tensorflow入门教程(二十二)使用slim对图像识别与检测(下)

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1、概述

上一节使用slim对图像进行识别，但是一张图片里就识别出一样东西，这节我们就来学习怎么检测图片里更多的物品。上一节我们使用的是Inception-ResNet-v2模型，这一节我们使用的是VGG模型。因为VGG在不仅在图像的识别上效果不错，而且在图像检测方面的效果也很好。

2、下载VGG19模型

和上节一样，我们也使用别人在ImageNet上训练好的模型来识别图片内容。打开以下网页，https://github.com/tensorflow/models/tree/master/research/slim

下载VGG 19模型。

3、导入模块

#encoding:utf-8
import tensorflow as tf
from matplotlib import pyplot as plt
from nets import vgg
import numpy as np
from datasets import imagenet
import os
import Image
# 加载像素均值及相关函数
from preprocessing.vgg_preprocessing import (_mean_image_subtraction,_R_MEAN, _G_MEAN, _B_MEAN)
slim = tf.contrib.slim
#这是我们下载的VGG19文件路径
vgg_checkpoint = 'checkpoint/vgg_19.ckpt'#获取imagenet所有分类的名字，这里有1000个分类
names = imagenet.create_readable_names_for_imagenet_labels()

4、预处理图片

#待检测图片
sample_image = '01.jpg'
#读取图片
image = tf.image.decode_jpeg(tf.read_file(sample_image), channels=3)
#将图片数据转成float型
image_float = tf.to_float(image, name='ToFloat')
#将每个像素减去像素的均值
processed_image = _mean_image_subtraction(image_float, [_R_MEAN, _G_MEAN, _B_MEAN])
#增加一个维度
processed_images = tf.expand_dims(processed_image, 0)

我比较想知道图片经过处理以后变成什么样，这里就可以用代码将其现实出来看看，

with tf.Session() as sess:reimg, np_image = sess.run([image, processed_image])
plt.figure()
p1 = plt.subplot(121)
p2 = plt.subplot(122)p1.set_title('Source image')
p1.imshow(reimg)
p1.axis('off')p2.set_title('Preprocessing image')
p2.imshow(np_image)
p2.axis('off')
plt.show()

运行结果如下，

可以看到，减去像素均值以后，图片被分为一块一块的了。

5、创建并载入模型

#创建模型
with slim.arg_scope(vgg.vgg_arg_scope()):logist, _ = vgg.vgg_19(processed_images,num_classes=1000,is_training=False,spatial_squeeze=False)#将logist的最大索引放到pred里，代表分类pred = tf.argmax(logist, dimension=3)#指定模型文件init_fn = slim.assign_from_checkpoint_fn(vgg_checkpoint, slim.get_model_variables('vgg_19'))#run
with tf.Session() as sess:init_fn(sess)reimg, seg, np_image = sess.run([image, pred, processed_image])

6、解析并显示检测结果

#去除空的维度
seg = np.squeeze(seg)
#去除其中重复的元素
unique_classes, relabeled_image = np.unique(seg, return_inverse=True)
seg_size = seg.shaperelabeled_image = relabeled_image.reshape(seg_size)#显示函数
def showlab(img, labels_str = []):plt.figure()p1 = plt.subplot(133)p2 = plt.subplot(132)p3 = plt.subplot(131)#显示原图片p1.set_title('Source image')p1.imshow(reimg)p1.axis('off')#显示预处理图片p2.set_title('Preprocessing image')p2.imshow(np_image)p2.axis('off')p3.set_title('Recognition image')minval = np.min(img)maxval = np.max(img)#显示识别图片cmap = plt.get_cmap('Paired', maxval - minval + 1)mat = p3.matshow(img, cmap = cmap, vmin = minval - 0.5, vmax = maxval + 0.5)cax = plt.colorbar(mat, ticks = np.arange(minval, maxval + 1), shrink = 1.2)cax.ax.set_yticklabels(labels_str)#显示plt.show()labels_names = []
for index, current_class_number in enumerate(unique_classes):labels_names.append(str(index) + ' ' + names[current_class_number + 1])print(str(index) + ' ' + names[current_class_number + 1])showlab(relabeled_image, labels_names)

