Spaces:
Running
on
A10G
Running
on
A10G
File size: 5,253 Bytes
2de1f98 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 |
from typing import Literal, Union
def process_mmdet_results(mmdet_results: list,
cat_id: int = 0,
multi_person: bool = True) -> list:
"""Process mmdet results, sort bboxes by area in descending order.
Args:
mmdet_results (list):
Result of mmdet.apis.inference_detector
when the input is a batch.
Shape of the nested lists is
(n_frame, n_category, n_human, 5).
cat_id (int, optional):
Category ID. This function will only select
the selected category, and drop the others.
Defaults to 0, ID of human category.
multi_person (bool, optional):
Whether to allow multi-person detection, which is
slower than single-person. If false, the function
only assure that the first person of each frame
has the biggest bbox.
Defaults to True.
Returns:
list:
A list of detected bounding boxes.
Shape of the nested lists is
(n_frame, n_human, 5)
and each bbox is (x, y, x, y, score).
"""
ret_list = []
only_max_arg = not multi_person
# for _, frame_results in enumerate(mmdet_results):
cat_bboxes = mmdet_results[cat_id]
# import pdb; pdb.set_trace()
sorted_bbox = qsort_bbox_list(cat_bboxes, only_max_arg)
if only_max_arg:
ret_list.append(sorted_bbox[0:1])
else:
ret_list.append(sorted_bbox)
return ret_list
def qsort_bbox_list(bbox_list: list,
only_max: bool = False,
bbox_convention: Literal['xyxy', 'xywh'] = 'xyxy'):
"""Sort a list of bboxes, by their area in pixel(W*H).
Args:
input_list (list):
A list of bboxes. Each item is a list of (x1, y1, x2, y2)
only_max (bool, optional):
If True, only assure the max element at first place,
others may not be well sorted.
If False, return a well sorted descending list.
Defaults to False.
bbox_convention (str, optional):
Bbox type, xyxy or xywh. Defaults to 'xyxy'.
Returns:
list:
A sorted(maybe not so well) descending list.
"""
# import pdb; pdb.set_trace()
if len(bbox_list) <= 1:
return bbox_list
else:
bigger_list = []
less_list = []
anchor_index = int(len(bbox_list) / 2)
anchor_bbox = bbox_list[anchor_index]
anchor_area = get_area_of_bbox(anchor_bbox, bbox_convention)
for i in range(len(bbox_list)):
if i == anchor_index:
continue
tmp_bbox = bbox_list[i]
tmp_area = get_area_of_bbox(tmp_bbox, bbox_convention)
if tmp_area >= anchor_area:
bigger_list.append(tmp_bbox)
else:
less_list.append(tmp_bbox)
if only_max:
return qsort_bbox_list(bigger_list) + \
[anchor_bbox, ] + less_list
else:
return qsort_bbox_list(bigger_list) + \
[anchor_bbox, ] + qsort_bbox_list(less_list)
def get_area_of_bbox(
bbox: Union[list, tuple],
bbox_convention: Literal['xyxy', 'xywh'] = 'xyxy') -> float:
"""Get the area of a bbox_xyxy.
Args:
(Union[list, tuple]):
A list of [x1, y1, x2, y2].
bbox_convention (str, optional):
Bbox type, xyxy or xywh. Defaults to 'xyxy'.
Returns:
float:
Area of the bbox(|y2-y1|*|x2-x1|).
"""
# import pdb;pdb.set_trace()
if bbox_convention == 'xyxy':
return abs(bbox[2] - bbox[0]) * abs(bbox[3] - bbox[1])
elif bbox_convention == 'xywh':
return abs(bbox[2] * bbox[3])
else:
raise TypeError(f'Wrong bbox convention: {bbox_convention}')
def calculate_iou(bbox1, bbox2):
# Calculate the Intersection over Union (IoU) between two bounding boxes
x1 = max(bbox1[0], bbox2[0])
y1 = max(bbox1[1], bbox2[1])
x2 = min(bbox1[2], bbox2[2])
y2 = min(bbox1[3], bbox2[3])
intersection_area = max(0, x2 - x1 + 1) * max(0, y2 - y1 + 1)
bbox1_area = (bbox1[2] - bbox1[0] + 1) * (bbox1[3] - bbox1[1] + 1)
bbox2_area = (bbox2[2] - bbox2[0] + 1) * (bbox2[3] - bbox2[1] + 1)
union_area = bbox1_area + bbox2_area - intersection_area
iou = intersection_area / union_area
return iou
def non_max_suppression(bboxes, iou_threshold):
# Sort the bounding boxes by their confidence scores (e.g., the probability of containing an object)
bboxes = sorted(bboxes, key=lambda x: x[4], reverse=True)
# Initialize a list to store the selected bounding boxes
selected_bboxes = []
# Perform non-maximum suppression
while len(bboxes) > 0:
current_bbox = bboxes[0]
selected_bboxes.append(current_bbox)
bboxes = bboxes[1:]
remaining_bboxes = []
for bbox in bboxes:
iou = calculate_iou(current_bbox, bbox)
if iou < iou_threshold:
remaining_bboxes.append(bbox)
bboxes = remaining_bboxes
return selected_bboxes |