def polygons_to_bitmask(polygons: List[np.ndarray], height: int,
width: int) -> np.ndarray:
"""
Args:
polygons (list[ndarray]): each array has shape (Nx2,)
height, width (int)
Returns:
ndarray: a bool mask of shape (height, width)
"""
assert len(polygons) > 0, "COCOAPI does not support empty polygons"
rles = mask_util.frPyObjects(polygons, height, width)
if len(rles) >= 2:
import pycocotools._mask as _mask
rle = _mask.decode(rles)
for i in range(rle.shape[2] - 1):
if i == 0:
temp = rle[:, :, i]
temp = np.logical_xor(temp, rle[:, :, i + 1])
rle = temp.astype('uint8').reshape([28, 28, 1])
rle = _mask.encode(rle)
rle = mask_util.merge(rle)
else:
rle = mask_util.merge(rles)
return mask_util.decode(rle).astype(np.bool)
def createInstanceImage_old(annotations, img): # create and draw annotation mask. semImg = np.zeros((img['height'], img['width']), dtype = np.uint8) for ann in annotations: # pdb.set_trace() catgr_id = ann['category_id'] # c = ([catgr_id, catgr_id,catgr_id]).tolist()[0] if type(ann['segmentation']) == list: # polygon for seg in ann['segmentation']: seg = [round(elem, 0) for elem in seg] # rounding seg = map(int, seg) # convert to int, floor poly = np.array(seg).reshape((len(seg)/2), 2) canvas = np.zeros((img['height'],img['width'],1), dtype = int) poly = poly.tolist() poly = [tuple([elem[1],elem[0]]) for elem in poly] mahotas.polygon.fill_polygon(poly, canvas, catgr_id) idx = canvas.nonzero() annImg[idx[0],idx[1]] = catgr_id else: # if type(ann['segmentation']) == dict # mask if type(ann['segmentation']['counts']) == list: rle = mask.frPyObjects([ann['segmentation']], img['height'], img['width']) else: rle = [ann['segmentation']] m = mask.decode(rle) idx = m.nonzero() annImg[idx[0],idx[1]] = catgr_id return annImg
def load_annotations(self, image_index, mask=True):
# get ground truth annotations
annotations_ids = self.coco.getAnnIds(
imgIds=self.image_ids[image_index], iscrowd=False)
annotations = np.zeros((0, 5))
# some images appear to miss annotations (like image with id 257034)
if len(annotations_ids) == 0:
return annotations, None
# parse annotations
coco_annotations = self.coco.loadAnns(annotations_ids)
for idx, a in enumerate(coco_annotations):
# some annotations have basically no width / height, skip them
if a['bbox'][2] < 1 or a['bbox'][3] < 1:
continue
annotation = np.zeros((1, 5))
annotation[0, :4] = a['bbox']
annotation[0, 4] = self.coco_label_to_label(a['category_id'])
annotations = np.append(annotations, annotation, axis=0)
# transform from [x, y, w, h] to [x1, y1, x2, y2]
annotations[:, 2] = annotations[:, 0] + annotations[:, 2]
annotations[:, 3] = annotations[:, 1] + annotations[:, 3]
if all([("counts" in x) for x in coco_annotations]):
masks = decode(coco_annotations)
classes_ = annotations[:, -1].astype(np.uint64)
if self.sparse:
mask = np.zeros(masks.shape[:2], dtype=np.uint8)
for nn, cc in enumerate(classes_):
cc = int(cc + 1)
mask = np.maximum(cc * (masks[:, :, nn] > 0), mask)
else:
mask = np.zeros((masks.shape[:2] + (1 + len(self.classes), )),
dtype=np.uint8)
for nn, cc in enumerate(classes_):
cc = int(cc + 1)
mask[:, :, cc] = np.maximum(mask[:, :, cc], masks[:, :,
nn])
# ADD BACKGROUND CHANNEL
mask[:, :, 0] = 1 - mask.sum(2, )
#padded_masks = .cat((bgmask, padded_masks), dim=1)
else:
mask = None
return annotations, mask
def createInstanceImage(annotations, img):
# an image we want to create
instanceImg = Image.new("I", (img.shape[1], img.shape[0]), 0)
semanticImg = Image.new("I", (img.shape[1], img.shape[0]), 0)
#directionImg = Image.new("I", (img['width'], img['height']), 0)
# a drawer to draw into the image
drawer_ins = ImageDraw.Draw(instanceImg)
drawer_sem = ImageDraw.Draw(semanticImg)
#drawer_dir = ImageDraw.Draw(directionImg)
#loop over all objects
insId_list = []
instanceId = 0
for ann in annotations:
if ann['id'] not in insId_list:
insId_list.append(ann['id'])
instanceId = instanceId + 1
if type(ann['segmentation']) == list:
# polygon
for seg in ann['segmentation']:
poly = np.array(seg).reshape((len(seg) / 2), 2)
poly = poly.tolist()
poly = [tuple([elem[0], elem[1]]) for elem in poly]
# pdb.set_trace()
drawer_ins.polygon(poly, fill=instanceId,
outline=None) # outline = None
drawer_sem.polygon(poly, fill=ann['category_id'], outline=None)
elif False: # (type(ann['segmentation'])== dict): ignore crowd object.
# mask
if type(ann['segmentation']['counts']) == list:
rle = mask.frPyObjects([ann['segmentation']], img.shape[0],
img.shape[1])
else:
rle = [ann['segmentation']]
m = mask.decode(rle)
idx = m.nonzero()
points = [
tuple([elem[1], elem[0]]) for elem in zip(idx[0], idx[1])
]
drawer_ins.point(points, fill=instanceId)
drawer_sem.point(points, fill=ann['category_id'])
return instanceImg, semanticImg
def decode(rleObjs):
if type(rleObjs) == list:
return _mask.decode(rleObjs)
else:
return _mask.decode([rleObjs])[:,:,0]
def decode(rleObjs):
if isinstance(rleObjs, list):
return _mask.decode(rleObjs)
else:
return _mask.decode([rleObjs])[:, :, 0]
def decode(self):
return decode([self.enc])[:, :, 0]
def mask_as_poly_list(mask):
polygons = Mask(_mask.decode([mask])).polygons().points
polygons = map(lambda p: p.reshape(-1), polygons) # Detectron requirement
polygons = filter(lambda p: len(p) >= 6, polygons) # Detectron requirement
return list(polygons)
def decode(rleObjs):
if type(rleObjs) == list:
return _mask.decode(rleObjs)
else:
return _mask.decode([rleObjs])[:,:,0]
