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utils.py
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utils.py
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import numpy as np
import math
import random
from retriever import IMG_PX_SIZE
import matplotlib.pyplot as plt
#images: [(image_class, image_array), ......]
#This function returns an array that has equal number of images per class and not randomized
#length: total length of batch_x
def nextImageBatch(images, length=150, classes=15, index=0):
perClass = int(length/classes)
i = 0
classLength = int(len(images)/classes)
print('perClass: {0}, classLength: {1}'.format(perClass, classLength))
#batch_x contains the images, batch_y contains the index of the associated class in the subfolders array
batchx, batchy = np.zeros((length, IMG_PX_SIZE, IMG_PX_SIZE, 3)), np.zeros((length, classes))
counter = 0
for cla in range(classes):
for i in range(perClass):
#Following is the index of the image to be appended
point = (cla*classLength) + (index*perClass) + i
#print(point)
#need to convert the 2d image to 3d
newimage = images[point][1]
#Do not convert image to 3d, it is already in 3d
batchx[counter] = newimage
batchy[counter][images[point][0]] = 1
counter = counter + 1
print('batch_x shape: {}'.format(batchx.shape))
print('batch_y shape: {}'.format(batchy.shape))
return batchx, batchy, index+1
#This function returns an array of random images
def nextImageRandomBatch(images, length, classes):
print('Length: {}'.format(length))
batchx, batchy = np.zeros((length, IMG_PX_SIZE, IMG_PX_SIZE, 1)), np.zeros((length, classes))
for counter in range(length):
index = random.randint(0, len(images)-1)
newimage = images[index][1]
batchx[counter] = newimage[:, :, np.newaxis]
batchy[counter][images[index][0]] = 1
print('batch_x shape: {}'.format(batchx.shape))
print('batch_y shape: {}'.format(batchy.shape))
return batchx, batchy
def nextFullBatch(images, length, classes):
print('Length: {}'.format(length))
batchx, batchy = np.zeros((length, IMG_PX_SIZE, IMG_PX_SIZE, 1)), np.zeros((length, classes))
for counter in range(length):
newimage = images[counter][1]
batchx[counter] = newimage[:, :, np.newaxis]
batchy[counter][images[counter][0]] = 1
print('batch_x shape: {}'.format(batchx.shape))
print('batch_y shape: {}'.format(batchy.shape))
return batchx, batchy
def nextNewBatch(images, length=200, classes=100, index=0):
#2
perClass = int(length/classes)
i = 0
#261
classLength = int(len(images)/classes)
print('perClass: {0}, classLength: {1}'.format(perClass, classLength))
#batch_x contains the images, batch_y contains the index of the associated class in the subfolders array
batchx, batchy = np.zeros((length, IMG_PX_SIZE, IMG_PX_SIZE, 3)), np.zeros((length, classes))
counter = 0
increment = int(len(images)/length)
for i in range(0, len(images), increment):
point1 = i + (index)
batchx[counter] = images[point1][1]
batchy[counter][images[point1][0]] = 1
counter = counter + 1
print('batch_x shape: {}'.format(batchx.shape))
print('batch_y shape: {}'.format(batchy.shape))
return batchx, batchy, index+1