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image-compression
Commit 96e5883b authored by Bryce Hepner's avatar Bryce Hepner
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trying to convolve, don't know how to deconvolve

parent 5476cdb4
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Showing with 215 additions and 59 deletions
.gitignore
View file @ 96e5883b
...@@ -18,3 +18,4 @@ attic ...@@ -18,3 +18,4 @@ attic
*.DS_Store *.DS_Store
lwir16.tar.gz lwir16.tar.gz
*.mp4 *.mp4
*.jpg
\ No newline at end of file
FullTester.ipynb
View file @ 96e5883b
...@@ -2,7 +2,7 @@ ...@@ -2,7 +2,7 @@
"cells": [ "cells": [
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 2, "execution_count": 10,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -15,21 +15,21 @@ ...@@ -15,21 +15,21 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "execution_count": 11,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"3\n", "5\n",
"3\n", "5\n",
"0.5416965429002373\n" "0.30596738362283216\n"
] ]
} }
], ],
"source": [ "source": [
"scenes = file_extractor(\"averaged_images(11)\")\n", "scenes = file_extractor(\"betterimages\")\n",
"images = image_extractor(scenes)\n", "images = image_extractor(scenes)\n",
"print(len(images))\n", "print(len(images))\n",
"newnamesforlater = []\n", "newnamesforlater = []\n",
...@@ -102,14 +102,15 @@ ...@@ -102,14 +102,15 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 4, "execution_count": 12,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"0.6922330946285837\n" "0.5066909897511674\n",
"0.6485158723863795\n"
] ]
} }
], ],
...@@ -123,26 +124,20 @@ ...@@ -123,26 +124,20 @@
" writer = png.Writer(newimage.shape[1], newimage.shape[0], greyscale=True, bitdepth=16)\n", " writer = png.Writer(newimage.shape[1], newimage.shape[0], greyscale=True, bitdepth=16)\n",
" writer.write(f, newimage)\n", " writer.write(f, newimage)\n",
" pngsizes.append(os.path.getsize(newnamesforlater[i][:-4] + \".png\"))\n", " pngsizes.append(os.path.getsize(newnamesforlater[i][:-4] + \".png\"))\n",
"print(np.sum(pngsizes)/np.sum(file_sizes_old))\n" "print(np.sum(pngsizes)/np.sum(file_sizes_old))\n",
"print(0.6485158723863795)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 12, "execution_count": 8,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"[[-190.26562 -177.99805 -177.38672 ... -166.95898 -156.64062 -145.72461]\n", "0.6493572339400003\n"
" [-175.46094 -163.56055 -183.07617 ... -169.2207 -167.58789 -154.14062]\n",
" [-171.45898 -179.1582 -174.68164 ... -173.10547 -157.51758 -156.97461]\n",
" ...\n",
" [-143.66992 -127.69531 -143.9043 ... 156.90625 170.79102 165.55469]\n",
" [-139.97656 -134.71875 -152.26953 ... 169.49805 158.11914 172.49023]\n",
" [-145.33984 -127.59375 -134.07227 ... 182.88672 169.80273 183.32812]]\n",
"0.3016682254469224\n"
] ]
} }
], ],
......
Remove_Noise.py
View file @ 96e5883b
from audioop import mul
from matplotlib.image import composite_images from matplotlib.image import composite_images
from WorkingPyDemo import * from WorkingPyDemo import *
from scipy.ndimage.filters import gaussian_filter from scipy.ndimage.filters import gaussian_filter
...@@ -102,24 +103,24 @@ def find_only_in_channel(images, channel_name = "10"): ...@@ -102,24 +103,24 @@ def find_only_in_channel(images, channel_name = "10"):
def adjust_to_original(new_image, average_image): def adjust_to_original(new_image, average_image):
original_image_min = np.min(new_image) original_image_min = np.min(new_image)
original_image_max = np.max(new_image) original_image_max = np.max(new_image)
# average_image = average_image - np.mean(average_image) average_image = average_image - np.mean(average_image)
# adjusted_image = average_image - new_image # adjusted_image = average_image - new_image
# adjusted_image = gaussian_filter(new_image,sigma=1) # adjusted_image = gaussian_filter(new_image,sigma=1)
adjusted_image = new_image - (average_image - gaussian_filter(average_image,sigma=1)) adjusted_image = new_image - (average_image - gaussian_filter(average_image,sigma=1))
