如何读取(解码).tfrecords文件,查看内部图像并进行扩充? [英] How to read (decode) .tfrecords file, see the images inside and do augmentation?
问题描述
我有一个 .tfrecords
文件,我想提取文件,查看文件中的图像并进行扩充.我正在使用
噪声:
模糊:
噪声模糊:
噪声模糊镜像:
增强后每个标签的图像数量(每个标签精确平衡30张图像):
与上面相同的扩充,但是对于带有标签图像的输入和输出文件夹,而不是TFRecordDataset,请将 c_inp_dir
和 c_out_dir
更改为您的文件夹路径:
import osos.environ ['TF_CPP_MIN_LOG_LEVEL'] ='3'将tensorflow导入为tf,将tensorflow_addons导入为tfa,将PIL.Image,将numpy导入为np,将数学,将matplotlib.pyplot导入为pltc_inp_dir ='./images/'c_out_dir ='./images_out/'c_augment_types =('noise','blur','noise_blur','noise_blur_mirror')c_res_class_size = None#如果为None,则自动配置为最大类大小def calc_labels(dirn = None):如果dirn为None:礼服= c_inp_dircnts,标签= {},[]对于已排序的标签(os.listdir(f'{dirn}')):label = int(标签)labels.append(标签)cnts [label] = len(os.listdir(f'{dirn}/{label}/'))返回cnts,标签def img_gen():cnts = {}对于已排序的标签(os.listdir(c_inp_dir)):label = int(标签)对于已排序的fname(os.listdir(f'{c_inp_dir}/{label}/')):img_arr = np.array(PIL.Image.open(f'{c_inp_dir}/{label}/{fname}'))产量标签,img_arr,fnamedef gaussian_noise(inp,stddev):噪声= tf.random.normal(shape = tf.shape(inp),均值= 0.0,stddev = stddev,dtype = inp.dtype)返回inp +噪声def扩充(a,cnt):min_noise_stddev,max_noise_stddev = 5.,20.blur_kern,min_blur_stddev,max_blur_stddev = 3、1、5.断言cnt> = 1pad_a = lambda x:np.pad(x,((0,2 ** math.ceil(math.log(x.shape [0])/math.log(2))-x.shape [0]),(0,2 ** math.ceil(math.log(x.shape [1])/math.log(2))-x.shape [1]),(0,0)),constant_values = 0)post_a = lambda x:np.clip(x [:a.shape [0],:a.shape [1]],0,255).astype(np.uint8)产生'orig',a中位数-= 1res = []fcnt = math.ceil(cnt/len(c_augment_types))linsp = lambda l,r,c:[(l +(i +1)*(r-l)/(c +1))对于范围(c)中的i]对于zip(linsp(min_noise_stddev,max_noise_stddev,fcnt),linsp(min_blur_stddev,max_blur_stddev,fcnt)中的noise_stddev,blur_stddev :)如果c_augment_types中的噪声"为:#yield'noise',post_a(tf.keras.layers.GaussianNoise(stddev = noise_stddev)(prep_a,training = True).numpy())res.append(('noise',post_a(gaussian_noise(a.astype(np.float32),stddev = noise_stddev).numpy())))如果c_augment_types中的模糊":res.append(('blur',post_a(tfa.image.gaussian_filter2d(pad_a(a).astype(np.float32),filter_shape = blur_kern,sigma = blur_stddev).numpy())))如果c_augment_types中为'noise_blur'或c_augment_types中为'noise_blur_mirror':nbr = post_a(tfa.image.gaussian_filter2d(pad_a(gaussian_noise(a.astype(np.float32),stddev = noise_stddev).numpy()),filter_shape = blur_kern,sigma = blur_stddev).numpy())如果c_augment_types中的'noise_blur':res.append(('noise_blur',nbr))如果c_augment_types中的'noise_blur_mirror':res.append(('noise_blur_mirror',tf.image.flip_left_right(nbr).numpy().astype(np.uint8)))断言cnt< = len(res)< = cnt + len(c_augment_types),(cnt,len(res),len(c_augment_types))res的收益[:cnt]def process():labels_cnts,标签= calc_labels()max_class_size = max(labels_cnts.values())如果c_res_class_size不为None:断言max_class_size< = c_res_class_size,f'最大类大小为{max_class_size},而请求的res类大小更小,{c_res_class_size}!"class_size = c_res_class_size别的:class_size = max_class_sizecur_labels_cnts = {}对于枚举(img_gen())中的iimg((label,imga,fname)):os.makedirs(f'{c_out_dir}/{label}/',exist_ok = True)cur_labels_cnts [label] = cur_labels_cnts.get(label,0)+ 1need_cnt = class_size//labels_cnts [label] + int(cur_labels_cnts [label]< = class_size%labels_cnts [label])对于iaug,(taug,aug)枚举(augment(imga,need_cnt)):PIL.Image.fromarray(aug).save(f'{c_out_dir}/{label}/{fname}.{iaug} _ {taug} .png')如果(iimg%10)== 0:打印(iimg,'',sep ='',end ='',flush = True)def plot_cnts(dirn):labels_cnts = calc_labels(dirn)[0]x,y = zip(* sorted(labels_cnts.items(),key = lambda e:e [0]))plt.xlabel('标签')plt.ylabel('num images')plt.plot(x,y)plt.xticks(x)plt.show()def main():过程()plot_cnts(c_inp_dir)plot_cnts(c_out_dir)主要的()
I have a .tfrecords
file and I want to extract, see the images in the file and augment them.
