1676013840
How to Fix Python [WinError 3] The System Cannot Find The Path Specifi
Python error [WinError 3] is a variation of the [WinError 2] error. The complete error message is as follows:
FileNotFoundError: [WinError 3] The system cannot find the path specified
This error usually occurs when you use the Python os module to interact with the Windows filesystem.
While [WinError 2] means that a file can’t be found, [WinError 3] means that the path you specified doesn’t exist.
This article will show you several examples that can cause this error and how to fix it.
1. Typed the wrong name when calling os.listdir() method
Suppose you have the following directory structure on your computer:
.
├── assets
│ ├── image.png
│ └── photo.png
└── main.py
Next, suppose you try to get the names of the files inside the assets/ directory using the os.listdir() method.
But you specified the wrong directory name when calling the method as follows:
import os
files = os.listdir("asset")
print(files)
Because you have an assets folder and not asset, the os module can’t find the directory:
Traceback (most recent call last):
File "main.py", line 3, in <module>
files = os.listdir("asset")
^^^^^^^^^^^^^^^^^^^
FileNotFoundError: [WinError 3] The system cannot find the path specified: 'asset'
To fix this error, make sure that the path you passed as the parameter to the listdir() method exists.
For our example, replacing asset with assets should fix the error.
2. Specifying non-existent path in os.rename() method.
The error also appears when you specified the wrong path name when calling the os.rename() method.
Suppose you have a different directory structure on your computer as follows:
.
├── docs
│ └── file.txt
└── main.py
Now, you want to rename the file.txt file into article.txt file.
You called the os.rename() method as follows:
import os
os.rename("doc/file.txt", "article.txt")
The code above incorrectly specified the path docs/ as doc/, so the error is triggered:
Traceback (most recent call last):
File "main.py", line 3, in <module>
os.rename("doc/file.txt", "article.txt")
FileNotFoundError: [WinError 3] The system cannot find the path specified:
'doc/file.txt' -> 'article.txt'
To fix this error, you need to specify the correct path to the file, which is docs/file.txt.
Please note that the extension of the file must be specified in the arguments. If you type file.txt as file, then you’ll get the same error.
This is because Python will think you’re instructing to rename a directory or folder and not a file.
When renaming a file, always include the file extension.
Also, keep in mind that directory and file names are case-sensitive, so you need to use the right capitalization.
3. Use absolute path instead of relative
At times, you might want to access a folder or file that’s a bit difficult to reach from the location of your script.
Suppose you have a directory structure as follows on your computer:
. C:
├── assets
│ └── text
│ └── file.txt
└── scripts
└── test
└── main.py
In this structure, the path to main.py is C:/scripts/test/main.py, while the file.txt is in C:/assets/text/file.txt
Suppose you want to rename file.txt to article.txt, this is how you specify the name with relative paths:
import os
os.rename("../../assets/text/file.txt", "../../assets/text/article.txt")
It’s easy for you to specify the wrong path when using relative paths, so it’s recommended to use absolute paths when the path is complex.
Here’s what the arguments look like using absolute paths:
import os
os.rename("C:/assets/text/file.txt", "C:/assets/text/article.txt")
As you can see, absolute paths are easier to read and understand. In Windows, the absolute path usually starts with the drive letter you have in your system like C: or D:.
To find the absolute path of a file, right-click on the file and select Properties from the context menu.
You’ll see the location of the file as follows:
File location in WindowsAdd the file name at the end of the location path, and you get the absolute path.
I hope this tutorial is helpful. See you in other articles! 👋
Original article source at: https://sebhastian.com/
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1653475560
A Pure PHP Implementation Of The MessagePack Serialization Format
msgpack.php
A pure PHP implementation of the MessagePack serialization format.
Features
- Fully compliant with the latest MessagePack specification
- Supports streaming unpacking
- Supports unsigned 64-bit integers handling
- Supports object serialization
- Fully tested
- Relatively fast
Installation
The recommended way to install the library is through Composer:
composer require rybakit/msgpack
Usage
Packing
To pack values you can either use an instance of a Packer:
$packer = new Packer();
$packed = $packer->pack($value);
or call a static method on the MessagePack class:
$packed = MessagePack::pack($value);
In the examples above, the method pack automatically packs a value depending on its type. However, not all PHP types can be uniquely translated to MessagePack types. For example, the MessagePack format defines map and array types, which are represented by a single array type in PHP. By default, the packer will pack a PHP array as a MessagePack array if it has sequential numeric keys, starting from 0 and as a MessagePack map otherwise:
$mpArr1 = $packer->pack([1, 2]); // MP array [1, 2]
$mpArr2 = $packer->pack([0 => 1, 1 => 2]); // MP array [1, 2]
$mpMap1 = $packer->pack([0 => 1, 2 => 3]); // MP map {0: 1, 2: 3}
$mpMap2 = $packer->pack([1 => 2, 2 => 3]); // MP map {1: 2, 2: 3}
$mpMap3 = $packer->pack(['a' => 1, 'b' => 2]); // MP map {a: 1, b: 2}
However, sometimes you need to pack a sequential array as a MessagePack map. To do this, use the packMap method:
$mpMap = $packer->packMap([1, 2]); // {0: 1, 1: 2}
Here is a list of type-specific packing methods:
$packer->packNil(); // MP nil
$packer->packBool(true); // MP bool
$packer->packInt(42); // MP int
$packer->packFloat(M_PI); // MP float (32 or 64)
$packer->packFloat32(M_PI); // MP float 32
$packer->packFloat64(M_PI); // MP float 64
$packer->packStr('foo'); // MP str
$packer->packBin("\x80"); // MP bin
$packer->packArray([1, 2]); // MP array
$packer->packMap(['a' => 1]); // MP map
$packer->packExt(1, "\xaa"); // MP ext
Check the "Custom types" section below on how to pack custom types.
Packing options
The Packer object supports a number of bitmask-based options for fine-tuning the packing process (defaults are in bold):
| Name | Description |
|---|---|
FORCE_STR | Forces PHP strings to be packed as MessagePack UTF-8 strings |
FORCE_BIN | Forces PHP strings to be packed as MessagePack binary data |
DETECT_STR_BIN | Detects MessagePack str/bin type automatically |
FORCE_ARR | Forces PHP arrays to be packed as MessagePack arrays |
FORCE_MAP | Forces PHP arrays to be packed as MessagePack maps |
DETECT_ARR_MAP | Detects MessagePack array/map type automatically |
FORCE_FLOAT32 | Forces PHP floats to be packed as 32-bits MessagePack floats |
FORCE_FLOAT64 | Forces PHP floats to be packed as 64-bits MessagePack floats |
The type detection mode (
DETECT_STR_BIN/DETECT_ARR_MAP) adds some overhead which can be noticed when you pack large (16- and 32-bit) arrays or strings. However, if you know the value type in advance (for example, you only work with UTF-8 strings or/and associative arrays), you can eliminate this overhead by forcing the packer to use the appropriate type, which will save it from running the auto-detection routine. Another option is to explicitly specify the value type. The library provides 2 auxiliary classes for this,MapandBin. Check the "Custom types" section below for details.
Examples:
// detect str/bin type and pack PHP 64-bit floats (doubles) to MP 32-bit floats
$packer = new Packer(PackOptions::DETECT_STR_BIN | PackOptions::FORCE_FLOAT32);
// these will throw MessagePack\Exception\InvalidOptionException
$packer = new Packer(PackOptions::FORCE_STR | PackOptions::FORCE_BIN);
$packer = new Packer(PackOptions::FORCE_FLOAT32 | PackOptions::FORCE_FLOAT64);
Unpacking
To unpack data you can either use an instance of a BufferUnpacker:
$unpacker = new BufferUnpacker();
$unpacker->reset($packed);
$value = $unpacker->unpack();
or call a static method on the MessagePack class:
$value = MessagePack::unpack($packed);
If the packed data is received in chunks (e.g. when reading from a stream), use the tryUnpack method, which attempts to unpack data and returns an array of unpacked messages (if any) instead of throwing an InsufficientDataException:
while ($chunk = ...) {
$unpacker->append($chunk);
if ($messages = $unpacker->tryUnpack()) {
return $messages;
}
}
If you want to unpack from a specific position in a buffer, use seek:
$unpacker->seek(42); // set position equal to 42 bytes
$unpacker->seek(-8); // set position to 8 bytes before the end of the buffer
To skip bytes from the current position, use skip:
$unpacker->skip(10); // set position to 10 bytes ahead of the current position
To get the number of remaining (unread) bytes in the buffer:
$unreadBytesCount = $unpacker->getRemainingCount();
To check whether the buffer has unread data:
$hasUnreadBytes = $unpacker->hasRemaining();
If needed, you can remove already read data from the buffer by calling:
$releasedBytesCount = $unpacker->release();
With the read method you can read raw (packed) data:
$packedData = $unpacker->read(2); // read 2 bytes
Besides the above methods BufferUnpacker provides type-specific unpacking methods, namely:
$unpacker->unpackNil(); // PHP null
$unpacker->unpackBool(); // PHP bool
$unpacker->unpackInt(); // PHP int
$unpacker->unpackFloat(); // PHP float
$unpacker->unpackStr(); // PHP UTF-8 string
$unpacker->unpackBin(); // PHP binary string
$unpacker->unpackArray(); // PHP sequential array
$unpacker->unpackMap(); // PHP associative array
$unpacker->unpackExt(); // PHP MessagePack\Type\Ext object
Unpacking options
The BufferUnpacker object supports a number of bitmask-based options for fine-tuning the unpacking process (defaults are in bold):
| Name | Description |
|---|---|
BIGINT_AS_STR | Converts overflowed integers to strings [1] |
BIGINT_AS_GMP | Converts overflowed integers to GMP objects [2] |
BIGINT_AS_DEC | Converts overflowed integers to Decimal\Decimal objects [3] |
1. The binary MessagePack format has unsigned 64-bit as its largest integer data type, but PHP does not support such integers, which means that an overflow can occur during unpacking.
