Praveen76

#Write a function to choose one image randomly from your dataset and predict using Trained model.
def show_image_with_predictions(df, threshold=0.6):
  # choose a random image
  row = df.sample()
  filepath = row['fileName'].values[0]
  print("filepath:", filepath)

  # get all rows for this image
  df2 = df[df['fileName'] == filepath]
  im = np.array(Image.open(filepath))

Praveen76

from glob import glob
model_paths = glob('snapshots/resnet50_csv_0*.h5')
latest_path = sorted(model_paths)[-1]
print("path:", latest_path)

from keras_retinanet import models

model = models.load_model(latest_path, backbone_name='resnet50')
model = models.convert_model(model)

Praveen76

python keras_retinanet/bin/train.py --freeze-backbone 
            --random-transform \
            --weights {PRETRAINED_MODEL} 
            --batch-size 8 
            --steps 500 
            --epochs 15  
            csv maskDetectorData.csv maskDetectorClasses.csv

Praveen76

#Put your training data path & file that has labels for your training data 
!keras_retinanet/bin/train.py 
        --freeze-backbone
        --random-transform
        --weights {PRETRAINED_MODEL}
        --batch-size 8
        --steps 500
        --epochs 15
        csv maskDetectorData.csv 
        maskDetectorClasses.csv

Praveen76

URL_MODEL = 'https://github.com/fizyr/keras-retinanet/releases/download/0.5.1/resnet50_coco_best_v2.1.0.h5'
urllib.request.urlretrieve(URL_MODEL, PRETRAINED_MODEL)

Praveen76

#Define labels & write them in a file
classes = ['mask','noMask']
with open('../maskDetectorClasses.csv', 'w') as f:
  for i, class_name in enumerate(classes):
    f.write(f'{class_name},{i}\n')
            
if not os.path.exists('snapshots'):
  os.mkdir('snapshots')

Praveen76

# pick a random image
  filepath = df.sample()['fileName'].values[0]

  # get all rows for this image
  df2 = df[df['fileName'] == filepath]
  im = np.array(Image.open(filepath))

  # if there's a PNG it will have alpha channel
  im = im[:,:,:3]

Praveen76

pngPath='C:/Users/PraveenKumar/RetinaNet//maskDetectorJPEGImages/'
annotPath='C:/Users/PraveenKumar/RetinaNet//maskDetectorXMLfiles/'

data=pd.DataFrame(columns=['fileName','xmin','ymin','xmax','ymax','class'])

os.getcwd()
#read All files
allfiles = [f for f in listdir(annotPath) if isfile(join(annotPath, f))]

#Read all pdf files in images and then in text and store that in temp folder 

Praveen76

import numpy as np
import shutil
import pandas as pd
import os, sys, random
import xml.etree.ElementTree as ET
import pandas as pd
from os import listdir
from os.path import isfile, join
import matplotlib.pyplot as plt
from PIL import Image

Praveen76

import os
print(os.getcwd())

git clone https://github.com/fizyr/keras-retinanet.git
%cd keras-retinanet/

!pip install .
!python setup.py build_ext --inplace