def pologons_to_mask(polygons, size):
height, width = size
# formatting for COCO PythonAPI
rles = mask_utils.frPyObjects(polygons, height, width)
rle = mask_utils.merge(rles)
mask = mask_utils.decode(rle)
return mask
def annToRLE(self, ann):
"""
Convert annotation which can be polygons, uncompressed RLE to RLE.
:return: binary mask (numpy 2D array)
"""
t = self.imgs[ann['image_id']]
h, w = t['height'], t['width']
segm = ann['segmentation']
if type(segm) == list:
# polygon -- a single object might consist of multiple parts
# we merge all parts into one mask rle code
rles = maskUtils.frPyObjects(segm, h, w)
rle = maskUtils.merge(rles)
elif type(segm['counts']) == list:
# uncompressed RLE
rle = maskUtils.frPyObjects(segm, h, w)
else:
# rle
rle = ann['segmentation']
return rle
def convert_coco_poly_to_mask(segmentations, height, width):
masks = []
for polygons in segmentations:
rles = coco_mask.frPyObjects(polygons, height, width)
mask = coco_mask.decode(rles)
if len(mask.shape) < 3:
mask = mask[..., None]
mask = torch.as_tensor(mask, dtype=torch.uint8)
mask = mask.any(dim=2)
masks.append(mask)
if masks:
masks = torch.stack(masks, dim=0)
else:
masks = torch.zeros((0, height, width), dtype=torch.uint8)
return masks
def __init__(self, id, category, category_id, bbox, area, segment,
crop_builder, image, which_set):
self.id = id
self.category = category
self.category_id = category_id
self.bbox = bbox
self.area = area
self.segment = segment
# https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocotools/mask.py
self.rle_mask = None
if use_coco:
self.rle_mask = cocoapi.frPyObjects(self.segment,
h=image.height,
w=image.width)
# logger.info("Guess_What = {} ".format(self.rle_mask))
# logger.info("get_mask_shape A= {} ".format(self.get_mask().shape ))
# get_mask_or = self.get_mask()
# get_mask = np.reshape(get_mask_or,(360,640))
# logger.info("get_mask_shape B= {} ".format(get_mask.shape))
# img_path = os.path.join("data/img/raw", "{}.jpg".format(image.id))
# img = PIL_Image.open(img_path).convert('RGB')
# img = resize_image(img, self.width , self.height)
# # logger.info("/*/*/*/*/******* Image ********/*/*/*/*/*/*/*/ {}".format(self.img_path))
# # logger.info("img_shape = {} {} ".format(self.width,self.height))
# img_segment = np.multiply(img,get_mask_or)
# plt.imshow(img_segment)
# # plt.imshow(get_mask)
# plt.show()
if crop_builder is not None:
filename = "{}.jpg".format(image.id)
# logger.info("id = {} ,filemane = {} ,which_set = {},bbox = {}".format(id,filename,which_set,bbox))
self.crop_loader = crop_builder.build(
id, filename=filename, which_set=which_set,
bbox=bbox) # logger.info("Image_id=",image.id)
def __init__(self, crop_id, category, category_id, bbox, area, segment,
crop_builder, image):
self.id = crop_id
self.category = category
self.category_id = category_id
self.bbox = bbox
self.area = area
self.segment = segment
# https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocotools/mask.py
if type(segment) == dict or type(segment[0]) == list: # polygon
self.rle_mask = cocoapi.frPyObjects(segment,
h=image.height,
w=image.width)
else:
self.rle_mask = segment
if crop_builder is not None:
self.crop_loader = crop_builder.build(crop_id,
filename=image.filename,
bbox=bbox)
self.crop_scale = crop_builder.scale
def __init__(self, id, category, category_id, bbox, area, segment,
crop_builder, image, which_set):
self.id = id
self.category = category
self.category_id = category_id
self.bbox = bbox
self.area = area
self.segment = segment
# https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocotools/mask.py
self.rle_mask = None
if use_coco:
self.rle_mask = cocoapi.frPyObjects(self.segment,
h=image.height,
w=image.width)
self.crop_loader = None
if crop_builder is not None:
filename = "{}.jpg".format(image.id)
self.crop_loader = crop_builder.build(id,
filename=filename,
which_set=which_set,
bbox=bbox)
self.crop_scale = crop_builder.scale
def showAnns(self, anns):
"""
Display the specified annotations.
:param anns (array of object): annotations to display
:return: None
"""
if len(anns) == 0:
return 0
if 'segmentation' in anns[0] or 'keypoints' in anns[0]:
datasetType = 'instances'
elif 'caption' in anns[0]:
datasetType = 'captions'
else:
raise Exception('datasetType not supported')
if datasetType == 'instances':
ax = plt.gca()
ax.set_autoscale_on(False)
polygons = []
color = []
for ann in anns:
c = (np.random.random((1, 3)) * 0.6 + 0.4).tolist()[0]
if 'segmentation' in ann:
if type(ann['segmentation']) == list:
# polygon
for seg in ann['segmentation']:
poly = np.array(seg).reshape(
(int(len(seg) / 2), 2))
polygons.append(Polygon(poly))
color.append(c)
else:
# mask
t = self.imgs[ann['image_id']]
if type(ann['segmentation']['counts']) == list:
rle = maskUtils.frPyObjects([ann['segmentation']],
t['height'],
t['width'])
else:
rle = [ann['segmentation']]
m = maskUtils.decode(rle)
img = np.ones((m.shape[0], m.shape[1], 3))
if ann['iscrowd'] == 1:
color_mask = np.array([2.0, 166.0, 101.0]) / 255
if ann['iscrowd'] == 0:
color_mask = np.random.random((1, 3)).tolist()[0]
for i in range(3):
img[:, :, i] = color_mask[i]
ax.imshow(np.dstack((img, m * 0.5)))
if 'keypoints' in ann and type(ann['keypoints']) == list:
# turn skeleton into zero-based index
sks = np.array(
self.loadCats(ann['category_id'])[0]['skeleton']) - 1
kp = np.array(ann['keypoints'])
x = kp[0::3]
y = kp[1::3]
v = kp[2::3]
for sk in sks:
if np.all(v[sk] > 0):
plt.plot(x[sk], y[sk], linewidth=3, color=c)
plt.plot(x[v > 0],
y[v > 0],
'o',
markersize=8,
markerfacecolor=c,
markeredgecolor='k',
markeredgewidth=2)
plt.plot(x[v > 1],
y[v > 1],
'o',
markersize=8,
markerfacecolor=c,
markeredgecolor=c,
markeredgewidth=2)
p = PatchCollection(polygons,
facecolor=color,
linewidths=0,
alpha=0.4)
ax.add_collection(p)
p = PatchCollection(polygons,
facecolor='none',
edgecolors=color,
linewidths=2)
ax.add_collection(p)
elif datasetType == 'captions':
for ann in anns:
print(ann['caption'])