| #!/usr/bin/env python | |
| import cv2 | |
| import sys | |
| def drawBoundingBoxes(imageData, imageOutputPath, inferenceResults, color): | |
| """Draw bounding boxes on an image. | |
| imageData: image data in numpy array format | |
| imageOutputPath: output image file path | |
| inferenceResults: inference results array off object (l,t,w,h) | |
| colorMap: Bounding box color candidates, list of RGB tuples. |
| ::########################################################################################################################## | |
| :: | |
| :: This script can ruin your day, if you run it without fully understanding what it does, you don't know what you are doing, | |
| :: | |
| :: OR BOTH!!! | |
| :: | |
| :: YOU HAVE BEEN WARNED!!!!!!!!!! | |
| :: | |
| :: This script is provided "AS IS" with no warranties, and confers no rights. | |
| :: Feel free to challenge me, disagree with me, or tell me I'm completely nuts in the comments section, |
| fun <T : Any> Optional<T>.toNullable(): T? { | |
| return if (this.isPresent) { | |
| this.get() | |
| } else { | |
| null | |
| } | |
| } |
| :: Windows 10 Hardening Script | |
| :: This is based mostly on my own personal research and testing. My objective is to secure/harden Windows 10 as much as possible while not impacting usability at all. (Think being able to run on this computer's of family members so secure them but not increase the chances of them having to call you to troubleshoot something related to it later on). References for virtually all settings can be found at the bottom. Just before the references section, you will always find several security settings commented out as they could lead to compatibility issues in common consumer setups but they're worth considering. | |
| :: Obligatory 'views are my own'. :) | |
| :: Thank you @jaredhaight for the Win Firewall config recommendations! | |
| :: Thank you @ricardojba for the DLL Safe Order Search reg key! | |
| :: Thank you @jessicaknotts for the help on testing Exploit Guard configs and checking privacy settings! | |
| :: Best script I've found for Debloating Windows 10: https://github.com/Sycnex/Windows10Debloater | |
| : |
telegram-scripts.js into JS consoleshowContacts() to get the list of contacts with idssaveChat(userId) where userId is the id from step 3Process can take a while, check console for progress. Occasionall FLOOD_WAIT errors are expected. Once done, browser will download the JSON file.
Past August 2024, Authy stopped supported the desktop version of their apps:
See Authy is shutting down its desktop app | The 2FA app Authy will only be available on Android and iOS starting in August for details.
And indeed, after a while, Authy changed something in their backend which now prevents the old desktop app from logging in. If you are already logged in, then you are in luck, and you can follow the instructions below to export your tokens.
If you are not logged in anymore, but can find a backup of the necessary files, then restore those files, and re-install Authy 2.2.3 following the instructions below, and it should work as expected.
After installing the latest Wine release, which currently is 2.0 (I chose the development branch) on XQuartz 2.7.11, I was having problems with blurry text in both winecfg, regedit and other programs launched through Wine. After trying to enable font smoothing and font replacements (source) with only slight changes I found someone trying to solve the same issues (source), albeit compiling everything from scratch which I don't want to do. It turns out that the Retina display on my MacBook Pro was causing the issues with blurry fonts because Wine was not using the "real" resolution, only the reported "lower resolution".
To enable Retina support in Wine open the registry editor via a terminal, preferably through Wine Devel.app installed with Wine:
$ wine regedit
Then find the folder/key:
This is an Keras implementation of ResNet-152 with ImageNet pre-trained weights. I converted the weights from Caffe provided by the authors of the paper. The implementation supports both Theano and TensorFlow backends. Just in case you are curious about how the conversion is done, you can visit my blog post for more details.
ResNet Paper:
Deep Residual Learning for Image Recognition.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
arXiv:1512.03385
To improve collaboration this guide is now available on GitHub.
| def dot_product(x, kernel): | |
| """ | |
| Wrapper for dot product operation, in order to be compatible with both | |
| Theano and Tensorflow | |
| Args: | |
| x (): input | |
| kernel (): weights | |
| Returns: | |
| """ | |
| if K.backend() == 'tensorflow': |