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predataset_RGBT_CC.py
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import glob
import math
import os
import torch
import cv2
import h5py
import numpy as np
import scipy.io as io
import scipy.spatial
from scipy.ndimage.filters import gaussian_filter
import random
# 先224*224
'''set your data path'''
root = 'F:/DataSets/'
rgbt_cc_train = os.path.join(root, 'RGBT-CC-use/train')
rgbt_cc_test = os.path.join(root, 'RGBT-CC-use/test')
rgbt_cc_val = os.path.join(root, 'RGBT-CC-use/val')
# 记得与之修改后面的路径
# path_sets = [rgbt_cc_train]
path_sets = [rgbt_cc_test]
# path_sets = [rgbt_cc_val]
'''for part A'''
# if not os.path.exists(rgbt_cc_train.replace('train', 'new_train_224')):
# os.makedirs(rgbt_cc_train.replace('train', 'new_trian_224'))
if not os.path.exists(rgbt_cc_test.replace('test', 'new_test_224')):
os.makedirs(rgbt_cc_test.replace('test', 'new_test_224'))
#
# if not os.path.exists(rgbt_cc_val.replace('val', 'new_val_224')):
# os.makedirs(rgbt_cc_val.replace('val', 'new_val_224'))
img_paths = []
for path in path_sets:
for img_path in glob.glob(os.path.join(path, '*RGB.jpg')):
img_paths.append(img_path)
img_paths.sort()
np.random.seed(0)
random.seed(0)
for img_path in img_paths:
# print(img_path)
Img_data = cv2.imread(img_path)
T_data = cv2.imread(img_path.replace('_RGB', '_T'))
Gt_data = np.load(img_path.replace('_RGB.jpg', '_GT.npy'))
# 448和672
rate = 1
rate_1 = 1
rate_2 = 1
flag = 0
if Img_data.shape[1] >= Img_data.shape[0]: # 后面的大
rate_1 = 672.0 / Img_data.shape[1]
rate_2 = 448.0 / Img_data.shape[0]
Img_data = cv2.resize(Img_data, (0, 0), fx=rate_1, fy=rate_2)
T_data = cv2.resize(T_data, (0, 0), fx=rate_1, fy=rate_2)
Gt_data[:, 0] = Gt_data[:, 0] * rate_1
Gt_data[:, 1] = Gt_data[:, 1] * rate_2
print("1111111")
elif Img_data.shape[0] > Img_data.shape[1]: # 前面的大
rate_1 = 672.0 / Img_data.shape[0]
rate_2 = 448.0 / Img_data.shape[1]
Img_data = cv2.resize(Img_data, (0, 0), fx=rate_2, fy=rate_1)
T_data = cv2.resize(T_data, (0, 0), fx=rate_2, fy=rate_1)
Gt_data[:, 0] = Gt_data[:, 0] * rate_2 # 对应的坐标进行扩大映射
Gt_data[:, 1] = Gt_data[:, 1] * rate_1 # 对应的坐标进行扩大映射
print("22222")
# kpoint = np.zeros((Img_data.shape[0], Img_data.shape[1]))
#
# for i in range(0, len(Gt_data)):
# if int(Gt_data[i][1]) < Img_data.shape[0] and int(Gt_data[i][0]) < Img_data.shape[1]:
# kpoint[int(Gt_data[i][1]), int(Gt_data[i][0])] = 1
# height, width = Img_data.shape[0], Img_data.shape[1]
# m = int(width / 224)
# n = int(height / 224)
# fname = img_path.split('/')[-1]
# root_path = img_path.split('IMG_')[0].replace('images', 'images_crop')
# kpoint = kpoint.copy()
# if root_path.split('/')[-3] == 'train_data':
#
# for i in range(0, m):
# for j in range(0, n):
# crop_img = Img_data[j * 224: 224 * (j + 1), i * 224:(i + 1) * 224, ]
# crop_kpoint = kpoint[j * 224: 224 * (j + 1), i * 224:(i + 1) * 224]
# gt_count = np.sum(crop_kpoint)
#
# save_fname = str(i) + str(j) + str('_') + fname
# save_path = root_path + save_fname
# h5_path = save_path.replace('.jpg', '.h5').replace('images', 'gt_density_map')
# if gt_count == 0:
# print(save_path, h5_path)
# with h5py.File(h5_path, 'w') as hf:
# hf['gt_count'] = gt_count
# cv2.imwrite(save_path, crop_img)
# else: # 训练才需要位置，验证，测试不需要
img_path = img_path.replace('test', 'new_test_224')
print(img_path)
T_path = img_path.replace('_RGB','_T')
gt_save_path = img_path.replace('_RGB.jpg', '_GT.npy')
cv2.imwrite(img_path, Img_data)
cv2.imwrite(T_path, T_data)
np.save(gt_save_path, Gt_data)
# gt_count = np.sum(kpoint)
# with h5py.File(img_path.replace('.jpg', '.h5').replace('images', 'gt_density_map'), 'w') as hf:
# hf['gt_count'] = gt_count