# Source code for scannerpy.stdlib.bboxes

import numpy as np

from scannerpy.table import Table

[docs]def proto_to_np(bboxes):
return np.array([[
box.x1, box.y1, box.x2, box.y2, box.score, box.label, box.track_id,
box.track_score
] for box in bboxes])

# Frome https://www.pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/
[docs]def iou(bbox_a, bbox_b):
# determine the (x, y)-coordinates of the intersection rectangle
xA = max(bbox_a.x1, bbox_b.x1)
yA = max(bbox_a.y1, bbox_b.y1)
xB = min(bbox_a.x2, bbox_b.x2)
yB = min(bbox_a.y2, bbox_b.y2)

# compute the area of intersection rectangle
interArea = (xB - xA + 1) * (yB - yA + 1)

# compute the area of both the prediction and ground-truth
# rectangles
boxAArea = (bbox_a.x2 - bbox_a.x1 + 1) * (bbox_a.y2 - bbox_a.y1 + 1)
boxBArea = (bbox_b.x2 - bbox_b.x1 + 1) * (bbox_b.y2 - bbox_b.y1 + 1)

# compute the intersection over union by taking the intersection
# area and dividing it by the sum of prediction + ground-truth
# areas - the interesection area
iou = interArea / float(boxAArea + boxBArea - interArea)

# return the intersection over union value
return iou

[docs]def nms(orig_boxes, overlapThresh):
# if there are no boxes, return an empty list
if len(orig_boxes) == 0:
return []
elif len(orig_boxes) == 1:
return orig_boxes

if type(orig_boxes[0]) != np.ndarray:
boxes = proto_to_np(orig_boxes)
else:
boxes = orig_boxes

npboxes = np.array(boxes[0])
for box in boxes[1:]:
npboxes = np.vstack((npboxes, box))
boxes = npboxes
# if the bounding boxes integers, convert them to floats --
# this is important since we'll be doing a bunch of divisions

# initialize the list of picked indexes
pick = []

# grab the coordinates of the bounding boxes
x1 = boxes[:, 0]
y1 = boxes[:, 1]
x2 = boxes[:, 2]
y2 = boxes[:, 3]
score = boxes[:, 4]

# compute the area of the bounding boxes and sort the bounding
# boxes by the bottom-right y-coordinate of the bounding box
area = (x2 - x1 + 1) * (y2 - y1 + 1)
idxs = np.argsort(score)

# keep looping while some indexes still remain in the indexes
# list
while len(idxs) > 0:
# grab the last index in the indexes list and add the
# index value to the list of picked indexes
last = len(idxs) - 1
i = idxs[last]
pick.append(i)

# find the largest (x, y) coordinates for the start of
# the bounding box and the smallest (x, y) coordinates
# for the end of the bounding box
xx1 = np.maximum(x1[i], x1[idxs[:last]])
yy1 = np.maximum(y1[i], y1[idxs[:last]])
xx2 = np.minimum(x2[i], x2[idxs[:last]])
yy2 = np.minimum(y2[i], y2[idxs[:last]])

# compute the width and height of the bounding box
w = np.maximum(0, xx2 - xx1 + 1)
h = np.maximum(0, yy2 - yy1 + 1)

# compute the ratio of overlap
overlap = (w * h) / area[idxs[:last]]

# delete all indexes from the index list that have
idxs = np.delete(idxs,
np.concatenate(
([last], np.where(overlap > overlapThresh)[0])))

# return only the bounding boxes that were picked
return np.array(orig_boxes)[pick]