"""
Downloads the CamVid data if necessary, and converts it to COCO.
"""
from os.path import relpath
from os.path import exists
from os.path import join
import ubelt as ub
def _devcheck_sample_full_image():
"""
"""
import kwimage
import numpy as np
sampler = grab_camvid_sampler()
cid_to_cidx = sampler.catgraph.id_to_idx
classes = sampler.catgraph
# Try loading an entire image
img, annots = sampler.load_image_with_annots(1)
file = img['imdata']
imdata = file[:]
aids = [ann['id'] for ann in annots]
_annots = sampler.dset.annots(aids)
sseg_list = []
for s in _annots.lookup('segmentation'):
m = kwimage.MultiPolygon.coerce(s)
sseg_list.append(m)
aids = _annots.aids
cids = _annots.cids
boxes = _annots.boxes
segmentations = kwimage.PolygonList(sseg_list)
class_idxs = np.array([cid_to_cidx[cid] for cid in cids])
dets = kwimage.Detections(
aids=aids,
boxes=boxes,
class_idxs=class_idxs,
segmentations=segmentations,
classes=classes,
datakeys=['aids'],
)
if 1:
print('dets = {!r}'.format(dets))
print('dets.data = {!r}'.format(dets.data))
print('dets.meta = {!r}'.format(dets.meta))
if ub.argflag('--show'):
import kwplot
with ub.Timer('dets.draw_on'):
canvas = imdata.copy()
canvas = dets.draw_on(canvas)
kwplot.imshow(canvas, pnum=(1, 2, 1), title='dets.draw_on')
with ub.Timer('dets.draw'):
kwplot.imshow(imdata, pnum=(1, 2, 2), docla=True, title='dets.draw')
dets.draw()
def _devcheck_load_sub_image():
import kwimage
import numpy as np
sampler = grab_camvid_sampler()
cid_to_cidx = sampler.catgraph.id_to_idx
classes = sampler.catgraph
# Try loading a subregion of an image
sample = sampler.load_positive(2)
imdata = sample['im']
annots = sample['annots']
aids = annots['aids']
cids = annots['cids']
boxes = annots['rel_boxes']
class_idxs = np.array([cid_to_cidx[cid] for cid in cids])
segmentations = annots['rel_ssegs']
raw_dets = kwimage.Detections(
aids=aids,
boxes=boxes,
class_idxs=class_idxs,
segmentations=segmentations,
classes=classes,
datakeys=['aids'],
)
# Clip boxes to the image boundary
input_dims = imdata.shape[0:2]
raw_dets.data['boxes'] = raw_dets.boxes.clip(0, 0, input_dims[1], input_dims[0])
keep = []
for i, s in enumerate(raw_dets.data['segmentations']):
# TODO: clip polygons
m = s.to_mask(input_dims)
if m.area > 0:
keep.append(i)
dets = raw_dets.take(keep)
heatmap = dets.rasterize(bg_size=(1, 1), input_dims=input_dims)
if 1:
print('dets = {!r}'.format(dets))
print('dets.data = {!r}'.format(dets.data))
print('dets.meta = {!r}'.format(dets.meta))
if ub.argflag('--show'):
import kwplot
kwplot.autompl()
heatmap.draw()
draw_boxes = 1
kwplot.figure(doclf=True)
with ub.Timer('dets.draw_on'):
canvas = imdata.copy()
# TODO: add logic to color by class
canvas = dets.draw_on(canvas, boxes=draw_boxes, color='random')
kwplot.imshow(canvas, pnum=(1, 2, 1), title='dets.draw_on')
with ub.Timer('dets.draw'):
kwplot.imshow(imdata, pnum=(1, 2, 2), docla=True, title='dets.draw')
dets.draw(boxes=draw_boxes, color='random')
[docs]def grab_camvid_train_test_val_splits(coco_dset, mode='segnet'):
# Use the split from SegNet: https://github.com/alexgkendall/SegNet-Tutorial
split_files = {
'train': ub.grabdata('https://raw.githubusercontent.com/alexgkendall/SegNet-Tutorial/master/CamVid/train.txt'),
'vali': ub.grabdata('https://raw.githubusercontent.com/alexgkendall/SegNet-Tutorial/master/CamVid/val.txt'),
'test': ub.grabdata('https://raw.githubusercontent.com/alexgkendall/SegNet-Tutorial/master/CamVid/test.txt'),
}
gid_subsets = {}
for tag, fpath in split_files.items():
text = open(fpath, 'r').read()
parts = text.replace('\n', ' ').split(' ')
parts = [p for p in parts if p]
from os.path import basename
names = sorted(set(basename(p) for p in parts))
gids = [coco_dset.index.file_name_to_img['701_StillsRaw_full/' + name]['id'] for name in names]
gid_subsets[tag] = gids
return gid_subsets
[docs]def grab_camvid_sampler():
"""
Grab a kwcoco.CocoSampler object for the CamVid dataset.
