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helper_train_mean.py
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helper_train_mean.py
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from tqdm import tqdm
import numpy as np
import os.path
import sys
import random
import math
import cv2
# define the path to the dataset and the files containing the train and test ground truths
directory = '/path/to/the/dataset/Gradmag-Syn-Car/'
dataset_train = 'groundtruth_GradmagSynCar.txt'
dataset_test = 'groundtruth_GradmagReal.txt'
class datasource(object):
def __init__(self, images, poses):
self.images = images
self.poses = poses
# function for performing centre croppping
def centeredCrop(img, output_side_length):
height, width, depth = img.shape
new_height = output_side_length
new_width = output_side_length
if height > width:
new_height = output_side_length * height / width
else:
new_width = output_side_length * width / height
height_offset = (new_height - output_side_length) / 2
width_offset = (new_width - output_side_length) / 2
cropped_img = img[height_offset:height_offset + output_side_length,
width_offset:width_offset + output_side_length]
return cropped_img
# function for croppping and mean subtraction train images
def preprocess_train(images):
images_out = [] #final result
#Resize and crop and compute mean!
images_cropped = []
for i in tqdm(range(len(images))):
X = cv2.imread(images[i])
X = cv2.resize(X, (320, 240))
X = centeredCrop(X, 224)
images_cropped.append(X)
#compute images mean
N = 0
mean = np.zeros((1, 3, 224, 224))
for X in tqdm(images_cropped):
mean[0][0] += X[:,:,0]
mean[0][1] += X[:,:,1]
mean[0][2] += X[:,:,2]
N += 1
mean[0] /= N
#Subtract mean from all images
for X in tqdm(images_cropped):
X = np.transpose(X,(2,0,1))
X = X - mean
X = np.squeeze(X)
X = np.transpose(X, (1,2,0))
Y = np.expand_dims(X, axis=0)
images_out.append(Y)
return images_out
# function for croppping and mean subtraction test images
def preprocess_test(images_train, images_test):
images_out_test = [] #final result
#Resize and crop and compute mean!
images_cropped_train = []
images_cropped_test = []
for i in tqdm(range(len(images_train))):
X = cv2.imread(images_train[i])
X = cv2.resize(X, (320, 240))
X = centeredCrop(X, 224)
images_cropped_train.append(X)
#compute images mean
N = 0
mean = np.zeros((1, 3, 224, 224))
for X in tqdm(images_cropped_train):
mean[0][0] += X[:,:,0]
mean[0][1] += X[:,:,1]
mean[0][2] += X[:,:,2]
N += 1
mean[0] /= N
for i in tqdm(range(len(images_test))):
X = cv2.imread(images_test[i])
X = cv2.resize(X, (320, 240))
X = centeredCrop(X, 224)
images_cropped_test.append(X)
#Subtract mean from all images
for X in tqdm(images_cropped_test):
X = np.transpose(X,(2,0,1))
X = X - mean
X = np.squeeze(X)
X = np.transpose(X, (1,2,0))
Y = np.expand_dims(X, axis=0)
images_out_test.append(Y)
return images_out_test
# reading ground truth data from files
def get_data(dataset_train, dataset_test):
poses_train = []
images_train = []
poses_test = []
images_test = []
with open(directory+dataset_train) as f:
next(f) # skip the 3 header lines
next(f)
next(f)
for line in f:
fname, p0,p1,p2,p3,p4,p5,p6 = line.split()
p0 = float(p0)
p1 = float(p1)
p2 = float(p2)
p3 = float(p3)
p4 = float(p4)
p5 = float(p5)
p6 = float(p6)
poses_train.append((p0,p1,p2,p3,p4,p5,p6))
images_train.append(directory+fname)
images_out_train = preprocess_train(images_train)
with open(directory+dataset_test) as f:
next(f) # skip the 3 header lines
next(f)
next(f)
for line in f:
fname, p0,p1,p2,p3,p4,p5,p6 = line.split()
p0 = float(p0)
p1 = float(p1)
p2 = float(p2)
p3 = float(p3)
p4 = float(p4)
p5 = float(p5)
p6 = float(p6)
poses_test.append((p0,p1,p2,p3,p4,p5,p6))
images_test.append(directory+fname)
images_out_test = preprocess_test(images_train, images_test)
return datasource(images_out_train, poses_train), datasource(images_out_test, poses_test)
# creating the final datasource containing images and the respective camera poses for training and testing datasets
def getKings():
datasource_train, datasource_test = get_data(dataset_train, dataset_test)
images_train = []
poses_train = []
images_test = []
poses_test = []
for i in range(len(datasource_train.images)):
# print(i)
images_train.append(datasource_train.images[i])
poses_train.append(datasource_train.poses[i])
for i in range(len(datasource_test.images)):
# print(i)
images_test.append(datasource_test.images[i])
poses_test.append(datasource_test.poses[i])
return datasource(images_train, poses_train), datasource(images_test, poses_test)