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image-compression
Commit fced29e2 authored by Kelly Chang's avatar Kelly Chang
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Merge branch 'master' of https://git.elphel.com/Elphel/master

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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"
]
},
{
"cell_type": "code",
"execution_count": 44,
"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": 51,
"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",
" A = np.array([[3,0,-1],[0,3,3],[1,-3,-4]]) # the matrix for system of equation\n",
" z0 = image[0:-2,0:-2] # get all the first pixel for the entire image\n",
" z1 = image[0:-2,1:-1] # get all the second pixel for the entire image\n",
" z2 = image[0:-2,2::] # get all the third pixel for the entire image\n",
" z3 = image[1:-1,0:-2] # get all the forth pixel for the entire image\n",
" # calculate the out put of the system of equation\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",
" # use numpy solver to solve the system of equations all at once\n",
" predict = np.linalg.solve(A,y)[-1]\n",
" # flatten the neighbor pixlels and stack them together\n",
" z0 = np.ravel(z0)\n",
" z1 = np.ravel(z1)\n",
" z2 = np.ravel(z2)\n",
" z3 = np.ravel(z3)\n",
" neighbor = np.vstack((z0,z1,z2,z3)).T\n",
" # calculate the difference\n",
" diff = np.max(neighbor,axis = 1) - np.min(neighbor, axis=1)\n",
" # flatten the image to a vector\n",
" image = np.ravel(image[1:-1,1:-1])\n",
" return image, predict, diff\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 49,
"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, difference = 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)))"
]
},
{
"cell_type": "code",
"execution_count": 52,
"id": "fa65dcd6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Average Error First and Second Added: 20.017164930235474\n",
"Standard Deviaiton of Mean Errors: 0.16101183692475135\n",
"Average Difference: 53.678648426455226\n",
"Average Time per Image for First: 0.04535740613937378\n"
]
}
],
"source": [
"print(f\"Average Error First and Second Added: {np.mean(error_mean)}\")\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": 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": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
Encoding_decoding.ipynb
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......@@ -567,7 +567,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
"version": "3.8.11"
}
},
"nbformat": 4,
......
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compress_start.py deleted 100644 → 0
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'''
Created on Jan 11, 2022
@author: nathanielc
python file located in the Image Compression project
folder under nathanielc user. This python file is for
exploring possible image compression techniques and is by
no means a final product. Goal is to learn how to download
images and extract important statistics from them.
'''
import numpy as np
from matplotlib import pyplot as plt
from itertools import product
import os
import sys
from PIL import Image
def file_extractor(dirname="images"):
files = os.listdir(dirname)
scenes = []
for file in files:
scenes.append(os.path.join(dirname, file))
return scenes
def image_extractor(scenes):
image_folder = []
for scene in scenes:
files = os.listdir(scene)
for file in files:
image_folder.append(os.path.join(scene, file))
images = []
for folder in image_folder:
ims = os.listdir(folder)
for im in ims:
if im[-4:] == ".jp4" or im[-7:] == "_6.tiff":
continue
else:
images.append(os.path.join(folder, im))
return images #returns a list of file paths to .tiff files in the specified directory given in file_extractor
def im_distribution(images, num):
"""
Function that extracts tiff files from specific cameras and returns a list of all
the tiff files corresponding to that camera. i.e. all pictures labeled "_7.tiff" or otherwise
specified camera numbers.
Parameters:
images (list): list of all tiff files, regardless of classification. This is NOT a list of directories but
of specific tiff files that can be opened right away. This is the list that we iterate through and
divide.
num (str): a string designation for the camera number that we want to extract i.e. "14" for double digits
of "_1" for single digits.
Returns:
tiff (list): A list of tiff files that have the specified designation from num. They are the files extracted
from the 'images' list that correspond to the given num.
"""
tiff = []
for im in images:
if im[-7:-6] == num:
tiff.append(im)
return tiff
def plot_hist(tiff_list):
"""
This function is the leftovers from the first attempt to plot histograms.
