
這裡有多種方式，先上一個簡單的，然後來一個比較完全的。
import os
import random
def main():
random.seed(0) # 設定随機種子，保證随機結果可複現
files_path = "F:\\車道線大圖"
assert os.path.exists(files_path), "path: '{}' does not exist.".format(files_path)
val_rate = 0.5
# 讀取資料，并周遊圖檔名稱，通過'.'進行分割字元串，取第一部分，然後排序
files_name = sorted([file.split(".")[0] for file in os.listdir(files_path)])
files_num = len(files_name)
# 生成随機索引，k表示從輸入的序列中随機選擇k個無重複的元素
val_index = random.sample(range(0, files_num), k=int(files_num*val_rate))
train_files = []
val_files = []
for index, file_name in enumerate(files_name):
if index in val_index:
val_files.append(file_name)
else:
train_files.append(file_name)
try:
train_f = open("train.txt", "x")
eval_f = open("val.txt", "x")
train_f.write("\n".join(train_files))
eval_f.write("\n".join(val_files))
except FileExistsError as e:
print(e)
exit(1)
'''
Description: 學習使用
Version: 1.0
Author: 趙守風
Email: [email protected]
Date: 2021-03-24 14:17:19
LastEditors: zsf
LastEditTime: 2021-03-24 17:06:01
'''
import torch
import os
import random
import shutil
import math
'''
@description: 直接切分資料集，copy資料到對應的訓練集、測試集和驗證集，這裡不考慮類别的問題
@param {*} datapath: 原始資料路徑
@param {*} rootpath: 儲存切分後的路徑root
@param {*} train_rate: 訓練集占總數的百分比
@param {*} val_rate: 驗證集占總數百分比
@param {*} test_rate: 測試集占總數的百分比
@return {*}
@author: zsf
'''
def data_split(datapath, rootpath="./data_split", train_rate=0.8, val_rate=0.1, test_rate=0.1):
for parent, dirnames, filenames in os.walk(datapath):
for dirname in dirnames:
filepaths = os.listdir(os.path.join(parent, dirname))
# 删除其他的檔案
filepaths = [x for x in filepaths if x.lower().endswith(('.bmp', '.dib', '.png', '.jpg', '.jpeg', '.pbm', '.pgm', '.ppm', '.tif', '.tiff')) ]
# 開始切分
random.shuffle(filepaths)
for i in range(len(filepaths)):
if i < math.floor(train_rate*len(filepaths)):
sub_path = os.path.join(rootpath, 'train_set', dirname)
elif i < math.floor((train_rate+val_rate)*len(filepaths)):
sub_path = os.path.join(rootpath, 'val_set', dirname)
elif i < len(filepaths):
sub_path = os.path.join(rootpath, 'test_set', dirname)
if os.path.exists(sub_path) == 0:
os.makedirs(sub_path)
shutil.copy(os.path.join(datapath, dirname, filepaths[i]), os.path.join(sub_path, filepaths[i])) # 複制圖檔，從源到目的地
'''
@description: 這裡生成對應的txt檔案不挪動圖檔
@param {*} datapath: 原始資料路徑
@param {*} rootpath: 儲存切分後的路徑root
@param {*} train_rate: 訓練集占總數的百分比
@param {*} val_rate: 驗證集占總數百分比
@param {*} test_rate: 測試集占總數的百分比
@return {*}
@author: zsf
'''
def data_split_txt(datapath, rootpath="./data_split", train_rate=0.8, val_rate=0.1, test_rate=0.1):
for parent, dirnames, filenames in os.walk(datapath):
index = 0
for dirname in dirnames:
filepaths = os.listdir(os.path.join(parent, dirname))
# 删除其他的檔案
filepaths = [x for x in filepaths if x.lower().endswith(('.bmp', '.dib', '.png', '.jpg', '.jpeg', '.pbm', '.pgm', '.ppm', '.tif', '.tiff')) ]
# 開始切分
random.shuffle(filepaths)
for i in range(len(filepaths)):
if i < math.floor(train_rate*len(filepaths)):
txt_path = 'train_set.txt'
elif i < math.floor((train_rate+val_rate)*len(filepaths)):
txt_path = 'val_set.txt'
elif i < len(filepaths):
txt_path = 'test_set.txt'
with open(os.path.join(rootpath, txt_path), mode='a') as file:
file.write(str(index) + ' ' + os.path.join(datapath, dirname, filepaths[i]) + '\n')
index += 1
一張圖檔均勻切割
import os
from PIL import Image
path="D:\\SR\\USRNet-master\\USRNet-master\\testsets\\image\\20.jpg"
smallpath='D:\\SR\\USRNet-master\\USRNet-master\\testsets\\small'
def splitimage(src, rownum, colnum, dstpath):
img = Image.open(src)
w, h = img.size
if rownum <= h and colnum <= w:
print('Original image info: %sx%s, %s, %s' % (w, h, img.format, img.mode))
print('開始處理圖檔切割, 請稍候...')
s = os.path.split(src)
if dstpath == '':
dstpath = s[0]
fn = s[1].split('.')
basename = fn[0]
ext = fn[-1]
num = 0
rowheight = h // rownum
colwidth = w // colnum
for r in range(rownum):
for c in range(colnum):
box = (c * colwidth, r * rowheight, (c + 1) * colwidth, (r + 1) * rowheight)
p = os.path.join(dstpath, basename + '_' + str(num) + '.' + ext)
img.crop(box).save(p)
num = num + 1
print("all = {}, num = {}".format(rownum*colnum, num))
print('圖檔切割完畢，共生成 %s 張小圖檔。' % num)
else:
print('不合法的行列切割參數！')
splitimage(path, 10, 10, smallpath)
視訊轉圖檔
cap = cv2.VideoCapture('D:/SR/src/2.mp4')
i =0
while(1):
ret, frame = cap.read()
#cv2.imshow("image",frame)
cv2.imwrite("D:/SR/USRNet-master/USRNet-master/testsets/image/"+str(i)+'.jpg',frame)
i+=1
路徑字元串處理
def save_image(output_dir, image_path):
if not os.path.exists(output_dir):
os.makedirs(output_dir)
image_name = os.path.split(image_path)[-1]
name, ext = os.path.splitext(image_name)
return os.path.join(output_dir, "{}".format(name)) + ext
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