# adjusted_image = gaussian_filter(adjusted_image,sigma=20) # adjusted_image = gaussian_filter(adjusted_image,sigma=20)
# adjusted_image = new_image + (average_image - np.array(Image.fromarray(average_image).convert("L").filter(ImageFilter.GaussianBlur(radius=4)))) # adjusted_image = new_image + (average_image - np.array(Image.fromarray(average_image).convert("L").filter(ImageFilter.GaussianBlur(radius=4))))
plt.subplot(121) # plt.subplot(121)
plt.imshow(color_adjust((adjusted_image)),cmap='gray',vmin = 0, vmax=1) # plt.imshow(color_adjust((adjusted_image)),cmap='gray',vmin = 0, vmax=1)
plt.subplot(122) # plt.subplot(122)
plt.imshow(color_adjust(new_image),cmap='gray',vmin = 0, vmax=1) # plt.imshow(color_adjust(new_image),cmap='gray',vmin = 0, vmax=1)
plt.show() # plt.show()
adjusted_image = adjusted_image - np.min(adjusted_image) adjusted_image = adjusted_image - np.min(adjusted_image)
adjusted_image = adjusted_image*(original_image_max-original_image_min)/np.max(adjusted_image) adjusted_image = adjusted_image*(original_image_max-original_image_min)/np.max(adjusted_image)
adjusted_image = adjusted_image + original_image_min adjusted_image = adjusted_image + original_image_min
print(adjusted_image.dtype) # print(adjusted_image.dtype)
return adjusted_image.astype(np.uint16) return adjusted_image.astype(np.uint16)
def color_adjust(visual_array): def color_adjust(visual_array):
...@@ -144,7 +145,7 @@ def create_testable_images(images, selected_channel, quantity_of_images): ...@@ -144,7 +145,7 @@ def create_testable_images(images, selected_channel, quantity_of_images):
images = find_only_in_channel(images, selected_channel) images = find_only_in_channel(images, selected_channel)
# image_locations = np.random.choice(len(images), quantity_of_images, replace=False) # image_locations = np.random.choice(len(images), quantity_of_images, replace=False)
image_locations = np.arange(quantity_of_images) + 10000 image_locations = np.arange(quantity_of_images) + 5000
selected_images = np.array(images)[image_locations] selected_images = np.array(images)[image_locations]
...@@ -161,7 +162,7 @@ def create_testable_images(images, selected_channel, quantity_of_images): ...@@ -161,7 +162,7 @@ def create_testable_images(images, selected_channel, quantity_of_images):
wherelastslash = item.rfind("/") wherelastslash = item.rfind("/")
image = np.array(image)[1:] image = np.array(image)[1:]
savable_original = Image.fromarray(image) savable_original = Image.fromarray(image)
# savable_original.save("averaged_images(" + selected_channel + ")/innerfolder/original" + item[wherelastslash + 1:]) savable_original.save("original_images(" + selected_channel + ")/innerfolder/original" + item[wherelastslash + 1:])
altered_image = adjust_to_original(image, average_image) altered_image = adjust_to_original(image, average_image)
altered_image = Image.fromarray(altered_image) altered_image = Image.fromarray(altered_image)
...@@ -169,30 +170,84 @@ def create_testable_images(images, selected_channel, quantity_of_images): ...@@ -169,30 +170,84 @@ def create_testable_images(images, selected_channel, quantity_of_images):
# average_image = Image.fromarray(average_image) # average_image = Image.fromarray(average_image)
sftp_client.close() sftp_client.close()
def save_new_gauss():
"""\
creates gaussian kernel with side length `l` and a sigma of `sig`
"""
# x,y = np.mgrid[-1:1:.003125, -1:1:.003125]
x,y = np.mgrid[-1:1:.003911, -1:1:.003911]
# print(x.shape)
pos = np.dstack((x,y))
# grid = np.zeros((l,l))
# gauss = np.exp(-0.5 * np.square(ax) / np.square(sig))
from scipy.stats import multivariate_normal
# normal_grid = multivariate_normal.pdf(grid, mean = [0]*l, cov = [5]*l)
normal_grid = multivariate_normal([0, 0], [[2.0, 0.3], [0.3, 0.5]]).pdf(pos)
normal_grid = normal_grid/np.sum(normal_grid)
end_image = Image.fromarray(normal_grid)
# fig2 = plt.figure()
# ax2 = fig2.add_subplot(111)
# ax2.contourf(x, y, normal_grid)
# plt.show()
# print(np.sum(np.array(end_image)))
end_image.save("gaussian_kernel.tiff")
def little_inverter(initial_matrix):
n = initial_matrix.shape[0]
initial_matrix = np.hstack((initial_matrix,np.zeros_like(initial_matrix)))
print(initial_matrix.shape)
initial_matrix = initial_matrix.tolist()
for i in range(n):
for j in range(n):
if i == j:
initial_matrix[i][j+n] = 1
# Applying Guass Jordan Elimination
for i in range(n):
if initial_matrix[i][i] == 0.0:
sys.exit('Divide by zero detected!')
