def __getitem__(self, index):
image, labels = self.dataset[index]
np_arr = np.asarray(image)
ptimage = PTImage.from_numpy_array(np_arr)
objects = []
for t in labels:
box = Box.from_xywh(t['bbox'])
obj_type = self.coco.loadCats([t['category_id']])[0]['name']
# convert segmentation to polygon using the pycocotools
# note the segmentation could in one of several formats, for example the custom coco RLE,
# to convert the RLE back to polygon is bit of a pain so I will just ignore those right now
# according the COCO site, most of the data is in polygon form (not sure why theres a discrepency?)
# and I'd rather not store 2D binary masks with every object.
polygon = t.get('segmentation')
# reshape to 2d poly, assume its convex hull?
polys = []
if polygon and isinstance(polygon, list):
for seg in polygon:
polys.append(
Polygon(
np.array(seg).reshape((int(old_div(len(seg),
2)), 2))))
objects.append(Object(box, obj_type=obj_type, polygons=polys))
frame = Frame.from_image_and_objects(ptimage, objects)
return frame
def __getitem__(self,index):
pil_img,label = self.dataset[index]
# assert 2D here
np_arr = np.asarray(pil_img)
np_arr = np.expand_dims(np_arr, axis=2)
# create the PTImage, and object that span the frame
# add extra channel dimension
ptimage = PTImage.from_numpy_array(np_arr)
obj = Object(Box(0,0,pil_img.size[0],pil_img.size[1]))
frame = Frame.from_image_and_objects(ptimage,[obj])
return frame