├── README.md
├── finetune_alexnet_with_tensorflow
    ├── .idea
    │   ├── finetune_alexnet_with_tensorflow.iml
    │   ├── misc.xml
    │   ├── modules.xml
    │   └── workspace.xml
    ├── README.md
    ├── __pycache__
    │   ├── alexnet.cpython-37.pyc
    │   ├── caffe_classes.cpython-37.pyc
    │   ├── datagenerator.cpython-37.pyc
    │   └── test.cpython-37.pyc
    ├── alexnet.py
    ├── caffe_classes.py
    ├── dataAugmentation.py
    ├── datagenerator.py
    ├── finetune.py
    ├── main.py
    ├── static
    │   ├── css
    │   │   ├── __init__.py
    │   │   └── main.css
    │   ├── js
    │   │   ├── __init__.py
    │   │   └── jquery-3.4.1.js
    │   └── test_img
    │   │   ├── __init__.py
    │   │   ├── image_03102.jpg
    │   │   ├── image_03119.jpg
    │   │   └── test.jpg
    ├── templates
    │   ├── recognition.html
    │   └── recognition_ok.html
    ├── test.py
    ├── test.txt
    ├── train.txt
    ├── url.txt
    ├── valid.txt
    └── venv
    │   └── pyvenv.cfg
└── vgg16_oxford_flower_102
    ├── .idea
        ├── finetune_alexnet_with_tensorflow.iml
        ├── misc.xml
        ├── modules.xml
        └── workspace.xml
    ├── __pycache__
        ├── caffe_classes.cpython-37.pyc
        └── test.cpython-37.pyc
    ├── caffe_classes.py
    ├── main.py
    ├── network.py
    ├── readme.md
    ├── retrain.py
    ├── static
        ├── css
        │   ├── __init__.py
        │   └── main.css
        ├── js
        │   ├── __init__.py
        │   └── jquery-3.4.1.js
        └── test_img
        │   └── __init__.py
    ├── templates
        ├── recognition.html
        └── recognition_ok.html
    ├── test.py
    ├── url.txt
    └── venv
        └── pyvenv.cfg


/README.md:
--------------------------------------------------------------------------------
 1 | # flower_recognition
 2 | 花卉识别程序
 3 | 
 4 | 
 5 | ### finetune_alexnet_with_tensorflow(基于AlexNet的花卉识别)
 6 | 
 7 | finetune.py  训练神经网络的文件
 8 | 
 9 | alexnet.py AlexNet卷积神经网络模型
10 | 
11 | bvlc_alexnet.npy 训练好的参数
12 | 
13 | datagenerator.py 图片预处理
14 | 
15 | main.py 路由文件
16 | 
17 | 运行时需要先训练神经网络，因为虽然写了检查点但是看不到检查点文件，训练好以后再运行main.py文件。训练时将oxford文件夹放在train.txt文件中的指定位置，或者根据oxford文件夹的位置对train.txt进行相应修改。
18 | 
19 | ### vgg16_oxford_flower_102(基于VGG16迁移模型的花卉识别)
20 | 
21 | network.py 神经网络的初次训练
22 | 
23 | retrain.py 神经网络的再次训练
24 | 
25 | test.py 测试文件
26 | 
27 | 102flowermodel.h5 训练好的模型保存
28 | 
29 | main.py 路由文件
30 | 
31 | url.txt 花卉百度百科链接
32 | 
33 | ### 使用方法
34 | 运行main.py，访问http://127.0.0.1:5000/， 进入花卉识别主页面，点击“选择文件”按钮，上传要识别的花卉图片，然后点击“开始识别”按钮，进行识别。识别完成后，在页面下方显示识别图像、识别结果以及花卉相关介绍。
35 | 
36 | ### 数据集下载地址
37 | 
38 | http://www.robots.ox.ac.uk/~vgg/data/flowers/102/index.html
39 | 
40 | ### 训练好的参数文件 bvlc_alexnet.npy下载地址
41 | 
42 | http://www.cs.toronto.edu/~guerzhoy/tf_alexnet/bvlc_alexnet.npy
43 | 


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/finetune_alexnet_with_tensorflow/README.md:
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 1 | # README
 2 | 
 3 | ### finetune_alexnet_with_tensorflow(基于AlexNet的花卉识别)
 4 | 
 5 | finetune.py  训练神经网络的文件
 6 | 
 7 | alexnet.py AlexNet卷积神经网络模型
 8 | 
 9 | bvlc_alexnet.npy 训练好的参数
10 | 
11 | datagenerator.py 图片预处理
12 | 
13 | main.py 路由文件
14 | 
15 | 运行时需要先训练神经网络，因为虽然写了检查点但是看不到检查点文件，训练好以后再运行main.py文件。训练时将oxford文件夹放在train.txt文件中的指定位置，或者根据oxford文件夹的位置对train.txt进行相应修改。
16 | 
17 | ### vgg16_oxford_flower_102(基于VGG16迁移模型的花卉识别)
18 | 
19 | network.py 神经网络的初次训练
20 | 
21 | retrain.py 神经网络的再次训练
22 | 
23 | test.py 测试文件
24 | 
25 | 102flowermodel.h5 训练好的模型保存
26 | 
27 | main.py 路由文件
28 | 
29 | url.txt 花卉百度百科链接
30 | 
31 | ### 使用方法
32 | 运行main.py，访问http://127.0.0.1:5000/， 进入花卉识别主页面，点击“选择文件”按钮，上传要识别的花卉图片，然后点击“开始识别”按钮，进行识别。识别完成后，在页面下方显示识别图像、识别结果以及花卉相关介绍。
33 | 
34 | ### 数据集下载地址
35 | 
36 | http://www.robots.ox.ac.uk/~vgg/data/flowers/102/index.html
37 | 
38 | ### 训练好的参数文件 bvlc_alexnet.npy下载地址
39 | 
40 | http://www.cs.toronto.edu/~guerzhoy/tf_alexnet/bvlc_alexnet.npy
41 | 
42 | 


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/finetune_alexnet_with_tensorflow/alexnet.py:
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  1 | import tensorflow as tf
  2 | import numpy as np
  3 | 
  4 | class AlexNet(object):
  5 |   
  6 |   def __init__(self, x, keep_prob, num_classes, skip_layer, 
  7 |                weights_path = 'DEFAULT'):
  8 |     
  9 |     # Parse input arguments into class variables
 10 |     self.X = x
 11 |     self.NUM_CLASSES = num_classes
 12 |     self.KEEP_PROB = keep_prob
 13 |     self.SKIP_LAYER = skip_layer
 14 |     
 15 |     if weights_path == 'DEFAULT':      
 16 |       self.WEIGHTS_PATH = 'bvlc_alexnet.npy'
 17 |     else:
 18 |       self.WEIGHTS_PATH = weights_path
 19 |     
 20 |     # Call the create function to build the computational graph of AlexNet
 21 |     self.create()
 22 |     
 23 |   def create(self):
 24 |     
 25 |     # 1st Layer: Conv (w ReLu) -> Pool -> Lrn
 26 |     conv1 = conv(self.X, 11, 11, 96, 4, 4, padding = 'VALID', name = 'conv1')
 27 |     pool1 = max_pool(conv1, 3, 3, 2, 2, padding = 'VALID', name = 'pool1')
 28 |     norm1 = lrn(pool1, 2, 2e-05, 0.75, name = 'norm1')#局部响应归一化层
 29 |     
 30 |     # 2nd Layer: Conv (w ReLu) -> Pool -> Lrn with 2 groups
 31 |     conv2 = conv(norm1, 5, 5, 256, 1, 1, groups = 2, name = 'conv2')
 32 |     pool2 = max_pool(conv2, 3, 3, 2, 2, padding = 'VALID', name ='pool2')
 33 |     norm2 = lrn(pool2, 2, 2e-05, 0.75, name = 'norm2')
 34 |     
 35 |     # 3rd Layer: Conv (w ReLu)
 36 |     conv3 = conv(norm2, 3, 3, 384, 1, 1, name = 'conv3')
 37 |     
 38 |     # 4th Layer: Conv (w ReLu) splitted into two groups
 39 |     conv4 = conv(conv3, 3, 3, 384, 1, 1, groups = 2, name = 'conv4')
 40 |     
 41 |     # 5th Layer: Conv (w ReLu) -> Pool splitted into two groups
 42 |     conv5 = conv(conv4, 3, 3, 256, 1, 1, groups = 2, name = 'conv5')
 43 |     pool5 = max_pool(conv5, 3, 3, 2, 2, padding = 'VALID', name = 'pool5')
 44 |     
 45 |     # 6th Layer: Flatten -> FC (w ReLu) -> Dropout
 46 |     flattened = tf.reshape(pool5, [-1, 6*6*256])
 47 |     fc6 = fc(flattened, 6*6*256, 4096, name='fc6')
 48 |     dropout6 = dropout(fc6, self.KEEP_PROB)
 49 |     
 50 |     # 7th Layer: FC (w ReLu) -> Dropout
 51 |     fc7 = fc(dropout6, 4096, 4096, name = 'fc7')
 52 |     dropout7 = dropout(fc7, self.KEEP_PROB)
 53 |     
 54 |     # 8th Layer: FC and return unscaled activations (for tf.nn.softmax_cross_entropy_with_logits)
 55 |     self.fc8 = fc(dropout7, 4096, self.NUM_CLASSES, relu = False, name='fc8')
 56 | 
 57 |     
 58 |     
 59 |   def load_initial_weights(self, session):
 60 |     
 61 |     # Load the weights into memory
 62 |     weights_dict = np.load(self.WEIGHTS_PATH, encoding = 'bytes').item()
 63 |     
 64 |     # Loop over all layer names stored in the weights dict
 65 |     for op_name in weights_dict:
 66 |         
 67 |       # Check if the layer is one of the layers that should be reinitialized
 68 |       if op_name not in self.SKIP_LAYER:
 69 |         
 70 |         with tf.variable_scope(op_name, reuse = True):
 71 |             
 72 |           # Loop over list of weights/biases and assign them to their corresponding tf variable
 73 |           for data in weights_dict[op_name]:
 74 |             
 75 |             # Biases
 76 |             if len(data.shape) == 1:
 77 |               
 78 |               var = tf.get_variable('biases', trainable = False)
 79 |               session.run(var.assign(data))
 80 |               
 81 |             # Weights
 82 |             else:
 83 |               
 84 |               var = tf.get_variable('weights', trainable = False)
 85 |               session.run(var.assign(data))
 86 |             
 87 |      
 88 |   
 89 | """
 90 | Predefine all necessary layer for the AlexNet
 91 | """ 
 92 | def conv(x, filter_height, filter_width, num_filters, stride_y, stride_x, name,
 93 |          padding='SAME', groups=1):
 94 | 
 95 |   # Get number of input channels
 96 |   input_channels = int(x.get_shape()[-1])
 97 |   
 98 |   # Create lambda function for the convolution
 99 |   convolve = lambda i, k: tf.nn.conv2d(i, k, 
100 |                                        strides = [1, stride_y, stride_x, 1],
101 |                                        padding = padding)
102 |   
103 |   with tf.variable_scope(name) as scope:
104 |     # Create tf variables for the weights and biases of the conv layer
105 |     weights = tf.get_variable('weights', shape = [filter_height, filter_width, input_channels/groups, num_filters])
106 |     biases = tf.get_variable('biases', shape = [num_filters])  
107 |     
108 |     
109 |     if groups == 1:
110 |       conv = convolve(x, weights)
111 |       
112 |     # In the cases of multiple groups, split inputs & weights and
113 |     else:
114 |       # Split input and weights and convolve them separately
115 |       input_groups = tf.split(axis = 3, num_or_size_splits=groups, value=x)
116 |       weight_groups = tf.split(axis = 3, num_or_size_splits=groups, value=weights)
117 |       output_groups = [convolve(i, k) for i,k in zip(input_groups, weight_groups)]
118 |       
119 |       # Concat the convolved output together again
120 |       conv = tf.concat(axis = 3, values = output_groups)
121 |       
122 |     # Add biases 
123 |     bias = tf.reshape(tf.nn.bias_add(conv, biases), conv.get_shape().as_list())
124 |     
125 |     # Apply relu function
126 |     relu = tf.nn.relu(bias, name = scope.name)
127 |         
128 |     return relu
129 |   
130 | def fc(x, num_in, num_out, name, relu = True):
131 |   with tf.variable_scope(name) as scope:
132 |     
133 |     # Create tf variables for the weights and biases
134 |     weights = tf.get_variable('weights', shape=[num_in, num_out], trainable=True)
135 |     biases = tf.get_variable('biases', [num_out], trainable=True)
136 |     
137 |     # Matrix multiply weights and inputs and add bias
138 |     act = tf.nn.xw_plus_b(x, weights, biases, name=scope.name)
139 |     
140 |     if relu == True:
141 |       # Apply ReLu non linearity
142 |       relu = tf.nn.relu(act)      
143 |       return relu
144 |     else:
145 |       return act
146 |     
147 | 
148 | def max_pool(x, filter_height, filter_width, stride_y, stride_x, name, padding='SAME'):
149 |   return tf.nn.max_pool(x, ksize=[1, filter_height, filter_width, 1],
150 |                         strides = [1, stride_y, stride_x, 1],
151 |                         padding = padding, name = name)
152 |   
153 | def lrn(x, radius, alpha, beta, name, bias=1.0):
154 |   return tf.nn.local_response_normalization(x, depth_radius = radius, alpha = alpha,
155 |                                             beta = beta, bias = bias, name = name)
156 |   
157 | def dropout(x, keep_prob):
158 |   return tf.nn.dropout(x, keep_prob)
159 |   
160 |     


