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Image Compression
Commit 4af31d2e authored by Nathaniel Callens's avatar Nathaniel Callens
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added a file for Scout

parents 1b29c7d9 0e1fee10
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.ipynb_checkpoints/prediction_MSE_Scout-checkpoint.ipynb 0 → 100644
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "dbef8759",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from matplotlib import pyplot as plt\n",
"from itertools import product\n",
"import os\n",
"import sys\n",
"from PIL import Image\n",
"from scipy.optimize import minimize\n",
"from time import time\n",
"from numpy import linalg as la"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "b7a550e0",
"metadata": {},
"outputs": [],
"source": [
"def file_extractor(dirname=\"images\"):\n",
" files = os.listdir(dirname)\n",
" scenes = []\n",
" for file in files:\n",
" scenes.append(os.path.join(dirname, file))\n",
" return scenes\n",
"\n",
"def image_extractor(scenes):\n",
" image_folder = []\n",
" for scene in scenes:\n",
" files = os.listdir(scene)\n",
" for file in files:\n",
" image_folder.append(os.path.join(scene, file))\n",
" images = []\n",
" for folder in image_folder:\n",
" ims = os.listdir(folder)\n",
" for im in ims:\n",
" if im[-4:] == \".jp4\" or im[-7:] == \"_6.tiff\":\n",
" continue\n",
" else:\n",
" images.append(os.path.join(folder, im))\n",
" return images #returns a list of file paths to .tiff files in the specified directory given in file_extractor\n",
"\n",
"def im_distribution(images, num):\n",
" \"\"\"\n",
" Function that extracts tiff files from specific cameras and returns a list of all\n",
" the tiff files corresponding to that camera. i.e. all pictures labeled \"_7.tiff\" or otherwise\n",
" specified camera numbers.\n",
" \n",
" Parameters:\n",
" images (list): list of all tiff files, regardless of classification. This is NOT a list of directories but\n",
" of specific tiff files that can be opened right away. This is the list that we iterate through and \n",
" divide.\n",
" \n",
" num (str): a string designation for the camera number that we want to extract i.e. \"14\" for double digits\n",
" of \"_1\" for single digits.\n",
" \n",
" Returns:\n",
" tiff (list): A list of tiff files that have the specified designation from num. They are the files extracted\n",
" from the 'images' list that correspond to the given num.\n",
" \"\"\"\n",
" tiff = []\n",
" for im in images:\n",
" if im[-7:-5] == num:\n",
" tiff.append(im)\n",
" return tiff"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "9ed20f84",
"metadata": {},
"outputs": [],
"source": [
"def plot_hist(tiff_list, i):\n",
" \"\"\"\n",
" This function is the leftovers from the first attempt to plot histograms.\n",
" As it stands it needs some work in order to function again. We will\n",
" fix this later. 1/25/22\n",
" \"\"\"\n",
" \n",
"\n",
" image = tiff_list[i]\n",
" image = Image.open(image) #Open the image and read it as an Image object\n",
" image = np.array(image)[1:,:] #Convert to an array, leaving out the first row because the first row is just housekeeping data\n",
" image = image.astype(int)\n",
" row, col = image.shape\n",
" \"\"\"predict = np.empty([row,col]) # create a empty matrix to update prediction\n",
" predict[0,:] = np.copy(image[0,:]) # keep the first row from the image\n",
" predict[:,0] = np.copy(image[:,0]) # keep the first columen from the image\n",
" predict[-1,:] = np.copy(image[-1,:]) # keep the first row from the image\n",
