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image-compression
Commit de772659 authored by Bryce Hepner's avatar Bryce Hepner
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midday save

parent 9a11d800
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Showing with 401 additions and 73 deletions
ErrorImageCreator.py
View file @ de772659
......@@ -88,17 +88,32 @@ def color_adjust(visual_array):
adjusted_array = np.round(adjusted_array*255/np.max(adjusted_array))
return adjusted_array
if __name__ == "__main__":
scenes = file_extractor(folder_name)
scenes = file_extractor("averaged_images(11)")
images = image_extractor(scenes)
list_dic = np.load("first_dic.npy", allow_pickle="TRUE")
list_dic = np.load("second_dic.npy", allow_pickle="TRUE")
bins = [21,32,48]
encoded_string2 = bytes_to_bitstring(read_from_file(images[1][:-5] + "_Compressed.txt"))
print(images)
encoded_string2 = bytes_to_bitstring(read_from_file(images[0][:-5] + "_Compressed.txt"))
original_array, error_array = produce_error_array(encoded_string2, list_dic, bins, False)
adjusted_errors = color_adjust(abs(error_array))
original_array_adjusted = color_adjust(original_array)
# print(adjusted_errors)
print(error_array)
plt.subplot(121)
plt.imshow(original_array_adjusted,cmap='gray',vmin = 0, vmax=255)
plt.subplot(122)
plt.imshow(adjusted_errors,cmap = 'gray', vmin = 0, vmax=255)
# encoded_string2 = bytes_to_bitstring(read_from_file(images[1][:-5] + "_Compressed.txt"))
# original_array, error_array = produce_error_array(encoded_string2, list_dic, bins, False)
# adjusted_errors = color_adjust(abs(error_array))
# original_array_adjusted = color_adjust(original_array)
# plt.subplot(133)
# plt.imshow(adjusted_errors,cmap = 'gray', vmin = 0, vmax=255)
plt.show()
\ No newline at end of file
FullTester.ipynb
View file @ de772659
......@@ -254,14 +254,284 @@
},
{
"cell_type": "code",
"execution_count": 43,
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.6193594010686757\n"
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]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m/home/bryce/git/master/FullTester.ipynb Cell 13'\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m <a href='vscode-notebook-cell:/home/bryce/git/master/FullTester.ipynb#ch0000011?line=5'>6</a>\u001b[0m \u001b[39mprint\u001b[39m(newimage\u001b[39m.\u001b[39mdtype)\n\u001b[1;32m <a href='vscode-notebook-cell:/home/bryce/git/master/FullTester.ipynb#ch0000011?line=6'>7</a>\u001b[0m newimage \u001b[39m=\u001b[39m Image\u001b[39m.\u001b[39mfromarray(newimage)\n\u001b[0;32m----> <a href='vscode-notebook-cell:/home/bryce/git/master/FullTester.ipynb#ch0000011?line=7'>8</a>\u001b[0m newimage\u001b[39m.\u001b[39;49msave(newnamesforlater[i][:\u001b[39m-\u001b[39;49m\u001b[39m4\u001b[39;49m]\u001b[39m+\u001b[39;49m \u001b[39m\"\u001b[39;49m\u001b[39mlzw\u001b[39;49m\u001b[39m\"\u001b[39;49m \u001b[39m+\u001b[39;49m \u001b[39m\"\u001b[39;49m\u001b[39m.tiff\u001b[39;49m\u001b[39m\"\u001b[39;49m, compression\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mtiff_lzw\u001b[39;49m\u001b[39m'\u001b[39;49m, tiffinfo\u001b[39m=\u001b[39;49m{\u001b[39m317\u001b[39;49m: \u001b[39m2\u001b[39;49m})\n\u001b[1;32m <a href='vscode-notebook-cell:/home/bryce/git/master/FullTester.ipynb#ch0000011?line=9'>10</a>\u001b[0m lwz_sizes\u001b[39m.\u001b[39mappend(os\u001b[39m.\u001b[39mpath\u001b[39m.