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我的环境配置是python 3.6.2 + opencv 3.4.5。

下面是crowd counting计算人群密度图的代码。

# coding:utf-8

from __future__ import print_function

import numpy as np
import pylab
import matplotlib.pyplot as plt
import cv2
from cv2 import dnn
import time


cm_path = 'C:\\Users\\admin\\Desktop\\'



if __name__ == "__main__":

    fn = r'C:\Users\admin\Desktop\ShanghaiTech_Crowd_Counting_Dataset\part_B_final\test_data\images\IMG_191.jpg'

    im_ori = cv2.imread(fn)
    plt.figure(1)
    plt.imshow(im_ori)
    plt.axis('off')
    pylab.show()


    blob = dnn.blobFromImage(im_ori, 1, (1024, 768), (0, 0, 0), True)

    print("Input:", blob.shape, blob.dtype)

    net = dnn.readNetFromCaffe(cm_path + 'B_testdemo.prototxt', cm_path + 'B2_iter_93000.caffemodel')

    t = time.time()
    net.setInput(blob)
    density = net.forward()
    elapsed = time.time() - t

    print('inference image: %.4f seconds.' % elapsed)

    density = density/1000.0

    print("Output:", density.shape, density.dtype)

    person_num = np.sum(density[:])
    print("number: ",person_num)

    plt.figure(1)
    plt.imshow(density[0, 0, :, :])
    plt.axis('off')
    pylab.show()

dnn.blobFromImage(input_img,scalefactor, (width, height), mean, swapRB)

mean和scalefactor是用来对图像做标准化的，先减均值，再乘以一个系数。images -= mean；images *= scalefactor

swapRB是选择是否交换R与B颜色通道，opencv默认读取的图片是BGR格式，而训练模型时，往往是转换成RGB输入，所以这里通常设置为True，调换R与B通道。

dnn.readNetFromCaffe(modelTxt, caffe_modelBin)

输入的两个参数分别是网络结构.prototxt文件和模型文件。

【参考资料】

1、使用OpenCV_python中的DNN调用CaffeModel识别图像

2、https://github.com/linzhirui1992/Crowd-Counting