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remove class imports

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This commit is contained in:
lorenzo 2019-05-21 10:26:59 +02:00
parent 755688818f
commit 3c6c305606
2 changed files with 31 additions and 39 deletions
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69
src/features/preprocess_ki.py
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@ -1,4 +1,5 @@
"""Preprocess annnotations with KITTI ground-truth"""
import os
import glob
import math
@ -6,11 +7,25 @@ import logging
from collections import defaultdict
import json
import datetime
from utils.kitti import get_calibration, check_conditions
from utils.pifpaf import get_input_data, preprocess_pif
from utils.misc import get_idx_max, append_cluster
class PreprocessKitti:
"""Prepare arrays with same format as nuScenes preprocessing but using ground truth txt files"""
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
dic_jo = {'train': dict(X=[], Y=[], names=[], kps=[], K=[],
clst=defaultdict(lambda: defaultdict(list))),
'val': dict(X=[], Y=[], names=[], kps=[], K=[],
clst=defaultdict(lambda: defaultdict(list))),
'test': dict(X=[], Y=[], names=[], kps=[], K=[],
clst=defaultdict(lambda: defaultdict(list)))}
dic_names = defaultdict(lambda: defaultdict(list))
def __init__(self, dir_ann, iou_thresh=0.3):
self.dir_ann = dir_ann
@ -30,26 +45,6 @@ class PreprocessKitti:
path_train = os.path.join('splits', 'kitti_train.txt')
path_val = os.path.join('splits', 'kitti_val.txt')
logging.basicConfig(level=logging.INFO)
self.logger = logging.getLogger(__name__)
from utils.kitti import get_calibration, check_conditions
self.get_calibration = get_calibration
self.check_conditions = check_conditions
from utils.pifpaf import get_input_data, preprocess_pif
self.get_input_data = get_input_data
self.preprocess_pif = preprocess_pif
from utils.misc import get_idx_max, append_cluster
self.get_idx_max = get_idx_max
self.append_cluster = append_cluster
# self.clusters = ['all', '6', '10', '15', '20', '25', '30', '40', '50', '>50'
self.cnt_gt = 0
self.cnt_fnf = 0
self.dic_cnt = {'train': 0, 'val': 0, 'test': 0}
# Split training and validation images
set_gt = set(self.names_gt)
set_train = set()
@ -66,17 +61,13 @@ class PreprocessKitti:
self.set_val = set_gt.intersection(set_val)
assert self.set_train and self.set_val, "No validation or training annotations"
self.dic_jo = {'train': dict(X=[], Y=[], names=[], kps=[], K=[],
clst=defaultdict(lambda: defaultdict(list))),
'val': dict(X=[], Y=[], names=[], kps=[], K=[],
clst=defaultdict(lambda: defaultdict(list))),
'test': dict(X=[], Y=[], names=[], kps=[], K=[],
clst=defaultdict(lambda: defaultdict(list)))}
self.dic_names = defaultdict(lambda: defaultdict(list))
def run(self):
cnt_gt = 0
cnt_fnf = 0
dic_cnt = {'train': 0, 'val': 0, 'test': 0}
for name in self.names_gt:
# Extract ground truth
path_gt = os.path.join(self.dir_gt, name)
@ -89,17 +80,17 @@ class PreprocessKitti:
elif name in self.set_val:
phase = 'val'
else:
self.cnt_fnf += 1
cnt_fnf += 1
continue
# Extract keypoints
path_txt = os.path.join(self.dir_kk, basename + '.txt')
kk, tt = self.get_calibration(path_txt)
kk, tt = get_calibration(path_txt)
# Iterate over each line of the gt file and save box location and distances
with open(path_gt, "r") as f_gt:
for line_gt in f_gt:
if self.check_conditions(line_gt, mode='gt'):
if check_conditions(line_gt, mode='gt'):
box = [float(x) for x in line_gt.split()[4:8]]
boxes_gt.append(box)
loc_gt = [float(x) for x in line_gt.split()[11:14]]
@ -108,21 +99,21 @@ class PreprocessKitti:
self.dic_names[basename + '.png']['boxes'].append(box)
self.dic_names[basename + '.png']['dds'].append(dd)
self.dic_names[basename + '.png']['K'] = kk.tolist()
self.cnt_gt += 1
cnt_gt += 1
# Find the annotations if exists
try:
with open(os.path.join(self.dir_ann, basename + '.png.pifpaf.json'), 'r') as f:
annotations = json.load(f)
boxes, keypoints = self.preprocess_pif(annotations)
(inputs, _), (uv_kps, uv_boxes, _, _) = self.get_input_data(boxes, keypoints, kk)
boxes, keypoints = preprocess_pif(annotations)
(inputs, _), (uv_kps, uv_boxes, _, _) = get_input_data(boxes, keypoints, kk)
except FileNotFoundError:
uv_boxes = []
# Match each set of keypoint with a ground truth
for ii, box in enumerate(uv_boxes):
idx_max, iou_max = self.get_idx_max(box, boxes_gt)
idx_max, iou_max = get_idx_max(box, boxes_gt)
if iou_max >= self.iou_thresh:
@ -131,8 +122,8 @@ class PreprocessKitti:
self.dic_jo[phase]['Y'].append([dds[idx_max]]) # Trick to make it (nn,1)
self.dic_jo[phase]['K'] = kk.tolist()
self.dic_jo[phase]['names'].append(name) # One image name for each annotation
self.append_cluster(self.dic_jo, phase, inputs[ii], dds[idx_max], uv_kps[ii])
self.dic_cnt[phase] += 1
append_cluster(self.dic_jo, phase, inputs[ii], dds[idx_max], uv_kps[ii])
dic_cnt[phase] += 1
boxes_gt.pop(idx_max)
dds.pop(idx_max)
@ -142,9 +133,9 @@ class PreprocessKitti:
json.dump(self.dic_names, file)
for phase in ['train', 'val', 'test']:
print("Saved {} annotations for phase {}"
.format(self.dic_cnt[phase], phase))
.format(dic_cnt[phase], phase))
print("Number of GT files: {}. Files not found: {}"
.format(self.cnt_gt, self.cnt_fnf))
.format(cnt_gt, cnt_fnf))
print("\nOutput files:\n{}\n{}\n".format(self.path_names, self.path_joints))

1
src/features/preprocess_nu.py
View File

@ -17,6 +17,7 @@ from utils.nuscenes import select_categories
from utils.camera import project_3d
from utils.pifpaf import get_input_data, preprocess_pif
class PreprocessNuscenes:
"""
Preprocess Nuscenes dataset