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import argparse

def add_and_display(number_1, number_2, permission, display_value):
    if permission :
        print(display_value, number_1 + number_2)
        
        
if __name__ == "__main__":
    add_and_display(number_1 = 10, number_2 = 20, permission = True, 
                    display_value = "Adding two numbers")

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# Simple Demonstration of Python Argparser in command line arguments# Simple Demonstration of Python Argparser in command line arguments
import argparse
from distutils.util import strtobool

def add_and_display(args):
    print(args)
    print()
    if args.permissions :
        if args.operation == "add":
            print("Adding two numbers :", args.number_1 + args.number_2)

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# Simple Demonstration of Python Argparser in command line arguments with positional and optional arguments
import argparse
from distutils.util import strtobool
def add_and_display(args):
    print(args)
    print()
    if args.permissions :
        if args.operation == "add":
            print("Adding two numbers :", args.number_1 + args.number_2 + args.number_3 + args.number_4)
        else:

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# Dataset Extraction from github
!git clone 'https://github.com/Shenggan/BCCD_Dataset.git'

import os, sys, random, shutil
import xml.etree.ElementTree as ET
from glob import glob
import pandas as pd
from shutil import copyfile
import pandas as pd
from sklearn import preprocessing, model_selection

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img_width = 640
img_height = 480

def width(df):
  return int(df.xmax - df.xmin)
def height(df):
  return int(df.ymax - df.ymin)
def x_center(df):
  return int(df.xmin + (df.width/2))
def y_center(df):

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df_train, df_valid = model_selection.train_test_split(df, test_size=0.1, random_state=13, shuffle=True)
print(df_train.shape, df_valid.shape)

os.mkdir('/content/bcc/')
os.mkdir('/content/bcc/images/')
os.mkdir('/content/bcc/images/train/')
os.mkdir('/content/bcc/images/valid/')

os.mkdir('/content/bcc/labels/')
os.mkdir('/content/bcc/labels/train/')

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!git clone  'https://github.com/ultralytics/yolov5.git'

!pip install -qr '/content/yolov5/requirements.txt'  # install dependencies

## Create a yaml file and move it into the yolov5 folder ##
shutil.copyfile('/content/bcc.yaml', '/content/yolov5/bcc.yaml')

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# Start tensorboard (optional)
%load_ext tensorboard
%tensorboard --logdir runs/

!python yolov5/train.py --img 640 --batch 8 --epochs 100 \
    --data bcc.yaml --cfg models/yolov5s.yaml --name BCCM

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## TO PREDICT IMAGES IN A FOLDER ##
!python yolov5/detect.py --source /content/bcc/images/valid/ 
  --weights '/content/drive/My Drive/Machine Learning Projects/YOLO/best_yolov5_BCCM.pt'
  --output '/content/inference/output'
  
  
## TO PREDICT A SINGLE IMAGE FILE ##

output = !python yolov5/detect.py --source /content/bcc/images/valid/BloodImage_00000.jpg 
  --weights '/content/drive/My Drive/Machine Learning Projects/YOLO/best_yolov5_BCCM.pt'

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import os, sys, random
from glob import glob
import matplotlib.pyplot as plt
%matplotlib inline
!pip install -qr '/content/drive/My Drive/Machine Learning Projects/YOLO/SOURCE/requirements.txt'  # install dependencies

## Add the path where you have stored the neccessary supporting files to run detect.py ##
## Replace this with your path.##
sys.path.insert(0, '/content/drive/My Drive/Machine Learning Projects/YOLO/SOURCE/') 
print(sys.path)