Returns:
kwcoco.CocoSampler: sampler
Example:
>>> # xdoctest: +REQUIRES(--download)
>>> sampler = grab_camvid_sampler()
>>> print('sampler = {!r}'.format(sampler))
>>> # sampler.load_sample()
>>> for gid in ub.ProgIter(sampler.image_ids, desc='load image'):
>>> img = sampler.load_image(gid)
"""
import kwcoco
dset = grab_coco_camvid()
workdir = ub.ensure_app_cache_dir('camvid')
sampler = kwcoco.CocoSampler(dset, workdir=workdir)
return sampler
[docs]def grab_coco_camvid():
"""
Example:
>>> # xdoctest: +REQUIRES(--download)
>>> dset = grab_coco_camvid()
>>> print('dset = {!r}'.format(dset))
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> plt = kwplot.autoplt()
>>> plt.clf()
>>> dset.show_image(gid=1)
Ignore:
import xdev
gid_list = list(dset.imgs)
for gid in xdev.InteractiveIter(gid_list):
dset.show_image(gid)
xdev.InteractiveIter.draw()
"""
import kwcoco
cache_dpath = ub.ensure_app_cache_dir('kwcoco', 'camvid')
coco_fpath = join(cache_dpath, 'camvid.mscoco.json')
# Need to manually bump this if you make a change to loading
SCRIPT_VERSION = 'v4'
# Ubelt's stamp-based caches are super cheap and let you take control of
# the data format.
stamp = ub.CacheStamp('camvid_coco', depends=[SCRIPT_VERSION],
dpath=cache_dpath, product=coco_fpath, hasher='sha1',
verbose=3)
if stamp.expired():
camvid_raw_info = grab_raw_camvid()
dset = convert_camvid_raw_to_coco(camvid_raw_info)
with ub.Timer('dumping MS-COCO dset to: {}'.format(coco_fpath)):
dset.dump(coco_fpath)
# Mark this process as completed by saving a small file containing the
# hash of the "product" you are stamping.
stamp.renew()
# We can also cache the index build step independently. This uses
# ubelt.Cacher, which is pickle based, and writes the actual object to
# disk. Each type of caching has its own uses and tradeoffs.
cacher = ub.Cacher('prebuilt-coco', depends=[SCRIPT_VERSION],
dpath=cache_dpath, verbose=3)
dset = cacher.tryload()
if dset is None:
print('Reading coco_fpath = {!r}'.format(coco_fpath))
dset = kwcoco.CocoDataset(coco_fpath, tag='camvid')
# Directly save the file to disk.
dset._build_index()
dset._build_hashid()
cacher.save(dset)
camvid_dset = dset
print('Loaded camvid_dset = {!r}'.format(camvid_dset))
return camvid_dset
[docs]def grab_raw_camvid():
"""
Grab the raw camvid data.
"""
import zipfile
dpath = ub.ensure_app_cache_dir('kwcoco', 'camvid')
# url = 'http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/data/LabeledApproved_full.zip'
# url = 'https://github.com/mostafaizz/camvid/archive/master.zip'
url = 'https://data.kitware.com/api/v1/item/5cc0adce8d777f072b643503/download'
zip_fpath = ub.grabdata(url, fname='camvid-master.zip', dpath=dpath)
dset_root = join(dpath, 'camvid-master')
image_dpath = join(dset_root, '701_StillsRaw_full')
mask_dpath = join(dset_root, 'LabeledApproved_full')
label_path = join(dset_root, 'label_colors.txt')
if not exists(image_dpath):
zip_ref = zipfile.ZipFile(zip_fpath, 'r')
zip_ref.extractall(dpath)
zip_ref.close()
import glob
img_paths = sorted([relpath(fpath, dset_root)
for fpath in glob.glob(join(image_dpath, '*.png'))])
mask_paths = sorted([relpath(fpath, dset_root)
for fpath in glob.glob(join(mask_dpath, '*.png'))])
camvid_raw_info = {
'img_paths': img_paths,
'mask_paths': mask_paths,
'dset_root': dset_root,
'label_path': label_path,
}
return camvid_raw_info
[docs]def rgb_to_cid(r, g, b):
cid = (r << 16) + (g << 8) + (b << 0)
return cid
[docs]def cid_to_rgb(cid):
mask_b = (int(2 ** 8) - 1) << 0
mask_g = (int(2 ** 8) - 1) << 8
mask_r = (int(2 ** 8) - 1) << 16
r = (cid & mask_b) >> 0
g = (cid & mask_g) >> 8
b = (cid & mask_r) >> 16
rgb = (r, g, b)
return rgb
[docs]def convert_camvid_raw_to_coco(camvid_raw_info):
"""
Converts the raw camvid format to an MSCOCO based format, ( which lets use
use kwcoco's COCO backend).
