def get_next_batch(batch_size=64, cnt=0):
"""
# 生成一个训练batch
:param batch_size cnt
:return:
"""
batch_x = np.zeros([batch_size, IMAGE_HEIGHT * IMAGE_WIDTH])
batch_y = np.zeros([batch_size, MAX_CAPTCHA * CHAR_SET_LEN])
f = open(root + "mappings.txt", 'r')
lines = f.readlines()
f.close()
i = 0
for j in range(cnt * batch_size, (cnt + 1) * batch_size):
text = lines[j].split(",")[-1]
text = text.split("=")[0]
image = Image.open(root + str(j).zfill(4) + ".jpg")
# print(j)
# print(text)
# image.show()
image = convert2gray(image)
batch_x[
i, :] = image.flatten() / 255 # (image.flatten()-128)/128 mean为0
batch_y[i, :] = text2vec(text)
i += 1
return batch_x, batch_y
def get_batch(batch_size=128):
batch_x = np.zeros([batch_size, image_size])
batch_y = np.zeros([batch_size, char_size])
for i in range(batch_size):
text, image = captcha(char_set=char_set,
captcha_size=1,
width=image_w,
height=image_h)
batch_x[i, :] = img2vec(img2gray(image))
batch_y[i, :] = text2vec(char_set, text)
return batch_x, batch_y
def get_batch(batch_size=128):
batch_files = random.sample(files, batch_size)
batch_x = np.zeros([batch_size, image_size])
batch_y = np.zeros([batch_size, captcha_size * char_size])
for i in range(batch_size):
image = loadimg(os.path.join(fonts_dir, batch_files[i]))
text = batch_files[i][:4]
batch_x[i, :] = img2vec(img2gray(image))
batch_y[i, :] = text2vec(char_set, text)
return batch_x, batch_y
def get_next_batch_from_web(batch_size=64):
batch_x = np.zeros([batch_size, IMAGE_HEIGHT * IMAGE_WIDTH])
batch_y = np.zeros([batch_size, MAX_CAPTCHA * CHAR_SET_LEN])
text, image = wrap_gen_captcha_text_and_image(batch_size)
for i in range(batch_size):
batch_x[i, :] = image[i].flatten(
) / 255 # (image.flatten()-128)/128 mean为0
batch_y[i, :] = text2vec(text[i])
return batch_x, batch_y
def get_next_batch(batch_size=128):
"""
# 生成一个训练batch
:param batch_size:
:return:
"""
batch_x = np.zeros([batch_size, IMAGE_HEIGHT * IMAGE_WIDTH])
batch_y = np.zeros([batch_size, MAX_CAPTCHA * CHAR_SET_LEN])
for i in range(batch_size):
text, image = wrap_gen_captcha_text_and_image()
image = convert2gray(image)
batch_x[i, :] = image.flatten() / 255 # (image.flatten()-128)/128 mean为0
batch_y[i, :] = text2vec(text)
return batch_x, batch_y
def get_next_batch(batch_size=128):
"""
# 生成一个训练batch
:param batch_size:
:return:
"""
imgpath = "D:/gitrepos/captcha-tensorflow/vcode1/"
batch_x = np.zeros([batch_size, IMAGE_HEIGHT * IMAGE_WIDTH])
batch_y = np.zeros([batch_size, MAX_CAPTCHA * CHAR_SET_LEN])
td = train_data()
for i in range(batch_size):
#text, image = wrap_gen_captcha_text_and_image()
text, image = td.get_text_img(imgpath)
image = convert2gray(image)
batch_x[
i, :] = image.flatten() / 255 # (image.flatten()-128)/128 mean为0
batch_y[i, :] = text2vec(text)
return batch_x, batch_y
def get_next_batch(batch_size=128):
"""
# 生成一个训练batch
:param batch_size:
:return:
"""
batch_x = np.zeros([batch_size, IMAGE_HEIGHT * IMAGE_WIDTH])
batch_y = np.zeros([batch_size, MAX_CAPTCHA * CHAR_SET_LEN])
for i in range(batch_size):
text, image = wrap_gen_captcha_text_and_image()
if False: #此处改为Ture,用以输出查看训练图片
image_ = Image.fromarray(image)
image_.save('./test_out/%s.jpg' % text)
#exit()
batch_x[i, :] = image.flatten() / 255
batch_y[i, :] = text2vec(text)
return batch_x, batch_y
def get_next_batch(batch_size=128):
"""
# 生成一个训练batch
:param batch_size:
:return:
"""
batch_x = np.zeros([batch_size, IMAGE_HEIGHT * IMAGE_WIDTH])
#生成表示图片的二维向量，也就是输入图片的矩阵？？？？？？
batch_y = np.zeros([batch_size, MAX_CAPTCHA * CHAR_SET_LEN])
#生成一个二维向量，这里是过滤器？？？？？？
for i in range(batch_size):
text, image = wrap_gen_captcha_text_and_image()
image = convert2gray(image)
#x[:,i]表示取所有维中第i个数据，通常返回数组
#x[:,m:n]，即取所有维中第m到n-1个数据，含左不含右
#x[i,:]表示取第一维中下标为i的所有元素，通常返回数组
#flatten() 是将多维数组降位到一维并返回拷贝，默认降维是横向的
batch_x[
i, :] = image.flatten() / 255 # (image.flatten()-128)/128 mean为0
batch_y[i, :] = text2vec(text)
return batch_x, batch_y