import numpy as np
from clean_pvnet.infer.pvnet_inferer import PVNetFrameResultList
from common_utils.path_utils import get_all_files_of_extension, get_filename
from common_utils.file_utils import make_dir_if_not_exists
from annotation_utils.linemod.objects import Linemod_Dataset, LinemodCamera
from tqdm import tqdm
linemod_dataset = Linemod_Dataset.load_from_path(
'/home/clayton/workspace/prj/data_keep/data/misc_dataset/clayton_datasets/combined/train.json'
)
linemod_ann_sample = linemod_dataset.annotations[0]
kpt_3d = linemod_ann_sample.fps_3d.copy()
kpt_3d.append(linemod_ann_sample.center_3d)
corner_3d = linemod_ann_sample.corner_3d
kpt_3d = linemod_ann_sample.fps_3d.copy()
kpt_3d.append(linemod_ann_sample.center_3d)
# old_K = linemod_ann_sample.K
K = np.array([
517.799858, 0.000000, 303.876287, 0.000000, 514.807834, 238.157119,
0.000000, 0.000000, 1.000000
]).reshape(3, 3)
distortion = np.array([0.001647, -0.105636, -0.002094, -0.006446, 0.000000])
# frame_result_list = PVNetFrameResultList.load_from_path(f'{infer_data_dump}/{model_name}_infer.json')
# pbar = tqdm(total=len(frame_result_list), unit='frame(s)')
# for result in frame_result_list:
# for pred in result.pred_list:
# pred.recalculate(
# gt_kpt_3d=kpt_3d,
# corner_3d=corner_3d,
# K=K,
csv_paths = recursively_get_all_filepaths_of_extension(test_root_dir,
extension='csv')
test_names, datasets = [], []
for csv_path in csv_paths:
test_name = get_rootname_from_path(csv_path)
img_dir = f'{get_dirpath_from_filepath(csv_path)}/images'
assert dir_exists(img_dir), f"Couldn't find image directory: {img_dir}"
ann_path = f'{img_dir}/output.json'
if not file_exists(ann_path):
continue
dataset = COCO_Dataset.load_from_path(ann_path, img_dir=img_dir)
test_names.append(test_name)
datasets.append(dataset)
linemod_dataset = Linemod_Dataset.load_from_path(
f'/home/clayton/workspace/prj/data/misc_dataset/darwin_datasets/coco2linemod/darwin20210105_blackout/train.json'
)
linemod_ann_sample = linemod_dataset.annotations[0]
kpt_3d = linemod_ann_sample.fps_3d.copy()
kpt_3d.append(linemod_ann_sample.center_3d)
corner_3d = linemod_ann_sample.corner_3d
# K = linemod_ann_sample.K
K = np.array([
517.799858, 0.000000, 303.876287, 0.000000, 514.807834, 238.157119,
0.000000, 0.000000, 1.000000
]).reshape(3, 3)
distortion = np.array([0.001647, -0.105636, -0.002094, -0.006446, 0.000000])
linemod_image_sample = linemod_dataset.images[0]
dsize = (linemod_image_sample.width, linemod_image_sample.height)
infer_tests_pvnet(
from common_utils.file_utils import dir_exists, file_exists, make_dir_if_not_exists
from tqdm import tqdm
import numpy as np
inferer = PVNetInferer(
# weight_path='/home/clayton/workspace/git/clean-pvnet/data/model/pvnet/custom/99.pth',
# weight_path='/home/clayton/workspace/prj/data_keep/data/misc_dataset/darwin_weights/194.pth',
# weight_path='/home/clayton/workspace/prj/data_keep/data/weights/pvnet_hsr/20201119/499redo.pth',
weight_path=
'/home/clayton/workspace/prj/data_keep/data/weights/pvnet_hsr/20201119/499.pth'
)
model_name = 'pvnet20201119-epoch499dist'
# img_dir = '/home/clayton/workspace/git/pvnet-rendering/test/renders1'
img_dir = '/home/clayton/workspace/prj/data_keep/data/misc_dataset/darwin_datasets/nihonbashi2/organized'
linemod_dataset = Linemod_Dataset.load_from_path(f'{img_dir}/train.json')
linemod_ann_sample = linemod_dataset.annotations[0]
kpt_3d = linemod_ann_sample.fps_3d.copy()
kpt_3d.append(linemod_ann_sample.center_3d)
corner_3d = linemod_ann_sample.corner_3d
# K = linemod_ann_sample.K
K = np.array([
517.799858, 0.000000, 303.876287, 0.000000, 514.807834, 238.157119,
0.000000, 0.000000, 1.000000
]).reshape(3, 3)
distortion = np.array([0.001647, -0.105636, -0.002094, -0.006446, 0.000000])
linemod_image_sample = linemod_dataset.images[0]
dsize = (linemod_image_sample.width, linemod_image_sample.height)
robot_camera_dir = '/home/clayton/workspace/prj/data_keep/data/toyota/from_toyota/20201017/20201017_robot_camera'