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
|
%global _empty_manifest_terminate_build 0
Name: python-pdqhash
Version: 0.2.3
Release: 1
Summary: "Python bindings for Facebook's PDQ hash"
License: MIT
URL: https://github.com/faustomorales/pdqhash-python
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/e1/8b/7c4458eadda2a6224ea661bd835ecfe14b15f61feee2f7c2d08b06c1cdf9/pdqhash-0.2.3.tar.gz
%description
# pdqhash-python
These are Python bindings to the PDQ perceptual hash released by Facebook. Note that the bindings are provided under the MIT license but the PDQ source code is licensed separately under its own license (see the `ThreatExchange/hashing/pdq` folder).
## Installation
```
pip install pdqhash
```
## Usage
```python
import pdqhash
image = cv2.imread(os.path.join('tests', 'images', image_name))
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
hash_vector, quality = pdqhash.compute(image)
# Get all the rotations and flips in one pass.
# hash_vectors is a list of vectors in the following order
# - Original
# - Rotated 90 degrees
# - Rotated 180 degrees
# - Rotated 270 degrees
# - Flipped vertically
# - Flipped horizontally
# - Rotated 90 degrees and flipped vertically
# - Rotated 90 degrees and flipped horizontally
hash_vectors, quality = pdqhash.compute_dihedral(image)
# Get the floating point values of the hash.
hash_vector_float, quality = pdqhash.compute_float(image)
```
## Contributing
- Set up local development using `make init` (you need to have `pipenv` installed)
- Run tests using `make test`
- Run tests in Docker using `make docker_test`
%package -n python3-pdqhash
Summary: "Python bindings for Facebook's PDQ hash"
Provides: python-pdqhash
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
BuildRequires: python3-cffi
BuildRequires: gcc
BuildRequires: gdb
%description -n python3-pdqhash
# pdqhash-python
These are Python bindings to the PDQ perceptual hash released by Facebook. Note that the bindings are provided under the MIT license but the PDQ source code is licensed separately under its own license (see the `ThreatExchange/hashing/pdq` folder).
## Installation
```
pip install pdqhash
```
## Usage
```python
import pdqhash
image = cv2.imread(os.path.join('tests', 'images', image_name))
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
hash_vector, quality = pdqhash.compute(image)
# Get all the rotations and flips in one pass.
# hash_vectors is a list of vectors in the following order
# - Original
# - Rotated 90 degrees
# - Rotated 180 degrees
# - Rotated 270 degrees
# - Flipped vertically
# - Flipped horizontally
# - Rotated 90 degrees and flipped vertically
# - Rotated 90 degrees and flipped horizontally
hash_vectors, quality = pdqhash.compute_dihedral(image)
# Get the floating point values of the hash.
hash_vector_float, quality = pdqhash.compute_float(image)
```
## Contributing
- Set up local development using `make init` (you need to have `pipenv` installed)
- Run tests using `make test`
- Run tests in Docker using `make docker_test`
%package help
Summary: Development documents and examples for pdqhash
Provides: python3-pdqhash-doc
%description help
# pdqhash-python
These are Python bindings to the PDQ perceptual hash released by Facebook. Note that the bindings are provided under the MIT license but the PDQ source code is licensed separately under its own license (see the `ThreatExchange/hashing/pdq` folder).
## Installation
```
pip install pdqhash
```
## Usage
```python
import pdqhash
image = cv2.imread(os.path.join('tests', 'images', image_name))
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
hash_vector, quality = pdqhash.compute(image)
# Get all the rotations and flips in one pass.
# hash_vectors is a list of vectors in the following order
# - Original
# - Rotated 90 degrees
# - Rotated 180 degrees
# - Rotated 270 degrees
# - Flipped vertically
# - Flipped horizontally
# - Rotated 90 degrees and flipped vertically
# - Rotated 90 degrees and flipped horizontally
hash_vectors, quality = pdqhash.compute_dihedral(image)
# Get the floating point values of the hash.
hash_vector_float, quality = pdqhash.compute_float(image)
```
## Contributing
- Set up local development using `make init` (you need to have `pipenv` installed)
- Run tests using `make test`
- Run tests in Docker using `make docker_test`
%prep
%autosetup -n pdqhash-0.2.3
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-pdqhash -f filelist.lst
%dir %{python3_sitearch}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 0.2.3-1
- Package Spec generated
|