\u001b[39mgetsize(newnamesforlater[i][:\u001b[39m-\u001b[39m\u001b[39m4\u001b[39m]\u001b[39m+\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mlzw\u001b[39m\u001b[39m\"\u001b[39m \u001b[39m+\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m.tiff\u001b[39m\u001b[39m\"\u001b[39m))\n\u001b[1;32m <a href='vscode-notebook-cell:/home/bryce/git/master/FullTester.ipynb#ch0000011?line=10'>11</a>\u001b[0m \u001b[39mprint\u001b[39m(np\u001b[39m.\u001b[39msum(lwz_sizes)\u001b[39m/\u001b[39mnp\u001b[39m.\u001b[39msum(file_sizes_old))\n",
"File \u001b[0;32m~/.local/lib/python3.8/site-packages/PIL/Image.py:2300\u001b[0m, in \u001b[0;36mImage.save\u001b[0;34m(self, fp, format, **params)\u001b[0m\n\u001b[1;32m <a href='file:///home/bryce/.local/lib/python3.8/site-packages/PIL/Image.py?line=2296'>2297</a>\u001b[0m fp \u001b[39m=\u001b[39m builtins\u001b[39m.\u001b[39mopen(filename, \u001b[39m\"\u001b[39m\u001b[39mw+b\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m <a href='file:///home/bryce/.local/lib/python3.8/site-packages/PIL/Image.py?line=2298'>2299</a>\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> <a href='file:///home/bryce/.local/lib/python3.8/site-packages/PIL/Image.py?line=2299'>2300</a>\u001b[0m save_handler(\u001b[39mself\u001b[39;49m, fp, filename)\n\u001b[1;32m <a href='file:///home/bryce/.local/lib/python3.8/site-packages/PIL/Image.py?line=2300'>2301</a>\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m:\n\u001b[1;32m <a href='file:///home/bryce/.local/lib/python3.8/site-packages/PIL/Image.py?line=2301'>2302</a>\u001b[0m \u001b[39mif\u001b[39;00m open_fp:\n",
"File \u001b[0;32m~/.local/lib/python3.8/site-packages/PIL/TiffImagePlugin.py:1814\u001b[0m, in \u001b[0;36m_save\u001b[0;34m(im, fp, filename)\u001b[0m\n\u001b[1;32m <a href='file:///home/bryce/.local/lib/python3.8/site-packages/PIL/TiffImagePlugin.py?line=1810'>1811</a>\u001b[0m e\u001b[39m.\u001b[39msetimage(im\u001b[39m.\u001b[39mim, (\u001b[39m0\u001b[39m, \u001b[39m0\u001b[39m) \u001b[39m+\u001b[39m im\u001b[39m.\u001b[39msize)\n\u001b[1;32m <a href='file:///home/bryce/.local/lib/python3.8/site-packages/PIL/TiffImagePlugin.py?line=1811'>1812</a>\u001b[0m \u001b[39mwhile\u001b[39;00m \u001b[39mTrue\u001b[39;00m:\n\u001b[1;32m <a href='file:///home/bryce/.local/lib/python3.8/site-packages/PIL/TiffImagePlugin.py?line=1812'>1813</a>\u001b[0m \u001b[39m# undone, change to self.decodermaxblock:\u001b[39;00m\n\u001b[0;32m-> <a href='file:///home/bryce/.local/lib/python3.8/site-packages/PIL/TiffImagePlugin.py?line=1813'>1814</a>\u001b[0m l, s, d \u001b[39m=\u001b[39m e\u001b[39m.\u001b[39;49mencode(\u001b[39m16\u001b[39;49m \u001b[39m*\u001b[39;49m \u001b[39m1024\u001b[39;49m)\n\u001b[1;32m <a href='file:///home/bryce/.local/lib/python3.8/site-packages/PIL/TiffImagePlugin.py?line=1814'>1815</a>\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m _fp:\n\u001b[1;32m <a href='file:///home/bryce/.local/lib/python3.8/site-packages/PIL/TiffImagePlugin.py?line=1815'>1816</a>\u001b[0m fp\u001b[39m.\u001b[39mwrite(d)\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
],
......@@ -271,6 +541,7 @@
" newimage = Image.open(filename)\n",
" newimage = np.array(newimage)\n",
" newimage = newimage[1:]\n",
" print(newimage.dtype)\n",
" newimage = Image.fromarray(newimage)\n",
" newimage.save(newnamesforlater[i][:-4]+ \"lzw\" + \".tiff\", compression='tiff_lzw', tiffinfo={317: 2})\n",
"\n",
......
