矩形框生成密度图的代码

以下是生成矩形框密度图的Python代码：

import numpy as np
import cv2

def generate_density_map(image_size, bboxes, sigma=15):
    """
    根据矩形框生成密度图
    :param image_size: 图像大小
    :param bboxes: 矩形框坐标，格式为[x1, y1, x2, y2]
    :param sigma: 高斯核大小
    :return: 密度图
    """
    h, w = image_size
    density_map = np.zeros((h, w), dtype=np.float32)
    for bbox in bboxes:
        x1, y1, x2, y2 = bbox
        x1 = int(max(0, min(x1, w - 1)))
        y1 = int(max(0, min(y1, h - 1)))
        x2 = int(max(0, min(x2, w - 1)))
        y2 = int(max(0, min(y2, h - 1)))
        # 以矩形框中心点为中心，生成高斯分布
        center_x, center_y = (x1 + x2) // 2, (y1 + y2) // 2
        radius = sigma * 3
        sigma = max(1, sigma)
        height, width = y2 - y1, x2 - x1
        kernel_size = int(2 * radius + 1)
        kernel_size = min(kernel_size, min(height, width))
        x = np.arange(kernel_size) - kernel_size // 2
        y = np.arange(kernel_size) - kernel_size // 2
        xx, yy = np.meshgrid(x, y)
        kernel = np.exp(-(xx ** 2 + yy ** 2) / (2 * sigma ** 2))
        kernel = kernel / (2 * np.pi * sigma ** 2)
        if center_y - radius < 0:
            kernel = kernel[int(radius - center_y):]
        if center_x - radius < 0:
            kernel = kernel[:, int(radius - center_x):]
        if center_y + radius >= h:
            kernel = kernel[:int(kernel_size - (center_y + radius - h)), :]
        if center_x + radius >= w:
            kernel = kernel[:, :int(kernel_size - (center_x + radius - w))]
        density_map[y1:y2, x1:x2] += kernel
    return density_map

使用示例：

# 加载图像和矩形框坐标
image = cv2.imread('image.jpg')
bboxes = [[100, 100, 200, 200], [300, 300, 400, 400]]

# 生成密度图
density_map = generate_density_map(image.shape[:2], bboxes)

# 可视化密度图
cv2.imshow('Density Map', density_map)
cv2.waitKey()

其中`image_size`参数为图像大小，`bboxes`参数为矩形框坐标，格式为`[x1, y1, x2, y2]`，`sigma`参数为高斯核大小，用于控制密度图的模糊程度。函数返回生成的密度图。