ksdkamesh99

datagen=ImageDataGenerator(
    brightness_range=[0,2]
)
imagegen=datagen.flow_from_directory('images/',batch_size=1)
fig, rows =plt.subplots(nrows=1, ncols=4, figsize=(18,18))
for row in rows:
    row.imshow(imagegen.next()[0][0].astype('uint8'))
plt.show()

ksdkamesh99

datagen=ImageDataGenerator(
    rescale=1/255
)
imagegen=datagen.flow_from_directory('images/',batch_size=1)
fig, rows =plt.subplots(nrows=1, ncols=4, figsize=(18,18))
for row in rows:
    row.imshow(imagegen.next()[0][0].astype('uint8'))
plt.show()

ksdkamesh99

datagen=ImageDataGenerator(
    rescale=1/255
)
imagegen=datagen.flow_from_directory('images/',batch_size=1)
fig, rows =plt.subplots(nrows=1, ncols=4, figsize=(18,18))
for row in rows:
    row.imshow(imagegen.next()[0][0].astype('float32'))
plt.show()

ksdkamesh99

from keras.applications.mobilenet_v2 import preprocess_input
datagen=ImageDataGenerator(
    preprocessing_function=preprocess_input
)
imagegen=datagen.flow_from_directory('images/',batch_size=1)
fig, rows =plt.subplots(nrows=1, ncols=4, figsize=(18,18))
for row in rows:
    row.imshow(imagegen.next()[0][0].astype('float64'))
plt.show()

ksdkamesh99

datagen=ImageDataGenerator(
    channel_shift_range=100

)
imagegen=datagen.flow_from_directory('images/',batch_size=1)
fig, rows =plt.subplots(nrows=1, ncols=4, figsize=(18,18))
for row in rows:
    row.imshow(imagegen.next()[0][0].astype('float32'))
plt.show()

ksdkamesh99

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import cv2
from sklearn.cluster import KMeans

ksdkamesh99

img=cv2.imread('image color2.jpg')
plt.imshow(img)

ksdkamesh99

img=cv2.imread('image color2.jpg')
plt.imshow(img)

ksdkamesh99

img=cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
plt.imshow(img)

ksdkamesh99

img=img.reshape((img.shape[1]*img.shape[0],3))