As it stands it needs some work in order to function again. We will
fix this later. 1/25/22
"""
'''jj = 0
fig, axs = plt.subplots(nrows=2, ncols=2, figsize=(15,12))
for cam, ax in zip(cameras, axs.ravel()):
diff = []
for ii in range(len(cam)):
image = Image.open(cam[ii]) #Open the image and read it as an Image object
image = np.array(image)[1:,:] #Convert to an array, leaving out the first row because the first row is just housekeeping data
ar1, ar2 = image.shape
ind1, ind2 = np.random.randint(1,ar1-1), np.random.randint(1,ar2-1) #ind1 randomly selects a row, ind2 randomly selects a column,
#this is now a random pixel selection within the image
surrounding = [] #initialize a list to be filled the 8 surrounding pixels
for i,j in product(np.arange(-1,2), repeat=2): #Iterate through the combinations of surrounding pixel indices
if i == 0 and j == 0: #Avoid the target pixel
continue
else:
surrounding.append(image[ind1+i, ind1+j]) #Add the other 8 pixels to the list
diff.append(np.max(surrounding)-np.min(surrounding))
ax.hist(diff)
ax.set_title(f"tiff {jj}")
jj += 1
plt.tight_layout()
plt.show()
return '''
image = tiff_list[0]
image = Image.open(image) #Open the image and read it as an Image object
image = np.array(image)[1:,:] #Convert to an array, leaving out the first row because the first row is just housekeeping data
row, col = image.shape
predict = np.empty([row-1,col-1]) # create a empty matrix to update prediction
temp = image.copy
diff = np.empty([row-1,col-1])
for r in range(1,row-1): # loop through the rth row
for c in range(1,col-1): # loop through the cth column
surrounding = np.array([temp[r-1,c-1], temp[r-1,c], temp[r-1,c+1], temp[r,c-1]])
predict[r,c] = np.mean(surrounding) # take the mean of the previous 4 pixels
temp[r,c] = np.mean(surrounding)
diff[r,c] = (np.max(surrounding)-np.min(surrounding))
predict = np.ravel(predict)
diff = np.ravel(diff)
n = len(predict)
fig = plt.figure()
ax1 = fig.add_subplot(111, projection='3d')
z3 = np.zeros(n)
dx = np.ones(n)
dy = np.ones(n)
dz = np.arange(n)
ax1.bar3d(predict, diff, z3, dx, dy, dz, color="red")
ax1.axis('off')
plt.show()
return image, predict, diff
if __name__ == '__main__':
"""For boundary cases: Start by grabbing the shape of the images and saving those
as variables. Then, if statements for if row == 0 or row == maximum and if
col == 0 or col == maximum. Then grab corresponding open pixels. Then proceed to do
an and statement that handles the corners"""
scenes = file_extractor()
images = image_extractor(scenes)
#image, predict, difference = plot_hist(images)
#error = np.abs(image-predict)
plot_hist(images)
\ No newline at end of file
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prediction_MSE_Scout.py deleted 100644 → 0
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#!/usr/bin/env python
# coding: utf-8
# In[72]:
import numpy as np
from matplotlib import pyplot as plt
from itertools import product
import os
import sys
from PIL import Image
from scipy.optimize import minimize
from time import time
from numpy import linalg as la
from scipy.stats import gaussian_kde, entropy
import seaborn as sns
import pywt
import math
#import cv2
# In[15]:
def file_extractor(dirname="images"):
files = os.listdir(dirname)
scenes = []
for file in files:
scenes.append(os.path.join(dirname, file))
return scenes
def image_extractor(scenes):
image_folder = []
for scene in scenes:
files = os.listdir(scene)
for file in files:
if file[-4:] == ".jp4" or file[-7:] == "_6.tiff":
continue
else:
image_folder.append(os.path.join(scene, file))
return image_folder #returns a list of file paths to .tiff files in the specified directory given in file_extractor
def im_distribution(images, num):
"""
Function that extracts tiff files from specific cameras and returns a list of all
the tiff files corresponding to that camera. i.e. all pictures labeled "_7.tiff" or otherwise
specified camera numbers.
Parameters:
images (list): list of all tiff files, regardless of classification. This is NOT a list of directories but
of specific tiff files that can be opened right away. This is the list that we iterate through and
divide.
num (str): a string designation for the camera number that we want to extract i.e. "14" for double digits
of "_1" for single digits.
Returns:
tiff (list): A list of tiff files that have the specified designation from num. They are the files extracted
from the 'images' list that correspond to the given num.
"""
tiff = []
for im in images:
if im[-7:-5] == num:
tiff.append(im)
return tiff
# In[16]:
def plot_hist(tiff_list, i):
"""
This function is the leftovers from the first attempt to plot histograms.
