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generator.py
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generator.py
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from tensorflow.keras.preprocessing.image import load_img,img_to_array
from tensorflow.keras.utils import to_categorical
from sklearn.preprocessing import LabelEncoder
import numpy as np
import random
import os
import cv2
n_label = 6
#[其他,水体,建筑,耕地,林地,草地]
classes = [0.,29.,76.,150.,179.,226.]
labelencoder = LabelEncoder()
labelencoder.fit(classes)
filepath = './train/'
def randomcrop(img,size=128):
height,width,_ = img.shape
random_width = random.randint(0, width - size - 1)
random_height = random.randint(0, height - size - 1)
img_crop = img[random_height: random_height + size, random_width: random_width + size,:]
return img_crop
def get_train_val(val_rate=0.2,num_rate=0.5):
train_url = []
train_set = []
val_set = []
for pic in os.listdir(filepath + 'images'):
if(os.path.splitext(pic)[1] in ['.png','.tif','.jpg']):
train_url.append(pic)
random.shuffle(train_url)
total_num = len(train_url)
total_num = int(total_num * num_rate)
val_num = int(val_rate * total_num)
for i in range(total_num):
if i < val_num:
val_set.append(train_url[i])
else:
train_set.append(train_url[i])
return train_set,val_set
# def get_train_val(val_rate=0.2,num_rate=0.5,block_num=1000):
# train_url = []
# train_set = []
# val_set = []
# for pic in os.listdir(filepath + 'images'):
# if(os.path.splitext(pic)[1] in ['.png','.tif','.jpg']):
# train_url.append(pic)
# set_num = len(train_url) // block_num
# for i in range(set_num):
# block_url = train_url[i*block_num:(i+1)*block_num]
# random.shuffle(block_url)
# total_num = int(block_num * num_rate)
# val_num = int(val_rate * total_num)
# for j in range(total_num):
# if j < val_num:
# val_set.append(train_url[i*block_num+j])
# else:
# train_set.append(train_url[i*block_num+j])
# return train_set,val_set
# data for training
def generateData(batch_size,data=[],size=128):
while True:
train_data = []
train_label = []
batch = 0
for i in (range(len(data))):
url = data[i]
batch += 1
img = load_img(filepath + 'images/' + url)
img = img_to_array(img)
# img = randomcrop(img,size)
img = img / 255
train_data.append(img)
label = load_img(filepath + 'labels/' + url, color_mode='grayscale')
label = img_to_array(label)
# label = randomcrop(label,size)
label = label.reshape((size * size,))
train_label.append(label)
if batch % batch_size==0:
train_data = np.array(train_data)
train_label = np.array(train_label).flatten()
train_label = labelencoder.transform(train_label)
train_label = to_categorical(train_label, num_classes=n_label)
train_label = train_label.reshape((batch_size,size*size,n_label))
yield (train_data,train_label)
train_data = []
train_label = []
batch = 0
# data for validation
def generateValidData(batch_size,data=[],size=128):
while True:
valid_data = []
valid_label = []
batch = 0
for i in (range(len(data))):
url = data[i]
batch += 1
img = load_img(filepath + 'images/' + url)
img = img_to_array(img)
# img = randomcrop(img,size)
img = img / 255
valid_data.append(img)
label = load_img(filepath + 'labels/' + url, color_mode='grayscale')
label = img_to_array(label)
# label = randomcrop(label,size)
label = label.reshape((size * size,))
valid_label.append(label)
if batch % batch_size==0:
valid_data = np.array(valid_data)
valid_label = np.array(valid_label).flatten()
valid_label = labelencoder.transform(valid_label)
valid_label = to_categorical(valid_label, num_classes=n_label)
valid_label = valid_label.reshape((batch_size,size*size,n_label))
yield (valid_data,valid_label)
valid_data = []
valid_label = []
batch = 0