def __init__(self, config, subset, default_data_dir_suffix, data_keys_to_use, num_classes=2):
super().__init__(config, subset, data_keys_to_use, num_classes)
self._data_dir = config.dir("dataset_path", "/fastwork/" + username() + "/mywork/data/" + default_data_dir_suffix)
self._video_idx = None
self._object_idx = None
self._curr_video_data = None
self._keys_to_sample = []
# TODO: maybe later share the cache between the subsets
self._video_data_cache = {}
self._video_tags = self._read_video_tags()
def __init__(self, config, subset):
davis_sequence = config.string("model", '')
# data_list_path = LIST_PATH_ROOT + davis_sequence + '/'
self.data_dir = config.string('data_dir', DEFAULT_PATH)
annotation_file = "/fastwork/" + username(
) + "/mywork/data/coco/annotations/instances_train2014.json"
self.build_filename_to_coco_anns_dict(annotation_file)
super().__init__(config,
subset,
NAME,
self.data_dir,
"",
100,
cat_ids_to_use=None)
#!/usr/bin/env python3
import glob
from PIL import Image
import numpy as np
import tqdm
from refinement_net.datasets.util.Util import username
import datasets.cityscapes
PATH = "/fastwork/" + username() + "/mywork/data/cityscapes/"
CITYSCAPES_CODE_PATH = datasets.cityscapes.__file__.replace("__init__.py", "")
for subset, outfile in [("train", "training.txt"), ("val", "validation.txt")]:
with open(CITYSCAPES_CODE_PATH + outfile, "w") as f:
instances = glob.glob(PATH + "gtFine/" + subset +
"/*/*_instanceIds.png")
for inst in tqdm.tqdm(instances):
x = np.array(Image.open(inst))
ids = np.unique(x)
im = inst.replace("/gtFine/",
"/leftImg8bit/").replace("_gtFine_instanceIds",
"_leftImg8bit")
print(im.replace(PATH, ""),
inst.replace(PATH, ""),
file=f,
sep=" ",
end="")
for id_ in ids:
n = (x == id_).sum()
print(" ", id_, ":", n, file=f, sep="", end="")
print(file=f)
import glob
import tensorflow as tf
from refinement_net.datasets.Loader import register_dataset
from refinement_net.datasets.Dataset import FileListDataset
from refinement_net.datasets.util.Util import username
NUM_CLASSES = 2
VOID_LABEL = 255 # for translation augmentation
DAVIS_DEFAULT_PATH = "/fastwork/" + username() + "/mywork/data/DAVIS/"
DAVIS2017_DEFAULT_PATH = "/fastwork/" + username() + "/mywork/data/DAVIS2017/"
DAVIS_FLOW_DEFAULT_PATH = "/fastwork/" + username(
) + "/mywork/data/DAVIS_data/"
DAVIS_LUCID_DEFAULT_PATH = "/fastwork/" + username(
) + "/mywork/data/DAVIS_data/lucid/"
DAVIS2017_LUCID_DEFAULT_PATH = "/fastwork/" + username(
) + "/mywork/data/DAVIS2017_data/lucid/"
DAVIS_IMAGE_SIZE = (480, 854)
DAVIS2017_IMAGE_SIZE = (480, None)
def read_image_and_annotation_list(fn, data_dir):
imgs = []
ans = []
with open(fn) as f:
for l in f:
sp = l.split()
an = data_dir + sp[1]
im = data_dir + sp[0]
imgs.append(im)
#!/usr/bin/env python3
import glob
from PIL import Image
import numpy as np
import tqdm
from refinement_net.datasets.util.Util import username
import datasets.KITTI
PATH = "/home/" + username() + "/data/KITTI_instance/"
KITTI_CODE_PATH = datasets.KITTI.__file__.replace("__init__.py", "")
with open(KITTI_CODE_PATH + "training.txt", "w") as f:
instances = glob.glob(PATH + "instance/*.png")
for inst in tqdm.tqdm(instances):
x = np.array(Image.open(inst))
ids = np.unique(x)
im = inst.replace("/instance/", "/image_2/")
print(im.replace(PATH, ""),
inst.replace(PATH, ""),
file=f,
sep=" ",
end="")
for id_ in ids:
n = (x == id_).sum()
print(" ", id_, ":", n, file=f, sep="", end="")
print(file=f)
import json
import pycocotools.coco as coco
from pycocotools.cocoeval import COCOeval
from refinement_net.datasets.util.Util import username
coco_data_folder = "/fastwork/" + username() + "/mywork/data/coco/"
minival_gt_file = coco_data_folder + "annotations/instances_val2014.json"
minival_det_file = "/home/krause/vision/savitar2/forwarded/frcnn_test/frcnn_test-1-detections.json"
def evaluate_coco():
c = coco.COCO(minival_gt_file)
cocoDt = c.loadRes(minival_det_file)
cocoEval = COCOeval(c, cocoDt, 'bbox')
cocoEval.evaluate()
cocoEval.accumulate()
cocoEval.summarize()
print(cocoEval.stats[0])
def adjust_detections():
c = coco.COCO(minival_gt_file)
keys = list(c.cats.keys())
detections_list = json.load(open(minival_det_file))
for det in detections_list:
det['category_id'] = keys[det['category_id']]
json.dump(
detections_list,
open("/home/krause/vision/savitar2/forwarded/temp_edited.json", 'w'))
Convert polygons to binary masks.
