LearningToCountEverything
LearningToCountEverything copied to clipboard
Hello, could you tell me how the density map is generated
how the density map is generated?
好兄弟你知道了吗,我大概了解了不知道是不是对的,就是他通过高斯核生成的
他有代码可以直接生成的
好兄弟,细说,我做的这个毕设,之前我直接用的他的数据集,结果现在要改数据集,改成专门计人的,咋生成的啊?
他有代码可以直接生成的
请问你,生成密度图的代码在哪里呀,如果能告诉我的话不胜感激
谢谢好兄弟,研究生没考上,最近在找工作,近期没时间学深度学习了
------------------ 原始邮件 ------------------ 发件人: "cvlab-stonybrook/LearningToCountEverything" @.>; 发送时间: 2023年12月11日(星期一) 下午5:28 @.>; @.@.>; 主题: Re: [cvlab-stonybrook/LearningToCountEverything] Hello, could you tell me how the density map is generated (Issue #40)
姿态估计中有类似的方法
def putGaussianMaps(center, accumulate_confid_map, sigma, grid_y, grid_x, stride):
start = stride / 2.0 - 0.5 y_range = [i for i in range(int(grid_y))] x_range = [i for i in range(int(grid_x))] xx, yy = np.meshgrid(x_range, y_range) xx = xx * stride + start yy = yy * stride + start d2 = (xx - center[0]) ** 2 + (yy - center[1]) ** 2 exponent = d2 / 2.0 / sigma / sigma mask = exponent <= 4.6052 cofid_map = np.exp(-exponent) cofid_map = np.multiply(mask, cofid_map) accumulate_confid_map += cofid_map accumulate_confid_map[accumulate_confid_map > 1.0] = 1.0 return accumulate_confid_map
—
Reply to this email directly, view it on GitHub, or unsubscribe.
You are receiving this because you commented.Message ID: @.***>