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/finetune_alexnet_with_tensorflow/caffe_classes.py:
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  1 | class_names = '''pink primrose
  2 | hard-leaved pocket orchid
  3 | canterbury bells
  4 | sweet pea
  5 | english marigold
  6 | tiger lily
  7 | moon orchid
  8 | bird of paradise
  9 | monkshood
 10 | globe thistle
 11 | snapdragon
 12 | colt's foot
 13 | king protea
 14 | spear thistle
 15 | yellow iris
 16 | globe-flower
 17 | purple coneflower
 18 | peruvian lily
 19 | balloon flower
 20 | giant white arum lily
 21 | fire lily
 22 | pincushion flower
 23 | fritillary
 24 | red ginger
 25 | grape hyacinth
 26 | corn poppy
 27 | prince of wales feathers
 28 | stemless gentian
 29 | artichoke
 30 | sweet william
 31 | carnation
 32 | garden phlox
 33 | love in the mist
 34 | mexican aster
 35 | alpine sea holly
 36 | ruby-lipped cattleya
 37 | cape flower
 38 | great masterwort
 39 | siam tulip
 40 | lenten rose
 41 | barbeton daisy
 42 | daffodil
 43 | sword lily
 44 | poinsettia
 45 | bolero deep blue
 46 | wallflower
 47 | marigold
 48 | buttercup
 49 | oxeye daisy
 50 | common dandelion
 51 | petunia
 52 | wild pansy
 53 | primula
 54 | sunflower
 55 | pelargonium
 56 | bishop of llandaff
 57 | gaura
 58 | geranium
 59 | orange dahlia
 60 | pink-yellow dahlia
 61 | cautleya spicata
 62 | japanese anemone
 63 | black-eyed susan
 64 | silverbush
 65 | californian poppy
 66 | osteospermum
 67 | spring crocus
 68 | bearded iris
 69 | windflower
 70 | tree poppy
 71 | gazania
 72 | azalea
 73 | water lily
 74 | rose
 75 | thorn apple
 76 | morning glory
 77 | passion flower
 78 | lotus
 79 | toad lily
 80 | anthurium
 81 | frangipani
 82 | clematis
 83 | hibiscus
 84 | columbine
 85 | desert-rose
 86 | tree mallow
 87 | magnolia
 88 | cyclamen
 89 | watercress
 90 | canna lily
 91 | hippeastrum
 92 | bee balm
 93 | ball moss
 94 | foxglove
 95 | bougainvillea
 96 | camellia
 97 | mallow
 98 | mexican petunia
 99 | bromelia
100 | blanket flower
101 | trumpet creeper
102 | blackberry lily'''.split("\n")
103 | 


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/finetune_alexnet_with_tensorflow/dataAugmentation.py:
--------------------------------------------------------------------------------
 1 | from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
 2 | datagen = ImageDataGenerator(
 3 |     rotation_range=60)
 4 | 
 5 | img = load_img('image_00095.jpg')
 6 | x = img_to_array(img)
 7 | x = x.reshape((1,) + x.shape)
 8 | i = 0
 9 | for batch in datagen.flow(x,
10 |     batch_size=1,
11 |     save_to_dir='D:output',   #保存在这个文件夹下
12 |     save_prefix='lena',
13 |     save_format='jpg'):
14 |     i += 1
15 |     if i > 20:  #生成20张图
16 |         break
17 | 


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/finetune_alexnet_with_tensorflow/datagenerator.py:
--------------------------------------------------------------------------------
  1 | import numpy as np
  2 | import cv2
  3 | 
  4 | class ImageDataGenerator:
  5 |     def __init__(self, class_list, horizontal_flip=False, shuffle=False, 
  6 |                  mean = np.array([104., 117., 124.]), scale_size=(227, 227),
  7 |                  nb_classes = 102):
  8 |         
  9 |                 
 10 |         # Init params
 11 |         self.horizontal_flip = horizontal_flip
 12 |         self.n_classes = nb_classes
 13 |         self.shuffle = shuffle
 14 |         self.mean = mean
 15 |         self.scale_size = scale_size
 16 |         self.pointer = 0
 17 |         
 18 |         self.read_class_list(class_list)
 19 |         
 20 |         if self.shuffle:
 21 |             self.shuffle_data()
 22 | 
 23 |     def read_class_list(self,class_list):
 24 |         """
 25 |         Scan the image file and get the image paths and labels
 26 |         """
 27 |         with open(class_list) as f:
 28 |             lines = f.readlines()
 29 |             self.images = []
 30 |             self.labels = []
 31 |             for l in lines:
 32 |                 items = l.split()
 33 |                 self.images.append(items[0])
 34 |                 self.labels.append(int(items[1]))
 35 |             
 36 |             #store total number of data
 37 |             self.data_size = len(self.labels)
 38 |         
 39 |     def shuffle_data(self):
 40 |         """
 41 |         Random shuffle the images and labels
 42 |         """
 43 |         images = self.images.copy()
 44 |         labels = self.labels.copy()
 45 |         self.images = []
 46 |         self.labels = []
 47 |         
 48 |         #create list of permutated index and shuffle data accoding to list
 49 |         idx = np.random.permutation(len(labels))#不改变自身数组
 50 |         for i in idx:
 51 |             self.images.append(images[i])
 52 |             self.labels.append(labels[i])
 53 |                 
 54 |     def reset_pointer(self):
 55 |         """
 56 |         reset pointer to begin of the list
 57 |         """
 58 |         self.pointer = 0
 59 |         
 60 |         if self.shuffle:
 61 |             self.shuffle_data()
 62 |         
 63 |     
 64 |     def next_batch(self, batch_size):
 65 |         """
 66 |         This function gets the next n ( = batch_size) images from the path list
 67 |         and labels and loads the images into them into memory 
 68 |         """
 69 |         # Get next batch of image (path) and labels
 70 |         paths = self.images[self.pointer:self.pointer + batch_size]
 71 |         labels = self.labels[self.pointer:self.pointer + batch_size]
 72 |         
 73 |         #update pointer
 74 |         self.pointer += batch_size
 75 |         
 76 |         # Read images
 77 |         images = np.ndarray([batch_size, self.scale_size[0], self.scale_size[1], 3])
 78 |         for i in range(len(paths)):
 79 |             img = cv2.imread(paths[i])
 80 |             
 81 |             #flip image at random if flag is selected
 82 |             if self.horizontal_flip and np.random.random() < 0.5:
 83 |                 img = cv2.flip(img, 1)
 84 |             
 85 |             #rescale image
 86 |             img = cv2.resize(img, (self.scale_size[0], self.scale_size[1]))
 87 |             img = img.astype(np.float32)
 88 |             
 89 |             #subtract mean
 90 |             img -= self.mean
 91 |                                                                  
 92 |             images[i] = img
 93 | 
 94 |         # Expand labels to one hot encoding
 95 |         one_hot_labels = np.zeros((batch_size, self.n_classes))#一位有效编码
 96 |         for i in range(len(labels)):
 97 |             one_hot_labels[i][labels[i]] = 1
 98 | 
 99 |         #return array of images and labels
100 |         return images, one_hot_labels
101 | 


--------------------------------------------------------------------------------
/finetune_alexnet_with_tensorflow/finetune.py:
--------------------------------------------------------------------------------
  1 | import os
  2 | import numpy as np
  3 | import tensorflow as tf
  4 | from datetime import datetime
  5 | from alexnet import AlexNet
  6 | from datagenerator import ImageDataGenerator
  7 | 
  8 | """
  9 | Configuration settings
 10 | """
 11 | 
 12 | # Path to the textfiles for the trainings and validation set
 13 | 
 14 | train_file = 'train.txt'
 15 | val_file = 'valid.txt'
 16 | 
 17 | # Learning params
 18 | learning_rate = 0.01
 19 | num_epochs = 50
 20 | batch_size = 128
 21 | 
 22 | # Network params
 23 | dropout_rate = 0.5
 24 | num_classes = 102
 25 | train_layers = ['fc8', 'fc7']
 26 | 
 27 | # How often we want to write the tf.summary data to disk
 28 | display_step = 1
 29 | 
 30 | # Path for tf.summary.FileWriter and to store model checkpoints
 31 | filewriter_path = "/finetune_alexnet/flowers_summary"
 32 | checkpoint_path = "/finetune_alexnet/"
 33 | 
 34 | # Create parent path if it doesn't exist
 35 | if not os.path.isdir(checkpoint_path): os.mkdir(checkpoint_path)
 36 | 
 37 | 
 38 | # TF placeholder for graph input and output
 39 | x = tf.placeholder(tf.float32, [batch_size, 227, 227, 3])
 40 | y = tf.placeholder(tf.float32, [None, num_classes])
 41 | keep_prob = tf.placeholder(tf.float32)
 42 | 
 43 | # Initialize model
 44 | model = AlexNet(x, keep_prob, num_classes, train_layers)
 45 | 
 46 | # Link variable to model output
 47 | score = model.fc8
 48 | 
 49 | # List of trainable variables of the layers we want to train
 50 | var_list = [v for v in tf.trainable_variables() if v.name.split('/')[0] in train_layers]
 51 | 
 52 | # Op for calculating the loss
 53 | with tf.name_scope("cross_ent"):
 54 |   loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = score, labels = y))  
 55 | 
 56 | # Train op
 57 | with tf.name_scope("train"):
 58 |   # Get gradients of all trainable variables
 59 |   gradients = tf.gradients(loss, var_list)
 60 |   gradients = list(zip(gradients, var_list))
 61 |   
 62 |   # Create optimizer and apply gradient descent to the trainable variables
 63 |   optimizer = tf.train.GradientDescentOptimizer(learning_rate)
 64 |   train_op = optimizer.apply_gradients(grads_and_vars=gradients)
 65 | 
 66 | # Add gradients to summary  
 67 | for gradient, var in gradients:
 68 |   tf.summary.histogram(var.name + '/gradient', gradient)
 69 | 
 70 | # Add the variables we train to the summary  
 71 | for var in var_list:
 72 |   tf.summary.histogram(var.name, var)
 73 |   
 74 | # Add the loss to summary
 75 | tf.summary.scalar('cross_entropy', loss)
 76 |   
 77 | 
 78 | # Evaluation op: Accuracy of the model
 79 | with tf.name_scope("accuracy"):
 80 |   correct_pred = tf.equal(tf.argmax(score, 1), tf.argmax(y, 1))
 81 |   accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
 82 |   
 83 | # Add the accuracy to the summary
 84 | tf.summary.scalar('accuracy', accuracy)
 85 | 
 86 | # Merge all summaries together
 87 | merged_summary = tf.summary.merge_all()
 88 | 
 89 | # Initialize the FileWriter
 90 | writer = tf.summary.FileWriter(filewriter_path)
 91 | 
 92 | # Initialize an saver for store model checkpoints
 93 | saver = tf.train.Saver()
 94 | 
 95 | # Initalize the data generator seperately for the training and validation set
 96 | train_generator = ImageDataGenerator(train_file, 
 97 |                                      horizontal_flip = True, shuffle = True)
 98 | val_generator = ImageDataGenerator(val_file, shuffle = False) 
 99 | 
100 | # Get the number of training/validation steps per epoch
101 | train_batches_per_epoch = np.floor(train_generator.data_size / batch_size).astype(np.int16)
102 | val_batches_per_epoch = np.floor(val_generator.data_size / batch_size).astype(np.int16)
103 | 
104 | # Start Tensorflow session
105 | with tf.Session() as sess:
106 | 
107 |   # Initialize all variables
108 |   sess.run(tf.global_variables_initializer())
109 | 
110 |   # Add the model graph to TensorBoard
111 |   writer.add_graph(sess.graph)
112 | 
113 |   # Load the pretrained weights into the non-trainable layer
114 |   #model.load_initial_weights(sess)
115 |   saver.restore(sess, checkpoint_path + 'model_epoch50.ckpt')
116 |   print("恢复模型成功！")
117 | 
118 |   print("{} Start training...".format(datetime.now()))
119 |   print("{} Open Tensorboard at --logdir {}".format(datetime.now(),
120 |                                                     filewriter_path))
121 | 
122 |   # Loop over number of epochs
123 |   for epoch in range(num_epochs):
124 | 
125 |         print("{} Epoch number: {}".format(datetime.now(), epoch+1))
126 | 
127 |         step = 1
128 | 
129 |         while step < train_batches_per_epoch:
130 | 
131 |             # Get a batch of images and labels
132 |             batch_xs, batch_ys = train_generator.next_batch(batch_size)
133 | 
134 |             # And run the training op
135 |             sess.run(train_op, feed_dict={x: batch_xs,
136 |                                           y: batch_ys,
137 |                                           keep_prob: dropout_rate})
138 | 
139 |             # Generate summary with the current batch of data and write to file
140 |             if step%display_step == 0:
141 |                 s = sess.run(merged_summary, feed_dict={x: batch_xs,
142 |                                                         y: batch_ys,
143 |                                                         keep_prob: 1.})
144 |                 writer.add_summary(s, epoch*train_batches_per_epoch + step)
145 | 
146 |             step += 1
147 | 
148 |         # Validate the model on the entire validation set
149 |         print("{} Start validation".format(datetime.now()))
150 |         test_acc = 0.
151 |         test_count = 0
152 |         for _ in range(val_batches_per_epoch):
153 |             batch_tx, batch_ty = val_generator.next_batch(batch_size)
154 |             acc = sess.run(accuracy, feed_dict={x: batch_tx,
155 |                                                 y: batch_ty,
156 |                                                 keep_prob: 1.})
157 |             test_acc += acc
158 |             test_count += 1
159 |         test_acc /= test_count
160 |         print("{} Validation Accuracy = {:.4f}".format(datetime.now(), test_acc))
161 | 
162 |         # Reset the file pointer of the image data generator
163 |         val_generator.reset_pointer()
164 |         train_generator.reset_pointer()
165 | 
166 |         print("{} Saving checkpoint of model...".format(datetime.now()))
167 | 
168 |         #save checkpoint of the model
169 |         checkpoint_name = os.path.join(checkpoint_path, 'model_epoch'+str(epoch+1)+'.ckpt')
170 |         save_path = saver.save(sess, checkpoint_name)
171 | 
172 |         print("{} Model checkpoint saved at {}".format(datetime.now(), checkpoint_name))
173 | 
174 | #模型恢复
175 | # with tf.Session() as sess1:
176 | #     saver.restore(sess1, checkpoint_path+'model_epoch'
177 | #                                          '100.ckpt')
178 | #     print("恢复模型成功！")
179 | #
180 | #
181 | #     # Initialize all variables
182 | #     # sess1.run(tf.global_variables_initializer())
183 | #
184 | #     # Add the model graph to TensorBoard
185 | #     writer.add_graph(sess1.graph)
186 | #     print("{} Start training...".format(datetime.now()))
187 | #     print("{} Open Tensorboard at --logdir {}".format(datetime.now(),
188 | #                                                       filewriter_path))
189 | #     # Loop over number of epochs
190 | #     for epoch in range(num_epochs):
191 | #
192 | #         print("{} Epoch number: {}".format(datetime.now(), epoch + 1))
193 | #
194 | #         step = 1
195 | #
196 | #         while step < train_batches_per_epoch:
197 | #
198 | #             # Get a batch of images and labels
199 | #             batch_xs, batch_ys = train_generator.next_batch(batch_size)
200 | #
201 | #             # And run the training op
202 | #             sess1.run(train_op, feed_dict={x: batch_xs,
203 | #                                           y: batch_ys,
204 | #                                           keep_prob: dropout_rate})
205 | #
206 | #             # Generate summary with the current batch of data and write to file
207 | #             if step % display_step == 0:
208 | #                 s = sess1.run(merged_summary, feed_dict={x: batch_xs,
209 | #                                                         y: batch_ys,
210 | #                                                         keep_prob: 1.})
211 | #                 writer.add_summary(s, epoch * train_batches_per_epoch + step)
212 | #
213 | #             step += 1
214 | #
215 | #         # Validate the model on the entire validation set
216 | #         print("{} Start validation".format(datetime.now()))
217 | #         test_acc = 0.
218 | #         test_count = 0
219 | #         for _ in range(val_batches_per_epoch):
220 | #             batch_tx, batch_ty = val_generator.next_batch(batch_size)
221 | #             acc = sess1.run(accuracy, feed_dict={x: batch_tx,
222 | #                                                 y: batch_ty,
223 | #                                                 keep_prob: 1.})
224 | #             test_acc += acc
225 | #             test_count += 1
226 | #         test_acc /= test_count
227 | #         print("{} Validation Accuracy = {:.4f}".format(datetime.now(), test_acc))
228 | #
229 | #         # Reset the file pointer of the image data generator
230 | #         val_generator.reset_pointer()
231 | #         train_generator.reset_pointer()
232 | #
233 | #         print("{} Saving checkpoint of model...".format(datetime.now()))
234 | #
235 | #         # save checkpoint of the model
236 | #         checkpoint_name = os.path.join(checkpoint_path, 'model_epoch' + str(epoch + 1) + '.ckpt')
237 | #         save_path = saver.save(sess1, checkpoint_name)
238 | #
239 | #         print("{} Model checkpoint saved at {}".format(datetime.now(), checkpoint_name))
240 | 
241 |         
242 | 