" predict[:,-1] = np.copy(image[:,-1]) # keep the first columen from the image\n",
" diff = np.empty([row,col])\n",
" diff[0,:] = np.zeros(col) # keep the first row from the image\n",
" diff[:,0] = np.zeros(row)\n",
" diff[-1,:] = np.zeros(col) # keep the first row from the image\n",
" diff[:,-1] = np.zeros(row)\"\"\"\n",
" A = np.array([[3,0,-1],[0,3,3],[1,-3,-4]])\n",
" z0 = image[0:-2,0:-2]\n",
" z1 = image[0:-2,1:-1]\n",
" z2 = image[0:-2,2::]\n",
" z3 = image[1:-1,0:-2]\n",
" y0 = np.ravel(-z0+z2-z3)\n",
" y1 = np.ravel(z0+z1+z2)\n",
" y2 = np.ravel(-z0-z1-z2-z3)\n",
" y = np.vstack((y0,y1,y2))\n",
" predict = la.solve(A,y)[-1]\n",
" #diff = [(np.max([z0[r,c],z1[r,c],z2[r,c],z3[r,c]])-np.min([z0[r,c],z1[r,c],z2[r,c],z3[r,c]])) for r in range(0,row-2) for c in range(0,col-2)]\n",
" '''\n",
" for r in range(1,row-1): # loop through the rth row\n",
" for c in range(1,col-1): # loop through the cth column\n",
" actual_surrounding = np.array([image[r-1,c-1], image[r-1,c], image[r-1,c+1], image[r,c-1]])\n",
" #z = np.array([int(image[r-1,c-1]), int(image[r-1,c]), int(image[r-1,c+1]), int(image[r,c-1])])\n",
" z = np.array([image[r-1,c-1], image[r-1,c], image[r-1,c+1], image[r,c-1]])\n",
" y = np.array([-z[0]+z[2]-z[3], z[0]+z[1]+z[2], -z[0]-z[1]-z[2]-z[3]])\n",
" predict[r,c] = np.linalg.solve(A,y)[-1]\n",
" diff[r,c] = (np.max(actual_surrounding)-np.min(actual_surrounding))\n",
" predict = np.ravel(predict[1:-1,1:-1])\n",
" diff = np.ravel(diff[1:-1,1:-1])'''\n",
" image = np.ravel(image[1:-1,1:-1])\n",
" return image, predict#, diff"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "8e3ef654",
"metadata": {},
"outputs": [],
"source": [
"scenes = file_extractor()\n",
"images = image_extractor(scenes)\n",
"num_images = im_distribution(images, \"_9\")\n",
"error_mean = []\n",
"error_mean1 = []\n",
"diff_mean = []\n",
"times = []\n",
"times1 = []\n",
"all_error = []\n",
"for i in range(len(num_images)):\n",
" \"\"\"start1 = time()\n",
" image_1, predict_1, difference_1, x_s_1 = plot_hist(num_images, i, \"second\")\n",
" stop1 = time()\n",
" times1.append(stop1-start1)\n",
" error1 = np.abs(image_1-predict_1)\n",
" error_mean1.append(np.mean(np.ravel(error1)))\"\"\"\n",
" start = time()\n",
" image, predict = plot_hist(num_images, i)\n",
" stop = time()\n",
" times.append(stop-start)\n",
" error = np.abs(image-predict)\n",
" all_error.append(np.ravel(error))\n",
" error_mean.append(np.mean(np.ravel(error)))\n",
" #diff_mean.append(np.mean(np.ravel(difference)))\n",
" \n",
"#image, predict, difference = plot_hist(images, 0)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "fa65dcd6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Average Error First and Second Added: 20.017164930235474\n",
"233.22663391021266\n",
"Standard Deviaiton of Mean Errors: 0.16101183692475135\n",
"Average Time per Image for First: 0.023412495851516724\n"
]
}
],
"source": [
"print(f\"Average Error First and Second Added: {np.mean(error_mean)}\")\n",
"print(np.std(image))\n",
"print(f\"Standard Deviaiton of Mean Errors: {np.sqrt(np.var(error_mean))}\")\n",
"#print(f\"Average Difference: {np.mean(diff_mean)}\")\n",
"print(f\"Average Time per Image for First: {np.mean(times)}\")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "f592fa32",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[0 1 2]\n",
" [3 4 5]\n",
" [6 7 8]]\n",
"[4 5]\n",
"[4 5]\n"
]
}
],
"source": [
"b = np.arange(9).reshape((3,3))\n",
"print(b)\n",
"print(b[1,1::])\n",
"print(b[1,1:])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "dda442ae",
