predict.py

import argparse
import os
import numpy as np
import tensorflow as tf
from matplotlib import pyplot as plt
from PIL import Image
import cv2

import models

def predict(model_data_path, image_path):

    
    # Default input size
    height = 228
    width = 304
    channels = 3
    batch_size = 1
   
    # Read image
    img = Image.open(image_path)
    img = img.resize([width,height], Image.ANTIALIAS)
    img = np.array(img).astype('float32')
    img = np.expand_dims(np.asarray(img), axis = 0)
   
    # Create a placeholder for the input image
    input_node = tf.placeholder(tf.float32, shape=(None, height, width, channels))

    # Construct the network
    net = models.ResNet50UpProj({'data': input_node}, batch_size, 1, False)
        
    with tf.Session() as sess:

        # Load the converted parameters
        print('Loading the model')

        # Use to load from ckpt file
        saver = tf.train.Saver()     
        saver.restore(sess, model_data_path)

        # Use to load from npy file
        #net.load(model_data_path, sess) 

        # Evalute the network for the given image
        pred = sess.run(net.get_output(), feed_dict={input_node: img})
        
        # Plot result
        fig = plt.figure()
        ii = plt.imshow(pred[0,:,:,0], interpolation='nearest')
        fig.colorbar(ii)
	plt.savefig('final.jpg')
	image = cv2.imread('final.jpg')
	graysc = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
	cv2.imwrite('finalgray.jpg',graysc)

        
        return pred
        
                
def main():
    # Parse arguments
    parser = argparse.ArgumentParser()
    parser.add_argument('model_path', help='Converted parameters for the model')
    parser.add_argument('image_paths', help='Directory of images to predict')
    args = parser.parse_args()

    # Predict the image
    pred = predict(args.model_path, args.image_paths)
    
    os._exit(0)

if __name__ == '__main__':
    main()