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/finetune_alexnet_with_tensorflow/main.py:
--------------------------------------------------------------------------------
 1 | from flask import Flask, render_template, request, redirect, url_for, make_response, jsonify
 2 | from werkzeug.utils import secure_filename
 3 | import os
 4 | import cv2
 5 | import time
 6 | from test import test_image
 7 | import urllib.request
 8 | from bs4 import BeautifulSoup
 9 | 
10 | from datetime import timedelta
11 | 
12 | # 设置允许的文件格式
13 | ALLOWED_EXTENSIONS = set(['png', 'jpg', 'JPG', 'PNG', 'bmp'])
14 | 
15 | 
16 | def allowed_file(filename):
17 |     return '.' in filename and filename.rsplit('.', 1)[1] in ALLOWED_EXTENSIONS
18 | 
19 | 
20 | app = Flask(__name__)
21 | # 设置静态文件缓存过期时间
22 | app.send_file_max_age_default = timedelta(seconds=1)
23 | 
24 | 
25 | # @app.route('/upload', methods=['POST', 'GET'])
26 | @app.route('/', methods=['POST', 'GET'])  # 添加路由
27 | def upload():
28 |     if request.method == 'POST':
29 |         f = request.files['file']
30 | 
31 |         if not (f and allowed_file(f.filename)):
32 |             return jsonify({"error": 1001, "msg": "请检查上传的图片类型，仅限于png、PNG、jpg、JPG、bmp"})
33 | 
34 |         user_input = request.form.get("name")
35 | 
36 |         basepath = os.path.dirname(__file__)  # 当前文件所在路径
37 | 
38 |         upload_path = os.path.join(basepath, 'static/test_img', secure_filename(f.filename))
39 |         # 注意：没有的文件夹一定要先创建，不然会提示没有该路径
40 | 
41 |         f.save(upload_path)
42 | 
43 |         # 使用Opencv转换一下图片格式和名称
44 |         img = cv2.imread(upload_path)
45 |         cv2.imwrite(os.path.join(basepath, 'static/test_img', 'test.jpg'), img)
46 |         class_name=test_image(upload_path, num_class=102)
47 |         # class_name = 'water lily'
48 |         fo = open("url.txt", "r")
49 |         for line in fo.readlines():
50 |             temp = line.split(',')
51 |             if temp[0] == class_name:
52 |                 url = temp[1]
53 |         a = urllib.request.urlopen(url)
54 |         html = a.read()  # 读取网页源码
55 |         soup = BeautifulSoup(html, 'lxml')  # 生成标签树
56 | 
57 |         name = soup.find('h1')
58 |         name = name.get_text()
59 | 
60 |         text = soup.find('div', class_="para")
61 |         text = text.get_text()
62 | 
63 |         return render_template('recognition_ok.html', userinput=user_input, val1=time.time(),
64 |                                classname=class_name, name=name, text=text, url=url)
65 | 
66 |     return render_template('recognition.html')
67 | 
68 | 
69 | if __name__ == '__main__':
70 |     app.run(debug=True)
71 | 


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/finetune_alexnet_with_tensorflow/static/css/__init__.py:
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https://raw.githubusercontent.com/Amore-vv/flower_recognition/c4b210044a342f7e717ce09e7e929f993bea436b/finetune_alexnet_with_tensorflow/static/css/__init__.py


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/finetune_alexnet_with_tensorflow/static/css/main.css:
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  1 | body{
  2 |     background-color: ghostwhite;
  3 |     margin: 0 auto;
  4 |     text-align: center;
  5 | }
  6 | 
  7 | .t1{
  8 |     align-content: center;
  9 |     border-spacing: 250px 25px;
 10 |     margin-left: -55px;
 11 | 
 12 | }
 13 | 
 14 | hr{
 15 |     width:80%;
 16 |     margin:0 auto;
 17 |     border: 0;
 18 |     height: 1px;
 19 |     background-image: linear-gradient(to right, rgba(0, 0, 0, 0), rgba(0, 0, 0, 0.75), rgba(0, 0, 0, 0));
 20 | 
 21 | }
 22 | 
 23 | .bg1{
 24 |     width: 800px;
 25 |     height: 100px;
 26 |     background-color: dimgrey;
 27 |     color: floralwhite;
 28 |     text-align: center;
 29 |     line-height: 100px;
 30 |     margin-left: 150px;
 31 | }
 32 | 
 33 | .bg2{
 34 |     width: 500px;
 35 |     height: 595px;
 36 |     border: 2px solid dimgrey;
 37 |     float: left;
 38 |     margin-left: 50px;
 39 | }
 40 | 
 41 | .bg3{
 42 |     width: 500px;
 43 |     height: 600px;
 44 |     /*border: 1px solid dimgrey;*/
 45 |     float: left;
 46 |     margin-left: 10px;
 47 | }
 48 | .bg3 table{
 49 |     border: 2px solid dimgrey;
 50 |     border-collapse:collapse;
 51 |     padding: 10px;
 52 | }
 53 | .bg3 table td{
 54 |     border: 2px solid dimgrey;
 55 |     width: 500px;
 56 |     height: 295px;
 57 |     text-align: left;
 58 |     vertical-align: top;
 59 | }
 60 | 
 61 | .file1 {
 62 |     /*margin-top: 5px;*/
 63 |     background: #D0EEFF;
 64 |     border: 1px solid #99D3F5;
 65 |     border-radius: 4px;
 66 |     padding: 4px 12px;
 67 |     overflow: hidden;
 68 |     color: black;
 69 |     text-decoration: none;
 70 |     text-indent: 0;
 71 |     width: 200px;
 72 |     height: 60px;
 73 |     font-size: 30px;
 74 |     text-align: center;
 75 |     font-weight: 600;
 76 |     opacity: 0.5;
 77 | 
 78 | }
 79 | .file {
 80 |     position: relative;
 81 |     display: inline-block;
 82 |     background: #D0EEFF;
 83 |     border: 1px solid #99D3F5;
 84 |     border-radius: 4px;
 85 |     padding: 4px 12px;
 86 |     overflow: hidden;
 87 |     color: black;
 88 |     text-decoration: none;
 89 |     text-indent: 0;
 90 |     width: 180px;
 91 |     height: 50px;
 92 |     font-size: 30px;
 93 |     font-weight: 600;
 94 |     text-align: center;
 95 |     opacity: 0.5;
 96 | 
 97 | 
 98 | }
 99 | .file input {
100 |     position: absolute;
101 |     font-size: 30px;
102 |     right: 0;
103 |     top: 10px;
104 |     opacity: 0.5;
105 | 
106 | }
107 | .file:hover {
108 |     background: #AADFFD;
109 |     border-color: #78C3F3;
110 |     color: #004974;
111 |     text-decoration: none;
112 | }
113 | 


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/finetune_alexnet_with_tensorflow/static/js/__init__.py:
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https://raw.githubusercontent.com/Amore-vv/flower_recognition/c4b210044a342f7e717ce09e7e929f993bea436b/finetune_alexnet_with_tensorflow/static/js/__init__.py


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/finetune_alexnet_with_tensorflow/static/test_img/__init__.py:
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https://raw.githubusercontent.com/Amore-vv/flower_recognition/c4b210044a342f7e717ce09e7e929f993bea436b/finetune_alexnet_with_tensorflow/static/test_img/__init__.py


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/finetune_alexnet_with_tensorflow/static/test_img/image_03102.jpg:
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/finetune_alexnet_with_tensorflow/static/test_img/image_03119.jpg:
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/finetune_alexnet_with_tensorflow/static/test_img/test.jpg:
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https://raw.githubusercontent.com/Amore-vv/flower_recognition/c4b210044a342f7e717ce09e7e929f993bea436b/finetune_alexnet_with_tensorflow/static/test_img/test.jpg


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/finetune_alexnet_with_tensorflow/templates/recognition.html:
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 1 | <!DOCTYPE html>
 2 | <html lang="en">
 3 | <head>
 4 |     <meta charset="UTF-8">
 5 |     <link type="text/css" rel="stylesheet" href="{{url_for('static', filename='css/main.css')}}" />
 6 |     <title>花卉识别</title>
 7 | </head>
 8 | 
 9 | <body>
10 |     <div class="bg1">
11 |         <h1 style="font-size: 50px;">花卉识别</h1>
12 |     </div>
13 | 
14 |     <form action="" enctype='multipart/form-data' method='POST' >
15 |         <table class="t1">
16 |             <td>
17 |                 <a href="javascript:;" class="file">选择文件<input type="file" name="file" id=""></a>
18 |             </td>
19 |             <td>
20 |                 <input class="file1" type="submit" value="开始识别" />
21 |             </td>
22 |         </table>
23 |     </form>
24 | 
25 | <div class="bg2">
26 | 
27 | </div>
28 | <div class="bg3">
29 |     <table>
30 |         <tr>
31 |             <td>
32 |                 <h2 align="center">识别结果</h2>
33 |                 <hr>
34 |             </td>
35 |         </tr>
36 |         <tr>
37 |             <td>
38 |                 <h2 align="center">百度百科</h2>
39 |                 <hr>
40 | 
41 |             </td>
42 |         </tr>
43 |     </table>
44 | 
45 | </div>
46 | </body>
47 | 
48 | <script src="{{url_for('static', filename='js/jquery-3.4.1.js')}}"></script>
49 | 
50 | <script type="text/javascript">
51 |     $(function () {
52 | 
53 |         $(".file").on("change","input[type='file']",function(){
54 |             var filePath=$(this).val();
55 |             if(filePath.indexOf("jpg")!=-1 || filePath.indexOf("png")!=-1){
56 |                 $(".fileerrorTip").html("").hide();
57 |                 var arr=filePath.split('\\');
58 |                 var fileName=arr[arr.length-1];
59 |                 $(".showFileName").html(fileName);
60 |             }else{
61 |                 $(".showFileName").html("");
62 |                 $(".fileerrorTip").html("您未上传文件，或者您上传文件类型有误！").show();
63 |                 return false
64 |             }
65 |         });
66 | 
67 |     });
68 | </script>
69 | </html>
70 | 


