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Refactoring

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This commit is contained in:
Charles Joseph Pierre Beauville 2021-06-28 02:01:28 +02:00
parent 5870848da9
commit 9a8df6351d
3 changed files with 4 additions and 106 deletions
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2
monoloco/prep/__init__.py
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@ -1,3 +1,3 @@
from .preprocess_kitti import parse_ground_truth, factory_file
from .casr_preprocess import create_dic
from .preprocess_casr import create_dic

100
monoloco/prep/casr_preprocess.py
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@ -1,100 +0,0 @@
import pickle
import re
import json
import os
import glob
import datetime
import numpy as np
import torch
from .. import __version__
from ..network.process import preprocess_monoloco
gt_path = '/scratch/izar/beauvill/casr/data/annotations/casr_annotation.pickle'
res_path = '/scratch/izar/beauvill/casr/res_extended/casr*'
def bb_intersection_over_union(boxA, boxB):
xA = max(boxA[0], boxB[0])
yA = max(boxA[1], boxB[1])
xB = min(boxA[2], boxB[2])
yB = min(boxA[3], boxB[3])
interArea = max(0, xB - xA + 1) * max(0, yB - yA + 1)
boxAArea = (boxA[2] - boxA[0] + 1) * (boxA[3] - boxA[1] + 1)
boxBArea = (boxB[2] - boxB[0] + 1) * (boxB[3] - boxB[1] + 1)
iou = interArea / float(boxAArea + boxBArea - interArea)
return iou
def match_bboxes(bbox_gt, bbox_pred):
n_true = bbox_gt.shape[0]
n_pred = bbox_pred.shape[0]
iou_matrix = np.zeros((n_true, n_pred))
for i in range(n_true):
for j in range(n_pred):
iou_matrix[i, j] = bb_intersection_over_union(bbox_gt[i,:], bbox_pred[j,:])
return np.argmax(iou_matrix)
def standard_bbox(bbox):
return [bbox[0], bbox[1], bbox[0]+bbox[2], bbox[1]+bbox[3]]
def load_gt():
return pickle.load(open(gt_path, 'rb'), encoding='latin1')
def load_res():
mono = []
for folder in sorted(glob.glob(res_path), key=lambda x:float(re.findall(r"(\d+)",x)[0])):
data_list = []
for file in sorted(os.listdir(folder), key=lambda x:float(re.findall(r"(\d+)",x)[0])):
if 'json' in file:
json_path = os.path.join(folder, file)
json_data = json.load(open(json_path))
json_data['filename'] = json_path
data_list.append(json_data)
mono.append(data_list)
return mono
def create_dic(std=False):
gt=load_gt()
res=load_res()
dic_jo = {
'train': dict(X=[], Y=[], names=[], kps=[]),
'val': dict(X=[], Y=[], names=[], kps=[]),
'version': __version__,
}
split = ['3', '4']
if std:
wrong = [6, 8, 9, 10, 11, 12, 14, 21, 40, 43, 55, 70, 76, 92, 109,
110, 112, 113, 121, 123, 124, 127, 128, 134, 136, 139, 165, 173]
mode = 'std'
else:
wrong = []
mode = ''
for i in [x for x in range(len(res[:])) if x not in wrong]:
for j in [x for x in range(len(res[i][:])) if 'boxes' in res[i][x]]:
folder = gt[i][j]['video_folder']
phase = 'val'
if folder[7] in split:
phase = 'train'
gt_box = gt[i][j]['bbox_gt']
good_idx = match_bboxes(np.array([standard_bbox(gt_box)]), np.array(res[i][j]['boxes'])[:,:4])
keypoints = [res[i][j]['uv_kps'][good_idx]]
gt_turn = gt[i][j]['left_or_right']
if std and gt_turn == 3:
gt_turn = 2
inp = preprocess_monoloco(keypoints, torch.eye(3)).view(-1).tolist()
dic_jo[phase]['kps'].append(keypoints)
dic_jo[phase]['X'].append(inp)
dic_jo[phase]['Y'].append(gt_turn)
dic_jo[phase]['names'].append(folder+"_frame{}".format(j))
now_time = datetime.datetime.now().strftime("%Y%m%d-%H%M")[2:]
with open("/home/beauvill/joints-casr-" + mode + "-right-" +
split[0] + split[1] + "-" + now_time + ".json", 'w') as file:
json.dump(dic_jo, file)
return dic_jo

8
monoloco/run.py
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@ -82,6 +82,7 @@ def cli():
help='datasets to preprocess: nuscenes, nuscenes_teaser, nuscenes_mini, kitti',
default='kitti')
prep_parser.add_argument('--dir_nuscenes', help='directory of nuscenes devkit', default='data/nuscenes/')
prep_parser.add_argument('--casr_std', help='prep casr with only standard gestures', action='store_true')
prep_parser.add_argument('--iou_min', help='minimum iou to match ground truth', type=float, default=0.3)
prep_parser.add_argument('--variance', help='new', action='store_true')
prep_parser.add_argument('--activity', help='new', action='store_true')
@ -151,11 +152,8 @@ def main():
prep = PreprocessNuscenes(args.dir_ann, args.dir_nuscenes, args.dataset, args.iou_min)
prep.run()
elif 'casr' in args.dataset:
from .prep.casr_preprocess import create_dic
create_dic(std=False)
elif 'casr_std' in args.dataset:
from .prep.casr_preprocess import create_dic
create_dic(std=True)
from .prep import create_dic
create_dic(dir_ann=args.dir_ann, std=args.casr_std)
else:
from .prep.preprocess_kitti import PreprocessKitti
prep = PreprocessKitti(args.dir_ann, mode=args.mode, iou_min=args.iou_min)