for j in range(n):
if i != j:
ratio = initial_matrix[j][i]/initial_matrix[i][i]
for k in range(2*n):
initial_matrix[j][k] = initial_matrix[j][k] - ratio * initial_matrix[i][k]
# Row operation to make principal diagonal element to 1
for i in range(n):
divisor = initial_matrix[i][i]
for j in range(2*n):
initial_matrix[i][j] = initial_matrix[i][j]/divisor
return np.array(initial_matrix)[:,n:]
if __name__ == "__main__": if __name__ == "__main__":
# save_new_average(350,"11") # save_new_average(350,"11")
save_new_gauss()
gaussian_kernel = np.array(Image.open("gaussian_kernel.tiff"))
scenes = remote_file_extractor("/media/elphel/NVME/lwir16-proc/te0607/scenes/") scenes = remote_file_extractor("/media/elphel/NVME/lwir16-proc/te0607/scenes/")
images = remote_image_extractor(scenes) images = remote_image_extractor(scenes)
images = find_only_in_channel(images, "11") images = find_only_in_channel(images, "11")
# average_image = np.array(Image.open("Average_On_Channel(" + "11" + ").tiff")) # average_image = np.array(Image.open("Average_On_Channel(" + "11" + ").tiff"))
create_testable_images(images,"11",3) # create_testable_images(images,"11",6)
# plt.imshow(color_adjust(average_image),cmap='gray',vmin = 0, vmax=1) # plt.imshow(color_adjust(average_image),cmap='gray',vmin = 0, vmax=1)
# plt.show() # plt.show()
# print(len(images)) print(len(images))
average_image = remote_create_average(images[10000 - 10: 10000 + 10], "11") average_image = remote_create_average(images[5000 - 10: 5000 + 10], "11")
# plt.imshow(color_adjust((average_image - gaussian_filter(average_image,sigma=5))),cmap='gray',vmin = 0, vmax=1) # plt.imshow(color_adjust((average_image - gaussian_filter(average_image,sigma=5))),cmap='gray',vmin = 0, vmax=1)
# plt.show() # plt.show()
sftp_client = setup_remote_sftpclient() sftp_client = setup_remote_sftpclient()
print(len(images)) # print(len(images))
# print(images[10000]) # print(images[10000])
test_image = sftp_client.open(images[10000]) test_image = sftp_client.open(images[4700])
test_image = Image.open(test_image) test_image = Image.open(test_image)
test_image = np.array(test_image)[1:] test_image = np.array(test_image)[1:]
# print(test_image) # print(test_image)
# plt.imshow(color_adjust(adjust_to_original(test_image,average_image))) plt.imshow(color_adjust(adjust_to_original(test_image,average_image)),cmap='gray',vmin = 0, vmax=1)
# plt.show() plt.show()
little_more_blurred = gaussian_kernel@adjust_to_original(test_image,average_image)
print(little_inverter(gaussian_kernel)@gaussian_kernel)
plt.imshow(color_adjust(little_inverter(gaussian_kernel)@little_more_blurred),cmap='gray',vmin = 0, vmax=1)
plt.show()
# newimage = Image.fromarray(test_image - average_image) # newimage = Image.fromarray(test_image - average_image)
# newimage.save("NoInterference.tiff") # newimage.save("NoInterference.tiff")
......
VideoToImage.ipynb
View file @ 96e5883b
...@@ -2,18 +2,14 @@ ...@@ -2,18 +2,14 @@
"cells": [ "cells": [
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 8, "execution_count": 41,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"ename": "SystemError", "name": "stdout",
"evalue": "<built-in function imwrite> returned NULL without setting an error", "output_type": "stream",
"output_type": "error", "text": [
"traceback": [ "1190\n"
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mSystemError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m/home/bryce/git/master/VideoToImage.ipynb Cell 1'\u001b[0m in \u001b[0;36m<cell line: 5>\u001b[0;34m()\u001b[0m\n\u001b[1;32m <a href='vscode-notebook-cell:/home/bryce/git/master/VideoToImage.ipynb#ch0000000?line=3'>4</a>\u001b[0m count \u001b[39m=\u001b[39m \u001b[39m0\u001b[39m\n\u001b[1;32m <a href='vscode-notebook-cell:/home/bryce/git/master/VideoToImage.ipynb#ch0000000?line=4'>5</a>\u001b[0m \u001b[39mwhile\u001b[39;00m success \u001b[39mand\u001b[39;00m count \u001b[39m<\u001b[39m \u001b[39m5\u001b[39m:\n\u001b[0;32m----> <a href='vscode-notebook-cell:/home/bryce/git/master/VideoToImage.ipynb#ch0000000?line=5'>6</a>\u001b[0m cv2\u001b[39m.\u001b[39;49mimwrite(\u001b[39m\"\u001b[39;49m\u001b[39mbetterimages/innerfolder/fram\u001b[39;49m\u001b[39m%d\u001b[39;49;00m\u001b[39m.jpg\u001b[39;49m\u001b[39m\"\u001b[39;49m \u001b[39m%\u001b[39;49m count, image, \u001b[39m0\u001b[39;49m)\n\u001b[1;32m <a href='vscode-notebook-cell:/home/bryce/git/master/VideoToImage.ipynb#ch0000000?line=6'>7</a>\u001b[0m success, image \u001b[39m=\u001b[39m vidcap\u001b[39m.\u001b[39mread()\n\u001b[1;32m <a href='vscode-notebook-cell:/home/bryce/git/master/VideoToImage.ipynb#ch0000000?line=7'>8</a>\u001b[0m count \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n",
