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‎Pretreatment/PSNR_SSIM_Evaluate.ipynb

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"import math\n",
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"import numpy as np\n",
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"import cv2\n",
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"from PIL import Image"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"def calculate_psnr(img1, img2):\n",
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" # img1 and img2 have range [0, 255]\n",
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" img1 = img1.astype(np.float64)\n",
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" img2 = img2.astype(np.float64)\n",
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" mse = np.mean((img1 - img2)**2)\n",
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" if mse == 0:\n",
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" return float('inf')\n",
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" return 20 * math.log10(255.0 / math.sqrt(mse))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"def ssim(img1, img2):\n",
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" C1 = (0.01 * 255)**2\n",
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" C2 = (0.03 * 255)**2\n",
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"\n",
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" img1 = img1.astype(np.float64)\n",
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" img2 = img2.astype(np.float64)\n",
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" kernel = cv2.getGaussianKernel(11, 1.5)\n",
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" window = np.outer(kernel, kernel.transpose())\n",
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"\n",
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" mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5] # valid\n",
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" mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5]\n",
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" mu1_sq = mu1**2\n",
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" mu2_sq = mu2**2\n",
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" mu1_mu2 = mu1 * mu2\n",
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" sigma1_sq = cv2.filter2D(img1**2, -1, window)[5:-5, 5:-5] - mu1_sq\n",
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" sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq\n",
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" sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2\n",
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"\n",
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" ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) *\n",
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" (sigma1_sq + sigma2_sq + C2))\n",
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" return ssim_map.mean()\n",
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"\n",
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"\n",
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"def calculate_ssim(img1, img2):\n",
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" '''calculate SSIM\n",
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" the same outputs as MATLAB's\n",
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" img1, img2: [0, 255]\n",
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" '''\n",
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" if not img1.shape == img2.shape:\n",
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" raise ValueError('Input images must have the same dimensions.')\n",
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" if img1.ndim == 2:\n",
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" return ssim(img1, img2)\n",
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" elif img1.ndim == 3:\n",
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" if img1.shape[2] == 3:\n",
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" ssims = []\n",
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" for i in range(3):\n",
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" ssims.append(ssim(img1, img2))\n",
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" return np.array(ssims).mean()\n",
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" elif img1.shape[2] == 1:\n",
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" return ssim(np.squeeze(img1), np.squeeze(img2))\n",
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" else:\n",
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" raise ValueError('Wrong input image dimensions.')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"def calculate(path1, path2):\n",
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" img1=Image.open(path1)\n",
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" img2=Image.open(path2)\n",
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"\n",
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" img1_array = np.array(img1)\n",
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" img2_array = np.array(img2)\n",
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"\n",
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" result_psnr = calculate_psnr(img1_array,img2_array)\n",
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" result_ssim = calculate_ssim(img1_array, img2_array)\n",
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"\n",
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" print('PSNR: {}'.format(result_psnr))\n",
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" print('SSIM: {}'.format(result_ssim))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**🅰path1 为超分结果图** \n",
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"**🅱path2 为GT图**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"PSNR: 28.941417753051944\n",
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"SSIM: 0.8072565770834261\n"
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]
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}
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],
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"source": [
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"path1 = r\"D:\\A_result_compare\\HuangRW.tif\"\n",
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"path2 = r\"D:\\GraduationProjectBackUp\\Result_compare\\95\\95-2048pix-speed3-ave1.tif\" \n",
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"\n",
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"calculate(path1, path2)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"anaconda-cloud": {},
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"kernelspec": {
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"display_name": "Python [default]",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.5.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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}

‎Pretreatment/calculate_PSNR_SSIM.py

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import math
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import numpy as np
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import cv2
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from PIL import Image
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import argparse
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# Training settings
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parserI = argparse.ArgumentParser(description='Calculate PSNR and SSIM')
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parserI.add_argument('--SR', type=str, default=' ', help='your SR img')
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parserI.add_argument('--GT', type=str, default=' ', help='the GT img')
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optI = parserI.parse_args()
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print(optI)
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def calculate_psnr(img1, img2):
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# img1 and img2 have range [0, 255]
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img1 = img1.astype(np.float64)
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img2 = img2.astype(np.float64)
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mse = np.mean((img1 - img2)**2)
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if mse == 0:
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return float('inf')
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return 20 * math.log10(255.0 / math.sqrt(mse))
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def ssim(img1, img2):
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C1 = (0.01 * 255)**2
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C2 = (0.03 * 255)**2
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img1 = img1.astype(np.float64)
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img2 = img2.astype(np.float64)
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kernel = cv2.getGaussianKernel(11, 1.5)
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window = np.outer(kernel, kernel.transpose())
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mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5] # valid
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mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5]
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mu1_sq = mu1**2
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mu2_sq = mu2**2
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mu1_mu2 = mu1 * mu2
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sigma1_sq = cv2.filter2D(img1**2, -1, window)[5:-5, 5:-5] - mu1_sq
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sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq
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sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2
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ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) *
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(sigma1_sq + sigma2_sq + C2))
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return ssim_map.mean()
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def calculate_ssim(img1, img2):
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'''calculate SSIM
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the same outputs as MATLAB's
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img1, img2: [0, 255]
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'''
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if not img1.shape == img2.shape:
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raise ValueError('Input images must have the same dimensions.')
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if img1.ndim == 2:
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return ssim(img1, img2)
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elif img1.ndim == 3:
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if img1.shape[2] == 3:
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ssims = []
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for i in range(3):
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ssims.append(ssim(img1, img2))
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return np.array(ssims).mean()
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elif img1.shape[2] == 1:
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return ssim(np.squeeze(img1), np.squeeze(img2))
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else:
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raise ValueError('Wrong input image dimensions.')
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def calculate(path1, path2):
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img1=Image.open(path1)
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img2=Image.open(path2)
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img1_array = np.array(img1)
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img2_array = np.array(img2)
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result_psnr = calculate_psnr(img1_array,img2_array)
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result_ssim = calculate_ssim(img1_array, img2_array)
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print('PSNR: {}'.format(result_psnr))
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print('SSIM: {}'.format(result_ssim))
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if __name__ == '__main__':
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calculate(optI.SR, optI.GT)

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