check_duplicate.py

# Duplicate Image checker if two images are given 

from __future__ import absolute_import, division, print_function

import os
import os.path
import re
import sys
import tarfile
import glob
import json
import psutil
from collections import defaultdict
import numpy as np
from six.moves import urllib
import tensorflow as tf
from annoy import AnnoyIndex
from scipy import spatial
from nltk import ngrams
import random, os, codecs, random
import numpy as np
import argparse
FLAGS = tf.app.flags.FLAGS

tf.app.flags.DEFINE_string(
		'model_dir', '/tmp/imagenet',
		"""Path to classify_image_graph_def.pb, """
		"""imagenet_synset_to_human_label_map.txt, and """
		"""imagenet_2012_challenge_label_map_proto.pbtxt.""")
tf.app.flags.DEFINE_string('image_file', '',
													 """Absolute path to image file.""")
tf.app.flags.DEFINE_integer('num_top_predictions', 5,
														"""Display this many predictions.""")

DATA_URL = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz'

class NodeToReadable(object):
	"""Converts integer node ID's to English labels."""

	def __init__(self,
							 label_path=None,
							 uid_path=None):
		if not label_path:
			label_path = os.path.join(
					FLAGS.model_dir, 'imagenet_2012_challenge_label_map_proto.pbtxt')
		if not uid_path:
			uid_path = os.path.join(
					FLAGS.model_dir, 'imagenet_synset_to_human_label_map.txt')
		self.node_lookup = self.load(label_path, uid_path)

	def load(self, label_path, uid_path):
		"""Load English name for each softmax node.
		Argumentss:
			label_path: string UID to integer node_ID.
			uid_path: string UID to English string.
		Returns:
			dictionary from integer node_ID to English string.
		"""
		if not tf.gfile.Exists(uid_path):
			tf.logging.fatal('File does not exist %s', uid_path)
		if not tf.gfile.Exists(label_path):
			tf.logging.fatal('File does not exist %s', label_path)

		# Loads mapping from string UID to English string
		proto_as_ascii_lines = tf.gfile.GFile(uid_path).readlines()
		uid_to_human = {}
		p = re.compile(r'[n\d]*[ \S,]*')
		for line in proto_as_ascii_lines:
			parsed_items = p.findall(line)
			uid = parsed_items[0]
			human_string = parsed_items[2]
			uid_to_human[uid] = human_string

		# Loads mapping from string UID to integer node ID.
		node_id_to_uid = {}
		proto_as_ascii = tf.gfile.GFile(label_path).readlines()
		for line in proto_as_ascii:
			if line.startswith('	target_class:'):
				target_class = int(line.split(': ')[1])
			if line.startswith('	target_class_string:'):
				target_class_string = line.split(': ')[1]
				node_id_to_uid[target_class] = target_class_string[1:-2]

		# Loads the final mapping of integer node ID to English string
		node_id_to_name = {}
		for key, val in node_id_to_uid.items():
			if val not in uid_to_human:
				tf.logging.fatal('Failed to locate: %s', val)
			name = uid_to_human[val]
			node_id_to_name[key] = name

		return node_id_to_name

	def id_to_string(self, node_id):
		if node_id not in self.node_lookup:
			return ''
		return self.node_lookup[node_id]


def create_graph():
	"""Creates a graph from saved GraphDef file and returns a saver."""
	# Creates graph from saved graph_def.pb.
	with tf.gfile.FastGFile(os.path.join(
			FLAGS.model_dir, 'classify_image_graph_def.pb'), 'rb') as f:
		graph_def = tf.GraphDef()
		graph_def.ParseFromString(f.read())
		_ = tf.import_graph_def(graph_def, name='')


def imageAnalyser(image, output_dir):
	"""analyse images and create vectors.

	Args:
		image: An image to be analysed.
		output_dir: the directory in which image vectors will be saved

	Returns:
		image_with_labels: a dictionary with image file keys and predicted
			text label values
	"""
	image_with_labels = defaultdict(list)

	create_graph()

	with tf.Session() as sess:
		# Some useful tensors:
		# 'softmax:0': A tensor containing the normalized prediction across
		#	 1000 labels.
		# 'pool_3:0': A tensor containing the next-to-last layer containing 2048
		#	 float description of the image.
		# 'DecodeJpeg/contents:0': A tensor containing a string providing JPEG
		#	 encoding of the image.
		# Runs the softmax tensor by feeding the image_data as input to the graph.
		softmax_tensor = sess.graph.get_tensor_by_name('softmax:0')
		# for image_index, image in enumerate(image_list):
		try:
			print("parsing", image, "\n")
			if not tf.gfile.Exists(image):
				tf.logging.fatal('File does not exist %s', image)
			