As it stands it needs some work in order to function again. We will
fix this later. 1/25/22
"""
image = tiff_list[i]
image = Image.open(image) #Open the image and read it as an Image object
image = np.array(image)[1:,:] #Convert to an array, leaving out the first row because the first row is just housekeeping data
image = image.astype(int)
A = np.array([[3,0,-1],[0,3,3],[1,-3,-4]]) # the matrix for system of equation
z0 = image[0:-2,0:-2] # get all the first pixel for the entire image
z1 = image[0:-2,1:-1] # get all the second pixel for the entire image
z2 = image[0:-2,2::] # get all the third pixel for the entire image
z3 = image[1:-1,0:-2] # get all the forth pixel for the entire image
# calculate the out put of the system of equation
y0 = np.ravel(-z0+z2-z3)
y1 = np.ravel(z0+z1+z2)
y2 = np.ravel(-z0-z1-z2-z3)
y = np.vstack((y0,y1,y2))
# use numpy solver to solve the system of equations all at once
predict = np.linalg.solve(A,y)[-1]
#predict = []
# flatten the neighbor pixlels and stack them together
z0 = np.ravel(z0)
z1 = np.ravel(z1)
z2 = np.ravel(z2)
z3 = np.ravel(z3)
neighbor = np.vstack((z0,z1,z2,z3)).T
# calculate the difference
diff = np.max(neighbor,axis = 1) - np.min(neighbor, axis=1)
"""for i in range(len(neighbor)):
if neighbor[i][0] >= max(neighbor[i][3], neighbor[i][1]):
predict.append(min(neighbor[i][3], neighbor[i][1]))
elif neighbor[i][0] < min(neighbor[i][3], neighbor[i][1]):
predict.append(max(neighbor[i][3], neighbor[i][1]))
else:
predict.append(neighbor[i][3] + neighbor[i][1] - neighbor[i][0])"""
# flatten the image to a vector
image_ravel = np.ravel(image[1:-1,1:-1])
return image_ravel, predict, diff, image
# In[17]:
scenes = file_extractor()
images = image_extractor(scenes)
num_images = im_distribution(images, "_1")
error_mean = []
error_mean1 = []
diff_mean = []
times = []
times1 = []
all_error = []
for i in range(len(num_images)):
"""start1 = time()
image_1, predict_1, difference_1, x_s_1 = plot_hist(num_images, i, "second")
stop1 = time()
times1.append(stop1-start1)
error1 = np.abs(image_1-predict_1)
error_mean1.append(np.mean(np.ravel(error1)))"""
start = time()
image, predict, difference, non_ravel = plot_hist(num_images, i)
stop = time()
times.append(stop-start)
error = np.abs(image-predict)
all_error.append(np.ravel(error))
error_mean.append(np.mean(np.ravel(error)))
diff_mean.append(np.mean(np.ravel(difference)))
#image, predict, difference = plot_hist(images, 0)
# In[18]:
print(f"Average Error: {np.mean(error_mean)}")
print(f"Standard Deviaiton of Mean Errors: {np.sqrt(np.var(error_mean))}")
print(f"Average Difference: {np.mean(diff_mean)}")
print(f"Average Time per Image for First: {np.mean(times)}")
# In[19]:
new_image, new_pred, new_diff, no_ravel = plot_hist(images, 10)
# In[21]:
new_error = new_image-new_pred
plt.hist(new_error, bins=20, density=True)
sns.kdeplot(new_error)
plt.xlabel("error")
plt.show()
# In[41]:
image = Image.open(images[0]) #Open the image and read it as an Image object
image = np.array(image)[1:,:] #Convert to an array, leaving out the first row because the first row is just housekeeping data
image = image.astype(np.int64)
print("Std Deviation of E: ", np.std(new_error))
print("Normal bits: ", int(image[0][0]).bit_length())
H = np.log2(np.std(new_error)) + 1.943
print("Encoded Bits: ", H)
# In[47]:
# In[9]:
pred = new_pred.reshape((510,638))
real_pred = no_ravel.copy()
real_pred[1:-1, 1:-1] = pred
# In[10]:
coeffs = pywt.dwt2(no_ravel, 'bior1.3')
LL, (LH, HL, HH) = coeffs
print(HH.shape)
decompress = pywt.idwt2(coeffs, 'bior1.3')
"""print(decompress)
print(np.mean(np.abs(decompress-no_ravel)))"""
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