Args:
polys: a list of nx2 float array
Returns:
a binary matrix of (height, width)
"""
import pycocotools.mask as cocomask
polys = [p.flatten().tolist() for p in polys]
rles = cocomask.frPyObjects(polys, height, width)
rle = cocomask.merge(rles)
return cocomask.decode(rle)
DEFAULT_PATH = "/fastwork/" + username() + "/mywork/data/coco/"
# To clarify, COCO uses category IDs from 1 to 90 which we take verbatim (no +1/-1!!!)
# The classes_to_cat array contains the mapping
NUM_CLASSES = 81
N_MAX_DETECTIONS = 100
NAME = "COCO_detection"
@register_dataset(NAME)
class CocoDetectionDataset(FileListDataset):
def __init__(self, config, subset):
super().__init__(config, NAME, subset, DEFAULT_PATH, NUM_CLASSES)
self.add_masks = config.bool("add_masks", True)
self.exclude_crowd_images = config.bool("exclude_crowd_images", False)
self.exclude_crowd_annotations = config.bool(
#!/usr/bin/env python3
import glob
import os
from refinement_net.datasets.util.Util import username
PATH = "/fastwork/" + username() + "/mywork/data/mapillary/"
files = glob.glob(PATH + "training/*/-*.png") + glob.glob(PATH + "training/*/-*.jpg") + \
glob.glob(PATH + "validation/*/-*.png") + glob.glob(PATH + "validation/*/-*.jpg")
with open(PATH + "renamed.txt", "w") as f_out:
for f in files:
idx_begin = f.rfind("/-")
idx_end = idx_begin + 1
while f[idx_end] == "-":
idx_end += 1
f_new = f[:idx_begin + 1] + f[idx_end:]
print(f, "->", f_new)
os.rename(f, f_new)
print(f, f_new, file=f_out)
import glob
import tensorflow as tf
from refinement_net.datasets.Loader import register_dataset
from refinement_net.datasets.Dataset import FileListDataset
from refinement_net.datasets.util.Util import username
NUM_CLASSES = 2
VOID_LABEL = 255 # for translation augmentation
DAVIS_DEFAULT_IMAGES_PATH = "/work2/" + username() + "/data/DAVIS/train-val/val/"
DAVIS_DEFAULT_GT_PATH = "/work2/" + username() + "/data/DAVIS/train-val/val-gt/"
DAVIS_DEFAULT_PROPOSALS_PATH = "/home/" + username() + "/vision/maskrcnn_tensorpack/train_log/thesis1/notrain-val/"
DAVIS_IMAGE_SIZE = (480, 854)
@register_dataset("davisjono")
class DAVISjonoDataset(FileListDataset):
def __init__(self, config, subset, name="davisjono"):
self.image_dir = config.string("DAVIS_images_dir", DAVIS_DEFAULT_IMAGES_PATH)
self.gt_dir = config.string("DAVIS_gt_dir", DAVIS_DEFAULT_GT_PATH)
self.proposals_dir = config.string("DAVIS_proposals_dir", DAVIS_DEFAULT_PROPOSALS_PATH)
super().__init__(config, name, subset, DAVIS_DEFAULT_IMAGES_PATH, 2)
def read_inputfile_lists(self):
img_filenames = glob.glob(self.image_dir + "*/*.jpg")
seq_tags = [f.split("/")[-2] for f in img_filenames]
framenum = [f.split('/')[-1].split('.jpg')[0] for f in img_filenames]
label_filenames = [self.gt_dir + s + '/' + f + '.png' for s,f in zip(seq_tags,framenum)]
return img_filenames, label_filenames
#!/usr/bin/env python3
import glob
from PIL import Image
import numpy as np
from refinement_net.datasets.util.Util import username
import datasets.Mapillary
PATH = "/fastwork/" + username() + "/mywork/data/mapillary_quarter/"
MAPILLARY_CODE_PATH = datasets.Mapillary.__file__.replace("__init__.py", "")
for subset in ["training", "validation"]:
with open(MAPILLARY_CODE_PATH + subset, "w") as f:
instances = glob.glob(PATH + subset + "/instances/*.png")
for inst in instances:
x = np.array(Image.open(inst))
ids = np.unique(x)
im = inst.replace("/instances/",
"/images/").replace(".png", ".jpg")
print(im.replace(PATH, ""),
inst.replace(PATH, ""),
file=f,
sep=" ",
end="")
for id_ in ids:
n = (x == id_).sum()
print(" ", id_, ":", n, file=f, sep="", end="")
print(file=f)