I am using https://colab.research.google.com
TensorFlow version: 2.3.0
And for the following code
raw_dataset = tf.data.TFRecordDataset("*path.tfrecords")
for raw_record in raw_dataset.take(1):
example = tf.train.Example()
example.ParseFromString(raw_record.numpy())
print(example)
I am facing the following output:
features {
feature {
key: "depth"
value {
int64_list {
value: 3
}
}
}
feature {
key: "height"
value {
int64_list {
value: 333
}
}
}
feature {
key: "image_raw"
value {
bytes_list {
value:
}
}
}
feature {
key: "label"
value {
int64_list {
value: 16
}
}
}
feature {
key: "width"
value {
int64_list {
value: 500
}
}
}
}
Here is a simple code that can extract your .tfrecord images as .png format.
To run next codes you need to install one time pip modules through pip install tensorflow tensorflow_addons pillow numpy matplotlib
.
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import tensorflow as tf, PIL.Image, numpy as np
raw_dataset = tf.data.TFRecordDataset('max_32_set.tfrecords')
for i, raw_record in enumerate(raw_dataset.take(3)):
example = tf.train.Example()
example.ParseFromString(raw_record.numpy())
info = {}
for k, v in example.features.feature.items():
if k == 'image_raw':
info[k] = v.bytes_list.value[0]
elif k in ['depth', 'height', 'width']:
info[k] = v.int64_list.value[0]
img_arr = np.frombuffer(info['image_raw'], dtype = np.uint8).reshape(
info['height'], info['width'], info['depth']
)
# You can use img_arr numpy array above to directly augment/preprocess
# your image without saving it to .png.
img = PIL.Image.fromarray(img_arr)
img.save(f'max_32_set.tfrecords.{str(i).zfill(5)}.png')
First image from dataset:
Below is code for drawing number of images per each label. Labels inside max_32_set.tfrecords
file are represented as integers (not string names), probably names of labels are located in separate small file with meta information about dataset.
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import tensorflow as tf, numpy as np, matplotlib.pyplot as plt
raw_dataset = tf.data.TFRecordDataset('max_32_set.tfrecords')
labels_cnts = {}
for i, raw_record in enumerate(raw_dataset.as_numpy_iterator()):
example = tf.train.Example()
example.ParseFromString(raw_record)
info = {}
for k, v in example.features.feature.items():
if k == 'label':
info[k] = v.int64_list.value[0]
labels_cnts[info['label']] = labels_cnts.get(info['label'], 0) + 1
x, y = zip(*sorted(labels_cnts.items(), key = lambda e: e[0]))
plt.xlabel('label')
plt.ylabel('num images')
plt.plot(x, y)
plt.xticks(x)
plt.show()
Plot for max_32_set.tfrecords
:
Next code does augmentation using gaussian noise and gaussian blur, augmented tfrecord dataset is saved to max_32_set.augmented.tfrecords
file:
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import tensorflow as tf, tensorflow_addons as tfa, PIL.Image, numpy as np, math
c_inp_fname = 'max_32_set.tfrecords'
c_out_fname = 'max_32_set.augmented.tfrecords'
c_augment_types = ('noise', 'blur', 'noise_blur', 'noise_blur_mirror')
c_res_class_size = None # If None then auto configured to maximal class size
def calc_labels():
raw_dataset = tf.data.TFRecordDataset(c_inp_fname)
cnts, labels = {}, []
for i, raw_record in enumerate(raw_dataset):
example = tf.train.Example()
example.ParseFromString(raw_record.numpy())
label = example.features.feature['label'].int64_list.value[0]
cnts[label] = cnts.get(label, 0) + 1
labels.append(label)
return cnts, labels
def img_gen():
raw_dataset = tf.data.TFRecordDataset(c_inp_fname)
for i, raw_record in enumerate(raw_dataset):
example = tf.train.Example()
example.ParseFromString(raw_record.numpy())
info = {}
for k, v in example.features.feature.items():
if k == 'image_raw':
info[k] = v.bytes_list.value[0]
elif k in ['depth', 'height', 'width']:
info[k] = v.int64_list.value[0]
img_arr = np.frombuffer(info['image_raw'], dtype = np.uint8).reshape(
info['height'], info['width'], info['depth']
)
yield example, img_arr
def gaussian_noise(inp, stddev):
noise = tf.random.normal(shape = tf.shape(inp), mean = 0.0, stddev = stddev, dtype = inp.dtype)
return inp + noise
def augment(a, cnt):
min_noise_stddev, max_noise_stddev = 5., 20.