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'difference' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_10808/722042198.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_subplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mimage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdifference\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 5\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'o'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0malpha\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0.2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrcParams\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;34m'font.size'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;36m20\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mNameError\u001b[0m: name 'difference' is not defined"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 720x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize = (10,10))\n",
"ax = fig.add_subplot()\n",
"x = np.abs(predict-image)\n",
"y = difference\n",
"plt.plot(x,y,'o',alpha = 0.2)\n",
"plt.rcParams.update({'font.size': 20})\n",
"plt.xlabel(\"differnece to the true value\" )\n",
"plt.ylabel(\"differnece of min and max of true value of the surroundings\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "58da6063",
"metadata": {},
"outputs": [],
"source": [
"image = Image.open(images[0]) #Open the image and read it as an Image object\n",
"image = np.array(image)[1:,:]\n",
"#z = np.array([image[1-1,1-1], image[1-1,1], image[1-1,1+1], image[1,1-1]])\n",
"z = np.array([22554,22552,22519,22561])\n",
"print(z)\n",
"'''A = np.array([[-3,0,1],[0,-3,3],[-1,-3,4]])\n",
"y = np.array([z[0]-z[2]+z[3], z[0]+z[1]+z[2], -z[0]-z[1]-z[2]-z[3]])\n",
"a,b,c = np.linalg.solve(A,y)'''\n",
"A = np.array([[3,0,-1],[0,3,3],[1,-3,-4]])\n",
"y = np.array([-z[0]+z[2]-z[3], z[0]+z[1]+z[2], -z[0]-z[1]-z[2]-z[3]])\n",
"print(y)\n",
"a,b,c = np.linalg.solve(A,y)\n",
"print(a,b,c)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2562feeb",
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"i0 = (a*(-1) + b*(1) + c)\n",
"i1 = (a*(0) + b*(1) + c)\n",
"i2 = (a*(1) + b*(1) + c)\n",
"i3 = (a*(-1) + b*(0) + c)\n",
"print(sum([(i0-z[0])**2,(i1-z[1])**2,(i2-z[2])**2,(i3-z[3])**2]))\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "470cc137",
"metadata": {},
"outputs": [],
"source": [
"a = 0\n",
"b = 2\n",
"c = 2\n",
"i0 = (a*(-1) + b*(1) + c)\n",
"i1 = (a*(0) + b*(1) + c)\n",
"i2 = (a*(1) + b*(1) + c)\n",
"i3 = (a*(-1) + b*(0) + c)\n",
"print(sum([(i0-z[0])**2,(i1-z[1])**2,(i2-z[2])**2,(i3-z[3])**2]))\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3292b395",
"metadata": {},
"outputs": [],
"source": [
"#z = np.hstack((image[0,:3], image[1,0]))\n",
"#x = np.array([-1,0,1,-1])\n",
"#y = np.array([-1,-1,-1,0])\n",
"A = np.array([[-3,0,1],[0,-3,3],[1,3,-4]])\n",
"#y = np.array([z[0]-z[2]+z[3], z[0]+z[1]+z[2], -z[0]-z[1]-z[2]-z[3]])\n",
"y = np.array([[1,2,3],[4,5,6],[7,8,9]])\n",
"print(np.linalg.solve(A,y))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f9687830",
"metadata": {},
"outputs": [],
"source": [
"0.5**2 + 1.5**2"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e98eed4b",
"metadata": {},
"outputs": [],
"source": [
"y1= [0,1,2,3]\n",
"y2=[4,5,6,7]\n",
"y3=[8,9,10,11]\n",
"np.vstack((y1,y2,y3)).T"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b7e88aab",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
prediction_MSE.ipynb 0 → 100644
View file @ 4af31d2e
This source diff could not be displayed because it is too large. You can view the blob instead.
