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generate_density_map_perfect_names_SHAB_QNRF_NWPU_JHU.py
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generate_density_map_perfect_names_SHAB_QNRF_NWPU_JHU.py
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import cv2
import numpy as np
import scipy
import scipy.io as scio
from PIL import Image
import time
import math
import os
import glob
def get_density_map_gaussian(H, W, ratio_h, ratio_w, points, fixed_value=15):
h = H
w = W
density_map = np.zeros([h, w], dtype=np.float32)
num_gt = np.squeeze(points).shape[0]
if num_gt == 0:
return density_map
for idx, p in enumerate(points):
p = np.round(p).astype(int)
p[0], p[1] = min(h-1, math.floor(p[1] * ratio_h)), min(w-1, math.floor(p[0] * ratio_w))
sigma = fixed_value
sigma = max(1, sigma)
gaussian_radius = 7
gaussian_map = np.multiply(
cv2.getGaussianKernel(gaussian_radius*2+1, sigma),
cv2.getGaussianKernel(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] < 0 or p[0] < 0:
continue
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
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[y_up:y_down, x_left:x_right]
return density_map
def mkdir(path):
"""create a single empty directory if it didn't exist
Parameters:
path (str) -- a single directory path
"""
if not os.path.exists(path):
os.makedirs(path)
def mkdirs(paths):
"""create empty directories if they don't exist
Parameters:
paths (str list) -- a list of directory paths
"""
if isinstance(paths, list) and not isinstance(paths, str):
for path in paths:
mkdir(path)
else:
mkdir(paths)
if __name__ == "__main__":
is_train = 0 # '1' for train and '0' for test
train_test_for_gt_SH = 'train_data' if is_train else 'test_data'
train_test_for_gt_type2 = 'train' if is_train else 'test'
train_test_for_gt_type3 = 'train' if is_train else 'val'
train_test_for_den = 'den/train' if is_train else 'den/test'
dataset = 'QNRF_large'
if dataset == 'SHA':
# num_img = 300 if is_train else 182
image_dir_path = "ShanghaiTech/part_A_final/" + train_test_for_gt_SH + "/images"
ground_truth_dir_path = "ShanghaiTech/part_A_final/"+ train_test_for_gt_SH +"/ground_truth"
output_den_path = "./Processed_SHA_oriImg/" + train_test_for_den
# two extra paths
output_img_path = "./Processed_SHA_oriImg/ori/" + train_test_for_gt_SH + "/images"
output_mat_path = './Processed_SHA_oriImg/ori/' + train_test_for_gt_SH + "/ground_truth"
elif dataset == 'SHB':
# num_img = 400 if is_train else 316
image_dir_path = "ShanghaiTech/part_B_final/" + train_test_for_gt_SH + "/images"
ground_truth_dir_path = "ShanghaiTech/part_B_final/" + train_test_for_gt_SH + "/ground_truth"
output_den_path = "./Processed_SHB_oriImg/" + train_test_for_den
# two extra paths
output_img_path = "./Processed_SHB_oriImg/ori/" + train_test_for_gt_SH + "/images"
output_mat_path = './Processed_SHB_oriImg/ori/' + train_test_for_gt_SH + "/ground_truth"
elif dataset == 'QNRF_large':
# num_img = 1201 if is_train else 334
image_dir_path = "UCF-QNRF_ECCV18/" + train_test_for_gt_type2
ground_truth_dir_path = "UCF-QNRF_ECCV18/" + train_test_for_gt_type2
output_den_path = "./Processed_QNRF_large_oriImg/" + train_test_for_den
# two extra paths
output_img_path = "./Processed_QNRF_large_oriImg/ori/" + train_test_for_gt_SH + "/images" # using `gt_SH` here
output_mat_path = './Processed_QNRF_large_oriImg/ori/' + train_test_for_gt_SH + "/ground_truth"
# elif dataset == 'UCF50': # take all images as testing images
# num_img = 50
# image_dir_path = "UCF_CC_50/images/UCF_CC_50_img"
# ground_truth_dir_path = "UCF_CC_50/UCF_CC_50_mat"
# output_den_path = "./UCF50/" + train_test
elif dataset == 'NWPU_large':
image_dir_path = "NWPU/" + train_test_for_gt_type3 + "/imgs"
ground_truth_dir_path = "NWPU/" + train_test_for_gt_type3 + "/mats"
output_den_path = "./Processed_NWPU_large_oriImg/" + train_test_for_den
# two extra paths
output_img_path = "./Processed_NWPU_large_oriImg/ori/" + train_test_for_gt_SH + "/images"
output_mat_path = './Processed_NWPU_large_oriImg/ori/' + train_test_for_gt_SH + "/ground_truth"
elif dataset == 'JHU_large':
image_dir_path = "JHU_offical/" + train_test_for_gt_type2 + "/images"
ground_truth_dir_path = "JHU_offical/gt_npz/" + train_test_for_gt_type2
output_den_path = "./Processed_JHU_large_oriImg/" + train_test_for_den
# two extra paths
output_img_path = "./Processed_JHU_large_oriImg/ori/" + train_test_for_gt_SH + "/images"
