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create_txt.py
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import numpy
import numpy as np
import shutil
import os
import csv
import pandas as pd
import scipy.io as io
import cv2
import glob
path = r'D:\Lai\counting_dataset\test\fix_kernel\jhu++\test\images'
target_path = r'D:\Lai\counting_dataset\test\adaptive_kernel\UCF_QNRF'
cls = 'jhu++'
# SHH
if cls == 'SHH':
img_list = os.listdir(path + '/' + 'train_data/images')
file_list = os.listdir(path + '/' + 'train_data/ground-truth')
for i in range(len(file_list)):
name = img_list[i].split('.')
f2 = open(target_path + '/' + name[0] + '.txt', 'w')
f = io.loadmat(path + '/train_data/ground-truth/' + file_list[i])
f1 = f['image_info']
num = len(f1[0][0][0][0][0])
for j in range(num):
x = str(f1[0][0][0][0][0][j][0])
y = str(f1[0][0][0][0][0][j][1])
f2.write(x + ' ' + y + '\n')
f2.close()
# NWPU
if cls == 'NWPU':
img_path = path + 'image'
img_file = os.listdir(img_path)
for i in range(len(img_file)):
mat = io.loadmat((path + 'image/' + img_file[i]).replace('image', 'mats').replace('jpg', 'mat'))
gt = mat['annPoints']
num = len(gt)
if i < 3109:
target = target_path + 'train/'
if not os.path.isdir(target):
os.mkdir(target)
img = cv2.imread(path + 'image/' + img_file[i])
cv2.imwrite(target + img_file[i], img)
with open(target + img_file[i].replace('jpg', 'txt'), 'w') as f:
for j in range(num):
x = str(gt[j][0])
y = str(gt[j][1])
f.write(x + ' ' + y + '\n')
else:
target = target_path + 'test/'
if not os.path.isdir(target):
os.mkdir(target)
img = cv2.imread(path + 'image/' + img_file[i])
cv2.imwrite(target + img_file[i], img)
with open(target + img_file[i].replace('jpg', 'txt'), 'w') as f:
for j in range(num):
x = str(gt[j][0])
y = str(gt[j][1])
f.write(x + ' ' + y + '\n')
if cls == 'QNRF':
train = os.path.join(path, 'Train')
test = os.path.join(path, 'Test')
path_sets = [train, test]
# path_sets = [test]
img_paths = []
for path in path_sets:
for img_path in glob.glob(os.path.join(path, '*.jpg')):
img_paths.append(img_path)
j = 0
img_paths = img_paths[j:]
for img_path in img_paths:
print(img_path, j)
if j < 1201:
target_root = target_path + '/train'
img_name = img_path.split('\\')[-1]
name = img_name.split('.')
mat = io.loadmat(img_path.replace('.jpg', '_ann.mat'))
img = cv2.imread(img_path)
max_size = max(img.shape)
gt = mat['annPoints']
if max_size > 1920:
scale = 1920 / max_size
# scale the image and points
if len(gt) == 0:
gt = gt
else:
gt *= scale
f2 = open(target_path + '/train_dot/' + name[0] + '.txt', 'w')
for i in range(len(gt)):
if int(gt[i][1]) < img.shape[0] and int(gt[i][0]) < img.shape[1]:
x = str(gt[i][0])
y = str(gt[i][1])
f2.write(x + ' ' + y + '\n')
f2.close()
else:
target_root = target_path + '/test'
img_name = img_path.split('\\')[-1]
name = img_name.split('.')
mat = io.loadmat(img_path.replace('.jpg', '_ann.mat'))
img = cv2.imread(img_path)
max_size = max(img.shape)
gt = mat['annPoints']
if max_size > 1920:
scale = 1920 / max_size
# scale the image and points
img = cv2.resize(img, None, fx=scale, fy=scale)
if len(gt) == 0:
gt = gt
else:
gt *= scale
f2 = open(target_path + '/test_dot/' + name[0] + '.txt', 'w')
for i in range(len(gt)):
if int(gt[i][1]) < img.shape[0] and int(gt[i][0]) < img.shape[1]:
x = str(gt[i][0])
y = str(gt[i][1])
f2.write(x + ' ' + y + '\n')
f2.close()
j += 1
if cls == 'CC_50':
img_paths = []
for img_path in glob.glob(os.path.join(path, '*.jpg')):
img_paths.append(img_path)
j = 0
img_paths = img_paths[j:]
for img_path in img_paths:
print(img_path, j)
target_root = target_path
img_name = img_path.split('\\')[-1]
name = img_name.split('.')
mat = io.loadmat(img_path.replace('.jpg', '_ann.mat'))
img = cv2.imread(img_path)
max_size = max(img.shape)
gt = mat['annPoints']
if max_size > 1920:
scale = 1920 / max_size
# scale the image and points
if len(gt) == 0:
gt = gt
else:
gt *= scale
f2 = open(target_path + '/' + name[0] + '.txt', 'w')
for i in range(len(gt)):
x = str(gt[i][0])
y = str(gt[i][1])
f2.write(x + ' ' + y + '\n')
f2.close()
j += 1
# jhu++
if cls == 'jhu++':
img_paths = []
for img_file in glob.glob(os.path.join(path, '*.jpg')):
img_paths.append(img_file)
for img_path in img_paths:
point_path = img_path.replace('images', 'gt').replace('.jpg','.txt')
img = cv2.imread(img_path)
max_size = max(img.shape)
min_size = min(img.shape[0], img.shape[1])
with open(point_path, 'r') as files:
lines = files.readlines()
gt = np.zeros((len(lines), 2))
i = 0
for line in lines:
points = line.split(' ')
gt[i][0] = points[0]
gt[i][1] = points[1]
i += 1
if max_size > 1920:
scale = 1920 / max_size
# scale the image and points
if len(gt) == 0:
gt = gt
else:
gt *= scale
if min_size < 128:
scale = 128 / min_size
# scale the image and points
img = cv2.resize(img, None, fx=scale, fy=scale)
if len(gt) == 0:
gt = gt
else:
gt *= scale
with open(img_path.replace('jpg', 'txt'), 'w') as f:
for j in range(len(gt)):
x = str(gt[j][0])
y = str(gt[j][1])
f.write(x + ' ' + y + '\n')