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data_generation.py
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data_generation.py
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import os
import cv2
import random
import argparse
import numpy as np
from tqdm import tqdm
def removeHiddenfile(directory_list):
if '.' in directory_list:
directory_list.remove('.')
if '..' in directory_list:
directory_list.remove('.')
if '.DS_Store' in directory_list:
directory_list.remove('.DS_Store')
return directory_list
def homographyGeneration(raw_image, image_path, dataPath, pairs_per_img):
img = cv2.resize(cv2.imread(image_path, 0),(320,240))
with open(dataPath + 'homography_re' + '.txt', 'ab') as output_file_1, open(dataPath + 'homography_ab' + '.txt', 'ab') as output_file_2:
random_list = []
i = 1
while i < pairs_per_img + 1:
y_start = random.randint(32,80)
y_end = y_start + 128
x_start = random.randint(32,160)
x_end = x_start + 128
y_1 = y_start
x_1 = x_start
y_2 = y_end
x_2 = x_start
y_3 = y_end
x_3 = x_end
y_4 = y_start
x_4 = x_end
img_patch = img[y_start:y_end, x_start:x_end] # patch 1
y_1_offset = random.randint(-32,32)
x_1_offset = random.randint(-32,32)
y_2_offset = random.randint(-32,32)
x_2_offset = random.randint(-32,32)
y_3_offset = random.randint(-32,32)
x_3_offset = random.randint(-32,32)
y_4_offset = random.randint(-32,32)
x_4_offset = random.randint(-32,32)
y_1_p = y_1 + y_1_offset
x_1_p = x_1 + x_1_offset
y_2_p = y_2 + y_2_offset
x_2_p = x_2 + x_2_offset
y_3_p = y_3 + y_3_offset
x_3_p = x_3 + x_3_offset
y_4_p = y_4 + y_4_offset
x_4_p = x_4 + x_4_offset
pts_img_patch = np.array([[y_1,x_1],[y_2,x_2],[y_3,x_3],[y_4,x_4]]).astype(np.float32)
pts_img_patch_perturb = np.array([[y_1_p,x_1_p],[y_2_p,x_2_p],[y_3_p,x_3_p],[y_4_p,x_4_p]]).astype(np.float32)
h,status = cv2.findHomography(pts_img_patch, pts_img_patch_perturb, cv2.RANSAC)
img_perburb = cv2.warpPerspective(img, h, (320, 240))
img_perburb_patch = img_perburb[y_start:y_end, x_start:x_end] # patch 2
if not [y_1,x_1,y_2,x_2,y_3,x_3,y_4,x_4] in random_list:
random_list.append([y_1,x_1,y_2,x_2,y_3,x_3,y_4,x_4])
h_4pt_1 = np.array([y_1_offset,x_1_offset,y_2_offset,x_2_offset,y_3_offset,x_3_offset,y_4_offset,x_4_offset])
h_4pt_2 = np.array([y_1_p,x_1_p,y_2_p,x_2_p,y_3_p,x_3_p,y_4_p,x_4_p])
img_patch_path = os.path.join(dataPath, (raw_image.split('.')[0] + '_' + str(i) + '_1' +'.jpg'))
cv2.imwrite(img_patch_path, img_patch)
img_perburb_patch_path = os.path.join(dataPath, (raw_image.split('.')[0] + '_' + str(i) + '_2' +'.jpg'))
cv2.imwrite(img_perburb_patch_path, img_perburb_patch)
np.savetxt(output_file_1, h_4pt_1)
np.savetxt(output_file_2, h_4pt_2)
i += 1
def dataCollection(rawDataPath, dataPath, img_number,img_per_dir):
raw_image_list = removeHiddenfile(os.listdir(rawDataPath))
raw_image_list.sort()
for i in tqdm(range(img_number)):
raw_image = raw_image_list[i]
raw_image_path = os.path.join(rawDataPath, raw_image)
homographyGeneration(raw_image, raw_image_path, dataPath, img_per_dir)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--rawTrainDataPath', type=str, default='/media/csc105/Data/dataset/ms-coco/train2014', help='The raw data path.')
parser.add_argument('--trainDataPath', type=str, default='/home/jiange/dl/tensorflow_homographynet/data/generated_data/train/', help='The train data path')
parser.add_argument('--trainDataNumber', type=int, default=8000, help='The data size for traininig')
parser.add_argument('--rawValDataPath', type=str, default='/media/csc105/Data/dataset/ms-coco/val2014', help='The raw data path.')
parser.add_argument('--valDataPath', type=str, default='/home/jiange/dl/tensorflow_homographynet/data/generated_data/val/', help='The val data path')
parser.add_argument('--valDataNumber', type=int, default=2000, help='The data size for validate tuning')
parser.add_argument('--rawTestDataPath', type=str, default='/media/csc105/Data/dataset/ms-coco/train2014', help='The raw data path.')
parser.add_argument('--testDataPath', type=str, default='/home/jiange/dl/tensorflow_homographynet/data/generated_data/test/', help='The test data path')
parser.add_argument('--testDataNumber', type=int, default=800, help='The data size for testing')
print('<==================== Loading raw data ===================>\n')
args = parser.parse_args()
print('<================= Generating homography =================>\n')
# dataCollection(args.rawTrainDataPath, args.trainDataPath, args.trainDataNumber, 4)
dataCollection(args.rawValDataPath, args.valDataPath, args.valDataNumber, 4)
# dataCollection(args.rawTestDataPath, args.testDataPath, args.testDataNumber, 1)
print('<=================== Finishing data ===================>')
if __name__ == '__main__':
main()