# coding=utf-8
from __future__ import absolute_import
from __future__ import print_function
import argparse
import time
import urllib
import os
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from PIL import Image, ImageOps, ImageDraw, ImageFont
from numpy import *
from scipy import ndimage
from scipy.misc import imread
from scipy.misc import imresize
from scipy.spatial import distance
from sklearn import preprocessing
args = None
train_x = zeros((1, 227, 227, 3)).astype(float32)
train_y = zeros((1, 1000))
xdim = train_x.shape[1:]
ydim = train_y.shape[1]
net_data = load("bvlc_alexnet.npy").item()
def conv(input, kernel, biases, k_h, k_w, c_o, s_h, s_w, padding="VALID", group=1):
'''From https://github.com/ethereon/caffe-tensorflow
'''
c_i = input.get_shape()[-1]
assert c_i % group == 0
assert c_o % group == 0
convolve = lambda i, k: tf.nn.conv2d(i, k, [1, s_h, s_w, 1], padding=padding)
if group == 1:
conv = convolve(input, kernel)
else:
input_groups = tf.split(3, group, input)
kernel_groups = tf.split(3, group, kernel)
output_groups = [convolve(i, k) for i, k in zip(input_groups, kernel_groups)]
conv = tf.concat(3, output_groups)
return tf.reshape(tf.nn.bias_add(conv, biases), [-1] + conv.get_shape().as_list()[1:])
def initModel():
x = tf.placeholder(tf.float32, (None,) + xdim)
# conv1
# conv(11, 11, 96, 4, 4, padding='VALID', name='conv1')
k_h = 11;
k_w = 11;
c_o = 96;
s_h = 4;
s_w = 4
conv1W = tf.Variable(net_data["conv1"][0])
conv1b = tf.Variable(net_data["conv1"][1])
conv1_in = conv(x, conv1W, conv1b, k_h, k_w, c_o, s_h, s_w, padding="SAME", group=1)
conv1 = tf.nn.relu(conv1_in)
# lrn1
# lrn(2, 2e-05, 0.75, name='norm1')
radius = 2;
alpha = 2e-05;
beta = 0.75;
bias = 1.0
lrn1 = tf.nn.local_response_normalization(conv1,
depth_radius=radius,
alpha=alpha,
beta=beta,
bias=bias)
# maxpool1
# max_pool(3, 3, 2, 2, padding='VALID', name='pool1')
k_h = 3;
k_w = 3;
s_h = 2;
s_w = 2;
padding = 'VALID'
maxpool1 = tf.nn.max_pool(lrn1, ksize=[1, k_h, k_w, 1], strides=[1, s_h, s_w, 1], padding=padding)
# conv2
# conv(5, 5, 256, 1, 1, group=2, name='conv2')
k_h = 5;
k_w = 5;
c_o = 256;
s_h = 1;
s_w = 1;
group = 2
conv2W = tf.Variable(net_data["conv2"][0])
conv2b = tf.Variable(net_data["conv2"][1])
conv2_in = conv(maxpool1, conv2W, conv2b, k_h, k_w, c_o, s_h, s_w, padding="SAME", group=group)
conv2 = tf.nn.relu(conv2_in)
# lrn2
# lrn(2, 2e-05, 0.75, name='norm2')
radius = 2;
alpha = 2e-05;
beta = 0.75;
bias = 1.0
lrn2 = tf.nn.local_response_normalization(conv2,
depth_radius=radius,
alpha=alpha,
beta=beta,
bias=bias)
# maxpool2
# max_pool(3, 3, 2, 2, padding='VALID', name='pool2')
k_h = 3;
k_w = 3;
s_h = 2;
s_w = 2;
padding = 'VALID'
maxpool2 = tf.nn.max_pool(lrn2, ksize=[1, k_h, k_w, 1], strides=[1, s_h, s_w, 1], padding=padding)
# conv3
# conv(3, 3, 384, 1, 1, name='conv3')
k_h = 3;
k_w = 3;
c_o = 384;
s_h = 1;
s_w = 1;
group = 1
conv3W = tf.Variable(net_data["conv3"][0])
conv3b = tf.Variable(net_data["conv3"][1])
conv3_in = conv(maxpool2, conv3W, conv3b, k_h, k_w, c_o, s_h, s_w, padding="SAME", group=group)
conv3 = tf.nn.relu(conv3_in)
# conv4
# conv(3, 3, 384, 1, 1, group=2, name='conv4')
k_h = 3;
k_w = 3;
c_o = 384;
s_h = 1;
s_w = 1;
group = 2
conv4W = tf.Variable(net_data["conv4"][0])
conv4b = tf.Variable(net_data["conv4"][1])
conv4_in = conv(conv3, conv4W, conv4b, k_h, k_w, c_o, s_h, s_w, padding="SAME", group=group)
conv4 = tf.nn.relu(conv4_in)
# conv5
# conv(3, 3, 256, 1, 1, group=2, name='conv5')
k_h = 3;
k_w = 3;
c_o = 256;
s_h = 1;
s_w = 1;
group = 2
conv5W = tf.Variable(net_data["conv5"][0])
conv5b = tf.Variable(net_data["conv5"][1])
conv5_in = conv(conv4, conv5W, conv5b, k_h, k_w, c_o, s_h, s_w, padding="SAME", group=group)
conv5 = tf.nn.relu(conv5_in)
# maxpool5
# max_pool(3, 3, 2, 2, padding='VALID', name='pool5')
k_h = 3;
k_w = 3;
s_h = 2;
s_w = 2;
padding = 'VALID'
maxpool5 = tf.nn.max_pool(conv5, ksize=[1, k_h, k_w, 1], strides=[1, s_h, s_w, 1], padding=padding)
# fc6
# fc(4096, name='fc6')
fc6W = tf.Variable(net_data["fc6"][0])
fc6b = tf.Variable(net_data["fc6"][1])