blur_kern, min_blur_stddev, max_blur_stddev = 3, 1., 5.
assert cnt >= 1
pad_a = lambda x: np.pad(x, (
(0, 2 ** math.ceil(math.log(x.shape[0]) / math.log(2)) - x.shape[0]),
(0, 2 ** math.ceil(math.log(x.shape[1]) / math.log(2)) - x.shape[1]),
(0, 0)), constant_values = 0)
post_a = lambda x: np.clip(x[:a.shape[0], :a.shape[1]], 0, 255).astype(np.uint8)
yield 'orig', a
cnt -= 1
res = []
fcnt = math.ceil(cnt / len(c_augment_types))
linsp = lambda l, r, c: [(l + (i + 1) * (r - l) / (c + 1)) for i in range(c)]
for noise_stddev, blur_stddev in zip(linsp(min_noise_stddev, max_noise_stddev, fcnt), linsp(min_blur_stddev, max_blur_stddev, fcnt)):
if 'noise' in c_augment_types:
#yield 'noise', post_a(tf.keras.layers.GaussianNoise(stddev = noise_stddev)(prep_a, training = True).numpy())
res.append(('noise', post_a(gaussian_noise(a.astype(np.float32), stddev = noise_stddev).numpy())))
if 'blur' in c_augment_types:
res.append(('blur', post_a(tfa.image.gaussian_filter2d(pad_a(a).astype(np.float32), filter_shape = blur_kern, sigma = blur_stddev).numpy())))
if 'noise_blur' in c_augment_types or 'noise_blur_mirror' in c_augment_types:
nbr = post_a(tfa.image.gaussian_filter2d(
pad_a(gaussian_noise(a.astype(np.float32), stddev = noise_stddev).numpy()),
filter_shape = blur_kern, sigma = blur_stddev).numpy())
if 'noise_blur' in c_augment_types:
res.append(('noise_blur', nbr))
if 'noise_blur_mirror' in c_augment_types:
res.append(('noise_blur_mirror', tf.image.flip_left_right(nbr).numpy().astype(np.uint8)))
assert cnt <= len(res) <= cnt + len(c_augment_types), (cnt, len(res), len(c_augment_types))
yield from res[:cnt]
def process():
labels_cnts, labels = calc_labels()
max_class_size = max(labels_cnts.values())
if c_res_class_size is not None:
assert max_class_size <= c_res_class_size, f'Maximal class size is {max_class_size}, while requested res class size is smaller, {c_res_class_size}!'
class_size = c_res_class_size
else:
class_size = max_class_size
cur_labels_cnts = {}
for iimg, (proto, imga) in enumerate(img_gen()):
label = proto.features.feature['label'].int64_list.value[0]
cur_labels_cnts[label] = cur_labels_cnts.get(label, 0) + 1
need_cnt = class_size // labels_cnts[label] + int(cur_labels_cnts[label] <= class_size % labels_cnts[label])
for iaug, (taug, aug) in enumerate(augment(imga, need_cnt)):
#PIL.Image.fromarray(aug).save(f'max_32_set.tfrecords.aug.{str(iimg).zfill(5)}.{iaug}_{taug}.png')
protoc = type(proto)()
protoc.ParseFromString(proto.SerializeToString())
protoc.features.feature['image_raw'].bytes_list.value[0] = aug.tobytes()
yield protoc.SerializeToString()
if (iimg % 10) == 0:
print(iimg, ' ', sep = '', end = '', flush = True)
def main():
assert tf.executing_eagerly()
tf.data.experimental.TFRecordWriter(c_out_fname).write(
tf.data.TFRecordDataset.from_generator(process, tf.string)
)
main()
Example augmented images:
Original:
Noised:
Blurred:
Noised-blurred:
Noised-blurred-mirrored:
Number of images per label after augmentation (exactly balanced 30 images per label):
Same augmentation as above but for the case of input and output folders with labeled images, instead of TFRecordDataset, change c_inp_dir
and c_out_dir
to your folders paths:
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import tensorflow as tf, tensorflow_addons as tfa, PIL.Image, numpy as np, math, matplotlib.pyplot as plt
c_inp_dir = './images/'
c_out_dir = './images_out/'
c_augment_types = ('noise', 'blur', 'noise_blur', 'noise_blur_mirror')
c_res_class_size = None # If None then auto configured to maximal class size
def calc_labels(dirn = None):
if dirn is None:
dirn = c_inp_dir
cnts, labels = {}, []
for label in sorted(os.listdir(f'{dirn}')):
label = int(label)
labels.append(label)
cnts[label] = len(os.listdir(f'{dirn}/{label}/'))
return cnts, labels
def img_gen():
cnts = {}
for label in sorted(os.listdir(c_inp_dir)):
label = int(label)
for fname in sorted(os.listdir(f'{c_inp_dir}/{label}/')):
img_arr = np.array(PIL.Image.open(f'{c_inp_dir}/{label}/{fname}'))
yield label, img_arr, fname
def gaussian_noise(inp, stddev):
noise = tf.random.normal(shape = tf.shape(inp), mean = 0.0, stddev = stddev, dtype = inp.dtype)
return inp + noise
def augment(a, cnt):
min_noise_stddev, max_noise_stddev = 5., 20.
