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 from_dict(cls, objdict):
#print objdict
box = Box(objdict['box']['min'][0], objdict['box']['max'][0],
objdict['box']['min'][1], objdict['box']['max'][1])
uid = objdict['unique_identifier']
obj_type = objdict['attributes']['type']
return cls(box, uid, obj_type)
def find_negative_crop(self, frame, objects):
# pick a random crop, check that it does not overlap with an existing target
# TODO, this is inefficient, fix this algorithm later
frame_size = frame.image.get_wh()
max_attempts = 10
for i in range(0, max_attempts):
randcx = random.randrange(self.crop_size[0] / 2,
frame_size[0] - self.crop_size[0] / 2)
randcy = random.randrange(self.crop_size[1] / 2,
frame_size[1] - self.crop_size[1] / 2)
new_box = Box(randcx - self.crop_size[0] / 2,
randcy - self.crop_size[1] / 2,
randcx + self.crop_size[0] / 2,
randcy + self.crop_size[1] / 2)
box_found = all(
Box.intersection(x.box, new_box) is None for x in objects)
if box_found:
return new_box
return None
def test_tensor_to_boxes(self):
box0 = [0, 0, 1, 1]
box1 = [2, 2, 3, 3]
t1 = torch.Tensor([box0, box1])
boxes = Box.tensor_to_boxes(t1)
tboxes0 = boxes[0].to_single_array()
tboxes1 = boxes[1].to_single_array()
self.assertTrue(all(box0[i] == tboxes0[i]
for i in range(len(tboxes0))))
self.assertTrue(all(box1[i] == tboxes1[i]
for i in range(len(tboxes1))))
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
def perturb_frame(frame,params):
dims = frame.image.get_hw()
rand_affine = RandomPerturber.generate_random_affine(dims/2,dims,params)
perturbed_frame = Frame(frame.image_path)
perturbed_frame.image = rand_affine.apply_to_image(frame.image,dims)
for i,obj in enumerate(frame.objects):
# filter out completely out of bound objects
perturbed_obj_box = rand_affine.apply_to_box(obj.box)
perturbed_polygons = rand_affine.apply_to_polygons(obj.polygons)
if Box.intersection(perturbed_obj_box,perturbed_frame.image.get_bounding_box()) is not None:
obj_copy = copy.deepcopy(obj)
obj_copy.box = perturbed_obj_box
obj_copy.polygons = perturbed_polygons
perturbed_frame.objects.append(obj_copy)
return perturbed_frame
def apply_affine_to_frame(frame, affine, output_size):
perturbed_frame = Frame(frame.image_path)
perturbed_frame.image = affine.apply_to_image(frame.image, output_size)
for i, obj in enumerate(frame.objects):
# filter out completely out of bound objects
perturbed_obj_box = affine.apply_to_box(obj.box)
perturbed_polygons = affine.apply_to_polygons(obj.polygons)
if Box.intersection(
perturbed_obj_box,
perturbed_frame.image.get_bounding_box()) is not None:
obj_copy = copy.deepcopy(obj)
obj_copy.box = perturbed_obj_box
obj_copy.polygons = perturbed_polygons
perturbed_frame.objects.append(obj_copy)
return perturbed_frame
def __load_frames(self, cars_dir, labels_mat):
print('Loading Stanford Cars Frames')
labels = scipy.io.loadmat(labels_mat)['annotations'][0]
# load frames with labels
for label in labels:
if len(label) == 5:
xmin, ymin, xmax, ymax, path = label
elif len(label) == 6:
xmin, ymin, xmax, ymax, _, path = label
else:
assert False, 'unable to parse label!'
box = Box(float(xmin[0][0]), float(ymin[0][0]), float(xmax[0][0]),
float(ymax[0][0]))
obj = Object(box, obj_type='car')
image_path = os.path.join(cars_dir, path[0])
self.frames.append(Frame(image_path, [obj]))
def unapply_to_box(self,box):
transformed_box = np.dot(self.inverse,box.augmented_matrix())
return Box.from_augmented_matrix(transformed_box)
def to_object(self):
box_format = [self.bbox[0], self.bbox[1], self.bbox[2], self.bbox[3]]
return Object(Box.from_single_array(box_format), self.track_idx,
self.type)
def load_train(self):
frame1, frame2, neg_box, pos_box, anchor_box = None, None, None, None, None
# TODO, this should probably break if never find anything for a while
while neg_box is None:
indices = random.sample(self.frame_ids, 2)
frame1, frame2 = [self.source[x] for x in indices]
frame1_objs = filter(lambda x: x.obj_type in self.obj_types,
frame1.get_objects())
frame2_objs = filter(lambda x: x.obj_type in self.obj_types,
frame2.get_objects())
# get random pos boxes
pos_box = random.choice(frame1_objs).box
anchor_box = random.choice(frame2_objs).box
# find random neg crop
neg_box = self.find_negative_crop(frame1, frame1_objs)
perturbed_pos_box = RandomPerturber.perturb_crop_box(
pos_box, self.perturbations)
affine_crop0 = crop_image_resize(frame1.image, perturbed_pos_box,
self.crop_size)
pos_crop = affine_crop0.apply_to_image(frame1.image, self.crop_size)
affine_crop1 = crop_image_resize(frame2.image, anchor_box,
self.anchor_size)
anchor_crop = affine_crop1.apply_to_image(frame2.image,
self.anchor_size)
affine_crop2 = crop_image_resize(frame1.image, neg_box, self.crop_size)
neg_crop = affine_crop2.apply_to_image(frame1.image, self.crop_size)
# neg_crop.visualize(display=True,title='neg')
# now find all the boxes that intersect with the perturbed_pos_box
intersected_boxes = []
for obj in filter(lambda x: x.obj_type in self.obj_types,
frame1.get_objects()):
if Box.intersection(obj.box, perturbed_pos_box) is not None:
intersected_boxes.append(obj.box)
intersected_boxes = list(
map(lambda x: affine_crop0.apply_to_box(x), intersected_boxes))
# test display
# disp_frame = Frame.from_image_and_objects(pos_crop,[Object(box_crop)])
# disp_frame.visualize(display=True,title='pos frame')
# pos_crop.visualize(display=True,title='pos crop')
pos = torch.Tensor(
pos_crop.to_order_and_class(
Ordering.CHW, ValueClass.FLOAT01).get_data().astype(float))
neg = torch.Tensor(
neg_crop.to_order_and_class(
Ordering.CHW, ValueClass.FLOAT01).get_data().astype(float))
anchor = torch.Tensor(
anchor_crop.to_order_and_class(
Ordering.CHW, ValueClass.FLOAT01).get_data().astype(float))
# pos_map = generate_response_map_from_boxes(pos_crop.get_hw(),intersected_boxes)
# PTImage.from_2d_numpy(pos_map).visualize(display=True,title='pos frame')
pos_map = torch.Tensor(
generate_response_map_from_boxes(pos_crop.get_hw(),
intersected_boxes))
neg_map = torch.Tensor(
generate_response_map_from_boxes(pos_crop.get_hw()))
data = [pos, neg, anchor]
target = [pos_map, neg_map, anchor]
return TripletDetectionSample(data, target)
def __convert_to_objects(self, boxes, classes):
boxlist = Box.tensor_to_boxes(boxes.cpu())
objects = []
for x, y in zip(*(boxlist, classes.cpu().numpy())):
objects.append(Object(x, 0, self.class_lookup[y]))
return objects