prediction_MSE_Scout.ipynb 0 → 100644
View file @ 4af31d2e
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "dbef8759",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from matplotlib import pyplot as plt\n",
"from itertools import product\n",
"import os\n",
"import sys\n",
"from PIL import Image\n",
"from scipy.optimize import minimize\n",
"from time import time\n",
"from numpy import linalg as la"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "b7a550e0",
"metadata": {},
"outputs": [],
"source": [
"def file_extractor(dirname=\"images\"):\n",
" files = os.listdir(dirname)\n",
" scenes = []\n",
" for file in files:\n",
" scenes.append(os.path.join(dirname, file))\n",
" return scenes\n",
"\n",
"def image_extractor(scenes):\n",
" image_folder = []\n",
" for scene in scenes:\n",
" files = os.listdir(scene)\n",
" for file in files:\n",
" image_folder.append(os.path.join(scene, file))\n",
" images = []\n",
" for folder in image_folder:\n",
" ims = os.listdir(folder)\n",
" for im in ims:\n",
" if im[-4:] == \".jp4\" or im[-7:] == \"_6.tiff\":\n",
" continue\n",
" else:\n",
" images.append(os.path.join(folder, im))\n",
" return images #returns a list of file paths to .tiff files in the specified directory given in file_extractor\n",
"\n",
"def im_distribution(images, num):\n",
" \"\"\"\n",
" Function that extracts tiff files from specific cameras and returns a list of all\n",
" the tiff files corresponding to that camera. i.e. all pictures labeled \"_7.tiff\" or otherwise\n",
" specified camera numbers.\n",
" \n",
" Parameters:\n",
" images (list): list of all tiff files, regardless of classification. This is NOT a list of directories but\n",
" of specific tiff files that can be opened right away. This is the list that we iterate through and \n",
" divide.\n",
" \n",
" num (str): a string designation for the camera number that we want to extract i.e. \"14\" for double digits\n",
" of \"_1\" for single digits.\n",
" \n",
" Returns:\n",
" tiff (list): A list of tiff files that have the specified designation from num. They are the files extracted\n",
" from the 'images' list that correspond to the given num.\n",
" \"\"\"\n",
" tiff = []\n",
" for im in images:\n",
" if im[-7:-5] == num:\n",
" tiff.append(im)\n",
" return tiff"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "9ed20f84",
"metadata": {},
"outputs": [],
"source": [
"def plot_hist(tiff_list, i):\n",
" \"\"\"\n",
" This function is the leftovers from the first attempt to plot histograms.\n",
" As it stands it needs some work in order to function again. We will\n",
" fix this later. 1/25/22\n",
" \"\"\"\n",
" \n",
"\n",
" image = tiff_list[i]\n",
" image = Image.open(image) #Open the image and read it as an Image object\n",
" image = np.array(image)[1:,:] #Convert to an array, leaving out the first row because the first row is just housekeeping data\n",
" image = image.astype(int)\n",
" row, col = image.shape\n",
" \"\"\"predict = np.empty([row,col]) # create a empty matrix to update prediction\n",
" predict[0,:] = np.copy(image[0,:]) # keep the first row from the image\n",
" predict[:,0] = np.copy(image[:,0]) # keep the first columen from the image\n",
" predict[-1,:] = np.copy(image[-1,:]) # keep the first row from the image\n",
" predict[:,-1] = np.copy(image[:,-1]) # keep the first columen from the image\n",
" diff = np.empty([row,col])\n",
" diff[0,:] = np.zeros(col) # keep the first row from the image\n",