2. Make sure the GMP extension is enabled.
3. Make sure the Decimal extension is enabled.
Examples:
$packedUint64 = "\xcf"."\xff\xff\xff\xff"."\xff\xff\xff\xff";
$unpacker = new BufferUnpacker($packedUint64);
var_dump($unpacker->unpack()); // string(20) "18446744073709551615"
$unpacker = new BufferUnpacker($packedUint64, UnpackOptions::BIGINT_AS_GMP);
var_dump($unpacker->unpack()); // object(GMP) {...}
$unpacker = new BufferUnpacker($packedUint64, UnpackOptions::BIGINT_AS_DEC);
var_dump($unpacker->unpack()); // object(Decimal\Decimal) {...}
Custom types
In addition to the basic types, the library provides functionality to serialize and deserialize arbitrary types. This can be done in several ways, depending on your use case. Let's take a look at them.
Type objects
If you need to serialize an instance of one of your classes into one of the basic MessagePack types, the best way to do this is to implement the CanBePacked interface in the class. A good example of such a class is the Map type class that comes with the library. This type is useful when you want to explicitly specify that a given PHP array should be packed as a MessagePack map without triggering an automatic type detection routine:
$packer = new Packer();
$packedMap = $packer->pack(new Map([1, 2, 3]));
$packedArray = $packer->pack([1, 2, 3]);
More type examples can be found in the src/Type directory.
Type transformers
As with type objects, type transformers are only responsible for serializing values. They should be used when you need to serialize a value that does not implement the CanBePacked interface. Examples of such values could be instances of built-in or third-party classes that you don't own, or non-objects such as resources.
A transformer class must implement the CanPack interface. To use a transformer, it must first be registered in the packer. Here is an example of how to serialize PHP streams into the MessagePack bin format type using one of the supplied transformers, StreamTransformer:
$packer = new Packer(null, [new StreamTransformer()]);
$packedBin = $packer->pack(fopen('/path/to/file', 'r+'));
More type transformer examples can be found in the src/TypeTransformer directory.
Extensions
In contrast to the cases described above, extensions are intended to handle extension types and are responsible for both serialization and deserialization of values (types).
An extension class must implement the Extension interface. To use an extension, it must first be registered in the packer and the unpacker.
The MessagePack specification divides extension types into two groups: predefined and application-specific. Currently, there is only one predefined type in the specification, Timestamp.
Timestamp
The Timestamp extension type is a predefined type. Support for this type in the library is done through the TimestampExtension class. This class is responsible for handling Timestamp objects, which represent the number of seconds and optional adjustment in nanoseconds:
$timestampExtension = new TimestampExtension();
$packer = new Packer();
$packer = $packer->extendWith($timestampExtension);
$unpacker = new BufferUnpacker();
$unpacker = $unpacker->extendWith($timestampExtension);
$packedTimestamp = $packer->pack(Timestamp::now());
$timestamp = $unpacker->reset($packedTimestamp)->unpack();
$seconds = $timestamp->getSeconds();
$nanoseconds = $timestamp->getNanoseconds();
When using the MessagePack class, the Timestamp extension is already registered:
$packedTimestamp = MessagePack::pack(Timestamp::now());
$timestamp = MessagePack::unpack($packedTimestamp);
Application-specific extensions
In addition, the format can be extended with your own types. For example, to make the built-in PHP DateTime objects first-class citizens in your code, you can create a corresponding extension, as shown in the example. Please note, that custom extensions have to be registered with a unique extension ID (an integer from 0 to 127).
More extension examples can be found in the examples/MessagePack directory.
To learn more about how extension types can be useful, check out this article.
Exceptions
If an error occurs during packing/unpacking, a PackingFailedException or an UnpackingFailedException will be thrown, respectively. In addition, an InsufficientDataException can be thrown during unpacking.
An InvalidOptionException will be thrown in case an invalid option (or a combination of mutually exclusive options) is used.
Tests
Run tests as follows:
vendor/bin/phpunit
Also, if you already have Docker installed, you can run the tests in a docker container. First, create a container:
./dockerfile.sh | docker build -t msgpack -
The command above will create a container named msgpack with PHP 8.1 runtime. You may change the default runtime by defining the PHP_IMAGE environment variable:
PHP_IMAGE='php:8.0-cli' ./dockerfile.sh | docker build -t msgpack -
See a list of various images here.
Then run the unit tests:
docker run --rm -v $PWD:/msgpack -w /msgpack msgpack
Fuzzing
To ensure that the unpacking works correctly with malformed/semi-malformed data, you can use a testing technique called Fuzzing. The library ships with a help file (target) for PHP-Fuzzer and can be used as follows:
php-fuzzer fuzz tests/fuzz_buffer_unpacker.php
Performance
To check performance, run:
php -n -dzend_extension=opcache.so \
-dpcre.jit=1 -dopcache.enable=1 -dopcache.enable_cli=1 \
tests/bench.php
Example output
Filter: MessagePack\Tests\Perf\Filter\ListFilter
Rounds: 3
Iterations: 100000
=============================================
Test/Target Packer BufferUnpacker
---------------------------------------------
nil .................. 0.0030 ........ 0.0139
false ................ 0.0037 ........ 0.0144
true ................. 0.0040 ........ 0.0137
7-bit uint #1 ........ 0.0052 ........ 0.0120
7-bit uint #2 ........ 0.0059 ........ 0.0114
7-bit uint #3 ........ 0.0061 ........ 0.0119
5-bit sint #1 ........ 0.0067 ........ 0.0126
5-bit sint #2 ........ 0.0064 ........ 0.0132
5-bit sint #3 ........ 0.0066 ........ 0.0135
8-bit uint #1 ........ 0.0078 ........ 0.0200
8-bit uint #2 ........ 0.0077 ........ 0.0212
8-bit uint #3 ........ 0.0086 ........ 0.0203
16-bit uint #1 ....... 0.0111 ........ 0.0271
16-bit uint #2 ....... 0.0115 ........ 0.0260
16-bit uint #3 ....... 0.0103 ........ 0.0273
32-bit uint #1 ....... 0.0116 ........ 0.0326
32-bit uint #2 ....... 0.0118 ........ 0.0332
32-bit uint #3 ....... 0.0127 ........ 0.0325
64-bit uint #1 ....... 0.0140 ........ 0.0277
64-bit uint #2 ....... 0.0134 ........ 0.0294
64-bit uint #3 ....... 0.0134 ........ 0.0281
8-bit int #1 ......... 0.0086 ........ 0.0241
8-bit int #2 ......... 0.0089 ........ 0.0225
8-bit int #3 ......... 0.0085 ........ 0.0229
16-bit int #1 ........ 0.0118 ........ 0.0280
16-bit int #2 ........ 0.0121 ........ 0.0270
16-bit int #3 ........ 0.0109 ........ 0.0274
32-bit int #1 ........ 0.0128 ........ 0.0346
32-bit int #2 ........ 0.0118 ........ 0.0339
32-bit int #3 ........ 0.0135 ........ 0.0368
64-bit int #1 ........ 0.0138 ........ 0.0276
64-bit int #2 ........ 0.0132 ........ 0.0286
64-bit int #3 ........ 0.0137 ........ 0.0274
64-bit int #4 ........ 0.0180 ........ 0.0285
64-bit float #1 ...... 0.0134 ........ 0.0284
64-bit float #2 ...... 0.0125 ........ 0.0275
64-bit float #3 ...... 0.0126 ........ 0.0283
fix string #1 ........ 0.0035 ........ 0.0133
fix string #2 ........ 0.0094 ........ 0.0216
fix string #3 ........ 0.0094 ........ 0.0222
fix string #4 ........ 0.0091 ........ 0.0241
8-bit string #1 ...... 0.0122 ........ 0.0301
8-bit string #2 ...... 0.0118 ........ 0.0304
8-bit string #3 ...... 0.0119 ........ 0.0315
16-bit string #1 ..... 0.0150 ........ 0.0388
16-bit string #2 ..... 0.1545 ........ 0.1665
32-bit string ........ 0.1570 ........ 0.1756
wide char string #1 .. 0.0091 ........ 0.0236
wide char string #2 .. 0.0122 ........ 0.0313
8-bit binary #1 ...... 0.0100 ........ 0.0302
8-bit binary #2 ...... 0.0123 ........ 0.0324
8-bit binary #3 ...... 0.0126 ........ 0.0327
16-bit binary ........ 0.0168 ........ 0.0372
32-bit binary ........ 0.1588 ........ 0.1754
fix array #1 ......... 0.0042 ........ 0.0131
fix array #2 ......... 0.0294 ........ 0.0367
fix array #3 ......... 0.0412 ........ 0.0472
16-bit array #1 ...... 0.1378 ........ 0.1596
16-bit array #2 ........... S ............. S
32-bit array .............. S ............. S
complex array ........ 0.1865 ........ 0.2283
fix map #1 ........... 0.0725 ........ 0.1048
fix map #2 ........... 0.0319 ........ 0.0405
fix map #3 ........... 0.0356 ........ 0.0665
fix map #4 ........... 0.0465 ........ 0.0497
16-bit map #1 ........ 0.2540 ........ 0.3028
16-bit map #2 ............. S ............. S
32-bit map ................ S ............. S
complex map .......... 0.2372 ........ 0.2710
fixext 1 ............. 0.0283 ........ 0.0358
fixext 2 ............. 0.0291 ........ 0.0371
fixext 4 ............. 0.0302 ........ 0.0355
fixext 8 ............. 0.0288 ........ 0.0384
fixext 16 ............ 0.0293 ........ 0.0359
8-bit ext ............ 0.0302 ........ 0.0439
16-bit ext ........... 0.0334 ........ 0.0499
32-bit ext ........... 0.1845 ........ 0.1888
32-bit timestamp #1 .. 0.0337 ........ 0.0547
32-bit timestamp #2 .. 0.0335 ........ 0.0560
64-bit timestamp #1 .. 0.0371 ........ 0.0575
64-bit timestamp #2 .. 0.0374 ........ 0.0542
64-bit timestamp #3 .. 0.0356 ........ 0.0533
96-bit timestamp #1 .. 0.0362 ........ 0.0699
96-bit timestamp #2 .. 0.0381 ........ 0.0701
96-bit timestamp #3 .. 0.0367 ........ 0.0687
=============================================
Total 2.7618 4.0820
Skipped 4 4
Failed 0 0
Ignored 0 0
With JIT:
php -n -dzend_extension=opcache.so \
-dpcre.jit=1 -dopcache.jit_buffer_size=64M -dopcache.jit=tracing -dopcache.enable=1 -dopcache.enable_cli=1 \
tests/bench.php
Example output
Filter: MessagePack\Tests\Perf\Filter\ListFilter
Rounds: 3
Iterations: 100000
=============================================
Test/Target Packer BufferUnpacker
---------------------------------------------
nil .................. 0.0005 ........ 0.0054
false ................ 0.0004 ........ 0.0059
true ................. 0.0004 ........ 0.0059
7-bit uint #1 ........ 0.0010 ........ 0.0047
7-bit uint #2 ........ 0.0010 ........ 0.0046
7-bit uint #3 ........ 0.0010 ........ 0.0046
5-bit sint #1 ........ 0.0025 ........ 0.0046
5-bit sint #2 ........ 0.0023 ........ 0.0046
5-bit sint #3 ........ 0.0024 ........ 0.0045
8-bit uint #1 ........ 0.0043 ........ 0.0081
8-bit uint #2 ........ 0.0043 ........ 0.0079
8-bit uint #3 ........ 0.0041 ........ 0.0080
16-bit uint #1 ....... 0.0064 ........ 0.0095
16-bit uint #2 ....... 0.0064 ........ 0.0091
16-bit uint #3 ....... 0.0064 ........ 0.0094
32-bit uint #1 ....... 0.0085 ........ 0.0114
32-bit uint #2 ....... 0.0077 ........ 0.0122
32-bit uint #3 ....... 0.0077 ........ 0.0120
64-bit uint #1 ....... 0.0085 ........ 0.0159
64-bit uint #2 ....... 0.0086 ........ 0.0157
64-bit uint #3 ....... 0.0086 ........ 0.0158
8-bit int #1 ......... 0.0042 ........ 0.0080
8-bit int #2 ......... 0.0042 ........ 0.0080
8-bit int #3 ......... 0.0042 ........ 0.0081
16-bit int #1 ........ 0.0065 ........ 0.0095
16-bit int #2 ........ 0.0065 ........ 0.0090
16-bit int #3 ........ 0.0056 ........ 0.0085
32-bit int #1 ........ 0.0067 ........ 0.0107
32-bit int #2 ........ 0.0066 ........ 0.0106
32-bit int #3 ........ 0.0063 ........ 0.0104
64-bit int #1 ........ 0.0072 ........ 0.0162
64-bit int #2 ........ 0.0073 ........ 0.0174
64-bit int #3 ........ 0.0072 ........ 0.0164
64-bit int #4 ........ 0.0077 ........ 0.0161
64-bit float #1 ...... 0.0053 ........ 0.0135
64-bit float #2 ...... 0.0053 ........ 0.0135
64-bit float #3 ...... 0.0052 ........ 0.0135
fix string #1 ....... -0.0002 ........ 0.0044
fix string #2 ........ 0.0035 ........ 0.0067
fix string #3 ........ 0.0035 ........ 0.0077
fix string #4 ........ 0.0033 ........ 0.0078
8-bit string #1 ...... 0.0059 ........ 0.0110
8-bit string #2 ...... 0.0063 ........ 0.0121
8-bit string #3 ...... 0.0064 ........ 0.0124
16-bit string #1 ..... 0.0099 ........ 0.0146
16-bit string #2 ..... 0.1522 ........ 0.1474
32-bit string ........ 0.1511 ........ 0.1483
wide char string #1 .. 0.0039 ........ 0.0084
wide char string #2 .. 0.0073 ........ 0.0123
8-bit binary #1 ...... 0.0040 ........ 0.0112
8-bit binary #2 ...... 0.0075 ........ 0.0123
8-bit binary #3 ...... 0.0077 ........ 0.0129
16-bit binary ........ 0.0096 ........ 0.0145
32-bit binary ........ 0.1535 ........ 0.1479
fix array #1 ......... 0.0008 ........ 0.0061
fix array #2 ......... 0.0121 ........ 0.0165
fix array #3 ......... 0.0193 ........ 0.0222
16-bit array #1 ...... 0.0607 ........ 0.0479
16-bit array #2 ........... S ............. S
32-bit array .............. S ............. S
complex array ........ 0.0749 ........ 0.0824
fix map #1 ........... 0.0329 ........ 0.0431
fix map #2 ........... 0.0161 ........ 0.0189
fix map #3 ........... 0.0205 ........ 0.0262
fix map #4 ........... 0.0252 ........ 0.0205
16-bit map #1 ........ 0.1016 ........ 0.0927
16-bit map #2 ............. S ............. S
32-bit map ................ S ............. S
complex map .......... 0.1096 ........ 0.1030
fixext 1 ............. 0.0157 ........ 0.0161
fixext 2 ............. 0.0175 ........ 0.0183
fixext 4 ............. 0.0156 ........ 0.0185
fixext 8 ............. 0.0163 ........ 0.0184
fixext 16 ............ 0.0164 ........ 0.0182
8-bit ext ............ 0.0158 ........ 0.0207
16-bit ext ........... 0.0203 ........ 0.0219
32-bit ext ........... 0.1614 ........ 0.1539
32-bit timestamp #1 .. 0.0195 ........ 0.0249
32-bit timestamp #2 .. 0.0188 ........ 0.0260
64-bit timestamp #1 .. 0.0207 ........ 0.0281
64-bit timestamp #2 .. 0.0212 ........ 0.0291
64-bit timestamp #3 .. 0.0207 ........ 0.0295
96-bit timestamp #1 .. 0.0222 ........ 0.0358
96-bit timestamp #2 .. 0.0228 ........ 0.0353
96-bit timestamp #3 .. 0.0210 ........ 0.0319
=============================================
Total 1.6432 1.9674
Skipped 4 4
Failed 0 0
Ignored 0 0
You may change default benchmark settings by defining the following environment variables:
| Name | Default |
|---|---|
| MP_BENCH_TARGETS | pure_p,pure_u, see a list of available targets |
| MP_BENCH_ITERATIONS | 100_000 |
| MP_BENCH_DURATION | not set |
| MP_BENCH_ROUNDS | 3 |
| MP_BENCH_TESTS | -@slow, see a list of available tests |
For example:
export MP_BENCH_TARGETS=pure_p
export MP_BENCH_ITERATIONS=1000000
export MP_BENCH_ROUNDS=5
# a comma separated list of test names
export MP_BENCH_TESTS='complex array, complex map'
# or a group name
# export MP_BENCH_TESTS='-@slow' // @pecl_comp
# or a regexp
# export MP_BENCH_TESTS='/complex (array|map)/'
Another example, benchmarking both the library and the PECL extension:
MP_BENCH_TARGETS=pure_p,pure_u,pecl_p,pecl_u \
php -n -dextension=msgpack.so -dzend_extension=opcache.so \
-dpcre.jit=1 -dopcache.enable=1 -dopcache.enable_cli=1 \
tests/bench.php
Example output
Filter: MessagePack\Tests\Perf\Filter\ListFilter
Rounds: 3
Iterations: 100000
===========================================================================
Test/Target Packer BufferUnpacker msgpack_pack msgpack_unpack
---------------------------------------------------------------------------
nil .................. 0.0031 ........ 0.0141 ...... 0.0055 ........ 0.0064
false ................ 0.0039 ........ 0.0154 ...... 0.0056 ........ 0.0053
true ................. 0.0038 ........ 0.0139 ...... 0.0056 ........ 0.0044
7-bit uint #1 ........ 0.0061 ........ 0.0110 ...... 0.0059 ........ 0.0046
7-bit uint #2 ........ 0.0065 ........ 0.0119 ...... 0.0042 ........ 0.0029
7-bit uint #3 ........ 0.0054 ........ 0.0117 ...... 0.0045 ........ 0.0025
5-bit sint #1 ........ 0.0047 ........ 0.0103 ...... 0.0038 ........ 0.0022
5-bit sint #2 ........ 0.0048 ........ 0.0117 ...... 0.0038 ........ 0.0022
5-bit sint #3 ........ 0.0046 ........ 0.0102 ...... 0.0038 ........ 0.0023
8-bit uint #1 ........ 0.0063 ........ 0.0174 ...... 0.0039 ........ 0.0031
8-bit uint #2 ........ 0.0063 ........ 0.0167 ...... 0.0040 ........ 0.0029
8-bit uint #3 ........ 0.0063 ........ 0.0168 ...... 0.0039 ........ 0.0030
16-bit uint #1 ....... 0.0092 ........ 0.0222 ...... 0.0049 ........ 0.0030
16-bit uint #2 ....... 0.0096 ........ 0.0227 ...... 0.0042 ........ 0.0046
16-bit uint #3 ....... 0.0123 ........ 0.0274 ...... 0.0059 ........ 0.0051
32-bit uint #1 ....... 0.0136 ........ 0.0331 ...... 0.0060 ........ 0.0048
32-bit uint #2 ....... 0.0130 ........ 0.0336 ...... 0.0070 ........ 0.0048
32-bit uint #3 ....... 0.0127 ........ 0.0329 ...... 0.0051 ........ 0.0048
64-bit uint #1 ....... 0.0126 ........ 0.0268 ...... 0.0055 ........ 0.0049
64-bit uint #2 ....... 0.0135 ........ 0.0281 ...... 0.0052 ........ 0.0046
64-bit uint #3 ....... 0.0131 ........ 0.0274 ...... 0.0069 ........ 0.0044
8-bit int #1 ......... 0.0077 ........ 0.0236 ...... 0.0058 ........ 0.0044
8-bit int #2 ......... 0.0087 ........ 0.0244 ...... 0.0058 ........ 0.0048
8-bit int #3 ......... 0.0084 ........ 0.0241 ...... 0.0055 ........ 0.0049
16-bit int #1 ........ 0.0112 ........ 0.0271 ...... 0.0048 ........ 0.0045
16-bit int #2 ........ 0.0124 ........ 0.0292 ...... 0.0057 ........ 0.0049
16-bit int #3 ........ 0.0118 ........ 0.0270 ...... 0.0058 ........ 0.0050
32-bit int #1 ........ 0.0137 ........ 0.0366 ...... 0.0058 ........ 0.0051
32-bit int #2 ........ 0.0133 ........ 0.0366 ...... 0.0056 ........ 0.0049
32-bit int #3 ........ 0.0129 ........ 0.0350 ...... 0.0052 ........ 0.0048
64-bit int #1 ........ 0.0145 ........ 0.0254 ...... 0.0034 ........ 0.0025
64-bit int #2 ........ 0.0097 ........ 0.0214 ...... 0.0034 ........ 0.0025
64-bit int #3 ........ 0.0096 ........ 0.0287 ...... 0.0059 ........ 0.0050
64-bit int #4 ........ 0.0143 ........ 0.0277 ...... 0.0059 ........ 0.0046
64-bit float #1 ...... 0.0134 ........ 0.0281 ...... 0.0057 ........ 0.0052
64-bit float #2 ...... 0.0141 ........ 0.0281 ...... 0.0057 ........ 0.0050
64-bit float #3 ...... 0.0144 ........ 0.0282 ...... 0.0057 ........ 0.0050
fix string #1 ........ 0.0036 ........ 0.0143 ...... 0.0066 ........ 0.0053
fix string #2 ........ 0.0107 ........ 0.0222 ...... 0.0065 ........ 0.0068
fix string #3 ........ 0.0116 ........ 0.0245 ...... 0.0063 ........ 0.0069
fix string #4 ........ 0.0105 ........ 0.0253 ...... 0.0083 ........ 0.0077
8-bit string #1 ...... 0.0126 ........ 0.0318 ...... 0.0075 ........ 0.0088
8-bit string #2 ...... 0.0121 ........ 0.0295 ...... 0.0076 ........ 0.0086
8-bit string #3 ...... 0.0125 ........ 0.0293 ...... 0.0130 ........ 0.0093
16-bit string #1 ..... 0.0159 ........ 0.0368 ...... 0.0117 ........ 0.0086
16-bit string #2 ..... 0.1547 ........ 0.1686 ...... 0.1516 ........ 0.1373
32-bit string ........ 0.1558 ........ 0.1729 ...... 0.1511 ........ 0.1396
wide char string #1 .. 0.0098 ........ 0.0237 ...... 0.0066 ........ 0.0065
wide char string #2 .. 0.0128 ........ 0.0291 ...... 0.0061 ........ 0.0082
8-bit binary #1 ........... I ............. I ........... F ............. I
8-bit binary #2 ........... I ............. I ........... F ............. I
8-bit binary #3 ........... I ............. I ........... F ............. I
16-bit binary ............. I ............. I ........... F ............. I
32-bit binary ............. I ............. I ........... F ............. I
fix array #1 ......... 0.0040 ........ 0.0129 ...... 0.0120 ........ 0.0058
fix array #2 ......... 0.0279 ........ 0.0390 ...... 0.0143 ........ 0.0165
fix array #3 ......... 0.0415 ........ 0.0463 ...... 0.0162 ........ 0.0187
16-bit array #1 ...... 0.1349 ........ 0.1628 ...... 0.0334 ........ 0.0341
16-bit array #2 ........... S ............. S ........... S ............. S
32-bit array .............. S ............. S ........... S ............. S
complex array ............. I ............. I ........... F ............. F
fix map #1 ................ I ............. I ........... F ............. I
fix map #2 ........... 0.0345 ........ 0.0391 ...... 0.0143 ........ 0.0168
fix map #3 ................ I ............. I ........... F ............. I
fix map #4 ........... 0.0459 ........ 0.0473 ...... 0.0151 ........ 0.0163
16-bit map #1 ........ 0.2518 ........ 0.2962 ...... 0.0400 ........ 0.0490
16-bit map #2 ............. S ............. S ........... S ............. S
32-bit map ................ S ............. S ........... S ............. S
complex map .......... 0.2380 ........ 0.2682 ...... 0.0545 ........ 0.0579
fixext 1 .................. I ............. I ........... F ............. F
fixext 2 .................. I ............. I ........... F ............. F
fixext 4 .................. I ............. I ........... F ............. F
fixext 8 .................. I ............. I ........... F ............. F
fixext 16 ................. I ............. I ........... F ............. F
8-bit ext ................. I ............. I ........... F ............. F
16-bit ext ................ I ............. I ........... F ............. F
32-bit ext ................ I ............. I ........... F ............. F
32-bit timestamp #1 ....... I ............. I ........... F ............. F
32-bit timestamp #2 ....... I ............. I ........... F ............. F
64-bit timestamp #1 ....... I ............. I ........... F ............. F
64-bit timestamp #2 ....... I ............. I ........... F ............. F
64-bit timestamp #3 ....... I ............. I ........... F ............. F
96-bit timestamp #1 ....... I ............. I ........... F ............. F
96-bit timestamp #2 ....... I ............. I ........... F ............. F
96-bit timestamp #3 ....... I ............. I ........... F ............. F
===========================================================================
Total 1.5625 2.3866 0.7735 0.7243
Skipped 4 4 4 4
Failed 0 0 24 17
Ignored 24 24 0 7
With JIT:
MP_BENCH_TARGETS=pure_p,pure_u,pecl_p,pecl_u \
php -n -dextension=msgpack.so -dzend_extension=opcache.so \
-dpcre.jit=1 -dopcache.jit_buffer_size=64M -dopcache.jit=tracing -dopcache.enable=1 -dopcache.enable_cli=1 \
tests/bench.php
Example output
Filter: MessagePack\Tests\Perf\Filter\ListFilter
Rounds: 3
Iterations: 100000
===========================================================================
Test/Target Packer BufferUnpacker msgpack_pack msgpack_unpack
---------------------------------------------------------------------------
nil .................. 0.0001 ........ 0.0052 ...... 0.0053 ........ 0.0042
false ................ 0.0007 ........ 0.0060 ...... 0.0057 ........ 0.0043
true ................. 0.0008 ........ 0.0060 ...... 0.0056 ........ 0.0041
7-bit uint #1 ........ 0.0031 ........ 0.0046 ...... 0.0062 ........ 0.0041
7-bit uint #2 ........ 0.0021 ........ 0.0043 ...... 0.0062 ........ 0.0041
7-bit uint #3 ........ 0.0022 ........ 0.0044 ...... 0.0061 ........ 0.0040
5-bit sint #1 ........ 0.0030 ........ 0.0048 ...... 0.0062 ........ 0.0040
5-bit sint #2 ........ 0.0032 ........ 0.0046 ...... 0.0062 ........ 0.0040
5-bit sint #3 ........ 0.0031 ........ 0.0046 ...... 0.0062 ........ 0.0040
8-bit uint #1 ........ 0.0054 ........ 0.0079 ...... 0.0062 ........ 0.0050
8-bit uint #2 ........ 0.0051 ........ 0.0079 ...... 0.0064 ........ 0.0044
8-bit uint #3 ........ 0.0051 ........ 0.0082 ...... 0.0062 ........ 0.0044
16-bit uint #1 ....... 0.0077 ........ 0.0094 ...... 0.0065 ........ 0.0045
16-bit uint #2 ....... 0.0077 ........ 0.0094 ...... 0.0063 ........ 0.0045
16-bit uint #3 ....... 0.0077 ........ 0.0095 ...... 0.0064 ........ 0.0047
32-bit uint #1 ....... 0.0088 ........ 0.0119 ...... 0.0063 ........ 0.0043
32-bit uint #2 ....... 0.0089 ........ 0.0117 ...... 0.0062 ........ 0.0039
32-bit uint #3 ....... 0.0089 ........ 0.0118 ...... 0.0063 ........ 0.0044
64-bit uint #1 ....... 0.0097 ........ 0.0155 ...... 0.0063 ........ 0.0045
64-bit uint #2 ....... 0.0095 ........ 0.0153 ...... 0.0061 ........ 0.0045
64-bit uint #3 ....... 0.0096 ........ 0.0156 ...... 0.0063 ........ 0.0047
8-bit int #1 ......... 0.0053 ........ 0.0083 ...... 0.0062 ........ 0.0044
8-bit int #2 ......... 0.0052 ........ 0.0080 ...... 0.0062 ........ 0.0044
8-bit int #3 ......... 0.0052 ........ 0.0080 ...... 0.0062 ........ 0.0043
16-bit int #1 ........ 0.0089 ........ 0.0097 ...... 0.0069 ........ 0.0046
16-bit int #2 ........ 0.0075 ........ 0.0093 ...... 0.0063 ........ 0.0043
16-bit int #3 ........ 0.0075 ........ 0.0094 ...... 0.0062 ........ 0.0046
32-bit int #1 ........ 0.0086 ........ 0.0122 ...... 0.0063 ........ 0.0044
32-bit int #2 ........ 0.0087 ........ 0.0120 ...... 0.0066 ........ 0.0046
32-bit int #3 ........ 0.0086 ........ 0.0121 ...... 0.0060 ........ 0.0044
64-bit int #1 ........ 0.0096 ........ 0.0149 ...... 0.0060 ........ 0.0045
64-bit int #2 ........ 0.0096 ........ 0.0157 ...... 0.0062 ........ 0.0044
64-bit int #3 ........ 0.0096 ........ 0.0160 ...... 0.0063 ........ 0.0046
64-bit int #4 ........ 0.0097 ........ 0.0157 ...... 0.0061 ........ 0.0044
64-bit float #1 ...... 0.0079 ........ 0.0153 ...... 0.0056 ........ 0.0044
64-bit float #2 ...... 0.0079 ........ 0.0152 ...... 0.0057 ........ 0.0045
64-bit float #3 ...... 0.0079 ........ 0.0155 ...... 0.0057 ........ 0.0044
fix string #1 ........ 0.0010 ........ 0.0045 ...... 0.0071 ........ 0.0044
fix string #2 ........ 0.0048 ........ 0.0075 ...... 0.0070 ........ 0.0060
fix string #3 ........ 0.0048 ........ 0.0086 ...... 0.0068 ........ 0.0060
fix string #4 ........ 0.0050 ........ 0.0088 ...... 0.0070 ........ 0.0059
8-bit string #1 ...... 0.0081 ........ 0.0129 ...... 0.0069 ........ 0.0062
8-bit string #2 ...... 0.0086 ........ 0.0128 ...... 0.0069 ........ 0.0065
8-bit string #3 ...... 0.0086 ........ 0.0126 ...... 0.0115 ........ 0.0065
16-bit string #1 ..... 0.0105 ........ 0.0137 ...... 0.0128 ........ 0.0068
16-bit string #2 ..... 0.1510 ........ 0.1486 ...... 0.1526 ........ 0.1391
32-bit string ........ 0.1517 ........ 0.1475 ...... 0.1504 ........ 0.1370
wide char string #1 .. 0.0044 ........ 0.0085 ...... 0.0067 ........ 0.0057
wide char string #2 .. 0.0081 ........ 0.0125 ...... 0.0069 ........ 0.0063
8-bit binary #1 ........... I ............. I ........... F ............. I
8-bit binary #2 ........... I ............. I ........... F ............. I
8-bit binary #3 ........... I ............. I ........... F ............. I
16-bit binary ............. I ............. I ........... F ............. I
32-bit binary ............. I ............. I ........... F ............. I
fix array #1 ......... 0.0014 ........ 0.0059 ...... 0.0132 ........ 0.0055
fix array #2 ......... 0.0146 ........ 0.0156 ...... 0.0155 ........ 0.0148
fix array #3 ......... 0.0211 ........ 0.0229 ...... 0.0179 ........ 0.0180
16-bit array #1 ...... 0.0673 ........ 0.0498 ...... 0.0343 ........ 0.0388
16-bit array #2 ........... S ............. S ........... S ............. S
32-bit array .............. S ............. S ........... S ............. S
complex array ............. I ............. I ........... F ............. F
fix map #1 ................ I ............. I ........... F ............. I
fix map #2 ........... 0.0148 ........ 0.0180 ...... 0.0156 ........ 0.0179
fix map #3 ................ I ............. I ........... F ............. I
fix map #4 ........... 0.0252 ........ 0.0201 ...... 0.0214 ........ 0.0167
16-bit map #1 ........ 0.1027 ........ 0.0836 ...... 0.0388 ........ 0.0510
16-bit map #2 ............. S ............. S ........... S ............. S
32-bit map ................ S ............. S ........... S ............. S
complex map .......... 0.1104 ........ 0.1010 ...... 0.0556 ........ 0.0602
fixext 1 .................. I ............. I ........... F ............. F
fixext 2 .................. I ............. I ........... F ............. F
fixext 4 .................. I ............. I ........... F ............. F
fixext 8 .................. I ............. I ........... F ............. F
fixext 16 ................. I ............. I ........... F ............. F
8-bit ext ................. I ............. I ........... F ............. F
16-bit ext ................ I ............. I ........... F ............. F
32-bit ext ................ I ............. I ........... F ............. F
32-bit timestamp #1 ....... I ............. I ........... F ............. F
32-bit timestamp #2 ....... I ............. I ........... F ............. F
64-bit timestamp #1 ....... I ............. I ........... F ............. F
64-bit timestamp #2 ....... I ............. I ........... F ............. F
64-bit timestamp #3 ....... I ............. I ........... F ............. F
96-bit timestamp #1 ....... I ............. I ........... F ............. F
96-bit timestamp #2 ....... I ............. I ........... F ............. F
96-bit timestamp #3 ....... I ............. I ........... F ............. F
===========================================================================
Total 0.9642 1.0909 0.8224 0.7213
Skipped 4 4 4 4
Failed 0 0 24 17
Ignored 24 24 0 7
Note that the msgpack extension (v2.1.2) doesn't support ext, bin and UTF-8 str types.
License
The library is released under the MIT License. See the bundled LICENSE file for details.
Author: rybakit
Source Code: https://github.com/rybakit/msgpack.php
License: MIT License
1626775355
Why use Python for Software Development
No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas.
By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities.
Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly.
5 Reasons to Utilize Python for Programming Web Apps
Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.
Robust frameworks
Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions.
Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events.
Simple to read and compose
Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building.
The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties.
Utilized by the best
Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player.
Massive community support
Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions.
Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking.
Progressive applications
Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.
The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.
Summary
Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential.
The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.
#python development services #python development company #python app development #python development #python in web development #python software development
1669099573
What Is Face Recognition? Facial Recognition with Python and OpenCV
In this article, we will know what is face recognition and how is different from face detection. We will go briefly over the theory of face recognition and then jump on to the coding section. At the end of this article, you will be able to make a face recognition program for recognizing faces in images as well as on a live webcam feed.
What is Face Detection?
In computer vision, one essential problem we are trying to figure out is to automatically detect objects in an image without human intervention. Face detection can be thought of as such a problem where we detect human faces in an image. There may be slight differences in the faces of humans but overall, it is safe to say that there are certain features that are associated with all the human faces. There are various face detection algorithms but Viola-Jones Algorithm is one of the oldest methods that is also used today and we will use the same later in the article. You can go through the Viola-Jones Algorithm after completing this article as I’ll link it at the end of this article.
Face detection is usually the first step towards many face-related technologies, such as face recognition or verification. However, face detection can have very useful applications. The most successful application of face detection would probably be photo taking. When you take a photo of your friends, the face detection algorithm built into your digital camera detects where the faces are and adjusts the focus accordingly.
For a tutorial on Real-Time Face detection
What is Face Recognition?
Now that we are successful in making such algorithms that can detect faces, can we also recognise whose faces are they?
Face recognition is a method of identifying or verifying the identity of an individual using their face. There are various algorithms that can do face recognition but their accuracy might vary. Here I am going to describe how we do face recognition using deep learning.
So now let us understand how we recognise faces using deep learning. We make use of face embedding in which each face is converted into a vector and this technique is called deep metric learning. Let me further divide this process into three simple steps for easy understanding:
Face Detection: The very first task we perform is detecting faces in the image or video stream. Now that we know the exact location/coordinates of face, we extract this face for further processing ahead.
Feature Extraction: Now that we have cropped the face out of the image, we extract features from it. Here we are going to use face embeddings to extract the features out of the face. A neural network takes an image of the person’s face as input and outputs a vector which represents the most important features of a face. In machine learning, this vector is called embedding and thus we call this vector as face embedding. Now how does this help in recognizing faces of different persons?
While training the neural network, the network learns to output similar vectors for faces that look similar. For example, if I have multiple images of faces within different timespan, of course, some of the features of my face might change but not up to much extent. So in this case the vectors associated with the faces are similar or in short, they are very close in the vector space. Take a look at the below diagram for a rough idea:
Now after training the network, the network learns to output vectors that are closer to each other(similar) for faces of the same person(looking similar). The above vectors now transform into:
We are not going to train such a network here as it takes a significant amount of data and computation power to train such networks. We will use a pre-trained network trained by Davis King on a dataset of ~3 million images. The network outputs a vector of 128 numbers which represent the most important features of a face.
Now that we know how this network works, let us see how we use this network on our own data. We pass all the images in our data to this pre-trained network to get the respective embeddings and save these embeddings in a file for the next step.
Comparing faces: Now that we have face embeddings for every face in our data saved in a file, the next step is to recognise a new t image that is not in our data. So the first step is to compute the face embedding for the image using the same network we used above and then compare this embedding with the rest of the embeddings we have. We recognise the face if the generated embedding is closer or similar to any other embedding as shown below:
So we passed two images, one of the images is of Vladimir Putin and other of George W. Bush. In our example above, we did not save the embeddings for Putin but we saved the embeddings of Bush. Thus when we compared the two new embeddings with the existing ones, the vector for Bush is closer to the other face embeddings of Bush whereas the face embeddings of Putin are not closer to any other embedding and thus the program cannot recognise him.
What is OpenCV
In the field of Artificial Intelligence, Computer Vision is one of the most interesting and Challenging tasks. Computer Vision acts like a bridge between Computer Software and visualizations around us. It allows computer software to understand and learn about the visualizations in the surroundings. For Example: Based on the color, shape and size determining the fruit. This task can be very easy for the human brain however in the Computer Vision pipeline, first we gather the data, then we perform the data processing activities and then we train and teach the model to understand how to distinguish between the fruits based on size, shape and color of fruit.
Currently, various packages are present to perform machine learning, deep learning and computer vision tasks. By far, computer vision is the best module for such complex activities. OpenCV is an open-source library. It is supported by various programming languages such as R, Python. It runs on most of the platforms such as Windows, Linux and MacOS.
To know more about how face recognition works on opencv, check out the free course on face recognition in opencv.
Advantages of OpenCV:
- OpenCV is an open-source library and is free of cost.
- As compared to other libraries, it is fast since it is written in C/C++.
- It works better on System with lesser RAM
- To supports most of the Operating Systems such as Windows, Linux and MacOS.
Installation:
Here we will be focusing on installing OpenCV for python only. We can install OpenCV using pip or conda(for anaconda environment).
- Using pip:
Using pip, the installation process of openCV can be done by using the following command in the command prompt.
pip install opencv-python
- Anaconda:
If you are using anaconda environment, either you can execute the above code in anaconda prompt or you can execute the following code in anaconda prompt.
conda install -c conda-forge opencv
Face Recognition using Python
In this section, we shall implement face recognition using OpenCV and Python. First, let us see the libraries we will need and how to install them:
- OpenCV
- dlib
- Face_recognition
OpenCV is an image and video processing library and is used for image and video analysis, like facial detection, license plate reading, photo editing, advanced robotic vision, optical character recognition, and a whole lot more.
The dlib library, maintained by Davis King, contains our implementation of “deep metric learning” which is used to construct our face embeddings used for the actual recognition process.
The face_recognition library, created by Adam Geitgey, wraps around dlib’s facial recognition functionality, and this library is super easy to work with and we will be using this in our code. Remember to install dlib library first before you install face_recognition.
To install OpenCV, type in command prompt
pip install opencv-python |
I have tried various ways to install dlib on Windows but the easiest of all of them is via Anaconda. First, install Anaconda (here is a guide to install it) and then use this command in your command prompt:
conda install -c conda-forge dlib |
Next to install face_recognition, type in command prompt
pip install face_recognition |
Now that we have all the dependencies installed, let us start coding. We will have to create three files, one will take our dataset and extract face embedding for each face using dlib. Next, we will save these embedding in a file.
In the next file we will compare the faces with the existing the recognise faces in images and next we will do the same but recognise faces in live webcam feed
Extracting features from Face
First, you need to get a dataset or even create one of you own. Just make sure to arrange all images in folders with each folder containing images of just one person.
Next, save the dataset in a folder the same as you are going to make the file. Now here is the code:
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Now that we have stored the embedding in a file named “face_enc”, we can use them to recognise faces in images or live video stream.
Face Recognition in Live webcam Feed
Here is the script to recognise faces on a live webcam feed:
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https://www.youtube.com/watch?v=fLnGdkZxRkg
Although in the example above we have used haar cascade to detect faces, you can also use face_recognition.face_locations to detect a face as we did in the previous script
Face Recognition in Images
The script for detecting and recognising faces in images is almost similar to what you saw above. Try it yourself and if you can’t take a look at the code below:
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Output:
Input
Output
This brings us to the end of this article where we learned about face recognition.
You can also upskill with Great Learning’s PGP Artificial Intelligence and Machine Learning Course. The course offers mentorship from industry leaders, and you will also have the opportunity to work on real-time industry-relevant projects.
Original article source at: https://www.mygreatlearning.com
1676013840
How to Fix Python [WinError 3] The System Cannot Find The Path Specifi
Python error [WinError 3] is a variation of the [WinError 2] error. The complete error message is as follows:
FileNotFoundError: [WinError 3] The system cannot find the path specified
This error usually occurs when you use the Python os module to interact with the Windows filesystem.
While [WinError 2] means that a file can’t be found, [WinError 3] means that the path you specified doesn’t exist.
This article will show you several examples that can cause this error and how to fix it.
1. Typed the wrong name when calling os.listdir() method
Suppose you have the following directory structure on your computer:
.
├── assets
│ ├── image.png
│ └── photo.png
└── main.py
Next, suppose you try to get the names of the files inside the assets/ directory using the os.listdir() method.
But you specified the wrong directory name when calling the method as follows:
import os
files = os.listdir("asset")
print(files)
Because you have an assets folder and not asset, the os module can’t find the directory:
Traceback (most recent call last):
File "main.py", line 3, in <module>
files = os.listdir("asset")
^^^^^^^^^^^^^^^^^^^
FileNotFoundError: [WinError 3] The system cannot find the path specified: 'asset'
To fix this error, make sure that the path you passed as the parameter to the listdir() method exists.
For our example, replacing asset with assets should fix the error.
2. Specifying non-existent path in os.rename() method.
The error also appears when you specified the wrong path name when calling the os.rename() method.
Suppose you have a different directory structure on your computer as follows:
.
├── docs
│ └── file.txt
└── main.py
Now, you want to rename the file.txt file into article.txt file.
You called the os.rename() method as follows:
import os
os.rename("doc/file.txt", "article.txt")
The code above incorrectly specified the path docs/ as doc/, so the error is triggered:
Traceback (most recent call last):
File "main.py", line 3, in <module>
os.rename("doc/file.txt", "article.txt")
FileNotFoundError: [WinError 3] The system cannot find the path specified:
'doc/file.txt' -> 'article.txt'
To fix this error, you need to specify the correct path to the file, which is docs/file.txt.
Please note that the extension of the file must be specified in the arguments. If you type file.txt as file, then you’ll get the same error.
This is because Python will think you’re instructing to rename a directory or folder and not a file.
When renaming a file, always include the file extension.
Also, keep in mind that directory and file names are case-sensitive, so you need to use the right capitalization.
3. Use absolute path instead of relative
At times, you might want to access a folder or file that’s a bit difficult to reach from the location of your script.
Suppose you have a directory structure as follows on your computer:
. C:
├── assets
│ └── text
│ └── file.txt
└── scripts
└── test
└── main.py
In this structure, the path to main.py is C:/scripts/test/main.py, while the file.txt is in C:/assets/text/file.txt
Suppose you want to rename file.txt to article.txt, this is how you specify the name with relative paths:
import os
os.rename("../../assets/text/file.txt", "../../assets/text/article.txt")
It’s easy for you to specify the wrong path when using relative paths, so it’s recommended to use absolute paths when the path is complex.
Here’s what the arguments look like using absolute paths:
import os
os.rename("C:/assets/text/file.txt", "C:/assets/text/article.txt")
As you can see, absolute paths are easier to read and understand. In Windows, the absolute path usually starts with the drive letter you have in your system like C: or D:.
To find the absolute path of a file, right-click on the file and select Properties from the context menu.
You’ll see the location of the file as follows:
Add the file name at the end of the location path, and you get the absolute path.
I hope this tutorial is helpful. See you in other articles! 👋
Original article source at: https://sebhastian.com/
1677907260
Chatgpt-api: Node.js client for the official ChatGPT API
ChatGPT API
Node.js client for the official ChatGPT API.
Intro
This package is a Node.js wrapper around ChatGPT by OpenAI. TS batteries included. ✨
Updates
March 1, 2023
The official OpenAI chat completions API has been released, and it is now the default for this package! 🔥
| Method | Free? | Robust? | Quality? |
|---|---|---|---|
ChatGPTAPI | ❌ No | ✅ Yes | ✅️ Real ChatGPT models |
ChatGPTUnofficialProxyAPI | ✅ Yes | ☑️ Maybe | ✅ Real ChatGPT |
Note: We strongly recommend using ChatGPTAPI since it uses the officially supported API from OpenAI. We may remove support for ChatGPTUnofficialProxyAPI in a future release.
ChatGPTAPI- Uses thegpt-3.5-turbo-0301model with the official OpenAI chat completions API (official, robust approach, but it's not free)ChatGPTUnofficialProxyAPI- Uses an unofficial proxy server to access ChatGPT's backend API in a way that circumvents Cloudflare (uses the real ChatGPT and is pretty lightweight, but relies on a third-party server and is rate-limited)
CLI
To run the CLI, you'll need an OpenAI API key:
export OPENAI_API_KEY="sk-TODO"
npx chatgpt "your prompt here"By default, the response is streamed to stdout, the results are stored in a local config file, and every invocation starts a new conversation. You can use -c to continue the previous conversation and --no-stream to disable streaming.
Under the hood, the CLI uses ChatGPTAPI with text-davinci-003 to mimic ChatGPT.
Usage:
$ chatgpt <prompt>
Commands:
<prompt> Ask ChatGPT a question
rm-cache Clears the local message cache
ls-cache Prints the local message cache path
For more info, run any command with the `--help` flag:
$ chatgpt --help
$ chatgpt rm-cache --help
$ chatgpt ls-cache --help
Options:
-c, --continue Continue last conversation (default: false)
-d, --debug Enables debug logging (default: false)
-s, --stream Streams the response (default: true)
-s, --store Enables the local message cache (default: true)
-t, --timeout Timeout in milliseconds
-k, --apiKey OpenAI API key
-n, --conversationName Unique name for the conversation
-h, --help Display this message
-v, --version Display version number
Install
npm install chatgptMake sure you're using node >= 18 so fetch is available (or node >= 14 if you install a fetch polyfill).
Usage
To use this module from Node.js, you need to pick between two methods:
| Method | Free? | Robust? | Quality? |
|---|---|---|---|
ChatGPTAPI | ❌ No | ✅ Yes | ✅️ Real ChatGPT models |
ChatGPTUnofficialProxyAPI | ✅ Yes | ☑️ Maybe | ✅ Real ChatGPT |
ChatGPTAPI - Uses the gpt-3.5-turbo-0301 model with the official OpenAI chat completions API (official, robust approach, but it's not free). You can override the model, completion params, and system message to fully customize your assistant.
ChatGPTUnofficialProxyAPI - Uses an unofficial proxy server to access ChatGPT's backend API in a way that circumvents Cloudflare (uses the real ChatGPT and is pretty lightweight, but relies on a third-party server and is rate-limited)
Both approaches have very similar APIs, so it should be simple to swap between them.
Note: We strongly recommend using ChatGPTAPI since it uses the officially supported API from OpenAI. We may remove support for ChatGPTUnofficialProxyAPI in a future release.
Usage - ChatGPTAPI
Sign up for an OpenAI API key and store it in your environment.
import { ChatGPTAPI } from 'chatgpt'
async function example() {
const api = new ChatGPTAPI({
apiKey: process.env.OPENAI_API_KEY
})
const res = await api.sendMessage('Hello World!')
console.log(res.text)
}You can override the default model (gpt-3.5-turbo-0301) and any OpenAI chat completion params using completionParams:
const api = new ChatGPTAPI({
apiKey: process.env.OPENAI_API_KEY,
completionParams: {
temperature: 0.5,
top_p: 0.8
}
})If you want to track the conversation, you'll need to pass the parentMessageId like this:
const api = new ChatGPTAPI({ apiKey: process.env.OPENAI_API_KEY })
// send a message and wait for the response
let res = await api.sendMessage('What is OpenAI?')
console.log(res.text)
// send a follow-up
res = await api.sendMessage('Can you expand on that?', {
parentMessageId: res.id
})
console.log(res.text)
// send another follow-up
res = await api.sendMessage('What were we talking about?', {
parentMessageId: res.id
})
console.log(res.text)You can add streaming via the onProgress handler:
const res = await api.sendMessage('Write a 500 word essay on frogs.', {
// print the partial response as the AI is "typing"
onProgress: (partialResponse) => console.log(partialResponse.text)
})
// print the full text at the end
console.log(res.text)You can add a timeout using the timeoutMs option:
// timeout after 2 minutes (which will also abort the underlying HTTP request)
const response = await api.sendMessage(
'write me a really really long essay on frogs',
{
timeoutMs: 2 * 60 * 1000
}
)If you want to see more info about what's actually being sent to OpenAI's chat completions API, set the debug: true option in the ChatGPTAPI constructor:
const api = new ChatGPTAPI({
apiKey: process.env.OPENAI_API_KEY,
debug: true
})We default to a basic systemMessage. You can override this in either the ChatGPTAPI constructor or sendMessage:
const res = await api.sendMessage('what is the answer to the universe?', {
systemMessage: `You are ChatGPT, a large language model trained by OpenAI. You answer as concisely as possible for each responseIf you are generating a list, do not have too many items.
Current date: ${new Date().toISOString()}\n\n`
})Note that we automatically handle appending the previous messages to the prompt and attempt to optimize for the available tokens (which defaults to 4096).