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/finetune_alexnet_with_tensorflow/templates/recognition_ok.html:
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 1 | <!DOCTYPE html>
 2 | <html lang="en">
 3 | <head>
 4 |     <meta charset="UTF-8">
 5 |     <link type="text/css" rel="stylesheet" href="{{url_for('static', filename='css/main.css')}}" />
 6 |     <title>花卉识别</title>
 7 | </head>
 8 | 
 9 | <body>
10 | <div class="bg1">
11 |     <h1 style="font-size: 50px;">花卉识别</h1>
12 | </div>
13 | 
14 |     <form action="" enctype='multipart/form-data' method='POST' >
15 |         <table class="t1">
16 |             <td>
17 |                 <a href="javascript:;" class="file">选择文件<input type="file" name="file" id=""></a>
18 |             </td>
19 |             <td>
20 |                 <input class="file1" type="submit" value="开始识别" />
21 |             </td>
22 |         </table>
23 |     </form>
24 | <div class="bg2">
25 |     <img src="{{ url_for('static', filename= './test_img/test.jpg',_t=val1) }}" width="500px" alt="你的图片被外星人劫持了～～"/>
26 | </div>
27 | <div class="bg3">
28 |     <table>
29 |         <tr>
30 |             <td>
31 |                 <h2 align="center">识别结果</h2>
32 |                 <hr>
33 |                 <p align="center">&nbsp;&nbsp;花卉名称：{{classname}}<br>
34 |                     &nbsp;&nbsp;译名：&nbsp;&nbsp;{{name}}</p>
35 |             </td>
36 |         </tr>
37 |         <tr>
38 |             <td>
39 |                 <h2 align="center">百度百科</h2>
40 |                 <hr>
41 |                 <p>{{text}}&nbsp;&nbsp;<a href="{{url}}">更多>></a></p>
42 |             </td>
43 |         </tr>
44 |     </table>
45 | 
46 | </div>
47 | 
48 | 
49 | </body>
50 | 
51 | </html>


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/finetune_alexnet_with_tensorflow/test.py:
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 1 | # -*- coding: utf-8 -*-
 2 | 
 3 | import tensorflow as tf
 4 | from alexnet import AlexNet  # import训练好的网络
 5 | import matplotlib.pyplot as plt
 6 | from caffe_classes import class_names
 7 | 
 8 | class_name = class_names  # oxford102种花的标签
 9 | 
10 | 
11 | def test_image(path_image, num_class, weights_path='Default'):
12 |     # 把新图片进行转换
13 |     img_string = tf.read_file(path_image)
14 |     # img_decoded = tf.image.decode_png(img_string, channels=3)
15 |     img_decoded = tf.image.decode_jpeg(img_string, channels=3)
16 |     img_resized = tf.image.resize_images(img_decoded, [227, 227])
17 |     img_resized = tf.reshape(img_resized, shape=[1, 227, 227, 3])
18 | 
19 |     # 图片通过AlexNet
20 |     model = AlexNet(img_resized, 0.5, 102, skip_layer='', weights_path=weights_path)
21 |     score = tf.nn.softmax(model.fc8)
22 |     max = tf.arg_max(score, 1)
23 |     saver = tf.train.Saver()
24 | 
25 |     with tf.Session() as sess:
26 |         sess.run(tf.global_variables_initializer())
27 |         saver.restore(sess,
28 |                       "/finetune_alexnet/model_epoch50.ckpt")  # 导入训练好的参数
29 |         # score = model.fc8
30 |         print(sess.run(model.fc8))
31 |         prob = sess.run(max)[0]
32 | 
33 |         # 在matplotlib中观测分类结果
34 |         plt.imshow(img_decoded.eval())
35 |         plt.title("Class:" + class_name[prob])#到标签文件caffe_classes.py文件中根据prob查找对应的标签
36 |         print(prob)
37 |         plt.show()
38 |     return class_name[prob]
39 | 
40 | 
41 | #test_image('image_06785.jpg', num_class=102)  # 输入一张新图片


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/finetune_alexnet_with_tensorflow/train.txt:
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 843 | C:\Users\Administrator\Desktop\oxford/data/jpg\image_05472.jpg 86
 844 | C:\Users\Administrator\Desktop\oxford/data/jpg\image_03719.jpg 52
 845 | C:\Users\Administrator\Desktop\oxford/data/jpg\image_07107.jpg 9
 846 | C:\Users\Administrator\Desktop\oxford/data/jpg\image_07872.jpg 98
 847 | C:\Users\Administrator\Desktop\oxford/data/jpg\image_00922.jpg 80
 848 | C:\Users\Administrator\Desktop\oxford/data/jpg\image_02927.jpg 59
 849 | C:\Users\Administrator\Desktop\oxford/data/jpg\image_07307.jpg 93
 850 | C:\Users\Administrator\Desktop\oxford/data/jpg\image_00864.jpg 80
 851 | C:\Users\Administrator\Desktop\oxford/data/jpg\image_01918.jpg 77
 852 | C:\Users\Administrator\Desktop\oxford/data/jpg\image_05906.jpg 67
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 854 | C:\Users\Administrator\Desktop\oxford/data/jpg\image_01126.jpg 45
 855 | C:\Users\Administrator\Desktop\oxford/data/jpg\image_05891.jpg 62
 856 | C:\Users\Administrator\Desktop\oxford/data/jpg\image_03582.jpg 71
 857 | C:\Users\Administrator\Desktop\oxford/data/jpg\image_08033.jpg 101
 858 | C:\Users\Administrator\Desktop\oxford/data/jpg\image_07709.jpg 96
 859 | C:\Users\Administrator\Desktop\oxford/data/jpg\image_02779.jpg 55
 860 | C:\Users\Administrator\Desktop\oxford/data/jpg\image_07140.jpg 44
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 864 | C:\Users\Administrator\Desktop\oxford/data/jpg\image_00514.jpg 87
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 867 | C:\Users\Administrator\Desktop\oxford/data/jpg\image_05798.jpg 37
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 869 | C:\Users\Administrator\Desktop\oxford/data/jpg\image_04488.jpg 70
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1000 | C:\Users\Administrator\Desktop\oxford/data/jpg\image_02879.jpg 85
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1006 | C:\Users\Administrator\Desktop\oxford/data/jpg\image_01987.jpg 79
1007 | C:\Users\Administrator\Desktop\oxford/data/jpg\image_01417.jpg 50
1008 | C:\Users\Administrator\Desktop\oxford/data/jpg\image_01602.jpg 81
1009 | C:\Users\Administrator\Desktop\oxford/data/jpg\image_07863.jpg 98
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1011 | C:\Users\Administrator\Desktop\oxford/data/jpg\image_04045.jpg 11
1012 | C:\Users\Administrator\Desktop\oxford/data/jpg\image_06142.jpg 63
1013 | C:\Users\Administrator\Desktop\oxford/data/jpg\image_07082.jpg 66
1014 | C:\Users\Administrator\Desktop\oxford/data/jpg\image_03345.jpg 7
1015 | C:\Users\Administrator\Desktop\oxford/data/jpg\image_07587.jpg 94
1016 | C:\Users\Administrator\Desktop\oxford/data/jpg\image_02944.jpg 59
1017 | C:\Users\Administrator\Desktop\oxford/data/jpg\image_07434.jpg 93
1018 | C:\Users\Administrator\Desktop\oxford/data/jpg\image_02684.jpg 57
1019 | C:\Users\Administrator\Desktop\oxford/data/jpg\image_01639.jpg 81
1020 | C:\Users\Administrator\Desktop\oxford/data/jpg\image_03165.jpg 10
1021 | 


