| import numpy as np |
| import cv2 |
| import random |
| import settings |
| from tqdm import tqdm |
| from similarityMeasures import getError |
| imageDistance = 1 |
|
|
|
|
| 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 tqdm(f): |
| fname, p0, p1, p2, p3, p4, p5, p6 = line.split() |
| timestamps.append(fname) |
| img = cv2.imread("../../../Self_loc_synthetic/dataset_real/dataset/" + 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. Each input |
| uses 3 images. The current one, the previous one and the next one. |
| """ |
| indices = range(len(source[0])) |
| for i in indices: |
| image = source[0][i] |
| image_left = source[0][max(0, i - imageDistance)] |
| image_right = source[0][min(i + imageDistance, len(source[0]) - 1)] |
| pose_x = source[1][0][i] |
| pose_q = source[1][1][i] |
| pose_x_left = source[1][0][max(0, i - imageDistance)] |
| pose_q_left = source[1][1][max(0, i - imageDistance)] |
| pose_x_right = source[1][0][min(i + imageDistance, len(source[0]) - 1)] |
| pose_q_right = source[1][1][min(i + imageDistance, len(source[0]) - 1)] |
| if settings.saveMean: |
| m1_2, a1_2 = getError(pose_x, pose_q, pose_x_left, pose_q_left) |
| m2_3, a2_3 = getError(pose_x, pose_q, pose_x_right, pose_q_right) |
| m1_3, a1_3 = getError(pose_x_left, pose_q_left, pose_x_right, pose_q_right) |
| if m1_2 > settings.distanceThreshold or m2_3 > settings.distanceThreshold: |
| continue |
| if a1_2 > settings.angleThreshold or a2_3 > settings.angleThreshold: |
| continue |
| yield np.asarray([image_left, image, image_right]), np.asarray([pose_x_left, pose_x, pose_x_right]), np.asarray([pose_q_left, pose_q, pose_q_right]) |
|
|
|
|
| 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] |
| image_left = source[0][max(0, i - imageDistance)] |
| image_right = source[0][min(i + imageDistance, len(source[0]) - 1)] |
| pose_x = source[1][0][i] |
| pose_q = source[1][1][i] |
| pose_x_left = source[1][0][max(0, i - imageDistance)] |
| pose_q_left = source[1][1][max(0, i - imageDistance)] |
| pose_x_right = source[1][0][min(i + imageDistance, len(source[0]) - 1)] |
| pose_q_right = source[1][1][min(i + imageDistance, len(source[0]) - 1)] |
| m1_2, a1_2 = getError(pose_x, pose_q, pose_x_left, pose_q_left) |
| m2_3, a2_3 = getError(pose_x, pose_q, pose_x_right, pose_q_right) |
| m1_3, a1_3 = getError(pose_x_left, pose_q_left, pose_x_right, pose_q_right) |
| if m1_2 > settings.distanceThreshold or m2_3 > settings.distanceThreshold: |
| continue |
| if a1_2 > settings.angleThreshold or a2_3 > settings.angleThreshold: |
| continue |
| yield np.asarray([image_left, image, image_right]), np.asarray([pose_x_left, pose_x, pose_x_right]), np.asarray([pose_q_left, pose_q, pose_q_right]) |
| |
|
|
| def gen_data_batch(source, batchSize): |
| """ 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) |
| 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)] |
|
|
