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louis030195 / simple_diablo2_socket_bot.py
Last active November 27, 2018 07:51
import socket
from struct import *
def read_string(buffer):
s = ''
i = 0
while 1:
c = buffer[i]
if c == 0:
@louis030195
louis030195 / error docker xmoto
Created November 15, 2018 18:59
louis@louis-P55V5:~/Documents/xmoto-gym$ sudo docker run -p 5900:5900 xmoto
###############################################################
#@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@#
#@ @#
#@ ** WARNING ** WARNING ** WARNING ** WARNING ** @#
#@ @#
#@ YOU ARE RUNNING X11VNC WITHOUT A PASSWORD!! @#
#@ @#
#@ This means anyone with network access to this computer @#
#@ may be able to view and control your desktop. @#
@louis030195
louis030195 / pytorch-cnn-visualizations2.ipynb
Last active February 12, 2019 13:22
pytorch-cnn-visualizations2.ipynb
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@louis030195
louis030195 / Animal_detector.txt
Last active March 16, 2019 17:25
Animal detector API (for a video file), count is the number of frames where this species has been detected, occurrences is the frame where it's appearing
{'snowplow, snowplough': {'count': 280, 'occurences': [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, 192, 193, 194, 195, 196, 221, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248,
@louis030195
louis030195 / microsoft-cameratraps.ipynb
Created July 8, 2019 08:40
Microsoft - CameraTraps.ipynb
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louis030195 / nasa_api.ipynb
Last active July 9, 2019 11:56
NASA_API.ipynb
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@louis030195
louis030195 / batch-prediction-ai-platform.ipynb
Created August 10, 2019 16:56
batch-prediction-ai-platform.ipynb
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@louis030195
louis030195 / Bot dump anti cheat
Last active August 24, 2019 07:43
connected to clientD2gs
d2gsToClient (uncompressed): D2GS_NEGOTIATECOMPRESSION {"compressionMode":1}
sidToServer : SID_NOTIFYJOIN {"productId":1,"productVersion":13,"gameName":"Waza","gamePassword":"Ok"}
sidToServer : SID_LEAVECHAT {}
sidToServer : SID_PING {"pingValue":1615000524}
d2gsToServer : D2GS_GAMELOGON { MCPCookie: 697872214,
gameId: 23,
characterClass: 1,
gameVersion: 13,
gameConstant: [ 2443516342, 3982347344 ],
@louis030195
louis030195 / Sniffer dump anti cheat
Created August 23, 2019 15:47
Start of d2gs session
d2gsToClient (uncompressed): D2GS_NEGOTIATECOMPRESSION {"compressionMode":1}
enable compression
d2gsToServer : D2GS_GAMELOGON {"MCPCookie":1503224318,"gameId":474,"characterClass":2,"gameVersion":13,"gameConstant":[2443516342,3982347344],"locale":0,"characterName":[71,114,101,101,110,68,117,100,101,108,111,118,101,114,0,0]}
raw d2gsToServer D2GS_GAMELOGON 68fe619959da01020d00000050cc5dedb619a59100477265656e447564656c6f7665720000
d2gsToClient : D2GS_GAMEFLAGS {"difficulty":0,"unknown":4,"hardcore":48,"expansion":1,"ladder":1}
d2gsToClient : D2GS_GAMELOADING {}
d2gsToServer : D2GS_PING {"tickCount":304281,"delay":0,"wardenResponse":0}
raw d2gsToServer D2GS_PING 6d99a404000000000000000000
d2gsToClient : D2GS_PONG {"tickCount":[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]}
@louis030195
louis030195 / Edit a Tensorflow graph to add a key.sh
Created September 5, 2019 13:08
see https://colab.research.google.com/drive/1CZxrvowmuzwfJJoUBjgIjsIpb-1gh53h
# Vector shape (-1, -1, -1, 3) = image_tensor
# Base64 string shape (-1) = encoded_image_string_tensor
INPUT_TYPE = 'encoded_image_string_tensor'
python models/research/object_detection/export_inference_graph.py \
--input_type $INPUT_TYPE \
--pipeline_config_path model/pipeline.config \
--trained_checkpoint_prefix model/model.ckpt \
--inference_graph_path output_inference_graph.pb \
--output_directory exported_model