forked from whn09/amazon-sagemaker-visual-search
-
Notifications
You must be signed in to change notification settings - Fork 0
/
similar_image.py
253 lines (198 loc) · 7.7 KB
/
similar_image.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
import cv2
import time
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import argparse
import mxnet as mx
from mxnet import nd, image
import gluoncv as gcv
gcv.utils.check_version('0.6.0')
from gluoncv.data import ImageNet1kAttr
from gluoncv.data.transforms.presets.imagenet import transform_eval
from gluoncv.model_zoo import get_model
from mxnet.gluon import nn
from scipy.spatial import distance
def compare_img_default(img1, img2):
"""
Strictly compare whether two pictures are equal
Attention: Even just a little tiny bit different (like 1px dot), will return false.
:param img1: img1 in MAT format(img1 = cv2.imread(image1))
:param img2: img2 in MAT format(img2 = cv2.imread(image2))
:return: true for equal or false for not equal
"""
difference = cv2.subtract(img1, img2)
result = not np.any(difference)
return result
def compare_img_hist(img1, img2):
"""
Compare the similarity of two pictures using histogram(直方图)
Attention: this is a comparision of similarity, using histogram to calculate
For example:
1. img1 and img2 are both 720P .PNG file,
and if compare with img1, img2 only add a black dot(about 9*9px),
the result will be 0.999999999953
:param img1: img1 in MAT format(img1 = cv2.imread(image1))
:param img2: img2 in MAT format(img2 = cv2.imread(image2))
:return: the similarity of two pictures
"""
# Get the histogram data of image 1, then using normalize the picture for better compare
img1_hist = cv2.calcHist([img1], [1], None, [256], [0, 256])
img1_hist = cv2.normalize(img1_hist, img1_hist, 0, 1, cv2.NORM_MINMAX, -1)
img2_hist = cv2.calcHist([img2], [1], None, [256], [0, 256])
img2_hist = cv2.normalize(img2_hist, img2_hist, 0, 1, cv2.NORM_MINMAX, -1)
similarity = cv2.compareHist(img1_hist, img2_hist, 0)
return similarity
def compare_img_p_hash(img1, img2):
"""
Get the similarity of two pictures via pHash
Generally, when:
ham_dist == 0 -> particularly like
ham_dist < 5 -> very like
ham_dist > 10 -> different image
Attention: this is not accurate compare_img_hist() method, so use hist() method to auxiliary comparision.
This method is always used for graphical search applications, such as Google Image(Use photo to search photo)
:param img1:
:param img2:
:return:
"""
hash_img1 = get_img_p_hash(img1)
hash_img2 = get_img_p_hash(img2)
return ham_dist(hash_img1, hash_img2)
def get_img_p_hash(img):
"""
Get the pHash value of the image, pHash : Perceptual hash algorithm(感知哈希算法)
:param img: img in MAT format(img = cv2.imread(image))
:return: pHash value
"""
hash_len = 32
# GET Gray image
gray_img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# Resize image, use the different way to get the best result
resize_gray_img = cv2.resize(gray_img, (hash_len, hash_len), cv2.INTER_AREA)
# resize_gray_img = cv2.resize(gray_img, (hash_len, hash_len), cv2.INTER_LANCZOS4)
# resize_gray_img = cv2.resize(gray_img, (hash_len, hash_len), cv2.INTER_LINEAR)
# resize_gray_img = cv2.resize(gray_img, (hash_len, hash_len), cv2.INTER_NEAREST)
# resize_gray_img = cv2.resize(gray_img, (hash_len, hash_len), cv2.INTER_CUBIC)
# Change the int of image to float, for better DCT
h, w = resize_gray_img.shape[:2]
vis0 = np.zeros((h, w), np.float32)
vis0[:h, :w] = resize_gray_img
# DCT: Discrete cosine transform(离散余弦变换)
vis1 = cv2.dct(cv2.dct(vis0))
vis1.resize(hash_len, hash_len)
img_list = vis1.flatten()
# Calculate the avg value
avg = sum(img_list) * 1. / len(img_list)
avg_list = []
for i in img_list:
if i < avg:
tmp = '0'
else:
tmp = '1'
avg_list.append(tmp)
# Calculate the hash value
p_hash_str = ''
for x in range(0, hash_len * hash_len, 4):
p_hash_str += '%x' % int(''.join(avg_list[x:x + 4]), 2)
return p_hash_str
def ham_dist(x, y):
"""
Get the hamming distance of two values.
