PatharaNor patharanordev

Solved SSL:CERTIFICATE_VERIFY_FAILED in Python

... [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate

import ssl

ssl._create_default_https_context = ssl._create_unverified_context

If You are Using MAC, I Actually Solved this Issue By Granting Permission for Terminal To Access Camera in Security and Privacy Section in System Preferences. Hope This Solves Issue In Your Mac Click Here To View Steps

Convert `.pt` to `.onnx`

The function using in Scaled-YOLOv4, please refer to Scaled-YOLOv4 repository.

Before start, the export script requires onnxsim, you need to install it first :

$ pip install -q onnx-simplifier

Fixed gem install cocoapods

If you found error message like this :

Error installing cocoapods: ERROR: Failed to build gem native extension

You should re-install ruby first before install cocoapods.

$ brew reinstall ruby

Encode image in nparray to base64

Encode base64 function:

import cv2
import base64

def encode_img(img, im_type):
 """Encodes an image as a png and encodes to base 64 for display."""

Long run CoLab

In CoLab, press (CTRL + SHIFT + i) to open inspect window. Go to console tab then press source code below and press enter :

function ClickConnect(){
console.log("Working"); 
document
 .querySelector('#top-toolbar &gt; colab-connect-button')

Plot image grid M x N via `matplotlib`

The example below, try to show predicted result image & label on VGG19 model, I have set grid size to N x 5 :

multi-rows
5-columns

I have directory structure like this

content

	from zipfile import ZipFile
	from google.colab import files

	!rm -rf kaggle.json
	!rm -rf /root/.kaggle/kaggle.json
	'''
	If you found an error "TypeError: Cannot read property '_uploadFiles' of undefined",
	please check cookie mode. You need to allow all cookie
	'''
	files.upload() #upload kaggle.json

	production_fname = '{}/production.csv'.format(os.getcwd())

	def write_prodfile(production_df):

	if os.path.isfile(production_fname):
	# Remove if exists
	os.remove(production_fname)

	production_df.to_csv(r'{}'.format(production_fname), index = False)

	#!/usr/bin/env python

	import numpy as np
	import os
	import time

	os.environ['KERAS_BACKEND'] = 'plaidml.keras.backend'

	import keras
	import keras.applications as kapp