ashhadulislam

! python train.py --data dataRoad.yaml --cfg yolov5s.yaml --batch-size 32 --epochs 1 --name RoadTrainModel

ashhadulislam

from pathlib import Path
path = Path(os.getcwd())
parent_path=path.parent.absolute()
final_data_path=os.path.join(parent_path,"data","finalRoad")
print(final_data_path)

yaml_data="train: "+final_data_path+"/images/train\n"
yaml_data+="val: "+final_data_path+"/images/val\n"
yaml_data+="test: "+final_data_path+"/images/test\n"

ashhadulislam

print("Number of train images = ",len(os.listdir("data/finalRoad/images/train/")))
print("Number of train annotations = ",len(os.listdir("data/finalRoad/labels/train/")))

print("Number of val images = ",len(os.listdir("data/finalRoad/images/val/")))
print("Number of val annotations = ",len(os.listdir("data/finalRoad/labels/val/")))

print("Number of test images = ",len(os.listdir("data/finalRoad/images/test/")))
print("Number of test annotations = ",len(os.listdir("data/finalRoad/labels/test/")))

ashhadulislam

import os
! git clone https://github.com/ultralytics/yolov5
os.chdir(r"yolov5")
! pip install -r requirements.txt

ashhadulislam

# Read images and annotations
images = [os.path.join('data/assortedFiles/images', x) for x in os.listdir('data/assortedFiles/images')]
annotations = [os.path.join('data/assortedFiles/annotations', x) for x in os.listdir('data/assortedFiles/annotations') if x[-3:] == "txt"]

images.sort()
annotations.sort()

print("Number of images",len(images),"\nNumber of annotation files",len(annotations))
# Split the dataset into train-valid-test splits 

ashhadulislam

def plot_bounding_box(image, annotation_list):
    annotations = np.array(annotation_list)
    w, h = image.size
    
    plotted_image = ImageDraw.Draw(image)

    transformed_annotations = np.copy(annotations)
    transformed_annotations[:,[1,3]] = annotations[:,[1,3]] * w
    transformed_annotations[:,[2,4]] = annotations[:,[2,4]] * h 

ashhadulislam

# Function to get the data from XML Annotation
def extract_info_from_xml(xml_file):
    root = ET.parse(xml_file).getroot()
    
    # Initialise the info dict 
    info_dict = {}
    info_dict['bboxes'] = []

    # Parse the XML Tree
    for elem in root:

ashhadulislam

input_path = Path("data/RoadDamageDataset")
output_path = Path("data/assortedFiles")

try:
    if not os.path.exists(output_path):
        os.mkdir(output_path)
    if not os.path.exists(output_path/'images'): 
        os.mkdir(output_path/'images')
    if not os.path.exists(output_path/'labels'):
        os.mkdir(output_path/'labels')

ashhadulislam

fname="data/RoadDamageDataset.tar.gz"
if fname.endswith("tar.gz"):
    tar = tarfile.open(fname, "r:gz")
    tar.extractall("data/")
    tar.close()
elif fname.endswith("tar"):
    tar = tarfile.open(fname, "r:")
    tar.extractall("data/")
    tar.close()

ashhadulislam

import tarfile
import os
from pathlib import Path
import shutil
import xml.etree.ElementTree as ET
from PIL import Image, ImageDraw
import numpy as np
import matplotlib.pyplot as plt
import random
from sklearn.model_selection import train_test_split