7、运行结果

截图中解析的结果有点小，我将结果打印出来，

0 hammerhead, hammerhead shark

1 stingray

2 sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita

3 coucal

4 hummingbird

5 spoonbill

6 ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus

7 sturgeon

8 abaya

9 aircraft carrier, carrier, flattop, attack aircraft carrier

10 airship, dirigible

11 balance beam, beam

12 balloon

13 bearskin, busby, shako

14 bell cote, bell cot

15 bikini, two-piece

16 breakwater, groin, groyne, mole, bulwark, seawall, jetty

17 cannon

18 chain

19 clog, geta, patten, sabot

20 dam, dike, dyke

21 drilling platform, offshore rig

22 dumbbell

23 fireboat

24 gown

25 guillotine

26 half track

27 hoopskirt, crinoline

28 knot

29 lumbermill, sawmill

30 maillot

31 maillot, tank suit

32 miniskirt, mini

33 motor scooter, scooter

34 overskirt

35 parachute, chute

36 patio, terrace

37 pot, flowerpot

38 punching bag, punch bag, punching ball, punchball

39 radio telescope, radio reflector

40 sarong

41 schooner

42 seat belt, seatbelt

43 shoe shop, shoe-shop, shoe store

44 suit, suit of clothes

45 swimming trunks, bathing trunks

46 swing

47 umbrella

48 volleyball

49 wing

50 worm fence, snake fence, snake-rail fence, Virginia fence

51 lakeside, lakeshore

52 groom, bridegroom

53 rapeseed

从上面可以看到，我们上一节识别出的裙子，这里也识别出来了，在第24项（gown）。第28项（knot），连裙子上的蝴蝶结也能识别出来了。

8、完整代码

#encoding:utf-8
import tensorflow as tf
from matplotlib import pyplot as plt
from nets import vgg
import numpy as np
from datasets import imagenet
import os# 加载像素均值及相关函数
from preprocessing.vgg_preprocessing import (_mean_image_subtraction,_R_MEAN, _G_MEAN, _B_MEAN)
slim = tf.contrib.slim
#原谅我穷屌丝，电脑显卡配置太低导致内存溢出，只能用cpu计算了
os.environ["CUDA_VISIBLE_DEVICES"]="-1"#这是我们下载的VGG19文件路径
vgg_checkpoint = 'checkpoint/vgg_19.ckpt'#获取imagenet所有分类的名字，这里有1000个分类
names = imagenet.create_readable_names_for_imagenet_labels()#待检测图片
sample_image = '01.jpg'
#读取图片
image = tf.image.decode_jpeg(tf.read_file(sample_image), channels=3)
#将图片数据转成float型
image_float = tf.to_float(image, name='ToFloat')
#将每个像素减去像素的均值
processed_image = _mean_image_subtraction(image_float, [_R_MEAN, _G_MEAN, _B_MEAN])
#增加一个维度
processed_images = tf.expand_dims(processed_image, 0)#创建模型
with slim.arg_scope(vgg.vgg_arg_scope()):logist, _ = vgg.vgg_19(processed_images,num_classes=1000,is_training=False,spatial_squeeze=False)#将logist的最大索引放到pred里，代表分类pred = tf.argmax(logist, axis=3)#指定模型文件init_fn = slim.assign_from_checkpoint_fn(vgg_checkpoint, slim.get_model_variables('vgg_19'))#run
with tf.Session() as sess:init_fn(sess)reimg, seg, np_image = sess.run([image, pred, processed_image])#去除空的维度
seg = np.squeeze(seg)
#去除其中重复的元素
unique_classes, relabeled_image = np.unique(seg, return_inverse=True)
seg_size = seg.shaperelabeled_image = relabeled_image.reshape(seg_size)#显示函数
def showlab(img, labels_str = []):plt.figure()p1 = plt.subplot(133)p2 = plt.subplot(132)p3 = plt.subplot(131)#显示原图片p1.set_title('Source image')p1.imshow(reimg)p1.axis('off')#显示预处理图片p2.set_title('Preprocessing image')p2.imshow(np_image)p2.axis('off')p3.set_title('Recognition image')minval = np.min(img)maxval = np.max(img)#显示识别图片cmap = plt.get_cmap('Paired', maxval - minval + 1)mat = p3.matshow(img, cmap = cmap, vmin = minval - 0.5, vmax = maxval + 0.5)cax = plt.colorbar(mat, ticks = np.arange(minval, maxval + 1), shrink = 1.2)cax.ax.set_yticklabels(labels_str)#显示plt.show()labels_names = []
for index, current_class_number in enumerate(unique_classes):labels_names.append(str(index) + ' ' + names[current_class_number + 1])print(str(index) + ' ' + names[current_class_number + 1])showlab(relabeled_image, labels_names)