fc6 = tf.nn.relu_layer(tf.reshape(maxpool5, [-1, int(prod(maxpool5.get_shape()[1:]))]), fc6W, fc6b)
# fc7
# fc(4096, name='fc7')
fc7W = tf.Variable(net_data["fc7"][0])
fc7b = tf.Variable(net_data["fc7"][1])
fc7 = tf.nn.relu_layer(fc6, fc7W, fc7b)
# fc8
# fc(1000, relu=False, name='fc8')
fc8W = tf.Variable(net_data["fc8"][0])
fc8b = tf.Variable(net_data["fc8"][1])
fc8 = tf.nn.xw_plus_b(fc7, fc8W, fc8b)
# prob
# softmax(name='prob'))
prob = tf.nn.softmax(fc8)
return (x, fc6)
def load_image(img_file_path):
img = imread(img_file_path)
img = (imresize(img, (227, 227))[:, :, :3]).astype(float32)
img = img - mean(img)
return img
def extract_feature(imgs):
x, fc6 = initModel()
# init = tf.initialize_all_variables()
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
return sess.run(fc6, feed_dict={x: imgs})
def load_image_names():
img_names = []
with open('img_names.txt') as f:
for img_name in f.readlines():
img_name = img_name.strip('\n')
img_names.append(img_name)
return img_names
def download_image(url):
tmp_img_path = 'img/tmp.jpg'
urllib.urlretrieve(url, tmp_img_path)
return tmp_img_path
def crop(np_img):
image = Image.fromarray(np_img)
width, height = image.size
box = (width * 0.2, height * 0.2, width * 0.8, height * 0.8)
crop = image.crop(box)
image = Image.new('RGBA', crop.size)
box = (0, 0, crop.size[0], crop.size[1])
image.paste(crop, box)
return np.asarray(image)
def rotate(np_img):
img = ndimage.rotate(np_img, 10, mode='nearest')
return img
def watermark(np_img):
image = Image.fromarray(np_img)
width, height = image.size
draw = ImageDraw.Draw(image)
text = "Deep Learning"
font = ImageFont.truetype('/Library/Fonts/Arial.ttf', 30)
text_width, text_height = draw.textsize(text, font)
margin = 10
x = width - text_width - margin
y = height / 2 - text_height - margin
draw.text((x, y), text, font=font, fill=(255, 0, 0))
return np.asarray(image)
def mirror(np_img):
image = Image.fromarray(np_img)
image = ImageOps.mirror(image)
data = np.asarray(image)
return data
def image_normalize(imgs):
new_images = []
for img in imgs:
img = (imresize(img, (227, 227))[:, :, :3]).astype(float32)
img -= mean(img)
new_images.append(img)
return new_images
def main():
t = time.time()
img = imread(args.img_file_path)
imgs = [img, watermark(img), rotate(img), crop(img), mirror(img)]
imgs_norm = image_normalize(imgs)
dataset_features = np.load('fc6.npy')
query_start = time.time()
query_features = extract_feature(imgs_norm)
binarizer = preprocessing.Binarizer().fit(query_features)
query_features = binarizer.transform(query_features)
print(dataset_features)
# https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.cdist.html#scipy.spatial.distance.cdist
cosine = distance.cdist(dataset_features, query_features, 'cosine')
print(cosine.shape)
dis = cosine
inds_all = argsort(dis, axis=0) # ζεζεΊ https://docs.scipy.org/doc/numpy/reference/generated/numpy.argsort.html
print('query cost: %f, dataset: %d, query: %d' % (time.time() - query_start, len(dataset_features), len(imgs)))
img_names = load_image_names()
fig, axes = plt.subplots(5, 11, figsize=(22, 10), subplot_kw={'xticks': [], 'yticks': []})
fig.subplots_adjust(hspace=0.15, wspace=0.01, left=.02, right=.98, top=.92, bottom=.08)
titles = ['original', 'watermark', 'rotate', 'crop', 'mirror']
for i in range(len(imgs)):
topK = []
inds = inds_all[:, i]
# print(inds)
for k in range(10):
topK.append(img_names[inds[k]])
print(inds[k], dis[inds[k], i], img_names[inds[k]])
original = axes[i, 0]
original.set_title(titles[i])
img = imgs[i]
original.imshow(img)
for j in range(10):
ax = axes[i, j + 1]
img = imread(topK[j])
ax.imshow(img)
title = '%d : %f' % (j + 1, dis[inds[j], i])
ax.set_title(title)
savePath = args.img_file_path + '_search_result.jpg'
plt.savefig(savePath)
print(time.time() - t)
# os.system('open -a Preview.app -F ' + savePath)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
'--img_file_path',
type=str,
default='041_0010.jpg',
help='Image file path.'
)
parser.add_argument(
'--img_url',
type=str,
default='',
help='Image Url.'
)
args = parser.parse_args()
if not args.img_url == '':
args.img_file_path = download_image(args.url)
main()
