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add factory method

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This commit is contained in:
lorenzo 2019-05-21 09:57:05 +02:00
parent d756678518
commit e96b37f8c8
1 changed files with 33 additions and 28 deletions
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61
src/features/preprocess_nu.py
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@ -10,7 +10,8 @@ from collections import defaultdict
import datetime import datetime
import numpy as np import numpy as np
from nuscenes.nuscenes import NuScenes
from nuscenes.utils import splits
class PreprocessNuscenes: class PreprocessNuscenes:
""" """
@ -45,9 +46,6 @@ class PreprocessNuscenes:
from utils.pifpaf import get_input_data, preprocess_pif from utils.pifpaf import get_input_data, preprocess_pif
self.get_input_data = get_input_data self.get_input_data = get_input_data
self.preprocess_pif = preprocess_pif self.preprocess_pif = preprocess_pif
from nuscenes.nuscenes import NuScenes
from nuscenes.utils import splits
self.splits = splits
# Initialize dicts to save joints for training # Initialize dicts to save joints for training
self.dic_jo = {'train': dict(X=[], Y=[], names=[], kps=[], boxes_3d=[], K=[], self.dic_jo = {'train': dict(X=[], Y=[], names=[], kps=[], boxes_3d=[], K=[],
@ -62,30 +60,6 @@ class PreprocessNuscenes:
self.cameras = ['CAM_FRONT', 'CAM_FRONT_LEFT', 'CAM_FRONT_RIGHT', 'CAM_BACK', 'CAM_BACK_LEFT', 'CAM_BACK_RIGHT'] self.cameras = ['CAM_FRONT', 'CAM_FRONT_LEFT', 'CAM_FRONT_RIGHT', 'CAM_BACK', 'CAM_BACK_LEFT', 'CAM_BACK_RIGHT']
# Split training and validation base on the dataset type
if dataset == 'nuscenes':
self.nusc = NuScenes(version='v1.0-trainval', dataroot=dir_nuscenes, verbose=True)
self.scenes = self.nusc.scene
split_scenes = self.splits.create_splits_scenes()
self.split_train, self.split_val = split_scenes['train'], split_scenes['val']
elif dataset == 'nuscenes_mini':
self.nusc = NuScenes(version='v1.0-mini', dataroot=dir_nuscenes, verbose=True)
self.scenes = self.nusc.scene
split_scenes = self.splits.create_splits_scenes()
self.split_train, self.split_val = split_scenes['train'], split_scenes['val']
elif dataset == 'nuscenes_teaser':
self.nusc = NuScenes(version='v1.0-trainval', dataroot=dir_nuscenes, verbose=True)
with open("splits/nuscenes_teaser_scenes.txt", "r") as file:
teaser_scenes = file.read().splitlines()
self.scenes = self.nusc.scene
self.scenes = [scene for scene in self.scenes if scene['token'] in teaser_scenes]
with open("splits/split_nuscenes_teaser.json", "r") as file:
dic_split = json.load(file)
self.split_train = [scene['name'] for scene in self.scenes if scene['token'] in dic_split['train']]
self.split_val = [scene['name'] for scene in self.scenes if scene['token'] in dic_split['val']]
def run(self): def run(self):
""" """
Prepare arrays for training Prepare arrays for training
@ -187,3 +161,34 @@ class PreprocessNuscenes:
print("\nSaved {} annotations for {} samples in {} scenes. Total time: {:.1f} minutes" print("\nSaved {} annotations for {} samples in {} scenes. Total time: {:.1f} minutes"
.format(cnt_ann, cnt_samples, cnt_scenes, (end-start)/60)) .format(cnt_ann, cnt_samples, cnt_scenes, (end-start)/60))
print("\nOutput files:\n{}\n{}\n".format(self.path_names, self.path_joints)) print("\nOutput files:\n{}\n{}\n".format(self.path_names, self.path_joints))
def factory(dataset, dir_nuscenes):
"""Define dataset type and split training and validation"""
assert dataset in ['nuscenes', 'nuscenes_mini', 'nuscenes_teaser']
if dataset == 'nuscenes':
nusc = NuScenes(version='v1.0-trainval', dataroot=dir_nuscenes, verbose=True)
scenes = nusc.scene
split_scenes = splits.create_splits_scenes()
split_train, split_val = split_scenes['train'], split_scenes['val']
elif dataset == 'nuscenes_mini':
nusc = NuScenes(version='v1.0-mini', dataroot=dir_nuscenes, verbose=True)
scenes = nusc.scene
split_scenes = splits.create_splits_scenes()
split_train, split_val = split_scenes['train'], split_scenes['val']
else:
nusc = NuScenes(version='v1.0-trainval', dataroot=dir_nuscenes, verbose=True)
with open("splits/nuscenes_teaser_scenes.txt", "r") as file:
teaser_scenes = file.read().splitlines()
scenes = nusc.scene
scenes = [scene for scene in scenes if scene['token'] in teaser_scenes]
with open("splits/split_nuscenes_teaser.json", "r") as file:
dic_split = json.load(file)
split_train = [scene['name'] for scene in scenes if scene['token'] in dic_split['train']]
split_val = [scene['name'] for scene in scenes if scene['token'] in dic_split['val']]
return nusc, scenes, split_train, split_val