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sort glob expression for batch of 2 and give name of the first

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This commit is contained in:
Lorenzo 2021-03-15 17:07:22 +01:00
parent dba966b512
commit 31172b6d58
3 changed files with 20 additions and 13 deletions
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2
monoloco/network/geom_baseline.py
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@ -6,7 +6,7 @@ from collections import defaultdict
import numpy as np
from monstereo.utils import pixel_to_camera, get_keypoints
from monoloco.utils import pixel_to_camera, get_keypoints
AVERAGE_Y = 0.48
CLUSTERS = ['10', '20', '30', 'all']

2
monoloco/network/process.py
View File

@ -88,12 +88,12 @@ def factory_for_gt(im_size, focal_length=5.7, name=None, path_gt=None, verbose=T
dic_gt = None
if im_size[0] / im_size[1] > 2.5: # KITTI default
kk = [[718.3351, 0., 600.3891], [0., 718.3351, 181.5122], [0., 0., 1.]] # Kitti calibration
logger.info("Using KITTI calibration matrix...")
else: # nuScenes camera parameters
kk = [
[im_size[0]*focal_length/Sx, 0., im_size[0]/2],
[0., im_size[1]*focal_length/Sy, im_size[1]/2],
[0., 0., 1.]]
if verbose:
logger.info("Using a standard calibration matrix...")
return kk, dic_gt

29
monoloco/predict.py
View File

@ -61,9 +61,14 @@ def factory_from_args(args):
if args.net == 'monstereo':
args.batch_size = 2
args.images = sorted(args.images)
else:
args.batch_size = 1
if args.batch_size == 2 and not args.long_edge:
args.long_edge = 1238
LOG.info("Long-edge set to %i".format(args.long_edge))
# Make default pifpaf argument
args.force_complete_pose = True
LOG.info("Force complete pose is active")
@ -108,14 +113,7 @@ def predict(args):
print('batch %d: %s', batch_i, meta['file_name'])
pred = [ann.inverse_transform(meta) for ann in pred]
if args.output_directory is None:
splits = os.path.split(meta['file_name'])
output_path = os.path.join(splits[0], 'out_' + splits[1])
else:
file_name = os.path.basename(meta['file_name'])
output_path = os.path.join(args.output_directory, 'out_' + file_name)
print('image', batch_i, meta['file_name'], output_path)
# Load image and collect pifpaf results
if idx == 0:
with open(meta_batch[0]['file_name'], 'rb') as f:
cpu_image = PIL.Image.open(f).convert('RGB')
@ -123,15 +121,24 @@ def predict(args):
'pred': pred,
'left': [ann.json_data() for ann in pred],
'image': cpu_image}
# Set output image name
if args.output_directory is None:
splits = os.path.split(meta['file_name'])
output_path = os.path.join(splits[0], 'out_' + splits[1])
else:
file_name = os.path.basename(meta['file_name'])
output_path = os.path.join(args.output_directory, 'out_' + file_name)
print('image', batch_i, meta['file_name'], output_path)
# Only for MonStereo
else:
pifpaf_outs['right'] = [ann.json_data() for ann in pred]
# 3D Predictions
if args.net in ('monoloco_pp', 'monstereo'):
im_name = os.path.basename(meta['file_name'])
im_size = (cpu_image.size[0], cpu_image.size[1]) # Original
kk, dic_gt = factory_for_gt(im_size, focal_length=args.focal, name=im_name, path_gt=args.path_gt)
kk, dic_gt = factory_for_gt(im_size, focal_length=args.focal, name=file_name, path_gt=args.path_gt)
# Preprocess pifpaf outputs and run monoloco
boxes, keypoints = preprocess_pifpaf(pifpaf_outs['left'], im_size, enlarge_boxes=False)