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find . -maxdepth 1 -type f \( -name "*.MOV" -or -name "*.mov" \) -exec echo {} \; -exec xattr -d com.apple.FinderInfo {} \;

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Setup for Mac / Linux

git config --global core.excludesfile ~/.gitignore

Global .gitignore content

# Custom folder
ignore

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sudo apt-get install build-essential
sudo apt-get install cmake git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev
sudo apt-get install python-dev python-numpy libtbb2 libtbb-dev libjpeg-dev libpng-dev libtiff-dev libjasper-dev libdc1394-22-dev
mkdir ~/Documents/temp
cd ~/Documents/temp
git clone https://github.com/opencv/opencv.git
cd opencv
git checkout 3.3.1
cd ..
git clone https://github.com/opencv/opencv_contrib.git

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Workshop Instructor Information

• Name: [Laksh Arora][1]

• Organisation: PyData Delhi

• Phone: +919871569215

• Email : laksharora28@gmail.com

• Background : Co-Organiser of PyData Delhi, Data Science Enthusiast

Workshop Information Overview

• Title: Introduction to CNN and Advanced Application

• Length: 90-120 min

• Proposed Date: Before 1st week of August

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# library
import numpy as np

# b_size is batch size
# inp_dim is input dimension
# hid_dim is hidden dimension
# out_dim is output dimension
b_size, inp_dim, hid_dim, out_dim = 64, 1000, 100, 10

# Create random dataset

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import os
import opencv as cv2

train_folder = 'path/to/Image_folder'
image_paths = [os.listdir(x[0]) for x in os.walk(train_folder)]
for a in image_paths:
  resized_a = cv2.resize(a, (32, 32))

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import cv2
import numpy as np
# from keras.models import load_model

rgb = cv2.VideoCapture(1)
facec = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
font = cv2.FONT_HERSHEY_SIMPLEX

# d_faces = np.load('./dataset/face_data.npy')
# l_faces = np.load('./dataset/face_lab.npy')

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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import os
import cv2

train_folder = 'GTRSB/train/GTSRB/Final_Training/Images'

image_paths = [os.listdir(x[0]) for x in os.walk(train_folder)]

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import os
import shutil
from tkinter import Tk
from tkinter import filedialog
import zipfile


def select_folder():
    root = Tk()
    root.withdraw()

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class LiFailed():
	pass

class TrFailed():
	pass


try:
	# whole code of li structure
	if a == " ":