model.py

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#
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import os
import numpy as np
from PIL import Image
from imageio import imread
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
import tf_slim as slim
from tf_slim.nets import inception
import tf_slim as slim
import cv2
import matplotlib.pyplot as plt

ckpt_path = "input\inception_v3.ckpt"
images_path = "test_input_images\*"
img_width = 299
img_height = 299
batch_size = 16
batch_shape = [batch_size, img_height, img_width, 3]
num_classes = 1001
predict_output = []
class_names_path = "input\imagenet_class_names.txt"
with open(class_names_path) as f:
    class_names = f.readlines()

X = tf.placeholder(tf.float32, shape=batch_shape)

with slim.arg_scope(inception.inception_v3_arg_scope()):
    logits, end_points = inception.inception_v3(
        X, num_classes = num_classes, is_training=False
    )

predictions = end_points["Predictions"]
saver = tf.train.Saver(slim.get_model_variables())

def load_images(input_dir):
    global batch_shape
    images = np.zeros(batch_shape)
    filenames = []
    idx=0
    batch_size = batch_shape[0]
    files = tf.gfile.Glob(input_dir)[:20]
    files.sort()
    for filepath in files:
        with tf.gfile.Open(filepath, "rb") as f:
            imgRaw = np.array(Image.fromarray(imread(f, as_gray=False, pilmode="RGB")).resize((299,299))).astype(np.float)/255.0
        images[idx, :, :, :] = imgRaw * 2.0 - 1.0
        filenames.append(os.path.basename(filepath))
        idx += 1
        if idx == batch_size:
            yield filenames, images
            filenames = []
            images = np.zeros(batch_shape)
            idx = 0
    if idx > 0:
        yield filenames, images

session_creator=tf.train.ChiefSessionCreator(
        scaffold=tf.train.Scaffold(saver=saver),
        checkpoint_filename_with_path=ckpt_path,
        master='')
with tf.train.MonitoredSession(session_creator=session_creator) as sess:
    for filenames, images in load_images(images_path):
        print("adding image")
        labels = sess.run(predictions, feed_dict={X: images})
        for filename, label, image in zip(filenames, labels, images):
            predict_output.append([filename, label, image])
# print("predict_output: ", predict_output)
for x in predict_output:
    # print("break?")
    out_list = list(x[1])
    topPredict = sorted(range(len(out_list)), key=lambda i: out_list[i], reverse=True)[:5]
    plt.imshow((((x[2]+1)/2)*255).astype(int))
    plt.show()
    print("Filename:",x[0])
    print("Displaying the top 5 Predictions for above image:")
    for p in topPredict:
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        print(class_names[p-1].strip())
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        print(class_names[p-1].strip())
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