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/finetune_alexnet_with_tensorflow/url.txt:
--------------------------------------------------------------------------------
  1 | pink primrose,https://baike.baidu.com/item/%E6%A8%B1%E8%8D%89%E8%8A%B1/10266812?fr=aladdin
  2 | hard-leaved pocket orchid,https://baike.baidu.com/item/%E7%A1%AC%E5%8F%B6%E5%85%9C%E5%85%B0
  3 | canterbury bells,https://baike.baidu.com/item/%E9%A3%8E%E9%93%83%E8%8D%89/680375
  4 | sweet pea,https://baike.baidu.com/item/%E8%B1%8C%E8%B1%86%E8%8A%B1
  5 | english marigold,https://baike.baidu.com/item/%E4%B8%87%E5%AF%BF%E8%8F%8A/946163
  6 | tiger lily,https://baike.baidu.com/item/%E8%99%8E%E7%9A%AE%E7%99%BE%E5%90%88
  7 | moon orchid,https://baike.baidu.com/item/%E6%9C%88%E5%85%B0%E8%8A%B1
  8 | bird of paradise,https://baike.baidu.com/item/%E9%B9%A4%E6%9C%9B%E5%85%B0/1012106?fromtitle=%E5%A4%A9%E5%A0%82%E9%B8%9F&fromid=21219
  9 | monkshood,https://baike.baidu.com/item/%E5%B7%9D%E4%B9%8C/766550
 10 | globe thistle,https://baike.baidu.com/item/%E8%93%9D%E5%88%BA%E5%A4%B4%E5%B1%9E
 11 | snapdragon,https://baike.baidu.com/item/%E9%87%91%E9%B1%BC%E8%8D%89/1665506
 12 | colt's foot,https://baike.baidu.com/item/%E6%AC%BE%E5%86%AC/2639838
 13 | king protea,https://baike.baidu.com/item/%E5%B8%9D%E7%8E%8B%E8%8A%B1/4390702
 14 | spear thistle,https://baike.baidu.com/item/%E7%BF%BC%E8%93%9F
 15 | yellow iris,https://baike.baidu.com/item/%E9%BB%84%E8%8F%96%E8%92%B2/1239542?fr=aladdin
 16 | globe-flower,https://baike.baidu.com/item/%E9%87%91%E8%8E%B2%E8%8A%B1%E5%B1%9E
 17 | purple coneflower,https://baike.baidu.com/item/%E7%B4%AB%E6%9D%BE%E6%9E%9C%E8%8F%8A
 18 | peruvian lily,https://baike.baidu.com/item/%E6%B0%B4%E4%BB%99%E7%99%BE%E5%90%88
 19 | balloon flower,https://baike.baidu.com/item/%E6%A1%94%E6%A2%97%E8%8A%B1/327581
 20 | giant white arum lily,https://baike.baidu.com/item/%E9%A9%AC%E8%B9%84%E8%8E%B2%E5%B1%9E
 21 | fire lily,https://baike.baidu.com/item/%E7%8F%A0%E8%8A%BD%E7%99%BE%E5%90%88
 22 | pincushion flower,https://baike.baidu.com/item/%E8%93%9D%E7%9B%86%E8%8A%B1%E5%B1%9E
 23 | fritillary,https://baike.baidu.com/item/%E8%B4%9D%E6%AF%8D
 24 | red ginger,https://baike.baidu.com/item/%E7%B4%AB%E8%8A%B1%E5%B1%B1%E5%A7%9C/23260746?fromtitle=Red%20Ginger&fromid=23271588&fr=aladdin
 25 | grape hyacinth,https://baike.baidu.com/item/%E8%91%A1%E8%90%84%E9%A3%8E%E4%BF%A1%E5%AD%90
 26 | corn poppy,https://baike.baidu.com/item/%E8%A7%92%E7%BD%82%E7%B2%9F%E8%8A%B1/4076456?fr=aladdin
 27 | prince of wales feathers,https://baike.baidu.com/item/%E9%B8%A1%E5%86%A0%E8%8A%B1/404974#viewPageContent
 28 | stemless gentian,https://baike.baidu.com/item/%E6%97%A0%E8%8C%8E%E9%BE%99%E8%83%86
 29 | artichoke,https://baike.baidu.com/item/%E6%B4%8B%E8%93%9F
 30 | sweet william,https://baike.baidu.com/item/%E9%A1%BB%E8%8B%9E%E7%9F%B3%E7%AB%B9?fromtitle=%E7%BE%8E%E5%9B%BD%E7%9F%B3%E7%AB%B9&fromid=2953625
 31 | carnation,https://baike.baidu.com/item/%E9%A6%99%E7%9F%B3%E7%AB%B9
 32 | garden phlox,https://baike.baidu.com/item/%E8%8D%89%E5%A4%B9%E7%AB%B9%E6%A1%83/4396113?fr=aladdin
 33 | love in the mist,https://baike.baidu.com/item/%E9%BB%91%E7%A7%8D%E8%8D%89/4488927
 34 | mexican aster,https://baike.baidu.com/item/%E7%B4%AB%E8%8F%80/770771?fromtitle=%E7%B4%AB%E8%8B%91&fromid=3040571
 35 | alpine sea holly,https://baike.baidu.com/item/%E5%88%BA%E8%8A%B9%E5%B1%9E/8424980
 36 | ruby-lipped cattleya,https://baike.baidu.com/item/%E5%98%89%E5%BE%B7%E4%B8%BD%E9%9B%85%E5%85%B0%E5%B1%9E
 37 | cape flower,https://baike.baidu.com/item/%E7%9F%B3%E8%92%9C/300830
 38 | great masterwort,https://baike.baidu.com/item/%E5%A4%A7%E6%98%9F%E8%8A%B9
 39 | siam tulip,https://baike.baidu.com/item/%E5%A7%9C%E8%8D%B7%E8%8A%B1
 40 | lenten rose,https://baike.baidu.com/item/%E9%93%81%E7%AD%B7%E5%AD%90%E5%B1%9E
 41 | barbeton daisy,https://baike.baidu.com/item/%E9%9D%9E%E6%B4%B2%E8%8F%8A
 42 | daffodil,https://baike.baidu.com/item/%E6%B0%B4%E4%BB%99%E5%B1%9E
 43 | sword lily,https://baike.baidu.com/item/%E5%94%90%E8%8F%96%E8%92%B2%E5%B1%9E
 44 | poinsettia,https://baike.baidu.com/item/%E4%B8%80%E5%93%81%E7%BA%A2/803797
 45 | bolero deep blue,https://baike.baidu.com/item/%E6%B4%8B%E6%A1%94%E6%A2%97
 46 | wallflower,https://baike.baidu.com/item/%E7%B3%96%E8%8A%A5%E5%B1%9E
 47 | marigold,https://baike.baidu.com/item/%E4%B8%87%E5%AF%BF%E8%8F%8A%E5%B1%9E
 48 | buttercup,https://baike.baidu.com/item/%E6%AF%9B%E8%8C%9B%E5%B1%9E
 49 | oxeye daisy,https://baike.baidu.com/item/%E7%8E%9B%E6%A0%BC%E4%B8%BD%E7%89%B9%E8%8A%B1?fromtitle=%E6%B3%95%E5%85%B0%E8%A5%BF%E8%8F%8A&fromid=10807600
 50 | common dandelion,https://baike.baidu.com/item/%E8%A5%BF%E6%B4%8B%E8%92%B2%E5%85%AC%E8%8B%B1
 51 | petunia,https://baike.baidu.com/item/%E7%A2%A7%E5%86%AC%E8%8C%84%E5%B1%9E/1534055
 52 | wild pansy,https://baike.baidu.com/item/%E4%B8%89%E8%89%B2%E5%A0%87/200112#viewPageContent
 53 | primula,https://baike.baidu.com/item/%E6%8A%A5%E6%98%A5%E8%8A%B1%E5%B1%9E
 54 | sunflower,https://baike.baidu.com/item/%E5%90%91%E6%97%A5%E8%91%B5/6106
 55 | pelargonium,https://baike.baidu.com/item/%E5%A4%A9%E7%AB%BA%E8%91%B5%E5%B1%9E
 56 | bishop of llandaff,https://baike.baidu.com/item/%E5%A4%A9%E7%AB%BA%E7%89%A1%E4%B8%B9
 57 | gaura,https://baike.baidu.com/item/%E5%B1%B1%E6%A1%83%E8%8D%89%E5%B1%9E/3456547?fr=aladdin
 58 | geranium,https://baike.baidu.com/item/%E8%80%81%E9%B9%B3%E8%8D%89/393507
 59 | orange dahlia,https://baike.baidu.com/item/%E8%82%BF%E6%9F%84%E8%8F%8A
 60 | pink-yellow dahlia,https://baike.baidu.com/item/%E5%A4%A7%E4%B8%BD%E8%8A%B1/200399
 61 | cautleya spicata,https://baike.baidu.com/item/%E7%BA%A2%E8%8B%9E%E8%B7%9D%E8%8D%AF%E5%A7%9C
 62 | japanese anemone,https://baike.baidu.com/item/%E7%8C%AB%E7%88%AA%E8%8D%89/396369
 63 | black-eyed susan,https://baike.baidu.com/item/%E9%BB%91%E5%BF%83%E9%87%91%E5%85%89%E8%8F%8A
 64 | silverbush,https://baike.baidu.com/item/%E6%97%8B%E8%8A%B1/4507997
 65 | californian poppy,https://baike.baidu.com/item/%E8%8A%B1%E8%8F%B1%E8%8D%89
 66 | osteospermum,https://baike.baidu.com/item/%E8%93%9D%E7%9C%BC%E8%8F%8A
 67 | spring crocus,https://baike.baidu.com/item/%E8%8D%B7%E5%85%B0%E7%95%AA%E7%BA%A2%E8%8A%B1
 68 | bearded iris,https://baike.baidu.com/item/%E5%BE%B7%E5%9B%BD%E9%B8%A2%E5%B0%BE
 69 | windflower,https://baike.baidu.com/item/%E9%93%B6%E8%8E%B2%E8%8A%B1%E5%B1%9E
 70 | tree poppy,https://baike.baidu.com/item/%E7%BD%82%E7%B2%9F%E7%A7%91
 71 | gazania,https://baike.baidu.com/item/%E5%8B%8B%E7%AB%A0%E8%8F%8A/508160
 72 | azalea,https://baike.baidu.com/item/%E6%9D%9C%E9%B9%83/18876?fromtitle=%E6%9D%9C%E9%B9%83%E8%8A%B1&fromid=159279
 73 | water lily,https://baike.baidu.com/item/%E7%9D%A1%E8%8E%B2%E7%A7%91
 74 | rose,https://baike.baidu.com/item/%E7%8E%AB%E7%91%B0/63206
 75 | thorn apple,https://baike.baidu.com/item/%E6%9B%BC%E9%99%80%E7%BD%97%E8%8A%B1
 76 | morning glory,https://baike.baidu.com/item/%E7%89%B5%E7%89%9B/79184
 77 | passion flower,https://baike.baidu.com/item/%E8%A5%BF%E7%95%AA%E8%8E%B2%E5%B1%9E
 78 | lotus,https://baike.baidu.com/item/%E7%9D%A1%E8%8E%B2/11999986?fromtitle=%E8%8E%B2%E8%8A%B1&fromid=12428479
 79 | toad lily,https://baike.baidu.com/item/%E6%B2%B9%E7%82%B9%E8%8D%89
 80 | anthurium,https://baike.baidu.com/item/%E7%81%AB%E9%B9%A4%E8%8A%B1
 81 | frangipani,https://baike.baidu.com/item/%E7%BC%85%E6%A0%80%E8%8A%B1/873997
 82 | clematis,https://baike.baidu.com/item/%E9%93%81%E7%BA%BF%E8%8E%B2%E5%B1%9E
 83 | hibiscus,https://baike.baidu.com/item/%E6%9C%A8%E6%A7%BF%E5%B1%9E
 84 | columbine,https://baike.baidu.com/item/%E8%80%A7%E6%96%97%E8%8F%9C/767936?fr=aladdin
 85 | desert-rose,https://baike.baidu.com/item/%E6%B2%99%E6%BC%A0%E7%8E%AB%E7%91%B0
 86 | tree mallow,https://baike.baidu.com/item/%E8%8A%B1%E8%91%B5
 87 | magnolia,https://baike.baidu.com/item/%E6%9C%A8%E5%85%B0%E5%B1%9E
 88 | cyclamen,https://baike.baidu.com/item/%E4%BB%99%E5%AE%A2%E6%9D%A5%E5%B1%9E
 89 | watercress,https://baike.baidu.com/item/%E6%97%B1%E9%87%91%E8%8E%B2/892401
 90 | canna lily,https://baike.baidu.com/item/%E7%BE%8E%E4%BA%BA%E8%95%89/539429
 91 | hippeastrum,https://baike.baidu.com/item/%E6%9C%B1%E9%A1%B6%E7%BA%A2%E5%B1%9E
 92 | bee balm,https://baike.baidu.com/item/%E7%BE%8E%E5%9B%BD%E8%96%84%E8%8D%B7%E5%B1%9E
 93 | ball moss,https://baike.baidu.com/item/%E7%A9%BA%E6%B0%94%E5%87%A4%E6%A2%A8
 94 | foxglove,https://baike.baidu.com/item/%E6%AF%9B%E5%9C%B0%E9%BB%84%E5%B1%9E
 95 | bougainvillea,https://baike.baidu.com/item/%E5%85%89%E5%8F%B6%E5%AD%90%E8%8A%B1?fromtitle=%E7%B0%95%E6%9D%9C%E9%B9%83&fromid=1486412
 96 | camellia,https://baike.baidu.com/item/%E8%8C%B6%E8%8A%B1/315158?fr=aladdin
 97 | mallow,https://baike.baidu.com/item/%E9%94%A6%E8%91%B5/18212?fr=aladdin
 98 | mexican petunia,https://baike.baidu.com/item/%E7%8B%AD%E5%8F%B6%E7%BF%A0%E8%8A%A6%E8%8E%89/23445029?fr=aladdin&fromtitle=%E7%BF%A0%E8%8A%A6%E8%8E%89&fromid=7959962
 99 | bromelia,https://baike.baidu.com/item/%E8%A7%82%E8%B5%8F%E5%87%A4%E6%A2%A8/5100458
100 | blanket flower,https://baike.baidu.com/item/%E5%AE%BF%E6%A0%B9%E5%A4%A9%E4%BA%BA%E8%8F%8A
101 | trumpet creeper,https://baike.baidu.com/item/%E5%87%8C%E9%9C%84%E8%8A%B1
102 | blackberry lily,https://baike.baidu.com/item/%E5%B0%84%E5%B9%B2/769720?fr=aladdin


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1 | home = C:\Users\Administrator\Anaconda3
2 | include-system-site-packages = false
3 | version = 3.7.3
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/vgg16_oxford_flower_102/caffe_classes.py:
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  1 | class_names = '''pink primrose
  2 | hard-leaved pocket orchid
  3 | canterbury bells
  4 | sweet pea
  5 | english marigold
  6 | tiger lily
  7 | moon orchid
  8 | bird of paradise
  9 | monkshood
 10 | globe thistle
 11 | snapdragon
 12 | colt's foot
 13 | king protea
 14 | spear thistle
 15 | yellow iris
 16 | globe-flower
 17 | purple coneflower
 18 | peruvian lily
 19 | balloon flower
 20 | giant white arum lily
 21 | fire lily
 22 | pincushion flower
 23 | fritillary
 24 | red ginger
 25 | grape hyacinth
 26 | corn poppy
 27 | prince of wales feathers
 28 | stemless gentian
 29 | artichoke
 30 | sweet william
 31 | carnation
 32 | garden phlox
 33 | love in the mist
 34 | mexican aster
 35 | alpine sea holly
 36 | ruby-lipped cattleya
 37 | cape flower
 38 | great masterwort
 39 | siam tulip
 40 | lenten rose
 41 | barbeton daisy
 42 | daffodil
 43 | sword lily
 44 | poinsettia
 45 | bolero deep blue
 46 | wallflower
 47 | marigold
 48 | buttercup
 49 | oxeye daisy
 50 | common dandelion
 51 | petunia
 52 | wild pansy
 53 | primula
 54 | sunflower
 55 | pelargonium
 56 | bishop of llandaff
 57 | gaura
 58 | geranium
 59 | orange dahlia
 60 | pink-yellow dahlia
 61 | cautleya spicata
 62 | japanese anemone
 63 | black-eyed susan
 64 | silverbush
 65 | californian poppy
 66 | osteospermum
 67 | spring crocus
 68 | bearded iris
 69 | windflower
 70 | tree poppy
 71 | gazania
 72 | azalea
 73 | water lily
 74 | rose
 75 | thorn apple
 76 | morning glory
 77 | passion flower
 78 | lotus
 79 | toad lily
 80 | anthurium
 81 | frangipani
 82 | clematis
 83 | hibiscus
 84 | columbine
 85 | desert-rose
 86 | tree mallow
 87 | magnolia
 88 | cyclamen
 89 | watercress
 90 | canna lily
 91 | hippeastrum
 92 | bee balm
 93 | ball moss
 94 | foxglove
 95 | bougainvillea
 96 | camellia
 97 | mallow
 98 | mexican petunia
 99 | bromelia
100 | blanket flower
101 | trumpet creeper
102 | blackberry lily'''.split("\n")
103 | 


