Пример #1
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 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, copy.deepcopy(frame.objects))
     perturbed_frame.image = rand_affine.apply_to_image(frame.image, dims)
     for i, obj in enumerate(frame.objects):
         perturbed_frame.objects[i].box = rand_affine.apply_to_box(obj.box)
     return perturbed_frame
Пример #2
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 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
Пример #3
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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
Пример #4
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 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
Пример #5
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    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
Пример #6
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 def __load_labelled_frames(self, frame_dir, labels_file):
     files = [f for f in listdir(frame_dir) if isfile(join(frame_dir, f))]
     labels = KITTILabel.labels_from_file(
         labels_file) if labels_file is not None else []
     frames = []
     for f in files:
         file_index = int(splitext(basename(f))[0])
         objects = []
         for l in labels:
             if int(l.frame_idx) == file_index:
                 objects.append(l.to_object())
         frames.append(Frame(join(frame_dir, f), objects))
     return frames
Пример #7
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 def __load_labelled_frames(self,frame_dir,labels_file,calib_file=None):
     files = [f for f in listdir(frame_dir) if isfile(join(frame_dir, f))]
     labels = KITTILabel.labels_from_file(labels_file) if labels_file is not None else []
     calibration_mat = None
     if os.path.isfile(calib_file):
         calibration_mat = self.__load_camera_matrix_from_calib(calib_file)
     frames = []
     sorted_files = sorted(files, key=lambda x: int(splitext(basename(x))[0]))
     for f in sorted_files:
         file_index = int(splitext(basename(f))[0])
         objects = []
         for l in labels:
             if int(l.frame_idx) == file_index:
                 objects.append(l.to_object())
         frames.append(Frame(join(frame_dir,f),objs=objects,calib_mat=calibration_mat))
     return frames
Пример #8
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 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]))