Example:
>>> # xdoctest: +REQUIRES(--download)
>>> camvid_raw_info = grab_raw_camvid()
>>> # test with a reduced set of data
>>> del camvid_raw_info['img_paths'][2:]
>>> del camvid_raw_info['mask_paths'][2:]
>>> dset = convert_camvid_raw_to_coco(camvid_raw_info)
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> plt = kwplot.autoplt()
>>> kwplot.figure(fnum=1, pnum=(1, 2, 1))
>>> dset.show_image(gid=1)
>>> kwplot.figure(fnum=1, pnum=(1, 2, 2))
>>> dset.show_image(gid=2)
"""
import re
import kwimage
import kwcoco
print('Converting CamVid to MS-COCO format')
dset_root, img_paths, label_path, mask_paths = ub.take(
camvid_raw_info, 'dset_root, img_paths, label_path, mask_paths'.split(', '))
img_infos = {
'img_fname': img_paths,
'mask_fname': mask_paths,
}
keys = list(img_infos.keys())
next_vals = list(zip(*img_infos.values()))
image_items = [{k: v for k, v in zip(keys, vals)} for vals in next_vals]
dataset = {
'img_root': dset_root,
'images': [],
'categories': [],
'annotations': [],
}
lines = ub.readfrom(label_path).split('\n')
lines = [line for line in lines if line]
for line in lines:
color_text, name = re.split('\t+', line)
r, g, b = map(int, color_text.split(' '))
color = (r, g, b)
# Parse the special camvid format
cid = (r << 16) + (g << 8) + (b << 0)
cat = {
'id': cid,
'name': name,
'color': color,
}
dataset['categories'].append(cat)
for gid, img_item in enumerate(image_items, start=1):
img = {
'id': gid,
'file_name': img_item['img_fname'],
# nonstandard image field
'segmentation': img_item['mask_fname'],
}
dataset['images'].append(img)
dset = kwcoco.CocoDataset(dataset)
dset.rename_categories({'Void': 'background'})
assert dset.name_to_cat['background']['id'] == 0
dset.name_to_cat['background'].setdefault('alias', []).append('Void')
if False:
_define_camvid_class_hierarcy(dset)
if 1:
# TODO: Binarize CCs (and efficiently encode if possible)
import numpy as np
bad_info = []
once = False
# Add images
dset.remove_annotations(list(dset.index.anns.keys()))
for gid, img in ub.ProgIter(dset.imgs.items(), desc='parse label masks'):
mask_fpath = join(dset_root, img['segmentation'])
rgb_mask = kwimage.imread(mask_fpath, space='rgb')
r, g, b = rgb_mask.T.astype(np.int64)
cid_mask = np.ascontiguousarray(rgb_to_cid(r, g, b).T)
cids = set(np.unique(cid_mask)) - {0}
for cid in cids:
if cid not in dset.cats:
if gid == 618:
# Handle a known issue with image 618
c_mask = (cid == cid_mask).astype(np.uint8)
total_bad = c_mask.sum()
if total_bad < 32:
if not once:
print('gid 618 has a few known bad pixels, ignoring them')
once = True
continue
else:
raise Exception('more bad pixels than expected')
else:
raise Exception('UNKNOWN cid = {!r} in gid={!r}'.format(cid, gid))
# bad_rgb = cid_to_rgb(cid)
# print('bad_rgb = {!r}'.format(bad_rgb))
# print('WARNING UNKNOWN cid = {!r} in gid={!r}'.format(cid, gid))
# bad_info.append({
# 'gid': gid,
# 'cid': cid,
# })
else:
ann = {
'category_id': cid,
'image_id': gid
# 'segmentation': mask.to_coco()
}
assert cid in dset.cats
c_mask = (cid == cid_mask).astype(np.uint8)
mask = kwimage.Mask(c_mask, 'c_mask')
box = kwimage.Boxes([mask.get_xywh()], 'xywh')
# box = mask.to_boxes()
ann['bbox'] = ub.peek(box.to_coco())
ann['segmentation'] = mask.to_coco()
dset.add_annotation(**ann)
if 0:
bad_cids = [i['cid'] for i in bad_info]
print(sorted([c['color'] for c in dataset['categories']]))
print(sorted(set([cid_to_rgb(i['cid']) for i in bad_info])))
gid = 618
img = dset.imgs[gid]
mask_fpath = join(dset_root, img['segmentation'])
rgb_mask = kwimage.imread(mask_fpath, space='rgb')
r, g, b = rgb_mask.T.astype(np.int64)
cid_mask = np.ascontiguousarray(rgb_to_cid(r, g, b).T)
cid_hist = ub.dict_hist(cid_mask.ravel())
bad_cid_hist = {}
for cid in bad_cids:
bad_cid_hist[cid] = cid_hist.pop(cid)
import kwplot
kwplot.autompl()
kwplot.imshow(rgb_mask)
if 0:
import kwplot
plt = kwplot.autoplt()
plt.clf()
dset.show_image(1)
import xdev
gid_list = list(dset.imgs)
for gid in xdev.InteractiveIter(gid_list):
dset.show_image(gid)
xdev.InteractiveIter.draw()
dset._build_index()
dset._build_hashid()
return dset
def _define_camvid_class_hierarcy(dset):
# add extra supercategories
# NOTE: life-conscious, and life-inanimate are disjoint in this
# forumlation because we are restricted to a tree structure. If
# this changse, then we can try rencoding with multiple parents.
extra_structure = {
# Break down the image into things that are part of the system, and
# things that aren't
'background': 'root',
'system': 'root',
# The system is made up of environmental components and actor
# components.
'environment': 'system',
'actor': 'system',
# Break actors (things with complex movement) into subtypes
'life-conscious': 'actor',
'vehicle-land': 'actor',
'actor-other': 'actor',
# Break the environment (things with simple movement) info subtypes
'life-inanimate': 'environment',
'civil-structure': 'environment',
'civil-notice': 'environment',
'transport-way': 'environment',
# Subclassify transport mediums
'drive-way': 'transport-way',
'walk-way': 'transport-way',
}
for child, parent in extra_structure.items():
if child in dset.name_to_cat:
dset.name_to_cat[child]['supercategory'] = parent
else:
dset.add_category(name=child, supercategory=parent)
dset.name_to_cat['background']['supercategory'] = 'root'
dset.name_to_cat['Sky']['supercategory'] = 'environment'
dset.name_to_cat['Animal']['supercategory'] = 'life-conscious'
dset.name_to_cat['Bicyclist']['supercategory'] = 'life-conscious'
dset.name_to_cat['Pedestrian']['supercategory'] = 'life-conscious'
dset.name_to_cat['Child']['supercategory'] = 'life-conscious'
dset.name_to_cat['OtherMoving']['supercategory'] = 'actor-other'
dset.name_to_cat['CartLuggagePram']['supercategory'] = 'actor-other'
dset.name_to_cat['Car']['supercategory'] = 'vehicle-land'
dset.name_to_cat['Train']['supercategory'] = 'vehicle-land'
dset.name_to_cat['Truck_Bus']['supercategory'] = 'vehicle-land'
dset.name_to_cat['SUVPickupTruck']['supercategory'] = 'vehicle-land'
dset.name_to_cat['MotorcycleScooter']['supercategory'] = 'vehicle-land'
dset.name_to_cat['VegetationMisc']['supercategory'] = 'life-inanimate'
dset.name_to_cat['Tree']['supercategory'] = 'life-inanimate'
dset.name_to_cat['Column_Pole']['supercategory'] = 'civil-structure'
dset.name_to_cat['Fence']['supercategory'] = 'civil-structure'
dset.name_to_cat['Wall']['supercategory'] = 'civil-structure'
dset.name_to_cat['Building']['supercategory'] = 'civil-structure'
dset.name_to_cat['Archway']['supercategory'] = 'civil-structure'