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/vgg16_oxford_flower_102/main.py:
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 1 | from flask import Flask, render_template, request, redirect, url_for, make_response, jsonify
 2 | from werkzeug.utils import secure_filename
 3 | import os
 4 | import cv2
 5 | import time
 6 | from test import test_image
 7 | import urllib.request
 8 | from bs4 import BeautifulSoup
 9 | 
10 | from datetime import timedelta
11 | 
12 | # 设置允许的文件格式
13 | ALLOWED_EXTENSIONS = set(['png', 'jpg', 'JPG', 'PNG', 'bmp'])
14 | 
15 | 
16 | def allowed_file(filename):
17 |     return '.' in filename and filename.rsplit('.', 1)[1] in ALLOWED_EXTENSIONS
18 | 
19 | 
20 | app = Flask(__name__)
21 | # 设置静态文件缓存过期时间
22 | app.send_file_max_age_default = timedelta(seconds=1)
23 | 
24 | 
25 | # @app.route('/upload', methods=['POST', 'GET'])
26 | @app.route('/', methods=['POST', 'GET'])  # 添加路由
27 | def upload():
28 |     if request.method == 'POST':
29 |         f = request.files['file']
30 | 
31 |         if not (f and allowed_file(f.filename)):
32 |             return jsonify({"error": 1001, "msg": "Please check the image format, only png jpg bmp format"})
33 | 
34 |         user_input = request.form.get("name")
35 | 
36 |         basepath = os.path.dirname(__file__)  # 当前文件所在路径
37 | 
38 |         upload_path = os.path.join(basepath, 'static/test_img', secure_filename(f.filename))
39 |         # 注意：没有的文件夹一定要先创建，不然会提示没有该路径
40 | 
41 |         f.save(upload_path)
42 | 
43 |         # 使用Opencv转换一下图片格式和名称
44 |         img = cv2.imread(upload_path)
45 |         cv2.imwrite(os.path.join(basepath, 'static/test_img', 'test.jpg'), img)
46 |         class_name=test_image()
47 |         # class_name = 'water lily'
48 |         fo = open("url.txt", "r")
49 |         for line in fo.readlines():
50 |             temp = line.split(',')
51 |             if temp[0] == class_name:
52 |                 url = temp[1]
53 | 
54 |         a = urllib.request.urlopen(url)
55 |         html = a.read()  # 读取网页源码
56 |         soup = BeautifulSoup(html, 'lxml')  # 生成标签树
57 | 
58 |         name = soup.find('h1')
59 |         name = name.get_text()
60 | 
61 |         text = soup.find('div', class_="para")
62 |         text = text.get_text()
63 | 
64 |         return render_template('recognition_ok.html', userinput=user_input, val1=time.time(),
65 |                                classname=class_name, name=name, text=text, url=url)
66 | 
67 |     return render_template('recognition.html')
68 | 
69 | 
70 | if __name__ == '__main__':
71 |     app.run(debug=True)
72 | 


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/vgg16_oxford_flower_102/network.py:
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 1 | import os
 2 | import glob
 3 | import math
 4 | import numpy as np
 5 | from keras import optimizers
 6 | from keras import applications
 7 | from keras.models import Model
 8 | from keras.layers import Flatten, Dense, Dropout, Input
 9 | from keras.preprocessing.image import ImageDataGenerator
10 | from keras.callbacks import EarlyStopping, ModelCheckpoint
11 | from keras.models import load_model
12 | # 数据集
13 | train_dir = 'data/train'  # 训练集
14 | validation_dir = 'data/valid'  # 验证集
15 | nb_epoch = 50  # 迭代次数，原项目默认1000次
16 | batch_size = 32  # 批量大小
17 | img_size = (224, 224)  # 图片大小
18 | freeze_layers_number = 0  # 冻结层数
19 | 
20 | classes = sorted([o for o in os.listdir(train_dir)])  # 根据文件名分类
21 | nb_train_samples = len(glob.glob(train_dir + '/*/*.*'))  # 训练样本数
22 | nb_validation_samples = len(glob.glob(validation_dir + '/*/*.*'))  # 验证样本数
23 | 
24 | # 定义模型
25 | base_model = applications.VGG16(weights='imagenet', include_top=False, input_tensor=Input(shape=img_size + (3,)),
26 |                                 classes=len(classes))  # 预训练的VGG16网络，替换掉顶部网络
27 | 
28 | for layer in base_model.layers:  # 保留原有网络全部参数
29 |     print(layer.trainable)
30 |     layer.trainable = False
31 | 
32 | x = base_model.output  # 自定义网络
33 | x = Flatten()(x)  # 展平
34 | x = Dense(4096, activation='elu', name='fc1')(x)  # 全连接层，激活函数elu
35 | x = Dropout(0.6)(x)  # Droupout 0.6
36 | x = Dense(4096, activation='elu', name='fc2')(x)
37 | x = Dropout(0.6)(x)
38 | predictions = Dense(len(classes), activation='softmax', name='predictions')(x)  # 输出层，指定类数
39 | 
40 | model = Model(input=base_model.input, output=predictions)  # 新网络=预训练网络+自定义网络
41 | 
42 | model.compile(loss='categorical_crossentropy', optimizer=optimizers.Adam(lr=1e-5), metrics=['accuracy'])
43 | print(model.summary())
44 | 
45 | train_datagen = ImageDataGenerator(rotation_range=30., shear_range=0.2, zoom_range=0.2,
46 |                                    horizontal_flip=True)  # 30°内随机旋转，0.2几率应用错切，0.2几率缩放内部，水平随机旋转一半图像
47 | train_datagen.mean = np.array([103.939, 116.779, 123.68], dtype=np.float32).reshape((3, 1, 1))  # 去掉imagenet BGR均值
48 | train_data = train_datagen.flow_from_directory(train_dir, target_size=img_size, classes=classes)
49 | validation_datagen = ImageDataGenerator()  # 用于验证，无需数据增强
50 | validation_datagen.mean = np.array([103.939, 116.779, 123.68], dtype=np.float32).reshape((3, 1, 1))
51 | validation_data = validation_datagen.flow_from_directory(validation_dir, target_size=img_size,
52 |                                                          classes=classes)
53 | 
54 | 
55 | # 训练&保存
56 | def get_class_weight(d):
57 |     '''
58 |     calculate the weight of each class
59 |     :param d: dir path
60 |     :return: a dict
61 |     '''
62 |     white_list_formats = {'png', 'jpg', 'jpeg', 'bmp'}
63 |     class_number = dict()
64 |     dirs = sorted([o for o in os.listdir(d) if os.path.isdir(os.path.join(d, o))])
65 |     k = 0
66 |     for class_name in dirs:
67 |         class_number[k] = 0
68 |         iglob_iter = glob.iglob(os.path.join(d, class_name, '*.*'))
69 |         for i in iglob_iter:
70 |             _, ext = os.path.splitext(i)
71 |             if ext[1:] in white_list_formats:
72 |                 class_number[k] += 1
73 |         k += 1
74 |     total = np.sum(list(class_number.values()))
75 |     max_samples = np.max(list(class_number.values()))
76 |     mu = 1. / (total / float(max_samples))
77 |     keys = class_number.keys()
78 |     class_weight = dict()
79 |     for key in keys:
80 |         score = math.log(mu * total / float(class_number[key]))
81 |         class_weight[key] = score if score > 1. else 1.
82 | 
83 |     return class_weight
84 | 
85 | 
86 | class_weight = get_class_weight(train_dir)  # 计算每个类别所占数据集的比重
87 | 
88 | early_stopping = EarlyStopping(verbose=1, patience=30, monitor='val_loss')  # 30次微调后loss仍没下降便迭代下一轮
89 | model_checkpoint = ModelCheckpoint(filepath='102flowersmodel.h5', verbose=1, save_best_only=True, monitor='val_loss')
90 | callbacks = [early_stopping, model_checkpoint]
91 | 
92 | model.fit_generator(train_data, steps_per_epoch=nb_train_samples / float(batch_size), epochs=nb_epoch,
93 |                     validation_data=validation_data, validation_steps=nb_validation_samples / float(batch_size),
94 |                     callbacks=callbacks, class_weight=class_weight)
95 | 
96 | print('Training is finished!')


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/vgg16_oxford_flower_102/readme.md:
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 1 | # README
 2 | 
 3 | ### finetune_alexnet_with_tensorflow(基于AlexNet的花卉识别)
 4 | 
 5 | finetune.py  训练神经网络的文件
 6 | 
 7 | alexnet.py AlexNet卷积神经网络模型
 8 | 
 9 | bvlc_alexnet.npy 训练好的参数
10 | 
11 | datagenerator.py 图片预处理
12 | 
13 | main.py 路由文件
14 | 
15 | 运行时需要先训练神经网络，因为虽然写了检查点但是看不到检查点文件，训练好以后再运行main.py文件。训练时将oxford文件夹放在train.txt文件中的指定位置，或者根据oxford文件夹的位置对train.txt进行相应修改。
16 | 
17 | ### vgg16_oxford_flower_102(基于VGG16迁移模型的花卉识别)
18 | 
19 | network.py 神经网络的初次训练
20 | 
21 | retrain.py 神经网络的再次训练
22 | 
23 | test.py 测试文件
24 | 
25 | 102flowermodel.h5 训练好的模型保存
26 | 
27 | main.py 路由文件
28 | 
29 | url.txt 花卉百度百科链接
30 | 
31 | ### 使用方法
32 | 运行main.py，访问http://127.0.0.1:5000/， 进入花卉识别主页面，点击“选择文件”按钮，上传要识别的花卉图片，然后点击“开始识别”按钮，进行识别。识别完成后，在页面下方显示识别图像、识别结果以及花卉相关介绍。
33 | 
34 | ### 数据集下载地址
35 | 
36 | http://www.robots.ox.ac.uk/~vgg/data/flowers/102/index.html
37 | 
38 | ### 训练好的参数文件 bvlc_alexnet.npy下载地址
39 | 
40 | http://www.cs.toronto.edu/~guerzhoy/tf_alexnet/bvlc_alexnet.npy
41 | 
42 | 


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/vgg16_oxford_flower_102/retrain.py:
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 1 | import os
 2 | import glob
 3 | import math
 4 | import numpy as np
 5 | from keras import optimizers
 6 | from keras import applications
 7 | from keras.models import Model
 8 | from keras.layers import Flatten, Dense, Dropout, Input
 9 | from keras.preprocessing.image import ImageDataGenerator
10 | from keras.callbacks import EarlyStopping, ModelCheckpoint
11 | from keras.models import load_model
12 | # 数据集
13 | train_dir = 'data/train'  # 训练集
14 | validation_dir = 'data/valid'  # 验证集
15 | nb_epoch = 10  # 迭代次数，原项目默认1000次
16 | batch_size = 128  # 批量大小
17 | img_size = (224, 224)  # 图片大小
18 | freeze_layers_number = 0  # 冻结层数
19 | 
20 | classes = sorted([o for o in os.listdir(train_dir)])  # 根据文件名分类
21 | nb_train_samples = len(glob.glob(train_dir + '/*/*.*'))  # 训练样本数
22 | nb_validation_samples = len(glob.glob(validation_dir + '/*/*.*'))  # 验证样本数
23 | 
24 | # 定义模型
25 | base_model = applications.VGG16(weights='imagenet', include_top=False, input_tensor=Input(shape=img_size + (3,)),
26 |                                 classes=len(classes))  # 预训练的VGG16网络，替换掉顶部网络
27 | 
28 | for layer in base_model.layers:  # 保留原有网络全部参数
29 |     print(layer.trainable)
30 |     layer.trainable = False
31 | 
32 | x = base_model.output  # 自定义网络
33 | x = Flatten()(x)  # 展平
34 | x = Dense(4096, activation='elu', name='fc1')(x)  # 全连接层，激活函数elu
35 | x = Dropout(0.6)(x)  # Droupout 0.6
36 | x = Dense(4096, activation='elu', name='fc2')(x)
37 | x = Dropout(0.6)(x)
38 | predictions = Dense(len(classes), activation='softmax', name='predictions')(x)  # 输出层，指定类数
39 | 
40 | model = Model(input=base_model.input, output=predictions)  # 新网络=预训练网络+自定义网络
41 | 
42 | model.compile(loss='categorical_crossentropy', optimizer=optimizers.Adam(lr=1e-5), metrics=['accuracy'])
43 | print(model.summary())
44 | 
45 | train_datagen = ImageDataGenerator(rotation_range=30., shear_range=0.2, zoom_range=0.2,
46 |                                    horizontal_flip=True)  # 30°内随机旋转，0.2几率应用错切，0.2几率缩放内部，水平随机旋转一半图像
47 | train_datagen.mean = np.array([103.939, 116.779, 123.68], dtype=np.float32).reshape((3, 1, 1))  # 去掉imagenet BGR均值
48 | train_data = train_datagen.flow_from_directory(train_dir, target_size=img_size, classes=classes)
49 | validation_datagen = ImageDataGenerator()  # 用于验证，无需数据增强
50 | validation_datagen.mean = np.array([103.939, 116.779, 123.68], dtype=np.float32).reshape((3, 1, 1))
51 | validation_data = validation_datagen.flow_from_directory(validation_dir, target_size=img_size,
52 |                                                          classes=classes)
53 | 
54 | 
55 | # 训练&保存
56 | def get_class_weight(d):
57 |     '''
58 |     calculate the weight of each class
59 |     :param d: dir path
60 |     :return: a dict
61 |     '''
62 |     white_list_formats = {'png', 'jpg', 'jpeg', 'bmp'}
63 |     class_number = dict()
64 |     dirs = sorted([o for o in os.listdir(d) if os.path.isdir(os.path.join(d, o))])
65 |     k = 0
66 |     for class_name in dirs:
67 |         class_number[k] = 0
68 |         iglob_iter = glob.iglob(os.path.join(d, class_name, '*.*'))
69 |         for i in iglob_iter:
70 |             _, ext = os.path.splitext(i)
71 |             if ext[1:] in white_list_formats:
72 |                 class_number[k] += 1
73 |         k += 1
74 |     total = np.sum(list(class_number.values()))
75 |     max_samples = np.max(list(class_number.values()))
76 |     mu = 1. / (total / float(max_samples))
77 |     keys = class_number.keys()
78 |     class_weight = dict()
79 |     for key in keys:
80 |         score = math.log(mu * total / float(class_number[key]))
81 |         class_weight[key] = score if score > 1. else 1.
82 | 
83 |     return class_weight
84 | 
85 | 
86 | class_weight = get_class_weight(train_dir)  # 计算每个类别所占数据集的比重
87 | 
88 | early_stopping = EarlyStopping(verbose=1, patience=30, monitor='val_loss')  # 30次微调后loss仍没下降便迭代下一轮
89 | model_checkpoint = ModelCheckpoint(filepath='102flowersmodel.h5', verbose=1, save_best_only=True, monitor='val_loss')
90 | callbacks = [early_stopping, model_checkpoint]
91 | 
92 | rmodel = load_model('102flowersmodel.h5')
93 | 
94 | rmodel.fit_generator(train_data, steps_per_epoch=nb_train_samples / float(batch_size), epochs=nb_epoch,
95 |                     validation_data=validation_data, validation_steps=nb_validation_samples / float(batch_size),
96 |                     callbacks=callbacks, class_weight=class_weight)
97 | 
98 | print('Training is finished!')


