use the trained vgg19 model to predict a image

import some pakeges



In [1]:

    
import os
import keras
import tensorflow as tf
from keras.applications.vgg19 import VGG19
from keras.applications.vgg19 import preprocess_input, decode_predictions
from PIL import Image
import numpy as np
import os.path
import matplotlib.pyplot as plt









    



Using TensorFlow backend.

show image in jupyter notebook



In [2]:

    
%matplotlib inline

force to use gpu and limit the use of gpu memory



In [3]:

    
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
from keras.backend.tensorflow_backend import set_session
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.3
set_session(tf.Session(config=config))

load the model



In [4]:

    
model = VGG19(weights='imagenet')

start to predict image



In [16]:

    
def predict_image(img):
    ori_w,ori_h = img.size
    new_w = 224.0;
    new_h = 224.0;
    if ori_w > ori_h:
        bs = 224.0 / ori_h;
        new_w = ori_w * bs
        weight = int(new_w)
        height = int(new_h)
        img = img.resize( (weight, height), Image.BILINEAR )
        region = ( weight / 2 - 112, 0, weight / 2 + 112, height)
        img = img.crop( region )
    else:
        bs = 224.0 / ori_w;
        new_h = ori_h * bs
        weight = int(new_w)
        height = int(new_h)
        img = img.resize( (weight, height), Image.BILINEAR )
        region = ( 0, height / 2 - 112 , weight, height / 2 + 112  )
        img = img.crop( region )
    x = np.array( img, dtype = 'float32' )
    x[:, :, 0] = x[:, :, 0] - 123.680
    x[:, :, 1] = x[:, :, 1] - 116.779
    x[:, :, 2] = x[:, :, 2] - 103.939
    x = np.expand_dims(x, axis=0)
    results = model.predict(x)
    return decode_predictions(results, top=5)[0]



In [10]:

    
images_path = 'test_pic'
images = os.listdir(images_path)



In [11]:

    
print(images)









    



['cat.jpg', 'puzzle.jpeg', 'tiger.jpeg']



In [18]:

    
for image in images:
    image_path = os.path.join(images_path,image)
    print(image_path)
    img = plt.imread(image_path)
    label = predict_image(img)
    plt.title(label)
    plt.imshow(img)









    



test_pic/cat.jpg






    



---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-18-bbc9a767cd01> in <module>()
      3     print(image_path)
      4     img = plt.imread(image_path)
----> 5     label = predict_image(img)
      6     plt.title(label)
      7     plt.imshow(img)

<ipython-input-16-1090d482f81e> in predict_image(img)
      1 def predict_image(img):
----> 2     ori_w,ori_h = img.size
      3     new_w = 224.0;
      4     new_h = 224.0;
      5     if ori_w > ori_h:

TypeError: 'int' object is not iterable



In [ ]: