Unable to sign a partially signed transaction with solana-py - python
in order to buy something on MagicEden, I'm using an endpoint called buy_now that sends me back a payload with the following format :
with ['tx']['data'] the message and ['txSigned']['data'] a partially signed transaction. I'm trying to sign the message and to insert it in
the first slot of signatures[] of the transaction.
Payload in entry :
{'tx': {'type': 'Buffer', 'data': [2, 1, 9, 21, 196, 1, 121, 246, 8, 50, 175, 233, 165, 26, 58, 31, 47, 169, 127, 105, 114, 246, 195, 127, 107, 150, 107, 81, 27, 242, 42, 139, 211, 125, 28, 252, 5, 127, 54, 85, 153, 40, 206, 27, 171, 173, 182, 91, 139, 93, 158, 49, 186, 39, 248, 83, 155, 236, 96, 44, 203, 26, 220, 42, 251, 159, 70, 112, 234, 229, 246, 49, 247, 171, 199, 192, 75, 0, 164, 243, 164, 173, 173, 204, 108, 103, 77, 32, 29, 248, 152, 212, 87, 233, 255, 150, 147, 163, 18, 20, 46, 102, 223, 57, 126, 136, 59, 186, 161, 206, 130, 78, 143, 99, 68, 124, 54, 187, 28, 214, 169, 184, 137, 146, 121, 188, 11, 38, 234, 75, 163, 227, 159, 245, 230, 90, 36, 4, 85, 130, 248, 34, 4, 215, 246, 88, 214, 129, 157, 51, 165, 199, 101, 224, 234, 73, 209, 32, 159, 190, 135, 97, 212, 111, 105, 68, 93, 31, 113, 62, 39, 206, 222, 140, 109, 115, 71, 173, 36, 186, 212, 191, 186, 139, 47, 118, 15, 86, 147, 62, 225, 155, 19, 124, 188, 32, 130, 24, 74, 93, 25, 73, 136, 231, 60, 239, 217, 165, 75, 201, 251, 81, 250, 184, 172, 180, 74, 170, 178, 26, 93, 235, 115, 244, 5, 241, 178, 37, 12, 158, 58, 228, 224, 183, 152, 74, 250, 18, 157, 96, 7, 160, 158, 224, 142, 150, 46, 161, 202, 218, 73, 218, 230, 18, 50, 147, 194, 191, 195, 125, 8, 175, 246, 228, 16, 89, 36, 102, 175, 155, 72, 107, 229, 118, 121, 242, 246, 139, 65, 205, 220, 49, 224, 32, 146, 119, 74, 143, 99, 98, 237, 19, 164, 241, 157, 127, 75, 73, 147, 5, 131, 61, 229, 232, 186, 71, 117, 202, 167, 81, 61, 69, 95, 107, 207, 2, 115, 207, 210, 53, 247, 102, 81, 2, 23, 197, 60, 244, 161, 168, 23, 23, 33, 75, 127, 220, 222, 157, 73, 117, 58, 207, 101, 174, 28, 121, 154, 190, 255, 161, 186, 205, 218, 172, 143, 144, 113, 213, 119, 12, 213, 213, 20, 42, 184, 68, 163, 179, 252, 28, 238, 92, 2, 7, 247, 226, 10, 211, 129, 107, 192, 1, 198, 251, 136, 30, 2, 103, 195, 27, 24, 204, 62, 20, 138, 10, 82, 147, 129, 137, 32, 237, 250, 237, 171, 57, 30, 73, 51, 108, 11, 116, 219, 102, 157, 16, 71, 3, 66, 75, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 26, 136, 115, 139, 250, 198, 10, 13, 26, 130, 255, 192, 253, 225, 104, 17, 209, 176, 195, 48, 48, 68, 144, 55, 86, 179, 38, 200, 206, 101, 253, 239, 8, 209, 122, 249, 173, 94, 22, 105, 150, 178, 108, 233, 210, 121, 80, 93, 128, 143, 222, 100, 131, 254, 248, 217, 106, 235, 99, 8, 128, 8, 203, 113, 6, 221, 246, 225, 215, 101, 161, 147, 217, 203, 225, 70, 206, 235, 121, 172, 28, 180, 133, 237, 95, 91, 55, 145, 58, 140, 245, 133, 126, 255, 0, 169, 6, 167, 213, 23, 25, 44, 92, 81, 33, 140, 201, 76, 61, 74, 241, 127, 88, 218, 238, 8, 155, 161, 253, 68, 227, 219, 217, 138, 0, 0, 0, 0, 140, 151, 37, 143, 78, 36, 137, 241, 187, 61, 16, 41, 20, 142, 13, 131, 11, 90, 19, 153, 218, 255, 16, 132, 4, 142, 123, 216, 219, 233, 248, 89, 0, 11, 227, 225, 235, 161, 122, 71, 63, 137, 176, 247, 232, 226, 73, 64, 242, 10, 235, 142, 188, 167, 26, 136, 253, 233, 93, 75, 131, 183, 26, 9, 5, 33, 159, 137, 154, 129, 212, 255, 132, 251, 89, 61, 46, 223, 138, 144, 172, 27, 58, 179, 66, 88, 247, 223, 35, 62, 165, 3, 2, 177, 189, 46, 205, 33, 64, 224, 186, 130, 65, 227, 93, 10, 193, 134, 47, 123, 19, 30, 70, 242, 146, 250, 186, 131, 119, 230, 223, 15, 6, 200, 52, 135, 241, 92, 3, 20, 6, 0, 1, 2, 8, 12, 13, 17, 242, 35, 198, 137, 82, 225, 242, 182, 255, 0, 23, 100, 7, 0, 0, 0, 0, 20, 12, 0, 1, 14, 15, 2, 8, 12, 3, 8, 16, 13, 17, 34, 102, 6, 61, 18, 1, 218, 235, 234, 255, 255, 0, 23, 100, 7, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 255, 255, 255, 255, 255, 255, 255, 255, 20, 22, 0, 4, 1, 5, 14, 15, 2, 6, 8, 12, 7, 3, 8, 9, 8, 16, 13, 18, 19, 17, 10, 11, 42, 37, 74, 217, 157, 79, 49, 35, 6, 255, 250, 0, 23, 100, 7, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255]}, 'txSigned': {'type': 'Buffer', 'data': [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 30, 236, 72, 248, 210, 27, 195, 225, 195, 236, 128, 200, 183, 128, 195, 174, 10, 166, 181, 191, 241, 121, 21, 87, 232, 145, 169, 146, 168, 241, 1, 66, 204, 164, 143, 99, 121, 7, 163, 201, 176, 220, 191, 220, 99, 140, 228, 151, 111, 113, 138, 82, 86, 148, 253, 143, 194, 250, 145, 241, 152, 57, 242, 0, 2, 1, 9, 21, 196, 1, 121, 246, 8, 50, 175, 233, 165, 26, 58, 31, 47, 169, 127, 105, 114, 246, 195, 127, 107, 150, 107, 81, 27, 242, 42, 139, 211, 125, 28, 252, 5, 127, 54, 85, 153, 40, 206, 27, 171, 173, 182, 91, 139, 93, 158, 49, 186, 39, 248, 83, 155, 236, 96, 44, 203, 26, 220, 42, 251, 159, 70, 112, 234, 229, 246, 49, 247, 171, 199, 192, 75, 0, 164, 243, 164, 173, 173, 204, 108, 103, 77, 32, 29, 248, 152, 212, 87, 233, 255, 150, 147, 163, 18, 20, 8, 175, 246, 228, 16, 89, 36, 102, 175, 155, 72, 107, 229, 118, 121, 242, 246, 139, 65, 205, 220, 49, 224, 32, 146, 119, 74, 143, 99, 98, 237, 19, 46, 102, 223, 57, 126, 136, 59, 186, 161, 206, 130, 78, 143, 99, 68, 124, 54, 187, 28, 214, 169, 184, 137, 146, 121, 188, 11, 38, 234, 75, 163, 227, 159, 245, 230, 90, 36, 4, 85, 130, 248, 34, 4, 215, 246, 88, 214, 129, 157, 51, 165, 199, 101, 224, 234, 73, 209, 32, 159, 190, 135, 97, 212, 111, 105, 68, 93, 31, 113, 62, 39, 206, 222, 140, 109, 115, 71, 173, 36, 186, 212, 191, 186, 139, 47, 118, 15, 86, 147, 62, 225, 155, 19, 124, 188, 32, 130, 24, 74, 93, 25, 73, 136, 231, 60, 239, 217, 165, 75, 201, 251, 81, 250, 184, 172, 180, 74, 170, 178, 26, 93, 235, 115, 244, 5, 241, 178, 37, 12, 158, 58, 228, 224, 183, 152, 74, 250, 18, 157, 96, 7, 160, 158, 224, 142, 150, 46, 161, 202, 218, 73, 218, 230, 18, 50, 147, 194, 191, 195, 125, 164, 241, 157, 127, 75, 73, 147, 5, 131, 61, 229, 232, 186, 71, 117, 202, 167, 81, 61, 69, 95, 107, 207, 2, 115, 207, 210, 53, 247, 102, 81, 2, 23, 197, 60, 244, 161, 168, 23, 23, 33, 75, 127, 220, 222, 157, 73, 117, 58, 207, 101, 174, 28, 121, 154, 190, 255, 161, 186, 205, 218, 172, 143, 144, 113, 213, 119, 12, 213, 213, 20, 42, 184, 68, 163, 179, 252, 28, 238, 92, 2, 7, 247, 226, 10, 211, 129, 107, 192, 1, 198, 251, 136, 30, 2, 103, 195, 27, 24, 204, 62, 20, 138, 10, 82, 147, 129, 137, 32, 237, 250, 237, 171, 57, 30, 73, 51, 108, 11, 116, 219, 102, 157, 16, 71, 3, 66, 75, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 26, 136, 115, 139, 250, 198, 10, 13, 26, 130, 255, 192, 253, 225, 104, 17, 209, 176, 195, 48, 48, 68, 144, 55, 86, 179, 38, 200, 206, 101, 253, 239, 8, 209, 122, 249, 173, 94, 22, 105, 150, 178, 108, 233, 210, 121, 80, 93, 128, 143, 222, 100, 131, 254, 248, 217, 106, 235, 99, 8, 128, 8, 203, 113, 6, 221, 246, 225, 215, 101, 161, 147, 217, 203, 225, 70, 206, 235, 121, 172, 28, 180, 133, 237, 95, 91, 55, 145, 58, 140, 245, 133, 126, 255, 0, 169, 6, 167, 213, 23, 25, 44, 92, 81, 33, 140, 201, 76, 61, 74, 241, 127, 88, 218, 238, 8, 155, 161, 253, 68, 227, 219, 217, 138, 0, 0, 0, 0, 140, 151, 37, 143, 78, 36, 137, 241, 187, 61, 16, 41, 20, 142, 13, 131, 11, 90, 19, 153, 218, 255, 16, 132, 4, 142, 123, 216, 219, 233, 248, 89, 0, 11, 227, 225, 235, 161, 122, 71, 63, 137, 176, 247, 232, 226, 73, 64, 242, 10, 235, 142, 188, 167, 26, 136, 253, 233, 93, 75, 131, 183, 26, 9, 5, 33, 159, 137, 154, 129, 212, 255, 132, 251, 89, 61, 46, 223, 138, 144, 172, 27, 58, 179, 66, 88, 247, 223, 35, 62, 165, 3, 2, 177, 189, 46, 205, 33, 64, 224, 186, 130, 65, 227, 93, 10, 193, 134, 47, 123, 19, 30, 70, 242, 146, 250, 186, 131, 119, 230, 223, 15, 6, 200, 52, 135, 241, 92, 3, 20, 6, 0, 1, 2, 3, 12, 13, 17, 242, 35, 198, 137, 82, 225, 242, 182, 255, 0, 23, 100, 7, 0, 0, 0, 0, 20, 12, 0, 1, 14, 15, 2, 3, 12, 4, 3, 16, 13, 17, 34, 102, 6, 61, 18, 1, 218, 235, 234, 255, 255, 0, 23, 100, 7, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 255, 255, 255, 255, 255, 255, 255, 255, 20, 22, 0, 5, 1, 6, 14, 15, 2, 7, 3, 12, 8, 4, 3, 9, 3, 16, 13, 18, 19, 17, 10, 11, 42, 37, 74, 217, 157, 79, 49, 35, 6, 255, 250, 0, 23, 100, 7, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255]}}
and here is my code :
txSigned = tx_data['txSigned']['data']
tx = bytes(tx_data['tx']['data'])
client = AsyncClient("https://api.mainnet-beta.solana.com")
payer = Keypair.from_secret_key(b58decode(mypkey))
sig = payer.sign(tx)
tra = Transaction.deserialize(bytes(txSigned))
tra.signatures[0].signature = sig.signature
tra = Transaction.serialize(tra)
txn = await client.send_raw_transaction(tra)
I keep encountering : 'transaction has not been signed correctly'
Someone with a similar problem but using C# solnet : https://github.com/bmresearch/Solnet/issues/399
is this a bug? thanks a lot
This makes sense, since the previous signature from Magic Eden is getting removed during tra.signatures[0].signature = sig.signature
Instead, you should deserialize the transaction and then sign it yourself, e.g.:
tra = Transaction.deserialize(bytes(txSigned))
tra.sign(payer)
tra = Transaction.serialize(tra)
txn = await client.send_raw_transaction(tra)
Related
How to write shiftrow() in python
hi guys im doing shiftrows aes encryption
def rotate(word, n):
return word[n:]+word[0:n]
def shiftRows(state):
for i in range(4):
state[i*4:i*4+4] = rotate(state[i*4:i*4+4],i)
state = [0, 17, 34, 51, 68, 85, 102, 119, 136, 153, 170, 187, 204, 221, 238, 255]
shiftRows(state)
print("row: ", state)
Expected output:
[0, 85, 170, 255, 68, 153, 238, 51, 136, 221, 34, 119, 204, 17, 102, 187]
Current output:
[0, 17, 34, 51, 85, 102, 119, 68, 170, 187, 136, 153, 255, 204, 221, 238]
convert image from [0.0, 1.0] to [0, 255]
Suppose the image x consists of floats in the range [0, 1],
Torchvision adopts the transform of clip(x*255+0.5, 0, 255).as(uint8) .
Skimage seems similar to torch
TensorFlow uses an asymmetric approach
Details on the conversion follow below.
However, while investigating a few things, I found that this method gives an unfairly small chance for values of 0 and 255 compared to other values.
Why do these machine learning libraries use these unfair transformations?
pytorch
https://pytorch.org/vision/main/_modules/torchvision/utils.html#save_image
from collections import Counter, defaultdict
import numpy as np
DICT = defaultdict(list)
def as_uint8(X):
return np.clip(X * 255 + 0.5, 0, 255).astype(np.uint8)
for K, V in Counter(as_uint8(np.linspace(0/256, 256/256, 32 * 256))).items():
DICT[V].append(K)
print(DICT)
defaultdict(<class 'list'>, {17: [0, 255], 32: [1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 47, 48, 50, 51, 52, 53, 54, 55, 56, 57, 59, 60, 61, 62, 63, 64, 65, 67, 68, 69, 70, 71, 72, 73, 75, 76, 77, 78, 79, 80, 81, 83, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 95, 96, 97, 98, 100, 101, 102, 103, 104, 105, 106, 108, 109, 110, 111, 112, 113, 114, 116, 117, 118, 119, 120, 121, 122, 124, 125, 126, 127, 128, 129, 130, 131, 133, 134, 135, 136, 137, 138, 139, 141, 142, 143, 144, 145, 146, 147, 149, 150, 151, 152, 153, 154, 155, 157, 158, 159, 160, 161, 162, 163, 164, 166, 167, 168, 169, 170, 171, 172, 174, 175, 176, 177, 178, 179, 180, 182, 183, 184, 185, 186, 187, 188, 190, 191, 192, 193, 194, 195, 196, 198, 199, 200, 201, 202, 203, 204, 205, 207, 208, 209, 210, 211, 212, 213, 215, 216, 217, 218, 219, 220, 221, 223, 224, 225, 226, 227, 228, 229, 231, 232, 233, 234, 235, 236, 237, 238, 240, 241, 242, 243, 244, 245, 246, 248, 249, 250, 251, 252, 253, 254], 33: [8, 16, 25, 33, 41, 49, 58, 66, 74, 82, 90, 99, 107, 115, 123, 132, 140, 148, 156, 165, 173, 181, 189, 197, 206, 214, 222, 230, 239, 247]})
skimage https://scikit-image.org/docs/dev/user_guide/data_types.html
from skimage.util import img_as_ubyte
from collections import Counter, defaultdict
import numpy as np
DICT = defaultdict(list)
for K, V in Counter(img_as_ubyte(np.linspace(0/256, 256/256, 32 * 256).reshape(-1, 1, 1)).reshape(-1)).items():
DICT[V].append(K)
print(DICT)
defaultdict(<class 'list'>, {17: [0, 255], 32: [1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 47, 48, 50, 51, 52, 53, 54, 55, 56, 57, 59, 60, 61, 62, 63, 64, 65, 67, 68, 69, 70, 71, 72, 73, 75, 76, 77, 78, 79, 80, 81, 83, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 95, 96, 97, 98, 100, 101, 102, 103, 104, 105, 106, 108, 109, 110, 111, 112, 113, 114, 116, 117, 118, 119, 120, 121, 122, 124, 125, 126, 127, 128, 129, 130, 131, 133, 134, 135, 136, 137, 138, 139, 141, 142, 143, 144, 145, 146, 147, 149, 150, 151, 152, 153, 154, 155, 157, 158, 159, 160, 161, 162, 163, 164, 166, 167, 168, 169, 170, 171, 172, 174, 175, 176, 177, 178, 179, 180, 182, 183, 184, 185, 186, 187, 188, 190, 191, 192, 193, 194, 195, 196, 198, 199, 200, 201, 202, 203, 204, 205, 207, 208, 209, 210, 211, 212, 213, 215, 216, 217, 218, 219, 220, 221, 223, 224, 225, 226, 227, 228, 229, 231, 232, 233, 234, 235, 236, 237, 238, 240, 241, 242, 243, 244, 245, 246, 248, 249, 250, 251, 252, 253, 254], 33: [8, 16, 25, 33, 41, 49, 58, 66, 74, 82, 90, 99, 107, 115, 123, 132, 140, 148, 156, 165, 173, 181, 189, 197, 206, 214, 222, 230, 239, 247]})
tensorflow https://www.tensorflow.org/api_docs/python/tf/image/convert_image_dtype
import tensorflow as tf
from collections import Counter, defaultdict
import numpy as np
DICT = defaultdict(list)
img = tf.convert_to_tensor(np.linspace(0/256, 256/256, 32 * 256).reshape(-1, 1, 1))
img = tf.image.convert_image_dtype(img, dtype=tf.uint8, saturate=False)
img = tf.reshape(img, -1).numpy()
for K, V in Counter(img).items():
DICT[V].append(K)
print(DICT)
defaultdict(<class 'list'>, {33: [0, 17, 34, 51, 68, 85, 102, 119, 136, 153, 170, 187, 204, 221, 238], 32: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254], 17: [255]})
my suggestion 1
from collections import Counter, defaultdict
import numpy as np
DICT = defaultdict(list)
def as_uint8(X):
return np.clip(np.rint(X * 256 - 0.5), 0, 255).astype(np.uint8)
for K, V in Counter(as_uint8(np.linspace(0/256, 256/256, 32 * 256))).items():
DICT[V].append(K)
print(DICT)
defaultdict(<class 'list'>, {32: [0, 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, 254, 255]})
my suggestion 2
from collections import Counter, defaultdict
import numpy as np
DICT = defaultdict(list)
def as_uint8(X):
return np.clip(X * 256, 0, 255).astype(np.uint8)
for K, V in Counter(as_uint8(np.linspace(0/256, 256/256, 32 * 256))).items():
DICT[V].append(K)
print(DICT)
defaultdict(<class 'list'>, {32: [0, 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, 254, 255]})
how to convert list to RGB value in python
I'm trying to convert fibonacci series to rgb image. so
import matplotlib.pyplot as plt
import numpy as np
N = int(input("Number of elements in Fibonacci Series, N, (N>=2) : "))
#starting elements: 0, 1
fibonacciSeries = [0,1]
if N>2:
for i in range(2, N):
nextElement = fibonacciSeries[i-1] + fibonacciSeries[i-2]
fibonacciSeries.append(nextElement)
print(fibonacciSeries)
fib_arr = np.array(fibonacciSeries)
fib_arr
img =np.zeros((100,100,4))
rgb = []
for i in fibonacciSeries:
rgb.append(i % 255)
print(rgb)
all this process ı have a list of mod each index of fib_Arr like that!
[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 122, 100, 222, 67, 34, 101, 135, 236, 116, 97, 213, 55, 13, 68, 81, 149, 230, 124, 99, 223, 67, 35, 102, 137, 239, 121, 105, 226, 76, 47, 123, 170, 38, 208, 246, 199, 190, 134, 69, 203, 17, 220, 237, 202, 184, 131, 60, 191, 251, 187, 183, 115, 43, 158, 201, 104, 50, 154, 204, 103, 52, 155, 207, 107, 59, 166, 225, 136, 106, 242, 93, 80, 173, 253, 171, 169, 85, 254, 84, 83, 167, 250, 162, 157, 64, 221]
now how to convert this value to RGB image
I try to
plt.imshow(rgb)
plt.savefig("rgb.png")
but doesn't work
edited:
rgb_arr = np.array(rgb)
rgb_arr
array([ 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144,
233, 122, 100, 222, 67, 34, 101, 135, 236, 116, 97, 213, 55,
13, 68, 81, 149, 230, 124, 99, 223, 67, 35, 102, 137, 239,
121, 105, 226, 76, 47, 123, 170, 38, 208, 246, 199, 190, 134,
69, 203, 17, 220, 237, 202, 184, 131, 60, 191, 251, 187, 183,
115, 43, 158, 201, 104, 50, 154, 204, 103, 52, 155, 207, 107,
59, 166, 225, 136, 106, 242, 93, 80, 173, 253, 171, 169, 85,
254, 84, 83, 167, 250, 162, 157, 64])
from PIL import Image
img = Image.fromarray(rgb_arr, 'RGB')
img.save('test.png')
img.show()
picture
How to convert byte array to picture [closed]
Closed. This question needs to be more focused. It is not currently accepting answers. Want to improve this question? Update the question so it focuses on one problem only by editing this post. Closed 5 years ago. Improve this question How do I convert my byte array to a picture? I want it to be in saved JPG or BMP format, not just displayed as text or on the console. This is sample array: [255, 216, 255, 224, 0, 16, 74, 70, 73, 70, 0, 1, 1, 1, 0, 72, 0, 72, 0, 0, 255, 219, 0, 67, 0, 14, 10, 11, 13, 11, 9, 14, 13, 12, 13, 16, 15, 14, 17, 22, 36, 23, 22, 20, 20, 22, 44, 32, 33, 26, 36, 52, 46, 55, 54, 51, 46, 50, 50, 58, 65, 83, 70, 58, 61, 78, 62, 50, 50, 72, 98, 73, 78, 86, 88, 93, 94, 93, 56, 69, 102, 109, 101, 90, 108, 83, 91, 93, 89, 255, 219, 0, 67, 1, 15, 16, 16, 22, 19, 22, 42, 23, 23, 42, 89, 59, 50, 59, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 255, 192, 0, 17, 8, 0, 67, 0, 90, 3, 1, 34, 0, 2, 17, 1, 3, 17, 1, 255, 196, 0, 27, 0, 0, 2, 3, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 5, 0, 2, 4, 6, 1, 7, 255, 196, 0, 46, 16, 0, 2, 2, 1, 3, 4, 0, 6, 0, 6, 3, 0, 0, 0, 0, 0, 1, 2, 0, 3, 17, 4, 18, 33, 19, 49, 65, 81, 5, 20, 34, 50, 97, 129, 35, 51, 66, 82, 113, 161, 145, 177, 241, 255, 196, 0, 25, 1, 0, 3, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 4, 3, 0, 5, 255, 196, 0, 32, 17, 0, 3, 1, 0, 3, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 17, 3, 33, 49, 4, 18, 65, 50, 81, 97, 255, 218, 0, 12, 3, 1, 0, 2, 17, 3, 17, 0, 63, 0, 64, 23, 230, 43, 64, 123, 146, 48, 33, 151, 109, 78, 89, 185, 43, 244, 129, 234, 99, 171, 80, 90, 157, 152, 25, 7, 32, 226, 93, 89, 152, 41, 83, 245, 99, 13, 159, 18, 55, 254, 138, 57, 125, 101, 122, 132, 167, 40, 168, 16, 99, 129, 222, 69, 189, 107, 185, 171, 80, 25, 118, 131, 144, 115, 156, 192, 81, 131, 65, 177, 215, 42, 190, 120, 154, 180, 250, 122, 41, 2, 203, 47, 80, 196, 239, 36, 140, 136, 97, 82, 237, 5, 231, 140, 211, 165, 109, 129, 54, 18, 135, 183, 7, 17, 169, 212, 181, 40, 173, 96, 37, 79, 5, 189, 69, 86, 87, 180, 51, 171, 6, 243, 149, 237, 147, 54, 86, 83, 229, 16, 171, 51, 59, 113, 98, 191, 111, 212, 51, 78, 31, 125, 29, 245, 251, 46, 134, 212, 178, 218, 172, 235, 219, 196, 191, 76, 17, 145, 21, 211, 99, 212, 249, 175, 236, 30, 12, 105, 70, 173, 44, 93, 165, 72, 97, 46, 226, 249, 42, 186, 126, 146, 242, 112, 53, 218, 6, 213, 64, 189, 115, 122, 128, 227, 142, 227, 188, 27, 215, 42, 86, 77, 80, 208, 177, 235, 129, 233, 254, 35, 23, 174, 7, 100, 209, 80, 152, 112, 122, 95, 133, 88, 250, 94, 169, 179, 146, 50, 20, 119, 48, 26, 230, 21, 88, 136, 203, 176, 168, 236, 59, 159, 243, 49, 117, 110, 166, 210, 155, 217, 27, 177, 195, 120, 158, 239, 107, 92, 189, 132, 187, 118, 201, 158, 59, 149, 233, 234, 173, 28, 252, 57, 27, 90, 198, 189, 202, 170, 6, 78, 79, 113, 25, 184, 85, 78, 141, 149, 45, 71, 111, 117, 60, 17, 57, 154, 153, 171, 96, 245, 185, 87, 95, 34, 48, 249, 195, 171, 176, 29, 77, 228, 17, 129, 192, 238, 34, 185, 77, 96, 87, 79, 78, 155, 73, 69, 107, 163, 168, 217, 118, 75, 14, 63, 38, 21, 43, 61, 82, 21, 51, 248, 204, 207, 162, 27, 40, 110, 144, 123, 213, 120, 33, 187, 16, 61, 67, 13, 90, 223, 96, 122, 198, 8, 238, 177, 111, 142, 91, 218, 120, 141, 38, 158, 98, 44, 247, 109, 109, 140, 155, 88, 113, 159, 115, 222, 166, 208, 48, 199, 62, 79, 169, 109, 37, 226, 250, 109, 107, 16, 99, 171, 129, 159, 56, 255, 0, 201, 235, 168, 119, 42, 7, 62, 132, 202, 184, 190, 189, 203, 208, 167, 189, 52, 30, 141, 83, 161, 250, 142, 76, 217, 166, 212, 245, 9, 15, 199, 168, 156, 0, 167, 151, 24, 252, 201, 212, 193, 36, 28, 48, 154, 71, 45, 194, 79, 240, 202, 162, 95, 76, 126, 193, 76, 15, 76, 123, 139, 244, 218, 227, 140, 62, 61, 77, 95, 48, 159, 221, 44, 143, 149, 45, 118, 77, 92, 31, 209, 242, 64, 25, 156, 177, 28, 137, 161, 114, 216, 68, 4, 177, 30, 60, 192, 86, 72, 76, 159, 50, 201, 97, 22, 6, 4, 140, 30, 49, 49, 101, 33, 129, 193, 43, 130, 8, 134, 210, 186, 163, 146, 232, 31, 140, 115, 50, 245, 9, 39, 39, 36, 249, 133, 76, 240, 124, 67, 157, 4, 102, 150, 220, 41, 10, 150, 176, 172, 182, 72, 7, 180, 53, 90, 183, 210, 53, 157, 59, 3, 2, 177, 117, 119, 109, 98, 15, 57, 148, 123, 13, 231, 10, 112, 1, 201, 49, 90, 213, 140, 100, 240, 125, 240, 91, 152, 216, 149, 217, 119, 76, 31, 63, 153, 208, 116, 159, 78, 253, 71, 110, 165, 64, 99, 32, 114, 63, 83, 153, 248, 78, 152, 234, 237, 8, 70, 43, 79, 184, 231, 152, 254, 212, 170, 141, 37, 148, 37, 238, 89, 148, 133, 70, 96, 73, 63, 129, 30, 97, 53, 184, 35, 166, 186, 60, 179, 54, 218, 203, 75, 86, 219, 70, 67, 19, 156, 254, 160, 116, 203, 103, 68, 245, 173, 47, 110, 123, 145, 142, 34, 202, 236, 178, 187, 48, 114, 174, 135, 4, 71, 58, 166, 83, 66, 217, 253, 88, 236, 61, 153, 55, 29, 43, 151, 45, 102, 15, 200, 156, 180, 244, 206, 199, 248, 187, 43, 5, 136, 245, 39, 92, 249, 99, 42, 157, 55, 168, 216, 44, 41, 98, 248, 50, 163, 86, 216, 28, 87, 251, 19, 47, 4, 211, 131, 15, 244, 224, 152, 90, 233, 118, 165, 173, 82, 187, 83, 190, 76, 207, 129, 220, 96, 201, 146, 20, 228, 247, 241, 46, 195, 131, 212, 195, 60, 241, 152, 69, 99, 187, 129, 218, 100, 76, 130, 8, 239, 53, 212, 113, 201, 61, 160, 163, 130, 237, 12, 64, 108, 224, 247, 34, 71, 57, 33, 107, 224, 123, 131, 107, 25, 92, 21, 238, 124, 120, 158, 179, 251, 63, 84, 84, 16, 160, 90, 78, 90, 246, 3, 200, 94, 39, 77, 240, 199, 210, 232, 168, 172, 82, 203, 110, 170, 252, 2, 65, 201, 231, 223, 224, 78, 91, 113, 11, 140, 126, 230, 173, 14, 165, 180, 215, 173, 136, 161, 136, 247, 26, 107, 5, 164, 118, 90, 141, 62, 158, 203, 1, 101, 27, 148, 100, 145, 231, 159, 48, 26, 183, 85, 96, 120, 13, 216, 204, 21, 124, 75, 115, 90, 109, 82, 166, 204, 5, 2, 94, 219, 69, 141, 147, 140, 227, 7, 62, 102, 124, 183, 56, 243, 214, 116, 39, 189, 148, 102, 57, 35, 141, 179, 211, 91, 103, 238, 31, 238, 2, 195, 140, 159, 18, 163, 81, 102, 62, 227, 255, 0, 50, 83, 70, 142, 67, 204, 186, 114, 192, 30, 210, 73, 61, 1, 2, 40, 195, 15, 243, 46, 126, 252, 120, 245, 36, 145, 25, 199, 172, 126, 185, 226, 242, 78, 121, 146, 73, 223, 135, 4, 63, 202, 111, 212, 215, 167, 39, 253, 73, 36, 74, 240, 43, 211, 161, 214, 86, 155, 40, 59, 70, 70, 63, 234, 101, 14, 193, 73, 7, 28, 201, 36, 199, 155, 249, 6, 60, 51, 218, 236, 119, 101, 143, 136, 61, 199, 220, 146, 64, 135, 71, 255, 217]
Each MIME type has a signature(magic number). By first bytes its a JPEG img.
# your array
arr = [255, 216, 255, 224, 0, ...]
>>> bytearray(arr[:4])
bytearray(b'\xff\xd8\xff\xe0')
FF D8 FF E0 - its a jpeg signature image.
I tried:
f = open('/tmp/myimage.jpeg', 'wb')
f.write(bytearray(arr))
f.close()
and got a next image:
resizing image with numpy
lets say i have an image presented as this numpy array: array([[ 55, 229, 185, 21, 128, 50, 109, 121, 251], [138, 0, 143, 153, 22, 244, 102, 6, 63], [250, 235, 57, 28, 220, 15, 217, 147, 70], [121, 164, 128, 224, 56, 206, 104, 87, 154], [232, 51, 20, 235, 8, 200, 119, 234, 180], [182, 79, 79, 22, 221, 233, 54, 11, 209], [249, 64, 92, 70, 167, 151, 214, 188, 213]], dtype=uint8) this is 7X9 matrix and i want to double the width of the image to 7x18. i know what to do when you want to compress an image, but im not sure what i supposed to do if i want to increase the size. thanks! `
Put your array in a, then np.repeat(a, 2, axis=1) gives array([[ 55, 55, 229, 229, 185, 185, 21, 21, 128, 128, 50, 50, 109, 109, 121, 121, 251, 251], [138, 138, 0, 0, 143, 143, 153, 153, 22, 22, 244, 244, 102, 102, 6, 6, 63, 63], [250, 250, 235, 235, 57, 57, 28, 28, 220, 220, 15, 15, 217, 217, 147, 147, 70, 70], [121, 121, 164, 164, 128, 128, 224, 224, 56, 56, 206, 206, 104, 104, 87, 87, 154, 154], [232, 232, 51, 51, 20, 20, 235, 235, 8, 8, 200, 200, 119, 119, 234, 234, 180, 180], [182, 182, 79, 79, 79, 79, 22, 22, 221, 221, 233, 233, 54, 54, 11, 11, 209, 209], [249, 249, 64, 64, 92, 92, 70, 70, 167, 167, 151, 151, 214, 214, 188, 188, 213, 213]]) Which has shape 7x18.