9、试试其他图片

将

#待检测图片
sample_image = '01.jpg'

改成

#待检测图片
sample_image = '02.jpg'

运行结果，

$ python demo7.py

terminate called after throwing an instance of 'std::bad_alloc'

what(): std::bad_alloc

Aborted

我靠？怎么回事？百度一下，说是数据量太大造成的。那先试试bus.jpg，将代码改成，

#待检测图片
sample_image = 'bus.jpg'

运行结果，

这张图只识别了校车。那张02.jpg图片真是有毒。重启电脑再运行试试......

我丢，重启以后运行就好了，运行结果如下，

居然识别出109个物体？？？识别打印如下，

0 stingray

1 water ouzel, dipper

2 bald eagle, American eagle, Haliaeetus leucocephalus

3 great grey owl, great gray owl, Strix nebulosa

4 axolotl, mud puppy, Ambystoma mexicanum

5 ringneck snake, ring-necked snake, ring snake

6 horned viper, cerastes, sand viper, horned asp, Cerastes cornutus

7 black widow, Latrodectus mactans

8 tick

9 ptarmigan

10 African grey, African gray, Psittacus erithacus

11 sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita

12 platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus

13 nematode, nematode worm, roundworm

14 conch

15 American egret, great white heron, Egretta albus

16 bustard

17 king penguin, Aptenodytes patagonica

18 albatross, mollymawk

19 Chihuahua

20 Maltese dog, Maltese terrier, Maltese

21 Italian greyhound

22 Scottish deerhound, deerhound

23 Kerry blue terrier

24 Sealyham terrier, Sealyham

25 komondor

26 Bouvier des Flandres, Bouviers des Flandres

27 miniature pinscher

28 Bernese mountain dog

29 boxer

30 dalmatian, coach dog, carriage dog

31 affenpinscher, monkey pinscher, monkey dog

32 toy poodle

33 Mexican hairless

34 Persian cat

35 Siamese cat, Siamese

36 Egyptian cat

37 lacewing, lacewing fly

38 Angora, Angora rabbit

39 hamster

40 porcupine, hedgehog

41 beaver

42 chimpanzee, chimp, Pan troglodytes

43 marmoset

44 ambulance

45 Band Aid

46 bathing cap, swimming cap

47 bathtub, bathing tub, bath, tub

48 bearskin, busby, shako

49 bonnet, poke bonnet

50 bow tie, bow-tie, bowtie

51 brassiere, bra, bandeau

52 broom

53 car mirror

54 cassette

55 Christmas stocking

56 crash helmet

57 garbage truck, dustcart

58 gown

59 jean, blue jean, denim

60 jersey, T-shirt, tee shirt

61 knot

62 lens cap, lens cover

63 letter opener, paper knife, paperknife

64 Loafer

65 mask

66 mitten

67 mortar

68 mouse, computer mouse

69 muzzle

70 oxygen mask

71 packet

72 paintbrush

73 paper towel

74 Petri dish

75 photocopier

76 plastic bag

77 pop bottle, soda bottle

78 punching bag, punch bag, punching ball, punchball

79 rubber eraser, rubber, pencil eraser

80 rugby ball

81 rule, ruler

82 sandal

83 scale, weighing machine

84 ski mask

85 sleeping bag

86 sock

87 spider web, spider's web

88 stethoscope

89 sunglass

90 sunglasses, dark glasses, shades

91 sunscreen, sunblock, sun blocker

92 swab, swob, mop

93 swimming trunks, bathing trunks

94 thimble

95 toilet seat

96 water bottle

97 wig

98 Windsor tie

99 wooden spoon

100 wool, woolen, woollen

101 crossword puzzle, crossword

102 ice cream, icecream

103 ice lolly, lolly, lollipop, popsicle

104 mashed potato

105 cucumber, cuke

106 Granny Smith

107 banana

108 custard apple

109 dough

看看有没有上一节的墨西哥玉米煎饼（burrito）？搜了一下，并没有......说明使用不同模型的识别还是有点区别的。但是它居然识别出了黄瓜（cucumber，第105项），但是，看看第105项附近的结果，