			with tf.gfile.FastGFile(image, 'rb') as f:
				image_data =	f.read()

				predictions = sess.run(softmax_tensor,
												{'DecodeJpeg/contents:0': image_data})

				predictions = np.squeeze(predictions)
				###
				# Get penultimate layer weights
				###
				feature_tensor = sess.graph.get_tensor_by_name('pool_3:0')
				feature_set = sess.run(feature_tensor,
												{'DecodeJpeg/contents:0': image_data})
				feature_vector = np.squeeze(feature_set)				
				outfile_name = os.path.basename(image) + ".npz"
				out_path = os.path.join(output_dir, outfile_name)
				np.savetxt(out_path, feature_vector, delimiter=',')
				# Creates node ID --> English string lookup.
				node_lookup = NodeToReadable()

				top_k = predictions.argsort()[-FLAGS.num_top_predictions:][::-1]
				for node_id in top_k:
					human_string = node_lookup.id_to_string(node_id)
					score = predictions[node_id]
					print("results for", image)
					print('%s (score = %.5f)' % (human_string, score))
					print("\n")

					image_with_labels[image].append(
						{
							"labels": human_string,
							"score": str(score)
						}
					)
			# close the open file handlers
			proc = psutil.Process()
			open_files = proc.open_files()

			for open_file in open_files:
				file_handler = getattr(open_file, "fd")
				os.close(file_handler)
		except Exception as e:
			print(e)
			print('could not process image index')

	return image_with_labels


def downloadModel():
	"""Download and extract model tar file."""
	dest_directory = FLAGS.model_dir
	if not os.path.exists(dest_directory):
		os.makedirs(dest_directory)
	filename = DATA_URL.split('/')[-1]
	filepath = os.path.join(dest_directory, filename)
	if not os.path.exists(filepath):
		def _progress(count, block_size, total_size):
			sys.stdout.write('\r>> Downloading %s %.1f%%' % (
					filename, float(count * block_size) / float(total_size) * 100.0))
			sys.stdout.flush()
		filepath, _ = urllib.request.urlretrieve(DATA_URL, filepath, _progress)
		print()
		statinfo = os.stat(filepath)
		print('Succesfully downloaded', filename, statinfo.st_size, 'bytes.')
	tarfile.open(filepath, 'r:gz').extractall(dest_directory)


def main(_):
	downloadModel()
	if len(sys.argv) < 2:
		print("please provide a glob path to one or more images, e.g.")
		print("python check_duplicate.py '../folder1/img1.jpg' ../folder2/img2.jpg")
		sys.exit()

	else:
		output_dir = "results"
		if not os.path.exists(output_dir):
			os.makedirs(output_dir)
		
		image1 = sys.argv[1]
		image2 = sys.argv[2]
		image1_file_name = sys.argv[1].split("/")[-1]
		image2_file_name = sys.argv[2].split("/")[-1]
		master_image1_labels = imageAnalyser(image1, output_dir)
		master_image2_labels = imageAnalyser(image2, output_dir)
		final_image_labels_dict = {**master_image1_labels, **master_image2_labels}
		'''with open("image1_to_labels.json", "w") as master_image_labels_out:
			json.dump(final_image_labels_dict, master_image_labels_out)'''
		print ("Dumping done.")


	# data structures
	file_index_to_file_name = {}
	file_index_to_file_vector = {}

	# config
	dims = 2048
	#n_nearest_neighbors = 1
	trees = 10000
	infiles = glob.glob('results/*.npz')

	# build ann index
	t = AnnoyIndex(dims)
	for file_index, i in enumerate(infiles):
		file_vector = np.loadtxt(i)
		file_name = os.path.basename(i).split('.')[0]
		file_index_to_file_name[file_index] = file_name
		file_index_to_file_vector[file_index] = file_vector
		t.add_item(file_index, file_vector)
	t.build(trees)

	image1_vectors = {}
	image2_vectors = {}
	image1_vectors['name'] = file_index_to_file_name[0]
	image1_vectors['vectors'] = file_index_to_file_vector[0]
	image2_vectors['name'] = file_index_to_file_name[1]
	image2_vectors['vectors'] = file_index_to_file_vector[1]

	similarity = 1 - spatial.distance.cosine(image1_vectors['vectors'], image2_vectors['vectors'])
	rounded_similarity = (int((similarity * 10000)) / 10000.0)*100
	print(rounded_similarity)
	if rounded_similarity > 75.00:
		print ("Duplicate")
	else:
		print ("Distinct")
	os.system("rm -rf results/*.npz")

if __name__ == '__main__':
	tf.app.run()
	
	
'''
Result after running.
python check_duplicate.py ../duplicate/l_1045416264.jpg ../duplicate/60_2091951686.jpg
.
.
.
.
77.89
Duplicate
'''