【记录】如何配置NWPU-CROD数据集

1.下载NWPU

地址：NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting and Localization

2.如何解压缩

 首先会得到如图所示的文件

创建空的三个文件夹：images，jsons，mats。

 将images 1-5b解压到images，part1-4都是1000张，5a 500张，5b 609张

最终images中会解压出5109张照片，不要文件夹里面套文件夹。

 

 

 mats中解压缩mats.zip，得到3609项，

 同理得到jsons，一共3609个json文件

 3.处理NWPU

习惯上我还是用cctrans的代码来处理，

地址：GitHub - wfs123456/CCTrans: Code Reproduction

主要涉及两个py文件，打开里面的preprocess_dataset.py，你也可以自己创建新的py文件导入代码。首先进行路径配置：

#  preprocess_dataset.py
# Preprocess images in QNRF and NWPU dataset.

import argparse

parser = argparse.ArgumentParser(description='Preprocess')
parser.add_argument('--dataset', default='nwpu',
                    help='dataset name, only support qnrf and nwpu')
parser.add_argument('--input-dataset-path', default='data/nwpu', # 改成你刚刚解压缩好的路径
                    help='original data directory')
parser.add_argument('--output-dataset-path', default='data/NWPU-Train-Val-Test', 
                    help='processed data directory') # 新建一个NWPU-Train-Val-Test文件夹，改成这个新建的文件夹路径
args = parser.parse_args()

if args.dataset.lower() == 'qnrf':
    from preprocess.preprocess_dataset_qnrf import main

    main(args.input_dataset_path, args.output_dataset_path, 512, 2048)
elif args.dataset.lower() == 'nwpu':
    from preprocess.preprocess_dataset_nwpu import main

    main(args.input_dataset_path, args.output_dataset_path, 384, 1920)
else:
    raise NotImplementedError

其他就可以不管了。如果你是自己创建py文件，还有写一个py文件：preprocess_dataset_nwpu.py

from scipy.io import loadmat
from PIL import Image
import numpy as np
import os
import cv2


def cal_new_size_v2(im_h, im_w, min_size, max_size):
    rate = 1.0 * max_size / im_h
    rate_w = im_w * rate
    if rate_w > max_size:
        rate = 1.0 * max_size / im_w
    tmp_h = int(1.0 * im_h * rate / 16) * 16

    if tmp_h < min_size:
        rate = 1.0 * min_size / im_h
    tmp_w = int(1.0 * im_w * rate / 16) * 16

    if tmp_w < min_size:
        rate = 1.0 * min_size / im_w
    tmp_h = min(max(int(1.0 * im_h * rate / 16) * 16, min_size), max_size)
    tmp_w = min(max(int(1.0 * im_w * rate / 16) * 16, min_size), max_size)

    rate_h = 1.0 * tmp_h / im_h
    rate_w = 1.0 * tmp_w / im_w
    assert tmp_h >= min_size and tmp_h <= max_size
    assert tmp_w >= min_size and tmp_w <= max_size
    return tmp_h, tmp_w, rate_h, rate_w


def gen_density_map_gaussian(im_height, im_width, points, sigma=4):
    """
    func: generate the density map.
    points: [num_gt, 2], for each row: [width, height]
    """
    density_map = np.zeros([im_height, im_width], dtype=np.float32)
    h, w = density_map.shape[:2]
    num_gt = np.squeeze(points).shape[0]
    if num_gt == 0:
        return density_map
    for p in points:
        p = np.round(p).astype(int)
        p[0], p[1] = min(h - 1, p[1]), min(w - 1, p[0])
        gaussian_radius = sigma * 2 - 1
        gaussian_map = np.multiply(
            cv2.getGaussianKernel(int(gaussian_radius * 2 + 1), sigma),
            cv2.getGaussianKernel(int(gaussian_radius * 2 + 1), sigma).T
        )
        x_left, x_right, y_up, y_down = 0, gaussian_map.shape[1], 0, gaussian_map.shape[0]
        # cut the gaussian kernel
        if p[1] < gaussian_radius:
            x_left = gaussian_radius - p[1]
        if p[0] < gaussian_radius:
            y_up = gaussian_radius - p[0]
        if p[1] + gaussian_radius >= w:
            x_right = gaussian_map.shape[1] - (gaussian_radius + p[1] - w) - 1
        if p[0] + gaussian_radius >= h:
            y_down = gaussian_map.shape[0] - (gaussian_radius + p[0] - h) - 1
        gaussian_map = gaussian_map[y_up:y_down, x_left:x_right]
        if np.sum(gaussian_map):
            gaussian_map = gaussian_map / np.sum(gaussian_map)
        density_map[
        max(0, p[0] - gaussian_radius):min(h, p[0] + gaussian_radius + 1),
        max(0, p[1] - gaussian_radius):min(w, p[1] + gaussian_radius + 1)
        ] += gaussian_map
    density_map = density_map / (np.sum(density_map / num_gt))
    return density_map