dset.name_to_cat['Bridge']['supercategory'] = 'civil-structure'
dset.name_to_cat['Tunnel']['supercategory'] = 'civil-structure'
dset.name_to_cat['TrafficCone']['supercategory'] = 'civil-notice'
dset.name_to_cat['TrafficLight']['supercategory'] = 'civil-notice'
dset.name_to_cat['LaneMkgsDriv']['supercategory'] = 'civil-notice'
dset.name_to_cat['LaneMkgsNonDriv']['supercategory'] = 'civil-notice'
dset.name_to_cat['SignSymbol']['supercategory'] = 'civil-notice'
dset.name_to_cat['ParkingBlock']['supercategory'] = 'civil-notice'
dset.name_to_cat['Misc_Text']['supercategory'] = 'civil-notice'
dset.name_to_cat['Road']['supercategory'] = 'drive-way'
dset.name_to_cat['RoadShoulder']['supercategory'] = 'drive-way'
dset.name_to_cat['Sidewalk']['supercategory'] = 'walk-way'
for cat in list(dset.cats.values()):
parent = cat.get('supercategory', None)
if parent is not None:
if parent not in dset.name_to_cat:
print('Missing parent = {!r}'.format(parent))
dset.add_category(name=parent, supercategory=parent)
if 0:
graph = dset.category_graph()
import graphid
graphid.util.show_nx(graph)
# Add in some hierarcy information
if 0:
for x in dset.name_to_cat:
print("dset.name_to_cat[{!r}]['supercategory'] = 'object'".format(x))
if 0:
example_cat_aids = []
for cat in dset.cats.values():
cname = cat['name']
aids = dset.index.cid_to_aids[dset.name_to_cat[cname]['id']]
if len(aids):
aid = ub.peek(aids)
example_cat_aids.append(aid)
else:
print('No examples of cat = {!r}'.format(cat))
import xdev
import kwplot
kwplot.autompl()
for aid in xdev.InteractiveIter(example_cat_aids):
print('aid = {!r}'.format(aid))
ann = dset.anns[aid]
cat = dset.cats[ann['category_id']]
print('cat = {!r}'.format(cat))
dset.show_image(aid=aid)
xdev.InteractiveIter.draw()
if 0:
cname = 'CartLuggagePram'
cname = 'ParkingBlock'
cname = 'LaneMkgsDriv'
aids = dset.index.cid_to_aids[dset.name_to_cat[cname]['id']]
if len(aids):
aid = ub.peek(aids)
print('aid = {!r}'.format(aid))
ann = dset.anns[aid]
cat = dset.cats[ann['category_id']]
print('cat = {!r}'.format(cat))
dset.show_image(aid=aid)
[docs]def main():
"""
Dump the paths to the coco file to stdout
By default these will go to in the path:
~/.cache/kwcoco/camvid/camvid-master
The four files will be:
~/.cache/kwcoco/camvid/camvid-master/camvid-full.mscoco.json
~/.cache/kwcoco/camvid/camvid-master/camvid-train.mscoco.json
~/.cache/kwcoco/camvid/camvid-master/camvid-vali.mscoco.json
~/.cache/kwcoco/camvid/camvid-master/camvid-test.mscoco.json
"""
# import scriptconfig as scfg
# class GrabCamVid_Config(scfg.Config):
# default = {
# 'dpath': scfg.Path(
# ub.get_app_cache_dir('kwcoco', 'camvid'), help='download location')
# }
# config = GrabCamVid_Config()
coco_dset = grab_coco_camvid()
# Use the same train/test/vali splits used in segnet
gid_subsets = grab_camvid_train_test_val_splits(coco_dset, mode='segnet')
dpath = coco_dset.dataset['img_root']
# Dump the full dataset
fpath = join(dpath, 'camvid-full.mscoco.json')
print(fpath)
coco_dset.dump(open(fpath, 'w'))
# Dump the train/vali/test splits
for tag, gids in gid_subsets.items():
subset = coco_dset.subset(gids)
fpath = join(dpath, 'camvid-{}.mscoco.json'.format(tag))
print(fpath)
subset.dump(open(fpath, 'w'))
if __name__ == '__main__':
"""
CommandLine:
python -m kwcoco.data.grab_camvid
"""
main()