"\u001b[0;31mSystemError\u001b[0m: <built-in function imwrite> returned NULL without setting an error"
] ]
} }
], ],
...@@ -22,44 +18,153 @@ ...@@ -22,44 +18,153 @@
"vidcap = cv2.VideoCapture(\"concat_mono-fg_realtime.mp4\")\n", "vidcap = cv2.VideoCapture(\"concat_mono-fg_realtime.mp4\")\n",
"success,image = vidcap.read()\n", "success,image = vidcap.read()\n",
"count = 0\n", "count = 0\n",
"while success and count < 5:\n", "while success :\n",
" cv2.imwrite(\"betterimages/innerfolder/fram%d.jpg\" % count, image, 0)\n", " grayimage = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
" if count < 926 and count >= 920:\n",
" cv2.imwrite(\"betterimages/innerfolder/fram%d.jpg\" % count, grayimage)\n",
" success, image = vidcap.read()\n", " success, image = vidcap.read()\n",
" count += 1" " count += 1\n",
"print(count)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 7, "execution_count": 42,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"(512, 640, 3)\n", "(512, 640)\n",
"(512, 640, 3)\n", "(512, 640)\n",
"(512, 640, 3)\n", "(512, 640)\n",
"(512, 640, 3)\n", "(512, 640)\n",
"(512, 640, 3)\n" "(512, 640)\n",
"(512, 640)\n"
] ]
} }
], ],
"source": [ "source": [
"from PIL import Image\n", "from PIL import Image\n",
"import numpy as np\n", "import numpy as np\n",
"for i in range(5):\n", "for i in range(920,926):\n",
" im = Image.open(\"betterimages/innerfolder/fram\" + str(i) + \".jpg\", )\n", " im = Image.open(\"betterimages/innerfolder/fram\" + str(i) + \".jpg\")\n",
" print(np.array(im).shape)\n", " print(np.array(im).shape)\n",
" im.save(\"betterimages/innerfolder/fram\" + str(i) + \".tiff\", 'TIFF')" " im.save(\"betterimages/innerfolder/fram\" + str(i) + \".tiff\", 'TIFF')"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 8,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
"source": [] {
"name": "stdout",
"output_type": "stream",
"text": [
"591360\n",
"327680\n"
]
}
],
"source": [
"print(672*880*1190)\n",
"print(512*640*1190)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.5541125541125541"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"327680/591360"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.421875"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"27/64"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(512, 512)\n",
"(512, 640)\n",
"(512, 640)\n"
]
}
],
"source": [
"im = Image.open(\"betterimages/innerfolder/fram\" + str(i) + \".jpg\")\n",
"def create_gaussian(l=5, sig=1.):\n",
" \"\"\"\n",
" creates gaussian kernel with side length `l` and a sigma of `sig`\n",
" \"\"\"\n",
" ax = np.linspace(-(l - 1) / 2., (l - 1) / 2., l)\n",
" gauss = np.exp(-0.5 * np.square(ax) / np.square(sig))\n",
" kernel = np.outer(gauss, gauss)\n",
" return kernel / np.sum(kernel)\n",
"\n",
"kernel = create_gaussian(512)\n",
"print(kernel.shape)\n",
"im = np.array(im)\n",
"print(im.shape)\n",
"print((kernel@im).shape)"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"320.5"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"205120/640"
]
}, },
{ {
"cell_type": "code", "cell_type": "code",
......
WorkingPyDemo.py
View file @ 96e5883b
...@@ -514,7 +514,7 @@ def text_to_tiff(filename, list_dic, bins): ...@@ -514,7 +514,7 @@ def text_to_tiff(filename, list_dic, bins):
if __name__ == "__main__": if __name__ == "__main__":
scenes = file_extractor("betterimages") scenes = file_extractor("original_images(11)")
images = image_extractor(scenes) images = image_extractor(scenes)
newnamesforlater = [] newnamesforlater = []
list_dic, bins = make_dictionary(images, 4, False) list_dic, bins = make_dictionary(images, 4, False)
......
second_dic.npy
View file @ 96e5883b
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