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- import tensorflow as tf
- import numpy as np
- import os,glob,cv2
- import sys,argparse
- # First, pass the path of the image
- dir_path = os.path.dirname(os.path.realpath(__file__))
- image_path=sys.argv[1]
- filename = dir_path +'/' +image_path
- image_size=128
- num_channels=3
- ims = ["cat.1110.jpg","cat.1111.jpg"]
- images=[]
- # Reading the image using OpenCV
- for filename in ims:
- image = cv2.imread(filename)
- # Resizing the image to our desired size and preprocessing will be done exactly as done during training
- image = cv2.resize(image, (image_size, image_size),0,0, cv2.INTER_LINEAR)
- images.append(image)
- images = np.array(images, dtype=np.uint8)
- images = images.astype('float32')
- images = np.multiply(images, 1.0/255.0)
- #The input to the network is of shape [None image_size image_size num_channels]. Hence we reshape.
- x_batch = images.reshape(2, image_size,image_size,num_channels)
- ## Let us restore the saved model
- sess = tf.Session()
- # Step-1: Recreate the network graph. At this step only graph is created.
- saver = tf.train.import_meta_graph('./trained_model/dogs-cats-model.meta')
- # Step-2: Now let's load the weights saved using the restore method.
- saver.restore(sess, tf.train.latest_checkpoint('./trained_model/'))
- # Accessing the default graph which we have restored
- graph = tf.get_default_graph()
- # Now, let's get hold of the op that we can be processed to get the output.
- # In the original network y_pred is the tensor that is the prediction of the network
- y_pred = graph.get_tensor_by_name("y_pred:0")
- ## Let's feed the images to the input placeholders
- x= graph.get_tensor_by_name("x:0")
- y_true = graph.get_tensor_by_name("y_true:0")
- y_test_images = np.zeros((1, 2))
- ### Creating the feed_dict that is required to be fed to calculate y_pred
- feed_dict_testing = {x: x_batch, y_true: y_test_images}
- result=sess.run(y_pred, feed_dict=feed_dict_testing)
- # result is of this format [probabiliy_of_rose probability_of_sunflower]
- print(result)
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