Automatic Image Labeling

 

import Augmentor
= Augmentor.Pipeline('one')
# p.random_distortion(probability=0.05, grid_width=4, grid_height=4, magnitude=4)
p.rotate(probability=0.3, max_left_rotation=25, max_right_rotation=25)
p.flip_left_right(probability=0.2)
p.flip_top_bottom(probability=0.02)
p.flip_random(probability=0.05)
p.rotate90(probability=0.005)
p.rotate180(probability=0.005)
p.rotate270(probability=0.005)
p.skew_tilt(probability=0.001)
p.skew_left_right(probability=0.005)
p.skew_top_bottom(probability=0.005)
p.skew_corner(probability=0.002)
p.skew(probability=0.002)
p.shear(probability=0.005, max_shear_left=10, max_shear_right=10)
p.sample(100)


https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html

https://machinelearningmastery.com/how-to-configure-image-data-augmentation-when-training-deep-learning-neural-networks/

https://www.tensorflow.org/tutorials/images/data_augmentation

https://towardsdatascience.com/how-to-augmentate-data-using-keras-38d84bd1c80c


from email.mime import image
import os
from PIL import Image
import cv2
# from PIL import Image
from os import listdir
 
# get the path or directory
folder_dir = "one"
for images in os.listdir(folder_dir):
 
    # check if the image end swith png or jpg or jpeg
    if (images.endswith(".png") or images.endswith(".jpg") or images.endswith(".jpeg")):
        # display
        # print(type(images))
        img = Image.open('one/'+images)
        # print(os.path.join(folder_dir, images))
        # width, height = image.size
        width = img.width
        height = img.height
        name=str(images)
        name=name.rsplit( ".", 1 )[ 0 ]
        # name.partition('.')
        f= open('one/'+name+'.txt',"w+")
        f.write("0 0 0 "+str(height)+" "+str(width))
        f.close
  
# # display width and height
        print("The height of the image is: ", height)
        print("The width of the image is: ", width)

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