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| import json | |
| import numpy as np | |
| import torch | |
| def convert_to_braille_unicode(str_input: str, path: str = "./braille_map.json") -> str: | |
| with open(path, "r") as fl: | |
| data = json.load(fl) | |
| if str_input in data.keys(): | |
| str_output = data[str_input] | |
| return str_output | |
| def parse_xywh_and_class(boxes: torch.Tensor) -> list: | |
| """ | |
| boxes input tensor | |
| boxes (torch.Tensor) or (numpy.ndarray): A tensor or numpy array containing the detection boxes, | |
| with shape (num_boxes, 6). | |
| orig_shape (torch.Tensor) or (numpy.ndarray): Original image size, in the format (height, width). | |
| Properties: | |
| xyxy (torch.Tensor) or (numpy.ndarray): The boxes in xyxy format. | |
| conf (torch.Tensor) or (numpy.ndarray): The confidence values of the boxes. | |
| cls (torch.Tensor) or (numpy.ndarray): The class values of the boxes. | |
| xywh (torch.Tensor) or (numpy.ndarray): The boxes in xywh format. | |
| xyxyn (torch.Tensor) or (numpy.ndarray): The boxes in xyxy format normalized by original image size. | |
| xywhn (torch.Tensor) or (numpy.ndarray): The boxes in xywh format normalized by original image size. | |
| """ | |
| # copy values from troublesome "boxes" object to numpy array | |
| new_boxes = np.zeros(boxes.shape) | |
| new_boxes[:, :4] = boxes.xywh.numpy() # first 4 channels are xywh | |
| new_boxes[:, 4] = boxes.conf.numpy() # 5th channel is confidence | |
| new_boxes[:, 5] = boxes.cls.numpy() # 6th channel is class which is last channel | |
| # sort according to y coordinate | |
| new_boxes = new_boxes[new_boxes[:, 1].argsort()] | |
| # find threshold index to break the line | |
| y_threshold = np.mean(new_boxes[:, 3]) // 2 | |
| boxes_diff = np.diff(new_boxes[:, 1]) | |
| threshold_index = np.where(boxes_diff > y_threshold)[0] | |
| # cluster according to threshold_index | |
| boxes_clustered = np.split(new_boxes, threshold_index + 1) | |
| boxes_return = [] | |
| for cluster in boxes_clustered: | |
| # sort according to x coordinate | |
| cluster = cluster[cluster[:, 0].argsort()] | |
| boxes_return.append(cluster) | |
| return boxes_return | |