自己看的
import numpy as np
import os
from pil import image
import random
os.environ['tf_cpp_min_log_level'] = '2'
train_file = './data/train'
test_file = './data/test'
class data:
def __init__(self, batch_size, istrain = true):
self.batch_size = batch_size # batch的大小
self.classific = 9 # 9種分類
self.istrain = istrain # 訓練
self.ls_imgs_labels = self.get_imgs_labels() # 獲得全部的路徑
self.epoch_imgs, self.epoch_labels = self.get_epoch() # 獲得全部的內容
def get_classific(self):
return self.classific
# 讀取全部資料的路徑
def get_imgs_labels(self):
ls_imgs =
cwd = os.getcwd()
if self.istrain:
folder = train_file
print("訓練檔案")
else:
folder = test_file
print("測試檔案")
folder = os.path.normpath(folder)
folder = os.path.join(cwd,folder)
# 每個資料夾的裡面的標籤
label = 0
# 遍歷每乙個資料夾
for files in os.listdir(folder):
if files == "airplane":
label = 0
elif files == "automobile":
label = 1
elif files == "bird":
label = 2
elif files == "cat":
label = 3
elif files == "deer":
label = 4
elif files == "dog":
label = 5
elif files == "horse":
label = 6
elif files == "ship":
label = 7
elif files == "truck":
label = 8
else:
print("沒有這個種類 :",files)
return
curr_folder = os.path.join(folder,files)
# print("當前處理 :",files,"標籤 :",label)
# 每一張
for pic in os.listdir(curr_folder):
ls_pic =
img_path = os.path.join(curr_folder,pic)
# img = image.open(img_path)
# img_raw = img.tobytes()
# labels = [0]*self.classific
# labels[label] = 1
print("全部記錄完成 %d" % len(ls_imgs))
return ls_imgs
# 獲得全部
def get_epoch(self):
print("begin load data")
# 得到所有路徑
ls_imgs_labels = self.ls_imgs_labels
imgs =
labels =
for name in ls_imgs_labels:
img = image.open(name[0])
img = np.asarray(img) / 255.0
label = [0] * self.classific
label[name[1]] = 1
print("data load successful")
return imgs, labels
# 從epoch裡面選擇batch個
def get_epoch_batch(self):
length = len(self.epoch_labels)
curr_imgs =
curr_labels =
count = 0
while(count < self.batch_size):
begin = int(random.random() * length)
count = count + 1
return curr_imgs, curr_labels
# 獲得batch_size個圖
def get_batch(self):
# 從乙個隨機數開始
length = len(self.ls_imgs_labels)
# 儲存需要得到的名
curr_img_name =
count = 0
while(count < self.batch_size):
begin = int(random.random() * length)
count = count + 1
imgs =
labels =
for name in curr_img_name:
img = image.open(name[0])
img = np.asarray(img) / 255.0
label = [0]*self.classific
label[name[1]] = 1
# print(name)
return imgs,labels
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