" diff[:,0] = np.zeros(row)\n",
" diff[-1,:] = np.zeros(col) # keep the first row from the image\n",
" diff[:,-1] = np.zeros(row)\"\"\"\n",
" A = np.array([[3,0,-1],[0,3,3],[1,-3,-4]])\n",
" z0 = image[0:-2,0:-2]\n",
" z1 = image[0:-2,1:-1]\n",
" z2 = image[0:-2,2::]\n",
" z3 = image[1:-1,0:-2]\n",
" y0 = np.ravel(-z0+z2-z3)\n",
" y1 = np.ravel(z0+z1+z2)\n",
" y2 = np.ravel(-z0-z1-z2-z3)\n",
" y = np.vstack((y0,y1,y2))\n",
" predict = la.solve(A,y)[-1]\n",
" #diff = [(np.max([z0[r,c],z1[r,c],z2[r,c],z3[r,c]])-np.min([z0[r,c],z1[r,c],z2[r,c],z3[r,c]])) for r in range(0,row-2) for c in range(0,col-2)]\n",
" '''\n",
" for r in range(1,row-1): # loop through the rth row\n",
" for c in range(1,col-1): # loop through the cth column\n",
" actual_surrounding = np.array([image[r-1,c-1], image[r-1,c], image[r-1,c+1], image[r,c-1]])\n",
" #z = np.array([int(image[r-1,c-1]), int(image[r-1,c]), int(image[r-1,c+1]), int(image[r,c-1])])\n",
" z = np.array([image[r-1,c-1], image[r-1,c], image[r-1,c+1], image[r,c-1]])\n",
" y = np.array([-z[0]+z[2]-z[3], z[0]+z[1]+z[2], -z[0]-z[1]-z[2]-z[3]])\n",
" predict[r,c] = np.linalg.solve(A,y)[-1]\n",
" diff[r,c] = (np.max(actual_surrounding)-np.min(actual_surrounding))\n",
" predict = np.ravel(predict[1:-1,1:-1])\n",
" diff = np.ravel(diff[1:-1,1:-1])'''\n",
" image = np.ravel(image[1:-1,1:-1])\n",
" return image, predict#, diff"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "8e3ef654",
"metadata": {},
"outputs": [],
"source": [
"scenes = file_extractor()\n",
"images = image_extractor(scenes)\n",
"num_images = im_distribution(images, \"_9\")\n",
"error_mean = []\n",
"error_mean1 = []\n",
"diff_mean = []\n",
"times = []\n",
"times1 = []\n",
"all_error = []\n",
"for i in range(len(num_images)):\n",
" \"\"\"start1 = time()\n",
" image_1, predict_1, difference_1, x_s_1 = plot_hist(num_images, i, \"second\")\n",
" stop1 = time()\n",
" times1.append(stop1-start1)\n",
" error1 = np.abs(image_1-predict_1)\n",
" error_mean1.append(np.mean(np.ravel(error1)))\"\"\"\n",
" start = time()\n",
" image, predict = plot_hist(num_images, i)\n",
" stop = time()\n",
" times.append(stop-start)\n",
" error = np.abs(image-predict)\n",
" all_error.append(np.ravel(error))\n",
" error_mean.append(np.mean(np.ravel(error)))\n",
" #diff_mean.append(np.mean(np.ravel(difference)))\n",
" \n",
"#image, predict, difference = plot_hist(images, 0)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "fa65dcd6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Average Error First and Second Added: 20.017164930235474\n",
"233.22663391021266\n",
"Standard Deviaiton of Mean Errors: 0.16101183692475135\n",
"Average Time per Image for First: 0.023412495851516724\n"
]
}
],
"source": [
"print(f\"Average Error First and Second Added: {np.mean(error_mean)}\")\n",
"print(np.std(image))\n",
"print(f\"Standard Deviaiton of Mean Errors: {np.sqrt(np.var(error_mean))}\")\n",
"#print(f\"Average Difference: {np.mean(diff_mean)}\")\n",
"print(f\"Average Time per Image for First: {np.mean(times)}\")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "f592fa32",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[0 1 2]\n",
" [3 4 5]\n",
" [6 7 8]]\n",
"[4 5]\n",
"[4 5]\n"
]
}
],
"source": [
"b = np.arange(9).reshape((3,3))\n",
"print(b)\n",
"print(b[1,1::])\n",
"print(b[1,1:])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "dda442ae",