def generate_data(im_path, mat_path, min_size, max_size):
    im = Image.open(im_path).convert('RGB')
    im_w, im_h = im.size
    points = loadmat(mat_path)['annPoints'].astype(np.float32)
    if len(points) > 0:  # some image has no crowd
        idx_mask = (points[:, 0] >= 0) * (points[:, 0] <= im_w) * (points[:, 1] >= 0) * (points[:, 1] <= im_h)
        points = points[idx_mask]
    im_h, im_w, rr_h, rr_w = cal_new_size_v2(im_h, im_w, min_size, max_size)
    im = np.array(im)
    if rr_h != 1.0 or rr_w != 1.0:
        im = cv2.resize(np.array(im), (im_w, im_h), cv2.INTER_CUBIC)
        if len(points) > 0:  # some image has no crowd
            points[:, 0] = points[:, 0] * rr_w
            points[:, 1] = points[:, 1] * rr_h

    density_map = gen_density_map_gaussian(im_h, im_w, points, sigma=8)
    return Image.fromarray(im), points, density_map


def generate_image(im_path, min_size, max_size):
    im = Image.open(im_path).convert('RGB')
    im_w, im_h = im.size
    im_h, im_w, rr_h, rr_w = cal_new_size_v2(im_h, im_w, min_size, max_size)
    im = np.array(im)
    if rr_h != 1.0 or rr_w != 1.0:
        im = cv2.resize(np.array(im), (im_w, im_h), cv2.INTER_CUBIC)
    return Image.fromarray(im)


def main(input_dataset_path, output_dataset_path, min_size=384, max_size=1920):
    ori_img_path = os.path.join(input_dataset_path, 'images')
    ori_anno_path = os.path.join(input_dataset_path, 'mats')

    for phase in ['train', 'val']:
        sub_save_dir = os.path.join(output_dataset_path, phase)
        if not os.path.exists(sub_save_dir):
            os.makedirs(sub_save_dir)
        with open(os.path.join(input_dataset_path, '{}.txt'.format(phase))) as f:
            lines = f.readlines()
            for i in lines:
                i = i.strip().split(' ')[0]
                im_path = os.path.join(ori_img_path, i + '.jpg')
                mat_path = os.path.join(ori_anno_path, i + '.mat')
                name = os.path.basename(im_path)
                im_save_path = os.path.join(sub_save_dir, name)
                print(name)
                # The Gaussian smoothed density map is just for visualization. It's not used in training.
                im, points, density_map = generate_data(im_path, mat_path, min_size, max_size)
                im.save(im_save_path)
                gd_save_path = im_save_path.replace('jpg', 'npy')
                np.save(gd_save_path, points)
                dm_save_path = im_save_path.replace('.jpg', '_densitymap.npy')
                np.save(dm_save_path, density_map)

    for phase in ['test']:
        sub_save_dir = os.path.join(output_dataset_path, phase)
        if not os.path.exists(sub_save_dir):
            os.makedirs(sub_save_dir)
        with open(os.path.join(input_dataset_path, '{}.txt'.format(phase))) as f:
            lines = f.readlines()
            for i in lines:
                i = i.strip().split(' ')[0]
                im_path = os.path.join(ori_img_path, i + '.jpg')
                name = os.path.basename(im_path)
                im_save_path = os.path.join(sub_save_dir, name)
                print(name)
                im = generate_image(im_path, min_size, max_size)
                im.save(im_save_path)

4.运行preprocess_dataset.py

直接命令台或者终端运行preprocess_dataset.py文件即可，处理时间比较长耐心等待

 5.最终结果

最终得到train，val，test三个文件夹。最好刚刚上一步骤的处理的输入和输出不要放在一个文件夹，容易混淆。

 train中内容

 val中内容

 test中内容