blur_kern, min_blur_stddev, max_blur_stddev = 3, 1., 5.
assert cnt >= 1
pad_a = lambda x: np.pad(x, (
(0, 2 ** math.ceil(math.log(x.shape[0]) / math.log(2)) - x.shape[0]),
(0, 2 ** math.ceil(math.log(x.shape[1]) / math.log(2)) - x.shape[1]),
(0, 0)), constant_values = 0)
post_a = lambda x: np.clip(x[:a.shape[0], :a.shape[1]], 0, 255).astype(np.uint8)
yield 'orig', a
cnt -= 1
res = []
fcnt = math.ceil(cnt / len(c_augment_types))
linsp = lambda l, r, c: [(l + (i + 1) * (r - l) / (c + 1)) for i in range(c)]
for noise_stddev, blur_stddev in zip(linsp(min_noise_stddev, max_noise_stddev, fcnt), linsp(min_blur_stddev, max_blur_stddev, fcnt)):
if 'noise' in c_augment_types:
#yield 'noise', post_a(tf.keras.layers.GaussianNoise(stddev = noise_stddev)(prep_a, training = True).numpy())
res.append(('noise', post_a(gaussian_noise(a.astype(np.float32), stddev = noise_stddev).numpy())))
if 'blur' in c_augment_types:
res.append(('blur', post_a(tfa.image.gaussian_filter2d(pad_a(a).astype(np.float32), filter_shape = blur_kern, sigma = blur_stddev).numpy())))
if 'noise_blur' in c_augment_types or 'noise_blur_mirror' in c_augment_types:
nbr = post_a(tfa.image.gaussian_filter2d(
pad_a(gaussian_noise(a.astype(np.float32), stddev = noise_stddev).numpy()),
filter_shape = blur_kern, sigma = blur_stddev).numpy())
if 'noise_blur' in c_augment_types:
res.append(('noise_blur', nbr))
if 'noise_blur_mirror' in c_augment_types:
res.append(('noise_blur_mirror', tf.image.flip_left_right(nbr).numpy().astype(np.uint8)))
assert cnt <= len(res) <= cnt + len(c_augment_types), (cnt, len(res), len(c_augment_types))
yield from res[:cnt]
def process():
labels_cnts, labels = calc_labels()
max_class_size = max(labels_cnts.values())
if c_res_class_size is not None:
assert max_class_size <= c_res_class_size, f'Maximal class size is {max_class_size}, while requested res class size is smaller, {c_res_class_size}!'
class_size = c_res_class_size
else:
class_size = max_class_size
cur_labels_cnts = {}
for iimg, (label, imga, fname) in enumerate(img_gen()):
os.makedirs(f'{c_out_dir}/{label}/', exist_ok = True)
cur_labels_cnts[label] = cur_labels_cnts.get(label, 0) + 1
need_cnt = class_size // labels_cnts[label] + int(cur_labels_cnts[label] <= class_size % labels_cnts[label])
for iaug, (taug, aug) in enumerate(augment(imga, need_cnt)):
PIL.Image.fromarray(aug).save(f'{c_out_dir}/{label}/{fname}.{iaug}_{taug}.png')
if (iimg % 10) == 0:
print(iimg, ' ', sep = '', end = '', flush = True)
def plot_cnts(dirn):
labels_cnts = calc_labels(dirn)[0]
x, y = zip(*sorted(labels_cnts.items(), key = lambda e: e[0]))
plt.xlabel('label')
plt.ylabel('num images')
plt.plot(x, y)
plt.xticks(x)
plt.show()
def main():
process()
plot_cnts(c_inp_dir)
plot_cnts(c_out_dir)
main()
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