Usage in CommonJS (Dynamic import)
async function example() {
// To use ESM in CommonJS, you can use a dynamic import
const { ChatGPTAPI } = await import('chatgpt')
const api = new ChatGPTAPI({ apiKey: process.env.OPENAI_API_KEY })
const res = await api.sendMessage('Hello World!')
console.log(res.text)
}Usage - ChatGPTUnofficialProxyAPI
The API for ChatGPTUnofficialProxyAPI is almost exactly the same. You just need to provide a ChatGPT accessToken instead of an OpenAI API key.
import { ChatGPTUnofficialProxyAPI } from 'chatgpt'
async function example() {
const api = new ChatGPTUnofficialProxyAPI({
accessToken: process.env.OPENAI_ACCESS_TOKEN
})
const res = await api.sendMessage('Hello World!')
console.log(res.text)
}See demos/demo-reverse-proxy for a full example:
npx tsx demos/demo-reverse-proxy.tsChatGPTUnofficialProxyAPI messages also contain a conversationid in addition to parentMessageId, since the ChatGPT webapp can't reference messages across
Reverse Proxy
You can override the reverse proxy by passing apiReverseProxyUrl:
const api = new ChatGPTUnofficialProxyAPI({
accessToken: process.env.OPENAI_ACCESS_TOKEN,
apiReverseProxyUrl: 'https://your-example-server.com/api/conversation'
})Known reverse proxies run by community members include:
| Reverse Proxy URL | Author | Rate Limits | Last Checked |
|---|---|---|---|
https://chat.duti.tech/api/conversation | @acheong08 | 120 req/min by IP | 2/19/2023 |
https://gpt.pawan.krd/backend-api/conversation | @PawanOsman | ? | 2/19/2023 |
Note: info on how the reverse proxies work is not being published at this time in order to prevent OpenAI from disabling access.
Access Token
To use ChatGPTUnofficialProxyAPI, you'll need an OpenAI access token from the ChatGPT webapp. To do this, you can use any of the following methods which take an email and password and return an access token:
- Node.js libs
- Python libs
These libraries work with email + password accounts (e.g., they do not support accounts where you auth via Microsoft / Google).
Alternatively, you can manually get an accessToken by logging in to the ChatGPT webapp and then opening https://chat.openai.com/api/auth/session, which will return a JSON object containing your accessToken string.
Access tokens last for days.
Note: using a reverse proxy will expose your access token to a third-party. There shouldn't be any adverse effects possible from this, but please consider the risks before using this method.
Docs
See the auto-generated docs for more info on methods and parameters.
Demos
Most of the demos use ChatGPTAPI. It should be pretty easy to convert them to use ChatGPTUnofficialProxyAPI if you'd rather use that approach. The only thing that needs to change is how you initialize the api with an accessToken instead of an apiKey.
To run the included demos:
- clone repo
- install node deps
- set
OPENAI_API_KEYin .env
A basic demo is included for testing purposes:
npx tsx demos/demo.tsA demo showing on progress handler:
npx tsx demos/demo-on-progress.tsThe on progress demo uses the optional onProgress parameter to sendMessage to receive intermediary results as ChatGPT is "typing".
npx tsx demos/demo-conversation.tsA persistence demo shows how to store messages in Redis for persistence:
npx tsx demos/demo-persistence.tsAny keyv adaptor is supported for persistence, and there are overrides if you'd like to use a different way of storing / retrieving messages.
Note that persisting message is required for remembering the context of previous conversations beyond the scope of the current Node.js process, since by default, we only store messages in memory. Here's an external demo of using a completely custom database solution to persist messages.
Note: Persistence is handled automatically when using ChatGPTUnofficialProxyAPI because it is connecting indirectly to ChatGPT.
Projects
All of these awesome projects are built using the chatgpt package. 🤯
- Twitter Bot powered by ChatGPT ✨
- Mention @ChatGPTBot on Twitter with your prompt to try it out
- ChatGPT API Server - API server for this package with support for multiple OpenAI accounts, proxies, and load-balancing requests between accounts.
- ChatGPT Prompts - A collection of 140+ of the best ChatGPT prompts from the community.
- Lovelines.xyz
- Chrome Extension (demo)
- VSCode Extension #1 (demo, updated version, marketplace)
- VSCode Extension #2 (marketplace)
- VSCode Extension #3 (marketplace)
- VSCode Extension #4 (marketplace)
- Raycast Extension #1 (demo)
- Raycast Extension #2
- Telegram Bot #1
- Telegram Bot #2
- Telegram Bot #3 (group privacy mode, ID-based auth)
- Telegram Bot #4 (queue system, ID-based chat thread)
- Deno Telegram Bot
- Go Telegram Bot
- Telegram Bot for YouTube Summaries
- GitHub ProBot
- Discord Bot #1
- Discord Bot #2
- Discord Bot #3
- Discord Bot #4 (selfbot)
- Discord Bot #5
- Discord Bot #6 (Shakespeare bot)
- WeChat Bot #1
- WeChat Bot #2
- WeChat Bot #3 (
- WeChat Bot #4
- WeChat Bot #5
- QQ Bot (plugin for Yunzai-bot)
- QQ Bot (plugin for KiviBot)
- QQ Bot (oicq)
- QQ Bot (oicq + RabbitMQ)
- QQ Bot (go-cqhttp)
- EXM smart contracts
- Flutter ChatGPT API
- Carik Bot
- Github Action for reviewing PRs
- WhatsApp Bot #1 (DALL-E + Whisper support 💪)
- WhatsApp Bot #2
- WhatsApp Bot #3 (multi-user support)
- WhatsApp Bot #4 (schedule periodic messages)
- WhatsApp Bot #5 (RaspberryPi + ngrok + Twilio)
- WhatsApp Bot #6 (Session and chat history storage with MongoStore)
- Matrix Bot
- Rental Cover Letter Generator
- Assistant CLI
- Teams Bot
- Askai
- TalkGPT
- ChatGPT With Voice
- iOS Shortcut
- Slack Bot #1
- Slack Bot #2 (with queueing mechanism)
- Slack Bot #3
- Slack Bot #4 ( Serverless AWS Lambda )
- Electron Bot
- Kodyfire CLI
- Twitch Bot
- Continuous Conversation
- Figma plugin
- NestJS server
- NestJS ChatGPT Starter Boilerplate
- Wordsmith: Add-in for Microsoft Word
- QuizGPT: Create Kahoot quizzes with ChatGPT
- openai-chatgpt: Talk to ChatGPT from the terminal
- Clippy the Saleforce chatbot ClippyJS joke bot
- ai-assistant Chat assistant
- Feishu Bot
- DomainGPT: Discover available domain names
- AI Poem Generator
- Next.js ChatGPT With Firebase
- ai-commit – GPT-3 Commit Message Generator
If you create a cool integration, feel free to open a PR and add it to the list.
Compatibility
- This package is ESM-only.
- This package supports
node >= 14. - This module assumes that
fetchis installed. - If you want to build a website using
chatgpt, we recommend using it only from your backend API
Credits
- Huge thanks to @waylaidwanderer, @abacaj, @wong2, @simon300000, @RomanHotsiy, @ElijahPepe, and all the other contributors 💪
- The original browser version was inspired by this Go module by Daniel Gross
- OpenAI for creating ChatGPT 🔥
Previous Updates
Feb 19, 2023
We now provide three ways of accessing the unofficial ChatGPT API, all of which have tradeoffs:
| Method | Free? | Robust? | Quality? |
|---|---|---|---|
ChatGPTAPI | ❌ No | ✅ Yes | ☑️ Mimics ChatGPT |
ChatGPTUnofficialProxyAPI | ✅ Yes | ☑️ Maybe | ✅ Real ChatGPT |
ChatGPTAPIBrowser (v3) | ✅ Yes | ❌ No | ✅ Real ChatGPT |
Note: I recommend that you use either ChatGPTAPI or ChatGPTUnofficialProxyAPI.
ChatGPTAPI- Usestext-davinci-003to mimic ChatGPT via the official OpenAI completions API (most robust approach, but it's not free and doesn't use a model fine-tuned for chat)ChatGPTUnofficialProxyAPI- Uses an unofficial proxy server to access ChatGPT's backend API in a way that circumvents Cloudflare (uses the real ChatGPT and is pretty lightweight, but relies on a third-party server and is rate-limited)ChatGPTAPIBrowser- (deprecated; v3.5.1 of this package) Uses Puppeteer to access the official ChatGPT webapp (uses the real ChatGPT, but very flaky, heavyweight, and error prone)
Feb 5, 2023
OpenAI has disabled the leaked chat model we were previously using, so we're now defaulting to text-davinci-003, which is not free.
We've found several other hidden, fine-tuned chat models, but OpenAI keeps disabling them, so we're searching for alternative workarounds.
Feb 1, 2023
This package no longer requires any browser hacks – it is now using the official OpenAI completions API with a leaked model that ChatGPT uses under the hood. 🔥
import { ChatGPTAPI } from 'chatgpt'
const api = new ChatGPTAPI({
apiKey: process.env.OPENAI_API_KEY
})
const res = await api.sendMessage('Hello World!')
console.log(res.text)Please upgrade to chatgpt@latest (at least v4.0.0). The updated version is significantly more lightweight and robust compared with previous versions. You also don't have to worry about IP issues or rate limiting.
Huge shoutout to @waylaidwanderer for discovering the leaked chat model!
If you run into any issues, we do have a pretty active Discord with a bunch of ChatGPT hackers from the Node.js & Python communities.
Lastly, please consider starring this repo and following me on twitter to help support the project.
Thanks && cheers, Travis
Download Details:
Author: Transitive-bullshit
Source Code: https://github.com/transitive-bullshit/chatgpt-api
License: MIT license