File size: 2,206 Bytes
1403119 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 | import numpy as np
import cv2
import random
def get_training_data(dataset):
""" Reads in the predicted pos/rotation data as well as the ground truth """
print(dataset)
images_batch = []
timestamps = []
po1 = []
po2 = []
with open(dataset) as f:
for line in f:
fname, p0, p1, p2, p3, p4, p5, p6 = line.split()
timestamps.append(fname)
img = np.load("path/to/txt_files/with/data/" + fname)
images_batch.append(img)
po1.append(np.array((np.float(p0), np.float(p1), np.float(p2))))
po2.append(np.array((np.float(p3), np.float(p4), np.float(p5), np.float(p6))))
return (images_batch, [np.asarray(po1), np.asarray(po2)]), timestamps
def limited_gen_data(source):
""" Yields the input/ground truth data in batches of size 1. Can be used for evaluating the test dataset"""
indices = range(len(source[0]))
for i in indices:
image = source[0][i]
pose_x_rel = source[1][0][i]
pose_q_rel = source[1][1][i]
yield image, pose_x_rel, pose_q_rel
def gen_data(source):
""" Data generator that randomly shuffles the training data and yields them as single elements. """
while True:
indices = list(range(len(source[0])))
random.shuffle(indices)
for i in indices:
image = source[0][i]
pose_x_rel = source[1][0][i]
pose_q_rel = source[1][1][i]
yield image, pose_x_rel, pose_q_rel
def gen_data_batch(source, batchSize, im_shape):
""" Uses the gen_data and yields batches of the size batchSize. """
data_gen = gen_data(source)
while True:
image_batch = []
pose_x_batch = []
pose_q_batch = []
for _ in range(batchSize):
image, pose_x, pose_q = next(data_gen)
if image.shape[0] != im_shape[1] or image.shape[1] != im_shape[2]:
image = cv2.resize(image, (im_shape[2], im_shape[1]))
image_batch.append(image)
pose_x_batch.append(pose_x)
pose_q_batch.append(pose_q)
yield np.asarray(image_batch), [np.asarray(pose_x_batch), np.asarray(pose_q_batch)]
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