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/vgg16_oxford_flower_102/static/css/__init__.py:
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https://raw.githubusercontent.com/Amore-vv/flower_recognition/c4b210044a342f7e717ce09e7e929f993bea436b/vgg16_oxford_flower_102/static/css/__init__.py


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/vgg16_oxford_flower_102/static/css/main.css:
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  1 | body{
  2 |     background-color: ghostwhite;
  3 |     margin: 0 auto;
  4 |     text-align: center;
  5 | }
  6 | 
  7 | .t1{
  8 |     align-content: center;
  9 |     border-spacing: 250px 25px;
 10 |     margin-left: -55px;
 11 | 
 12 | }
 13 | 
 14 | hr{
 15 |     width:80%;
 16 |     margin:0 auto;
 17 |     border: 0;
 18 |     height: 1px;
 19 |     background-image: linear-gradient(to right, rgba(0, 0, 0, 0), rgba(0, 0, 0, 0.75), rgba(0, 0, 0, 0));
 20 | 
 21 | }
 22 | 
 23 | .bg1{
 24 |     width: 800px;
 25 |     height: 100px;
 26 |     background-color: dimgrey;
 27 |     color: floralwhite;
 28 |     text-align: center;
 29 |     line-height: 100px;
 30 |     margin-left: 150px;
 31 | }
 32 | 
 33 | .bg2{
 34 |     width: 500px;
 35 |     height: 595px;
 36 |     border: 2px solid dimgrey;
 37 |     float: left;
 38 |     margin-left: 50px;
 39 | }
 40 | 
 41 | .bg3{
 42 |     width: 500px;
 43 |     height: 600px;
 44 |     /*border: 1px solid dimgrey;*/
 45 |     float: left;
 46 |     margin-left: 10px;
 47 | }
 48 | .bg3 table{
 49 |     border: 2px solid dimgrey;
 50 |     border-collapse:collapse;
 51 |     padding: 10px;
 52 | }
 53 | .bg3 table td{
 54 |     border: 2px solid dimgrey;
 55 |     width: 500px;
 56 |     height: 295px;
 57 |     text-align: left;
 58 |     vertical-align: top;
 59 | }
 60 | 
 61 | .file1 {
 62 |     /*margin-top: 5px;*/
 63 |     background: #D0EEFF;
 64 |     border: 1px solid #99D3F5;
 65 |     border-radius: 4px;
 66 |     padding: 4px 12px;
 67 |     overflow: hidden;
 68 |     color: black;
 69 |     text-decoration: none;
 70 |     text-indent: 0;
 71 |     width: 200px;
 72 |     height: 60px;
 73 |     font-size: 30px;
 74 |     text-align: center;
 75 |     font-weight: 600;
 76 |     opacity: 0.5;
 77 | 
 78 | }
 79 | .file {
 80 |     position: relative;
 81 |     display: inline-block;
 82 |     background: #D0EEFF;
 83 |     border: 1px solid #99D3F5;
 84 |     border-radius: 4px;
 85 |     padding: 4px 12px;
 86 |     overflow: hidden;
 87 |     color: black;
 88 |     text-decoration: none;
 89 |     text-indent: 0;
 90 |     width: 180px;
 91 |     height: 50px;
 92 |     font-size: 30px;
 93 |     font-weight: 600;
 94 |     text-align: center;
 95 |     opacity: 0.5;
 96 | 
 97 | 
 98 | }
 99 | .file input {
100 |     position: absolute;
101 |     font-size: 30px;
102 |     right: 0;
103 |     top: 10px;
104 |     opacity: 0.5;
105 | 
106 | }
107 | .file:hover {
108 |     background: #AADFFD;
109 |     border-color: #78C3F3;
110 |     color: #004974;
111 |     text-decoration: none;
112 | }
113 | 


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/vgg16_oxford_flower_102/static/js/__init__.py:
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https://raw.githubusercontent.com/Amore-vv/flower_recognition/c4b210044a342f7e717ce09e7e929f993bea436b/vgg16_oxford_flower_102/static/js/__init__.py


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/vgg16_oxford_flower_102/static/test_img/__init__.py:
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https://raw.githubusercontent.com/Amore-vv/flower_recognition/c4b210044a342f7e717ce09e7e929f993bea436b/vgg16_oxford_flower_102/static/test_img/__init__.py


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/vgg16_oxford_flower_102/templates/recognition.html:
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 1 | <!DOCTYPE html>
 2 | <html lang="en">
 3 | <head>
 4 |     <meta charset="UTF-8">
 5 |     <link type="text/css" rel="stylesheet" href="{{url_for('static', filename='css/main.css')}}" />
 6 |     <title>花卉识别</title>
 7 | </head>
 8 | 
 9 | <body>
10 |     <div class="bg1">
11 |         <h1 style="font-size: 50px;">花卉识别</h1>
12 |     </div>
13 | 
14 |     <form action="" enctype='multipart/form-data' method='POST' >
15 |         <table class="t1">
16 |             <td>
17 |                 <a href="javascript:;" class="file">选择文件<input type="file" name="file" id=""></a>
18 |             </td>
19 |             <td>
20 |                 <input class="file1" type="submit" value="开始识别" />
21 |             </td>
22 |         </table>
23 |     </form>
24 | 
25 | <div class="bg2">
26 | 
27 | </div>
28 | <div class="bg3">
29 |     <table>
30 |         <tr>
31 |             <td>
32 |                 <h2 align="center">识别结果</h2>
33 |                 <hr>
34 |             </td>
35 |         </tr>
36 |         <tr>
37 |             <td>
38 |                 <h2 align="center">百度百科</h2>
39 |                 <hr>
40 | 
41 |             </td>
42 |         </tr>
43 |     </table>
44 | 
45 | </div>
46 | </body>
47 | 
48 | <script src="{{url_for('static', filename='js/jquery-3.4.1.js')}}"></script>
49 | 
50 | <script type="text/javascript">
51 |     $(function () {
52 | 
53 |         $(".file").on("change","input[type='file']",function(){
54 |             var filePath=$(this).val();
55 |             if(filePath.indexOf("jpg")!=-1 || filePath.indexOf("png")!=-1){
56 |                 $(".fileerrorTip").html("").hide();
57 |                 var arr=filePath.split('\\');
58 |                 var fileName=arr[arr.length-1];
59 |                 $(".showFileName").html(fileName);
60 |             }else{
61 |                 $(".showFileName").html("");
62 |                 $(".fileerrorTip").html("您未上传文件，或者您上传文件类型有误！").show();
63 |                 return false
64 |             }
65 |         });
66 | 
67 |     });
68 | </script>
69 | </html>
70 | 


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/vgg16_oxford_flower_102/templates/recognition_ok.html:
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 1 | <!DOCTYPE html>
 2 | <html lang="en">
 3 | <head>
 4 |     <meta charset="UTF-8">
 5 |     <link type="text/css" rel="stylesheet" href="{{url_for('static', filename='css/main.css')}}" />
 6 |     <title>花卉识别</title>
 7 | </head>
 8 | 
 9 | <body>
10 | <div class="bg1">
11 |     <h1 style="font-size: 50px;">花卉识别</h1>
12 | </div>
13 | 
14 |     <form action="" enctype='multipart/form-data' method='POST' >
15 |         <table class="t1">
16 |             <td>
17 |                 <a href="javascript:;" class="file">选择文件<input type="file" name="file" id=""></a>
18 |             </td>
19 |             <td>
20 |                 <input class="file1" type="submit" value="开始识别" />
21 |             </td>
22 |         </table>
23 |     </form>
24 | <div class="bg2">
25 |     <img src="{{ url_for('static', filename= './test_img/test.jpg',_t=val1) }}" width="500px" alt="你的图片被外星人劫持了～～"/>
26 | </div>
27 | <div class="bg3">
28 |     <table>
29 |         <tr>
30 |             <td>
31 |                 <h2 align="center">识别结果</h2>
32 |                 <hr>
33 |                 <p align="center">&nbsp;&nbsp;花卉名称：{{classname}}<br>
34 |                     &nbsp;&nbsp;译名：&nbsp;&nbsp;{{name}}</p>
35 |             </td>
36 |         </tr>
37 |         <tr>
38 |             <td>
39 |                 <h2 align="center">百度百科</h2>
40 |                 <hr>
41 |                 <p>{{text}}&nbsp;&nbsp;<a href="{{url}}">更多>></a></p>
42 |             </td>
43 |         </tr>
44 |     </table>
45 | 
46 | </div>
47 | 
48 | 
49 | </body>
50 | 
51 | </html>


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/vgg16_oxford_flower_102/test.py:
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 1 | import time
 2 | import numpy as np
 3 | from keras.models import load_model
 4 | from keras.preprocessing import image
 5 | from caffe_classes import class_names
 6 | 
 7 | # # 加载模型
 8 | # start = time.clock()
 9 | # model = load_model('102flowersmodel.h5')
10 | # print('Warming up took {}s'.format(time.clock() - start))
11 | #
12 | # # 图片预处理
13 | # path = 'static/test_img/test.jpg'
14 | # img_height, img_width = 224, 224
15 | # x = image.load_img(path=path, target_size=(img_height, img_width))
16 | # x = image.img_to_array(x)
17 | # x = x[None]
18 | #
19 | # # 预测
20 | # start = time.clock()
21 | # y = model.predict(x)
22 | # print('Prediction took {}s'.format(time.clock() - start))
23 | #
24 | # # 置信度
25 | # for i in np.argsort(y[0])[::-1][:5]:
26 | #     print('{}:{:.2f}%'.format(i, y[0][i] * 100))
27 | 
28 | def test_image():
29 |     # 加载模型
30 |     path = 'static/test_img/test.jpg'
31 |     start = time.clock()
32 |     model = load_model('102flowersmodel.h5')
33 |     print('Warming up took {}s'.format(time.clock() - start))
34 | 
35 |     img_height, img_width = 224, 224
36 |     x = image.load_img(path=path, target_size=(img_height, img_width))
37 |     x = image.img_to_array(x)
38 |     x = x[None]
39 | 
40 |     # 预测
41 |     start = time.clock()
42 |     y = model.predict(x)
43 |     print('Prediction took {}s'.format(time.clock() - start))
44 | 
45 |     for i in np.argsort(y[0])[::-1][:5]:
46 |         flag = i
47 |         break
48 |     dir=['0', '1', '10', '100', '101', '11', '12', '13', '14', '15', '16', '17', '18', '19', '2', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '3', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '4', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '5', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '6', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '7', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '8', '80', '81',
49 |           '82', '83', '84', '85', '86', '87', '88', '89', '9', '90', '91', '92', '93', '94', '95', '96', '97', '98', '99']
50 |     k=int(dir[flag])
51 | 
52 |     flower_class = class_names[k]
53 |     return flower_class
54 | # ppath='C:/Users/Administrator/Desktop/image_00009.jpg'
55 | # print(test_image(ppath))


