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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import matplotlib.gridspec as gridspec\n",
"import cv2\n",
"import os\n",
"import numpy as np\n",
"import glob\n",
"from PIL import Image"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def random_shearing(img, num, border):\n",
" rows = img.shape[0]\n",
" cols = img.shape[1]\n",
" if num == 0:\n",
" pts1 = np.float32([[5,5],[20,5],[2,20]])\n",
" pts2 = np.float32([[10,10],[20,5],[5,25]])\n",
" elif num == 1:\n",
" pts1 = np.float32([[5,5],[15,5],[2,20]])\n",
" pts2 = np.float32([[5,10],[10,10],[5,25]])\n",
" elif num == 2:\n",
" pts1 = np.float32([[5,5],[15,5],[5,20]])\n",
" pts2 = np.float32([[5,10],[10,10],[5,25]])\n",
" elif num == 3:\n",
" pts1 = np.float32([[5,5],[10,5],[2,20]])\n",
" pts2 = np.float32([[5,10],[10,10],[5,25]])\n",
" elif num == 4:\n",
" pts1 = np.float32([[5,5],[10,5],[2,20]])\n",
" pts2 = np.float32([[5,10],[10,10],[5,30]])\n",
" else:\n",
" pts1 = np.float32([[5,5],[10,5],[10,20]])\n",
" pts2 = np.float32([[5,10],[10,10],[5,30]])\n",
" M = cv2.getAffineTransform(pts1,pts2)\n",
" return cv2.warpAffine(img, M, (cols,rows), borderValue=border)\n",
"\n",
"def random_rotation(img, degree, border):\n",
" rows = img.shape[0]\n",
" cols = img.shape[1]\n",
" M = cv2.getRotationMatrix2D((cols/2,rows/2),degree,1)\n",
" return cv2.warpAffine(img,M,(cols,rows), borderValue=border)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def transform_image(img, skt, ang_range, shear_range, trans_range):\n",
" '''\n",
" This function transforms images to generate new images.\n",
" The function takes in following arguments,\n",
" 1- Image\n",
" 2- ang_range: Range of angles for rotation\n",
" 3- shear_range: Range of values to apply affine transform to\n",
" 4- trans_range: Range of values to apply translations over.\n",
"\n",
" A Random uniform distribution is used to generate different parameters for transformation\n",
"\n",
" '''\n",
" # Rotation\n",
"\n",
" ang_rot = np.random.uniform(ang_range)-ang_range/2\n",
" rows,cols,ch = img.shape \n",
" Rot_M = cv2.getRotationMatrix2D((cols/2,rows/2),ang_rot,1)\n",
"\n",
" # Translation\n",
" tr_x = trans_range*np.random.uniform()-trans_range/2\n",
" tr_y = trans_range*np.random.uniform()-trans_range/2\n",
" Trans_M = np.float32([[1,0,tr_x],[0,1,tr_y]])\n",
"\n",
" # Shear\n",
" pts1 = np.float32([[5,5],[20,5],[5,20]])\n",
"\n",
" pt1 = 5+shear_range*np.random.uniform()-shear_range/2\n",
" pt2 = 20+shear_range*np.random.uniform()-shear_range/2\n",
"\n",
" pts2 = np.float32([[pt1,5],[pt2,pt1],[5,pt2]])\n",
"\n",
" shear_M = cv2.getAffineTransform(pts1,pts2)\n",
"\n",
" # Border\n",
" idx = 0\n",
" border_img = tuple([int(img[idx][0][0]), int(img[idx][0][1]), int(img[idx][0][2])])\n",
" border_skt = tuple([int(skt[0][0][0]), int(skt[0][0][1]), int(skt[0][0][2])])\n",
" \n",
" img = cv2.warpAffine(img,Rot_M,(cols,rows), borderValue=border_img)\n",
" img = cv2.warpAffine(img,Trans_M,(cols,rows), borderValue=border_img)\n",
" img = cv2.warpAffine(img,shear_M,(cols,rows), borderValue=border_img)\n",
" \n",
" skt = cv2.warpAffine(skt,Rot_M,(cols,rows), borderValue=border_skt)\n",
" skt = cv2.warpAffine(skt,Trans_M,(cols,rows), borderValue=border_skt)\n",
" skt = cv2.warpAffine(skt,shear_M,(cols,rows), borderValue=border_skt)\n",
"\n",
" return img, skt"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"sketch_dir = 'Dataset/Augmented sketch/'\n",
"photo_dir = 'Dataset/Augmented photo/'\n",
"\n",
"if not os.path.exists(sketch_dir):\n",
" os.mkdir(sketch_dir)\n",
"\n",
"if not os.path.exists(photo_dir):\n",
" os.mkdir(photo_dir)\n",
"\n",
"p_filenames = glob.glob('Dataset/CUHK/Training photo/*')\n",
"s_filenames = glob.glob('Dataset/CUHK/Training sketch/*')\n",
"\n",
"counter = 0\n",
"for i in range(len(p_filenames)):\n",
" im = cv2.imread(p_filenames[i])\n",
" sk = cv2.imread(s_filenames[i])\n",
"\n",
" for j in range(200):\n",
" img, skt = transform_image(im, sk, 40, 10, 10)\n",
"\n",
" cv2.imwrite(photo_dir + str(counter) + '.jpg', img)\n",
" cv2.imwrite(sketch_dir + str(counter) + '.jpg', skt)\n",
"\n",
" counter += 1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
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