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'difference' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_10808/722042198.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_subplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mimage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdifference\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 5\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'o'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0malpha\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0.2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrcParams\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;34m'font.size'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;36m20\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mNameError\u001b[0m: name 'difference' is not defined"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 720x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize = (10,10))\n",
"ax = fig.add_subplot()\n",
"x = np.abs(predict-image)\n",
"y = difference\n",
"plt.plot(x,y,'o',alpha = 0.2)\n",
"plt.rcParams.update({'font.size': 20})\n",
"plt.xlabel(\"differnece to the true value\" )\n",
"plt.ylabel(\"differnece of min and max of true value of the surroundings\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "58da6063",
"metadata": {},
"outputs": [],
"source": [
"image = Image.open(images[0]) #Open the image and read it as an Image object\n",
"image = np.array(image)[1:,:]\n",
"#z = np.array([image[1-1,1-1], image[1-1,1], image[1-1,1+1], image[1,1-1]])\n",
"z = np.array([22554,22552,22519,22561])\n",
"print(z)\n",
"'''A = np.array([[-3,0,1],[0,-3,3],[-1,-3,4]])\n",
"y = np.array([z[0]-z[2]+z[3], z[0]+z[1]+z[2], -z[0]-z[1]-z[2]-z[3]])\n",
"a,b,c = np.linalg.solve(A,y)'''\n",
"A = np.array([[3,0,-1],[0,3,3],[1,-3,-4]])\n",
"y = np.array([-z[0]+z[2]-z[3], z[0]+z[1]+z[2], -z[0]-z[1]-z[2]-z[3]])\n",
"print(y)\n",
"a,b,c = np.linalg.solve(A,y)\n",
"print(a,b,c)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2562feeb",
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"i0 = (a*(-1) + b*(1) + c)\n",
"i1 = (a*(0) + b*(1) + c)\n",
"i2 = (a*(1) + b*(1) + c)\n",
"i3 = (a*(-1) + b*(0) + c)\n",
"print(sum([(i0-z[0])**2,(i1-z[1])**2,(i2-z[2])**2,(i3-z[3])**2]))\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "470cc137",
"metadata": {},
"outputs": [],
"source": [
"a = 0\n",
"b = 2\n",
"c = 2\n",
"i0 = (a*(-1) + b*(1) + c)\n",
"i1 = (a*(0) + b*(1) + c)\n",
"i2 = (a*(1) + b*(1) + c)\n",
"i3 = (a*(-1) + b*(0) + c)\n",
"print(sum([(i0-z[0])**2,(i1-z[1])**2,(i2-z[2])**2,(i3-z[3])**2]))\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3292b395",
"metadata": {},
"outputs": [],
"source": [
"#z = np.hstack((image[0,:3], image[1,0]))\n",
"#x = np.array([-1,0,1,-1])\n",
"#y = np.array([-1,-1,-1,0])\n",
"A = np.array([[-3,0,1],[0,-3,3],[1,3,-4]])\n",
"#y = np.array([z[0]-z[2]+z[3], z[0]+z[1]+z[2], -z[0]-z[1]-z[2]-z[3]])\n",
"y = np.array([[1,2,3],[4,5,6],[7,8,9]])\n",
"print(np.linalg.solve(A,y))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f9687830",
"metadata": {},
"outputs": [],
"source": [
"0.5**2 + 1.5**2"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e98eed4b",
"metadata": {},
"outputs": [],
"source": [
"y1= [0,1,2,3]\n",
"y2=[4,5,6,7]\n",
"y3=[8,9,10,11]\n",
"np.vstack((y1,y2,y3)).T"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b7e88aab",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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