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utils.py
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utils.py
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import numpy as np
import cv2
import os
import glob
import random
def get_input(path):
im = cv2.imread(path)
return(im)
def get_files(path, ext):
files = []
label_files= []
for x in os.walk(path):
for y in glob.glob(os.path.join(x[0], '*.{}'.format(ext))):
files.append(y)
label_files = os.listdir(path)
label_files = sorted(label_files)
return files, label_files
def get_output(path, label_file):
img_id = path.split('/')[-2]
laba = []
for label in label_file:
if label == img_id:
laba.append(1)
else:
laba.append(0)
return laba
#RGB MODEL DATA GENERATOR
def rgbimage_generator(files, label_files, batch_size, dim):
while True:
batch_paths = np.random.choice(a = files,
size = batch_size)
batch_input_rgb = []
batch_output = []
for input_path in batch_paths:
input = get_input(input_path)
input = cv2.resize(input, dim)
output = get_output(input_path, label_files)
batch_input_rgb.append(input)
batch_output.append(output)
batch_input_rgb = np.array(batch_input_rgb)
batch_y = np.array(batch_output)
yield batch_input_rgb, batch_y
#BW Model Data Generator
def bwimage_generator(files, label_files, batch_size, dim):
while True:
batch_paths = np.random.choice(a = files,
size = batch_size)
batch_input_bw = []
batch_output = []
for input_path in batch_paths:
input = get_input(input_path)
input = cv2.resize(input, dim)
#GrayScale
img_gray = cv2.cvtColor(np.asarray(input).astype('uint8'), cv2.COLOR_BGR2GRAY)
img_gray = np.stack((img_gray,)*3, axis=-1)
output = get_output(input_path, label_files)
batch_input_bw.append(img_gray)
batch_output.append(output)
batch_input_bw = np.array(batch_input_bw)
batch_y = np.array(batch_output)
yield batch_input_bw, batch_y
#R Model Data Generator
def rimage_generator(files, label_files, batch_size, dim):
while True:
batch_paths = np.random.choice(a = files,
size = batch_size)
batch_input_r = []
batch_output = []
for input_path in batch_paths:
input = get_input(input_path)
input = cv2.resize(input, dim)
im_red = np.empty_like(input)
im_red[:] = input
#R
im_red[:, :, 0] = 0
im_red[:, :, 1] = 0
output = get_output(input_path, label_files)
batch_input_r.append(im_red)
batch_output.append(output)
batch_input_r = np.array(batch_input_r)
output = np.array(output)
batch_y = np.array(batch_output)
yield batch_input_r, batch_y
#G Model Data Generator
def gimage_generator(files, label_files, batch_size, dim):
while True:
batch_paths = np.random.choice(a = files,
size = batch_size)
batch_input_g = []
batch_output = []
for input_path in batch_paths:
input = get_input(input_path)
input = cv2.resize(input, dim)
im_green = np.empty_like(input)
im_green[:] = input
#G
im_green[:, :, 0] = 0
im_green[:, :, 2] = 0
output = get_output(input_path, label_files)
batch_input_g.append(im_green)
batch_output.append(output)
batch_input_g = np.array(batch_input_g)
batch_y = np.array(batch_output)
yield batch_input_g, batch_y
#B Model Data Generator
def bimage_generator(files, label_files, batch_size, dim):
while True:
batch_paths = np.random.choice(a = files,
size = batch_size)
batch_input_b = []
batch_output = []
for input_path in batch_paths:
input = get_input(input_path)
input = cv2.resize(input, dim)
im_blue = np.empty_like(input)
im_blue[:] = input
#B
im_blue[:, :, 1] = 0
im_blue[:, :, 2] = 0
output = get_output(input_path, label_files)
batch_input_b.append(im_blue)
batch_output.append(output)
batch_input_b = np.array(batch_input_b)
batch_y = np.array(batch_output)
yield batch_input_b, batch_y
#Ensembled Model Data Generator
def image_generator(files, label_files, batch_size, dim):
while True:
random.shuffle(files)
batch_paths = np.random.choice(a = files,
size = batch_size)
batch_input_rgb = []
batch_input_bw = []
batch_input_r = []
batch_input_g = []
batch_input_b = []
batch_output = []
for input_path in batch_paths:
input = get_input(input_path)
input = cv2.resize(input, dim)
im_red = np.empty_like(input)
im_red[:] = input
im_blue = np.empty_like(input)
im_blue[:] = input
im_green = np.empty_like(input)
im_green[:] = input
#GrayScale
img_gray = cv2.cvtColor(np.asarray(input).astype('uint8'), cv2.COLOR_BGR2GRAY)
img_gray = np.stack((img_gray,)*3, axis=-1)
#G
im_green[:, :, 0] = 0
im_green[:, :, 2] = 0
#R
im_red[:, :, 0] = 0
im_red[:, :, 1] = 0
#B
im_blue[:, :, 1] = 0
im_blue[:, :, 2] = 0
output = get_output(input_path, label_files)
batch_input_rgb.append(input)
batch_input_bw.append(img_gray)
batch_input_r.append(im_red)
batch_input_b.append(im_blue)
batch_input_g.append(im_green)
batch_output.append(output)
batch_input_rgb = np.array(batch_input_rgb)
batch_input_bw = np.array(batch_input_bw)
batch_input_b = np.array(batch_input_b)
batch_input_g = np.array(batch_input_g)
batch_input_r = np.array(batch_input_r)
batch_y = np.array(batch_output)
yield [batch_input_rgb, batch_input_bw, batch_input_r, batch_input_g, batch_input_b], batch_y