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/vgg16_oxford_flower_102/url.txt:
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  1 | pink primrose,https://baike.baidu.com/item/%E6%A8%B1%E8%8D%89%E8%8A%B1/10266812?fr=aladdin
  2 | hard-leaved pocket orchid,https://baike.baidu.com/item/%E7%A1%AC%E5%8F%B6%E5%85%9C%E5%85%B0
  3 | canterbury bells,https://baike.baidu.com/item/%E9%A3%8E%E9%93%83%E8%8D%89/680375
  4 | sweet pea,https://baike.baidu.com/item/%E8%B1%8C%E8%B1%86%E8%8A%B1
  5 | english marigold,https://baike.baidu.com/item/%E4%B8%87%E5%AF%BF%E8%8F%8A/946163
  6 | tiger lily,https://baike.baidu.com/item/%E8%99%8E%E7%9A%AE%E7%99%BE%E5%90%88
  7 | moon orchid,https://baike.baidu.com/item/%E6%9C%88%E5%85%B0%E8%8A%B1
  8 | bird of paradise,https://baike.baidu.com/item/%E9%B9%A4%E6%9C%9B%E5%85%B0/1012106?fromtitle=%E5%A4%A9%E5%A0%82%E9%B8%9F&fromid=21219
  9 | monkshood,https://baike.baidu.com/item/%E5%B7%9D%E4%B9%8C/766550
 10 | globe thistle,https://baike.baidu.com/item/%E8%93%9D%E5%88%BA%E5%A4%B4%E5%B1%9E
 11 | snapdragon,https://baike.baidu.com/item/%E9%87%91%E9%B1%BC%E8%8D%89/1665506
 12 | colt's foot,https://baike.baidu.com/item/%E6%AC%BE%E5%86%AC/2639838
 13 | king protea,https://baike.baidu.com/item/%E5%B8%9D%E7%8E%8B%E8%8A%B1/4390702
 14 | spear thistle,https://baike.baidu.com/item/%E7%BF%BC%E8%93%9F
 15 | yellow iris,https://baike.baidu.com/item/%E9%BB%84%E8%8F%96%E8%92%B2/1239542?fr=aladdin
 16 | globe-flower,https://baike.baidu.com/item/%E9%87%91%E8%8E%B2%E8%8A%B1%E5%B1%9E
 17 | purple coneflower,https://baike.baidu.com/item/%E7%B4%AB%E6%9D%BE%E6%9E%9C%E8%8F%8A
 18 | peruvian lily,https://baike.baidu.com/item/%E6%B0%B4%E4%BB%99%E7%99%BE%E5%90%88
 19 | balloon flower,https://baike.baidu.com/item/%E6%A1%94%E6%A2%97%E8%8A%B1/327581
 20 | giant white arum lily,https://baike.baidu.com/item/%E9%A9%AC%E8%B9%84%E8%8E%B2%E5%B1%9E
 21 | fire lily,https://baike.baidu.com/item/%E7%8F%A0%E8%8A%BD%E7%99%BE%E5%90%88
 22 | pincushion flower,https://baike.baidu.com/item/%E8%93%9D%E7%9B%86%E8%8A%B1%E5%B1%9E
 23 | fritillary,https://baike.baidu.com/item/%E8%B4%9D%E6%AF%8D
 24 | red ginger,https://baike.baidu.com/item/%E7%B4%AB%E8%8A%B1%E5%B1%B1%E5%A7%9C/23260746?fromtitle=Red%20Ginger&fromid=23271588&fr=aladdin
 25 | grape hyacinth,https://baike.baidu.com/item/%E8%91%A1%E8%90%84%E9%A3%8E%E4%BF%A1%E5%AD%90
 26 | corn poppy,https://baike.baidu.com/item/%E8%A7%92%E7%BD%82%E7%B2%9F%E8%8A%B1/4076456?fr=aladdin
 27 | prince of wales feathers,https://baike.baidu.com/item/%E9%B8%A1%E5%86%A0%E8%8A%B1/404974#viewPageContent
 28 | stemless gentian,https://baike.baidu.com/item/%E6%97%A0%E8%8C%8E%E9%BE%99%E8%83%86
 29 | artichoke,https://baike.baidu.com/item/%E6%B4%8B%E8%93%9F
 30 | sweet william,https://baike.baidu.com/item/%E9%A1%BB%E8%8B%9E%E7%9F%B3%E7%AB%B9?fromtitle=%E7%BE%8E%E5%9B%BD%E7%9F%B3%E7%AB%B9&fromid=2953625
 31 | carnation,https://baike.baidu.com/item/%E9%A6%99%E7%9F%B3%E7%AB%B9
 32 | garden phlox,https://baike.baidu.com/item/%E8%8D%89%E5%A4%B9%E7%AB%B9%E6%A1%83/4396113?fr=aladdin
 33 | love in the mist,https://baike.baidu.com/item/%E9%BB%91%E7%A7%8D%E8%8D%89/4488927
 34 | mexican aster,https://baike.baidu.com/item/%E7%B4%AB%E8%8F%80/770771?fromtitle=%E7%B4%AB%E8%8B%91&fromid=3040571
 35 | alpine sea holly,https://baike.baidu.com/item/%E5%88%BA%E8%8A%B9%E5%B1%9E/8424980
 36 | ruby-lipped cattleya,https://baike.baidu.com/item/%E5%98%89%E5%BE%B7%E4%B8%BD%E9%9B%85%E5%85%B0%E5%B1%9E
 37 | cape flower,https://baike.baidu.com/item/%E7%9F%B3%E8%92%9C/300830
 38 | great masterwort,https://baike.baidu.com/item/%E5%A4%A7%E6%98%9F%E8%8A%B9
 39 | siam tulip,https://baike.baidu.com/item/%E5%A7%9C%E8%8D%B7%E8%8A%B1
 40 | lenten rose,https://baike.baidu.com/item/%E9%93%81%E7%AD%B7%E5%AD%90%E5%B1%9E
 41 | barbeton daisy,https://baike.baidu.com/item/%E9%9D%9E%E6%B4%B2%E8%8F%8A
 42 | daffodil,https://baike.baidu.com/item/%E6%B0%B4%E4%BB%99%E5%B1%9E
 43 | sword lily,https://baike.baidu.com/item/%E5%94%90%E8%8F%96%E8%92%B2%E5%B1%9E
 44 | poinsettia,https://baike.baidu.com/item/%E4%B8%80%E5%93%81%E7%BA%A2/803797
 45 | bolero deep blue,https://baike.baidu.com/item/%E6%B4%8B%E6%A1%94%E6%A2%97
 46 | wallflower,https://baike.baidu.com/item/%E7%B3%96%E8%8A%A5%E5%B1%9E
 47 | marigold,https://baike.baidu.com/item/%E4%B8%87%E5%AF%BF%E8%8F%8A%E5%B1%9E
 48 | buttercup,https://baike.baidu.com/item/%E6%AF%9B%E8%8C%9B%E5%B1%9E
 49 | oxeye daisy,https://baike.baidu.com/item/%E7%8E%9B%E6%A0%BC%E4%B8%BD%E7%89%B9%E8%8A%B1?fromtitle=%E6%B3%95%E5%85%B0%E8%A5%BF%E8%8F%8A&fromid=10807600
 50 | common dandelion,https://baike.baidu.com/item/%E8%A5%BF%E6%B4%8B%E8%92%B2%E5%85%AC%E8%8B%B1
 51 | petunia,https://baike.baidu.com/item/%E7%A2%A7%E5%86%AC%E8%8C%84%E5%B1%9E/1534055
 52 | wild pansy,https://baike.baidu.com/item/%E4%B8%89%E8%89%B2%E5%A0%87/200112#viewPageContent
 53 | primula,https://baike.baidu.com/item/%E6%8A%A5%E6%98%A5%E8%8A%B1%E5%B1%9E
 54 | sunflower,https://baike.baidu.com/item/%E5%90%91%E6%97%A5%E8%91%B5/6106
 55 | pelargonium,https://baike.baidu.com/item/%E5%A4%A9%E7%AB%BA%E8%91%B5%E5%B1%9E
 56 | bishop of llandaff,https://baike.baidu.com/item/%E5%A4%A9%E7%AB%BA%E7%89%A1%E4%B8%B9
 57 | gaura,https://baike.baidu.com/item/%E5%B1%B1%E6%A1%83%E8%8D%89%E5%B1%9E/3456547?fr=aladdin
 58 | geranium,https://baike.baidu.com/item/%E8%80%81%E9%B9%B3%E8%8D%89/393507
 59 | orange dahlia,https://baike.baidu.com/item/%E8%82%BF%E6%9F%84%E8%8F%8A
 60 | pink-yellow dahlia,https://baike.baidu.com/item/%E5%A4%A7%E4%B8%BD%E8%8A%B1/200399
 61 | cautleya spicata,https://baike.baidu.com/item/%E7%BA%A2%E8%8B%9E%E8%B7%9D%E8%8D%AF%E5%A7%9C
 62 | japanese anemone,https://baike.baidu.com/item/%E7%8C%AB%E7%88%AA%E8%8D%89/396369
 63 | black-eyed susan,https://baike.baidu.com/item/%E9%BB%91%E5%BF%83%E9%87%91%E5%85%89%E8%8F%8A
 64 | silverbush,https://baike.baidu.com/item/%E6%97%8B%E8%8A%B1/4507997
 65 | californian poppy,https://baike.baidu.com/item/%E8%8A%B1%E8%8F%B1%E8%8D%89
 66 | osteospermum,https://baike.baidu.com/item/%E8%93%9D%E7%9C%BC%E8%8F%8A
 67 | spring crocus,https://baike.baidu.com/item/%E8%8D%B7%E5%85%B0%E7%95%AA%E7%BA%A2%E8%8A%B1
 68 | bearded iris,https://baike.baidu.com/item/%E5%BE%B7%E5%9B%BD%E9%B8%A2%E5%B0%BE
 69 | windflower,https://baike.baidu.com/item/%E9%93%B6%E8%8E%B2%E8%8A%B1%E5%B1%9E
 70 | tree poppy,https://baike.baidu.com/item/%E7%BD%82%E7%B2%9F%E7%A7%91
 71 | gazania,https://baike.baidu.com/item/%E5%8B%8B%E7%AB%A0%E8%8F%8A/508160
 72 | azalea,https://baike.baidu.com/item/%E6%9D%9C%E9%B9%83/18876?fromtitle=%E6%9D%9C%E9%B9%83%E8%8A%B1&fromid=159279
 73 | water lily,https://baike.baidu.com/item/%E7%9D%A1%E8%8E%B2%E7%A7%91
 74 | rose,https://baike.baidu.com/item/%E7%8E%AB%E7%91%B0/63206
 75 | thorn apple,https://baike.baidu.com/item/%E6%9B%BC%E9%99%80%E7%BD%97%E8%8A%B1
 76 | morning glory,https://baike.baidu.com/item/%E7%89%B5%E7%89%9B/79184
 77 | passion flower,https://baike.baidu.com/item/%E8%A5%BF%E7%95%AA%E8%8E%B2%E5%B1%9E
 78 | lotus,https://baike.baidu.com/item/%E7%9D%A1%E8%8E%B2/11999986?fromtitle=%E8%8E%B2%E8%8A%B1&fromid=12428479
 79 | toad lily,https://baike.baidu.com/item/%E6%B2%B9%E7%82%B9%E8%8D%89
 80 | anthurium,https://baike.baidu.com/item/%E7%81%AB%E9%B9%A4%E8%8A%B1
 81 | frangipani,https://baike.baidu.com/item/%E7%BC%85%E6%A0%80%E8%8A%B1/873997
 82 | clematis,https://baike.baidu.com/item/%E9%93%81%E7%BA%BF%E8%8E%B2%E5%B1%9E
 83 | hibiscus,https://baike.baidu.com/item/%E6%9C%A8%E6%A7%BF%E5%B1%9E
 84 | columbine,https://baike.baidu.com/item/%E8%80%A7%E6%96%97%E8%8F%9C/767936?fr=aladdin
 85 | desert-rose,https://baike.baidu.com/item/%E6%B2%99%E6%BC%A0%E7%8E%AB%E7%91%B0
 86 | tree mallow,https://baike.baidu.com/item/%E8%8A%B1%E8%91%B5
 87 | magnolia,https://baike.baidu.com/item/%E6%9C%A8%E5%85%B0%E5%B1%9E
 88 | cyclamen,https://baike.baidu.com/item/%E4%BB%99%E5%AE%A2%E6%9D%A5%E5%B1%9E
 89 | watercress,https://baike.baidu.com/item/%E6%97%B1%E9%87%91%E8%8E%B2/892401
 90 | canna lily,https://baike.baidu.com/item/%E7%BE%8E%E4%BA%BA%E8%95%89/539429
 91 | hippeastrum,https://baike.baidu.com/item/%E6%9C%B1%E9%A1%B6%E7%BA%A2%E5%B1%9E
 92 | bee balm,https://baike.baidu.com/item/%E7%BE%8E%E5%9B%BD%E8%96%84%E8%8D%B7%E5%B1%9E
 93 | ball moss,https://baike.baidu.com/item/%E7%A9%BA%E6%B0%94%E5%87%A4%E6%A2%A8
 94 | foxglove,https://baike.baidu.com/item/%E6%AF%9B%E5%9C%B0%E9%BB%84%E5%B1%9E
 95 | bougainvillea,https://baike.baidu.com/item/%E5%85%89%E5%8F%B6%E5%AD%90%E8%8A%B1?fromtitle=%E7%B0%95%E6%9D%9C%E9%B9%83&fromid=1486412
 96 | camellia,https://baike.baidu.com/item/%E8%8C%B6%E8%8A%B1/315158?fr=aladdin
 97 | mallow,https://baike.baidu.com/item/%E9%94%A6%E8%91%B5/18212?fr=aladdin
 98 | mexican petunia,https://baike.baidu.com/item/%E7%8B%AD%E5%8F%B6%E7%BF%A0%E8%8A%A6%E8%8E%89/23445029?fr=aladdin&fromtitle=%E7%BF%A0%E8%8A%A6%E8%8E%89&fromid=7959962
 99 | bromelia,https://baike.baidu.com/item/%E8%A7%82%E8%B5%8F%E5%87%A4%E6%A2%A8/5100458
100 | blanket flower,https://baike.baidu.com/item/%E5%AE%BF%E6%A0%B9%E5%A4%A9%E4%BA%BA%E8%8F%8A
101 | trumpet creeper,https://baike.baidu.com/item/%E5%87%8C%E9%9C%84%E8%8A%B1
102 | blackberry lily,https://baike.baidu.com/item/%E5%B0%84%E5%B9%B2/769720?fr=aladdin


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/vgg16_oxford_flower_102/venv/pyvenv.cfg:
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1 | home = C:\Users\Administrator\Anaconda3
2 | include-system-site-packages = false
3 | version = 3.7.3
4 | 


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