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| import numpy as np | |
| import torch | |
| from PIL import Image | |
| def adjust_bboxes_to_image_border(boxes, image_shape, threshold=20): | |
| '''Adjust bounding boxes to stick to image border if they are within a certain threshold. | |
| Args: | |
| boxes: (n, 4) | |
| image_shape: (height, width) | |
| threshold: pixel threshold | |
| Returns: | |
| adjusted_boxes: adjusted bounding boxes | |
| ''' | |
| # Image dimensions | |
| h, w = image_shape | |
| # Adjust boxes | |
| boxes[:, 0] = torch.where(boxes[:, 0] < threshold, torch.tensor( | |
| 0, dtype=torch.float, device=boxes.device), boxes[:, 0]) # x1 | |
| boxes[:, 1] = torch.where(boxes[:, 1] < threshold, torch.tensor( | |
| 0, dtype=torch.float, device=boxes.device), boxes[:, 1]) # y1 | |
| boxes[:, 2] = torch.where(boxes[:, 2] > w - threshold, torch.tensor( | |
| w, dtype=torch.float, device=boxes.device), boxes[:, 2]) # x2 | |
| boxes[:, 3] = torch.where(boxes[:, 3] > h - threshold, torch.tensor( | |
| h, dtype=torch.float, device=boxes.device), boxes[:, 3]) # y2 | |
| return boxes | |
| def convert_box_xywh_to_xyxy(box): | |
| x1 = box[0] | |
| y1 = box[1] | |
| x2 = box[0] + box[2] | |
| y2 = box[1] + box[3] | |
| return [x1, y1, x2, y2] | |
| def bbox_iou(box1, boxes, iou_thres=0.9, image_shape=(640, 640), raw_output=False): | |
| '''Compute the Intersection-Over-Union of a bounding box with respect to an array of other bounding boxes. | |
| Args: | |
| box1: (4, ) | |
| boxes: (n, 4) | |
| Returns: | |
| high_iou_indices: Indices of boxes with IoU > thres | |
| ''' | |
| boxes = adjust_bboxes_to_image_border(boxes, image_shape) | |
| # obtain coordinates for intersections | |
| x1 = torch.max(box1[0], boxes[:, 0]) | |
| y1 = torch.max(box1[1], boxes[:, 1]) | |
| x2 = torch.min(box1[2], boxes[:, 2]) | |
| y2 = torch.min(box1[3], boxes[:, 3]) | |
| # compute the area of intersection | |
| intersection = (x2 - x1).clamp(0) * (y2 - y1).clamp(0) | |
| # compute the area of both individual boxes | |
| box1_area = (box1[2] - box1[0]) * (box1[3] - box1[1]) | |
| box2_area = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1]) | |
| # compute the area of union | |
| union = box1_area + box2_area - intersection | |
| # compute the IoU | |
| iou = intersection / union # Should be shape (n, ) | |
| if raw_output: | |
| if iou.numel() == 0: | |
| return 0 | |
| return iou | |
| # get indices of boxes with IoU > thres | |
| high_iou_indices = torch.nonzero(iou > iou_thres).flatten() | |
| return high_iou_indices | |
| def image_to_np_ndarray(image): | |
| if type(image) is str: | |
| return np.array(Image.open(image)) | |
| elif issubclass(type(image), Image.Image): | |
| return np.array(image) | |
| elif type(image) is np.ndarray: | |
| return image | |
| return None | |