hamming distance(汉明距)
:param x:
:param y:
:return: the hamming distance
"""
assert len(x) == len(y)
return sum([ch1 != ch2 for ch1, ch2 in zip(x, y)])
def compare_img_sift(filename1, filename2):
start = time.time()
# queryImage
img1 = cv2.imread(filename1, 0)
# trainImage
img2 = cv2.imread(filename2, 0)
# Initiate SIFT detector
# sift = cv2.SIFT()
sift = cv2.xfeatures2d.SIFT_create()
# find the keypoints and descriptors with SIFT
kp1, des1 = sift.detectAndCompute(img1,None)
kp2, des2 = sift.detectAndCompute(img2,None)
# BFMatcher with default params
bf = cv2.BFMatcher()
matches = bf.knnMatch(des1,des2,k=2)
# Apply ratio test
good = []
for m,n in matches:
if m.distance < 0.75*n.distance:
good.append([m])
# cv2.drawMatchesKnn expects list of lists as matches.
img3 = cv2.drawMatchesKnn(img1,kp1,img2,kp2,good,flags=2,outImg=None)
plt.imshow(img3),plt.show()
score = 1.0 - len(good)/500.0
end = time.time()
# print('time:', end-start)
return score
def init_model():
parser = argparse.ArgumentParser(description='Predict ImageNet classes from a given image')
parser.add_argument('--model', type=str, default='ResNet50_v2',
help='name of the model to use')
parser.add_argument('--saved-params', type=str, default='endpoint/model/model-0000.params',
help='path to the saved model parameters') # 'model/model-0000.params'
# parser.add_argument('--input-pic', type=str, required=True,
# help='path to the input picture')
opt = parser.parse_args()
num_gpus = 0
ctx = [mx.gpu(i) for i in range(num_gpus)] if num_gpus > 0 else [mx.cpu()]
# print(ctx)
# Load Model
model_name = opt.model
pretrained = True if opt.saved_params == '' else False
if not pretrained:
classes = [i for i in range(5)]
net = get_model(model_name, classes=len(classes), pretrained=pretrained)
net.load_parameters(opt.saved_params)
else:
net = get_model(model_name, pretrained=pretrained)
classes = net.classes
net.collect_params().reset_ctx(ctx)
# print(len(net.features))
seq_net = nn.Sequential()
for i in range(len(net.features)):
seq_net.add(net.features[i])
return seq_net, ctx
def get_embedding_advance(input_pic):
# Load Images
img = image.imread(input_pic)
# Transform
img = transform_eval(img).copyto(ctx[0])
pred = None
use_layers = [len(seq_net)-1] # [i for i in range(len(seq_net))]
for i in range(len(seq_net)):
img = seq_net[i](img)
if i in use_layers:
# print(img.shape)
pred = img[0]
return pred.asnumpy()
def compare_img_image_embedding(filename1, filename2):
hash1 = get_embedding_advance(filename1)
hash2 = get_embedding_advance(filename2)
n1 = distance.cosine(hash1, hash2)
return n1
filename1 = '1,12,0,4,22432,3005,2000,8a28288b.jpg'
filename2 = '1,12,0,60,22846,3007,2000,ef9addd4.jpg'
img1 = cv2.imread(filename1)
img2 = cv2.imread(filename2)
result = compare_img_default(img1, img2)
print('compare_img_default:', result)
result = compare_img_hist(img1, img2)
print('compare_img_hist:', result)
result = compare_img_p_hash(img1, img2)
print('compare_img_p_hash:', result)
result = compare_img_sift(filename1, filename2)
print('compare_img_sift:', result)
seq_net, ctx = init_model()
result = compare_img_image_embedding(filename1, filename2)
print('compare_img_image_embedding:', result)