output_mat_path = './Processed_JHU_large_oriImg/ori/' + train_test_for_gt_SH + "/ground_truth"
elif dataset == 'BDdata_large':
assert is_train == 1
image_dir_path = "BDdata/filter_imgs_100"
ground_truth_dir_path = "BDdata/filter_mats_100"
output_den_path = "./Processed_BDdata_large_oriImg/" + train_test_for_den
# two extra paths
output_img_path = "./Processed_BDdata_large_oriImg/ori/" + train_test_for_gt_SH + "/images"
output_mat_path = './Processed_BDdata_large_oriImg/ori/' + train_test_for_gt_SH + "/ground_truth"
else:
assert 1==2
mkdirs(output_den_path)
mkdirs(output_img_path)
mkdirs(output_mat_path)
img_paths = None
if dataset.find("QNRF") != -1: # as gt and images are in the same folder for QNRF dataset
img_paths = glob.glob(image_dir_path + "/*.jpg")
else:
img_paths = glob.glob(image_dir_path + "/*")
for img_path in img_paths:
if dataset == 'SHA' or dataset == 'SHB':
gt_path = os.path.join(ground_truth_dir_path, "GT_" + os.path.basename(img_path)[:-4] + ".mat")
elif dataset == 'QNRF' or dataset == 'QNRF_large':
gt_path = os.path.join(ground_truth_dir_path, os.path.basename(img_path)[:-4] + "_ann.mat")
# elif dataset == 'UCF50':
# img_path = os.path.join(image_dir_path, "img_"+("%d" % (i+1))+".jpg")
# gt_path = os.path.join(image_dir_path, "img_"+("%d" % (i+1))+"_ann.mat")
elif dataset == 'NWPU' or dataset == 'NWPU_large' or dataset == 'BDdata_large':
gt_path = os.path.join(ground_truth_dir_path, os.path.basename(img_path)[:-4] + ".mat")
elif dataset == 'JHU' or dataset == 'JHU_large':
gt_path = os.path.join(ground_truth_dir_path, os.path.basename(img_path)[:-4] + ".npz")
else:
assert 1==2
print('Processing img: ', img_path)
img = Image.open(img_path).convert('RGB')
height = img.size[1]
width = img.size[0]
if dataset == 'SHA' or dataset == 'SHB':
points = scio.loadmat(gt_path)['image_info'][0][0][0][0][0]
elif dataset.find('QNRF') != -1 or dataset == 'UCF50' or dataset.find('NWPU') != -1 or dataset.find('BDdata') != -1:
points = scio.loadmat(gt_path)['annPoints']
elif dataset.find('JHU') != -1:
points = np.load(gt_path)['loc_info']
else:
assert 1==2
resize_height = height
resize_width = width
if dataset == 'SHA' or dataset == 'UCF50':
if resize_height <= 416:
tmp = resize_height
resize_height = 416
resize_width = (resize_height / tmp) * resize_width
if resize_width <= 416:
tmp = resize_width
resize_width = 416
resize_height = (resize_width / tmp) * resize_height
resize_height = math.ceil(resize_height / 32) * 32
resize_width = math.ceil(resize_width / 32) * 32
elif dataset == 'SHB':
resize_height = 768
resize_width = 1024
elif dataset == 'QNRF':
resize_height = 768
resize_width = 1024
elif dataset == 'QNRF_large' or dataset == 'NWPU_large' or dataset == 'JHU_large' or dataset == 'BDdata_large':
if resize_width >= 2048:
tmp = resize_width
resize_width = 2048
resize_height = (resize_width / tmp) * resize_height
if resize_height >= 2048:
tmp = resize_height
resize_height = 2048
resize_width = (resize_height / tmp) * resize_width
if resize_height <= 512:
tmp = resize_height
resize_height = 512
resize_width = (resize_height / tmp) * resize_width
if resize_width <= 512:
tmp = resize_width
resize_width = 512
resize_height = (resize_width / tmp) * resize_height
# other constraints
if resize_height < resize_width:
if resize_width / resize_height > 2048/512:
resize_width = 2048
resize_height = 512
else:
if resize_height / resize_width > 2048/512:
resize_height = 2048
resize_width = 512
resize_height = math.ceil(resize_height / 32) * 32
resize_width = math.ceil(resize_width / 32) * 32
else:
assert 1==2
ratio_h = (resize_height) / (height)
ratio_w = (resize_width) / (width)
gt = get_density_map_gaussian(resize_height, resize_width, ratio_h, ratio_w, points, 4)
gt = np.reshape(gt, [resize_height, resize_width]) # transpose into w, h
# transfer gt to float16 to save storage
gt = gt.astype(np.float16)
# Three stuffs to store
# 1. images with new folders
os.system('cp '+ img_path + ' ./' + os.path.join(output_img_path, dataset + '_' + os.path.basename(img_path)))
# 2. save density maps
np.save(os.path.join(output_den_path, dataset + "_" + os.path.basename(img_path)[:-4] + ".npy"), gt) # some extensions are '.JPG', so...
# 3. save mats
scio.savemat(os.path.join(output_mat_path, dataset + "_" + os.path.basename(img_path)[:-4] + ".mat"), {'annPoints':points})
print("complete!")