1.Numpy的几个骚操作

(1).np.maximum的使用

import numpy as np
box = [3,5,7,9]  # A single box with a first coordinate of 3
boxes = np.array([[1, 4], [5, 2], [2, 6]])  # An array of multiple boxes
#把box把3拿出来和boxes的第一列，逐个比较，返回最大值list
result = np.maximum(box[0], boxes[:, 0])
#result=[3 5 3]
print(result)

(2). np.argsort的使用：

import numpy as np
scores = np.array([0.5, 0.2, 0.8])
list1 = np.argsort(scores)
list2=np.argsort(scores)[::-1]
#list1=[1 0 2]
print(f"list1={list1}")
#list2=[2 0 1]
print(f"list2={list2}")
#scores=[0.5 0.2 0.8],原来的顺序没改
print(f"scores={scores}")

(3).向量和矩阵的乘法和加法

import numpy as np
box = [3,5]  # A single box with a first coordinate of 3
boxes = np.array([[1, 4], [5, 2], [2, 6]])  # An array of multiple boxes"""
矩阵乘法是"@"不是"*"
*不是矩阵乘法，把boxes的每一行都对应乘以box
box*boxes=[[ 3 20][15 10][ 6 30]]
"""
print(box*boxes)"""
boxes的每一列都对应乘以box
box+boxes=[[ 4  9][ 8  7][ 5 11]]
"""
print(box+boxes)

2.非极大值抑制

def non_max_suppression(boxes, scores, threshold):"""Apply non-maximum suppression to eliminate redundant bounding boxes.Parameters:- boxes: A list of bounding boxes in the format (x1, y1, x2, y2).- scores: A list of confidence scores for each bounding box.- threshold: The IoU (Intersection over Union) threshold for suppression.Returns:- selected_indices: A list of indices for the selected bounding boxes."""if len(boxes) == 0:return []# Convert the bounding boxes to the format (x1, y1, x2, y2)boxes = np.array(boxes)# Sort indices based on confidence scores (in descending order)#sorted_indices=[2,0,1]sorted_indices = np.argsort(scores)[::-1]selected_indices = []while len(sorted_indices) > 0:# Pick the bounding box with the highest confidence scorei = sorted_indices[0]selected_indices.append(i)# Calculate IoU with the remaining bounding boxesious = calculate_iou(boxes[i], boxes[sorted_indices[1:]])# Filter out bounding boxes with IoU higher than the threshold#np.where(ious <= threshold) is ([])#type(remaining_indices)=ndarrayremaining_indices = np.where(ious <= threshold)[0]print(f"remaining_indices={remaining_indices}")print(f"remaining_indices+1={remaining_indices+1}")#i=2的哪个框已经进入selectedsorted_indices = sorted_indices[remaining_indices + 1]print("sorted_indices=",sorted_indices)return selected_indicesdef calculate_iou(box, boxes):"""Calculate the Intersection over Union (IoU) between a bounding box and a list of bounding boxes.Parameters:- box: The bounding box in the format (x1, y1, x2, y2).- boxes: A list of bounding boxes.Returns:- ious: A numpy array of IoU values."""#计算intersection的4个坐标x1 = np.maximum(box[0], boxes[:, 0])y1 = np.maximum(box[1], boxes[:, 1])x2 = np.minimum(box[2], boxes[:, 2])y2 = np.minimum(box[3], boxes[:, 3])#没有交集的框的IoU就是0intersection = np.maximum(0, x2 - x1) * np.maximum(0, y2 - y1)area_box = (box[2] - box[0]) * (box[3] - box[1])area_boxes = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])union = area_box + area_boxes - intersectionious = intersection / unionreturn ious# Example usage:
import numpy as np
if __name__=="__main__":# Example bounding boxes and confidence scores#the 4 number in each box is the coordinate of left-top corner#and  right-bottom cornerboxes = [(10, 20, 50, 60), (15, 25, 55, 65), (30, 40, 70, 80)]scores = [0.9, 0.75, 0.95]# Set the IoU threshold for non-maximum suppressioniou_threshold = 0.5# Apply non-maximum suppressionselected_indices = non_max_suppression(boxes, scores, iou_threshold)# Display the selected bounding boxesfor i in selected_indices:print(f"Selected Box: {boxes[i]}, Score: {scores[i]}")

Selected Box: (30, 40, 70, 80), Score: 0.95
Selected Box: (10, 20, 50, 60), Score: 0.9