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#Prepare and upload your model artifacts to an Amazon S3 bucket. The bucket must be in the same region as the SageMaker endpoint you will create.

#Create an IAM role with the necessary permissions for SageMaker to access the S3 bucket and other AWS services on your behalf.

#Use the AWS SDK for Python (Boto3) to create a SageMaker model resource. The model resource describes the location of the model artifacts and the Docker image containing the inference code.

#Create an endpoint configuration, which defines the resources that SageMaker should use when hosting the endpoint. This includes the number and type of instances to use, and the configurations for the model and the data processing.

#Use the endpoint configuration to create an endpoint. This deploys the model and makes it available for inference.

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pip3 install --upgrade pip

pip3 install jupyter

jupyter notebook

goto http://127.0.0.1:8888

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• Go to this link https://github.com/argoproj/argo-workflows/releases and download argo-windows-amd64.exe.gz
• unzip argo-windows-amd64.exe.gz
• rename argo-windows-amd64.exe to argo.exe
• add the folder path to system path
• open a new terminal and type argo version

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# Python program to rename all file
# names in your directory
import os

os.chdir(r'C:\Users\oolaonipekun\Desktop\Car_damage_detection\data\validation\whole')
print(os.getcwd())
COUNT = 1

# Function to increment count
# to make the files sorted.

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import os
import glob
import json
import numpy as np
import pandas as pd
import xml.etree.ElementTree as ET

save_json_path = 'val_coco.json'

def xml_to_csv(path):

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import os
import re
from PIL import Image

folder_holding_yolo_files = "C:\\Users\\oolaonipekun\\Desktop\\ActionRecognition\\actionnet_yolo\\actionnet_yolo\\validation"
yolo_class_list_file = "classes.txt"

# Get a list of all the classes used in the yolo format
with open(yolo_class_list_file) as f:
    yolo_classes = f.readlines()

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import os
import shutil

dir_path = "data"
files = os.listdir(dir_path)
path = os.getcwd()
olddir_path = os.path.join(path, dir_path)
newpath = os.path.join(path,"newdata")
print(newpath)
print(olddir_path)

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import cv2
import sys

image = sys.argv[1]
image = cv2.imread(image)
gray_img = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

faceCascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
faces = faceCascade.detectMultiScale(
        gray_img,

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import cv2

def pad_img_to_fit_bbox(img, x1, x2, y1, y2):
    img = cv2.copyMakeBorder(img, - min(0, y1), max(y2 - img.shape[0], 0),
                            -min(0, x1), max(x2 - img.shape[1], 0),cv2.BORDER_REPLICATE)
    y2 += -min(0, y1)
    y1 += -min(0, y1)
    x2 += -min(0, x1)
    x1 += -min(0, x1)
    return img, x1, x2, y1, y2

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from imutils import paths
import cv2
import numpy as np
import argparse
import imutils
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--images", type=str, required=True,
	help="path to input directory of images to stitch")
ap.add_argument("-o", "--output", type=str, required=True,