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05-20
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import numpy as np
import cv2
def sigmoid(x):
return 1 / (1 + np.exp(-x))
def xywh2xyxy(x):
# x has shape [n, 4] where each row is (center_x, center_y, width, height)
y = np.zeros_like(x)
y[:, 0:2] = x[:, 0:2] - x[:, 2:4] / 2 # calculate min_x, min_y
y[:, 2:4] = x[:, 0:2] + x[:, 2:4] / 2 # calculate max_x, max_y
return y
def iou(box, boxes):
# Compute xmin, ymin, xmax, ymax for both boxes
xmin = np.maximum(box[0], boxes[:, 0])
ymin = np.maximum(box[1], boxes[:, 1])
xmax = np.minimum(box[2], boxes[:, 2])
ymax = np.minimum(box[3], boxes[:, 3])
# Compute intersection area
intersection_area = np.maximum(0, xmax - xmin) * np.maximum(0, ymax - ymin)
# Compute union area
box_area = (box[2] - box[0]) * (box[3] - box[1])
boxes_area = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
union_area = box_area + boxes_area - intersection_area
# Compute IoU
iou = intersection_area / union_area
return iou
def fast_nms(boxes, scores, iou_threshold):
sorted_indices = np.argsort(scores)[::-1]
selected_indices = []
while sorted_indices.size > 0:
box_id = sorted_indices[0]
selected_indices.append(box_id)
if sorted_indices.size == 1:
break
remaining_boxes = boxes[sorted_indices[1:]]
current_box = boxes[box_id].reshape(1, -1)
ious = np.array([iou(current_box[0], remaining_box) for remaining_box in remaining_boxes])
keep_indices = np.where(ious < iou_threshold)[0]
sorted_indices = sorted_indices[keep_indices + 1]
return selected_indices