Amey ameycodes

## image_inpainting.py
import cv2
import argparse

# --image : Source image
# --mask : Binary mask indicating pixels to be inpainted
# --method : Inpainting algorithm to be used Telea/NS
# --radius : Radius of neighborhood pixels to be considered for interpolation purpose

a = argparse.ArgumentParser()
a.add_argument("-i", "--image", type=str, required=True, help="path of image to be used")

## data_aug_zoom.py
from numpy import expand_dims
from tensorflow.keras.preprocessing.image import load_img
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
img = load_img('golden_retriever.jpg')
data = img_to_array(img)
samples = expand_dims(data, 0)
datagen = ImageDataGenerator(zoom_range=[0.5,1.5])
it = datagen.flow(samples, batch_size=1)

## data_aug_brightness.py
from numpy import expand_dims
from tensorflow.keras.preprocessing.image import load_img
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
img = load_img('golden_retriever.jpg')
data = img_to_array(img)
samples = expand_dims(data, 0)
datagen = ImageDataGenerator(brightness_range=[0.2,1.0])
it = datagen.flow(samples, batch_size=1)

## data_aug_rotation.py
from numpy import expand_dims
from tensorflow.keras.preprocessing.image import load_img
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
img = load_img('golden_retriever.jpg')
data = img_to_array(img)
samples = expand_dims(data, 0)
datagen = ImageDataGenerator(rotation_range=360)
it = datagen.flow(samples, batch_size=1)

## data_aug_flip.py
from numpy import expand_dims
from tensorflow.keras.preprocessing.image import load_img
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
img = load_img('golden_retriever.jpg')
data = img_to_array(img)
samples = expand_dims(data, 0)
# use parameter vertical_flip for vertical flip
datagen = ImageDataGenerator(horizontal_flip=True)

## data_aug_vertical.py
from numpy import expand_dims
from tensorflow.keras.preprocessing.image import load_img
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
img = load_img('golden_retriever.jpg')
data = img_to_array(img)
samples = expand_dims(data, 0)
datagen = ImageDataGenerator(height_shift_range=0.5)
it = datagen.flow(samples, batch_size=1)

## data_aug_horizontal.py
from numpy import expand_dims
from tensorflow.keras.preprocessing.image import load_img
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
img = load_img('golden_retriever.jpg')
data = img_to_array(img)
samples = expand_dims(data, 0)
# create generator instance
datagen = ImageDataGenerator(width_shift_range=[-0.3,0.3])
	import cv2
	import argparse

	# --image : Source image
	# --mask : Binary mask indicating pixels to be inpainted
	# --method : Inpainting algorithm to be used Telea/NS
	# --radius : Radius of neighborhood pixels to be considered for interpolation purpose

	a = argparse.ArgumentParser()
	a.add_argument("-i", "--image", type=str, required=True, help="path of image to be used")
	from numpy import expand_dims
	from tensorflow.keras.preprocessing.image import load_img
	from tensorflow.keras.preprocessing.image import img_to_array
	from tensorflow.keras.preprocessing.image import ImageDataGenerator
	import matplotlib.pyplot as plt
	img = load_img('golden_retriever.jpg')
	data = img_to_array(img)
	samples = expand_dims(data, 0)
	datagen = ImageDataGenerator(zoom_range=[0.5,1.5])
	it = datagen.flow(samples, batch_size=1)