Remove_Noise.py
View file @ de772659
from matplotlib.image import composite_images
from WorkingPyDemo import *
from scipy.ndimage.filters import gaussian_filter
import paramiko
from PIL import ImageFilter
def setup_remote_sftpclient():
client = paramiko.SSHClient()
client.load_system_host_keys()
......@@ -19,46 +21,39 @@ def remove_noise(images, which_sensor):
if image_name[-7:-5] == "_" + which_sensor:
same_sensor_images.append(image_name)
for i, image_name in enumerate(same_sensor_images):
# print(image_name)
image_object = Image.open(image_name)
# print(np.array(image_object).shape)
# print(np.array(image_object)[1:] + average_image)
average_image = np.array(image_object)[1:] + average_image
return average_image/len(same_sensor_images)
def remote_remove_noise(images, which_sensor):
def remote_create_average(images, which_sensor):
sftp_client = setup_remote_sftpclient()
averages = []
same_sensor_images = []
same_sensor_images = images
which_sensor = str(which_sensor)
first_image = sftp_client.open(images[0])
average_image = np.array(Image.open(first_image))[1:]
for i, image_name in enumerate(images):
if int(which_sensor) > 9:
if image_name[-7:-5] == which_sensor:
same_sensor_images.append(image_name)
else:
if image_name[-7:-5] == "_" + which_sensor:
same_sensor_images.append(image_name)
images = []
small_set_images = []
for i, image_name in enumerate(same_sensor_images):
# print(image_name)
image_object = sftp_client.open(image_name)
image_object = Image.open(image_object)
images.append(np.array(image_object)[1:])
small_set_images.append(np.array(image_object)[1:])
# print(np.array(image_object).shape)
# print(np.array(image_object)[1:] + average_image)
if (i % 100 == 0) and i!=0:
image_object = np.mean(np.array(images),axis = 0)
# print(image_object.shape)
image_object = np.mean(np.array(small_set_images),axis = 0)
averages.append(image_object)
# print(average_image.shape)
images = []
image_object = np.mean(np.array(images))
averages.append(image_object)
small_set_images = []
print(i)
if len(small_set_images) > 1:
image_object = np.mean(np.array(small_set_images),axis = 0)
averages.append(image_object)
sftp_client.close()
return np.mean(averages,axis=0)
if len(averages) > 1:
return np.mean(averages,axis=0)
else:
return np.array(averages[0])
def remote_file_extractor(headname = "/media/elphel/NVME/lwir16-proc/te0607/scenes/"):
"""Find all the files in the directory
......@@ -69,18 +64,16 @@ def remote_file_extractor(headname = "/media/elphel/NVME/lwir16-proc/te0607/scen
Returns:
files (list): a list of all the files in the directory
"""
client = paramiko.SSHClient()
client.load_system_host_keys()
client.connect("192.168.0.107", username="elphel")
sftp_client = client.open_sftp()
sftp_client = setup_remote_sftpclient()
# sftp_client.listdir("media/elphel/NVME/lwir16-proc/te0607/scenes/")
dirs_in_scenes = sftp_client.listdir("/media/elphel/NVME/lwir16-proc/te0607/scenes/")
dirs_in_scenes = sftp_client.listdir(headname)
scenes = []
for i, curr_folder in enumerate(dirs_in_scenes):
if "." not in curr_folder:
smaller_dirs = sftp_client.listdir(headname + curr_folder)
for small_folder in smaller_dirs:
scenes.append(headname + curr_folder + "/" + small_folder)
sftp_client.close()
return scenes
def remote_image_extractor(scenes):
......@@ -95,57 +88,106 @@ def remote_image_extractor(scenes):
image_folder.append(os.path.join(scene, file))
sftp_client.close()
return image_folder #returns a list of file paths to .tiff files in the specified directory given in file_extractor
def remove_the_noise(new_image, average_image):
original_image_min = np.min(newimage)
def find_only_in_channel(images, channel_name = "10"):
same_sensor_images = []
for i, image_name in enumerate(images):
if int(channel_name) > 9:
if image_name[-7:-5] == channel_name:
same_sensor_images.append(image_name)
else:
if image_name[-7:-5] == "_" + channel_name:
same_sensor_images.append(image_name)
return same_sensor_images
def adjust_to_original(new_image, average_image):
original_image_min = np.min(new_image)
original_image_max = np.max(new_image)
adjusted_image = new_image - average_image
# average_image = average_image - np.mean(average_image)
adjusted_image = new_image - (average_image - gaussian_filter(average_image,sigma=5))
# adjusted_image = new_image - (average_image - np.array(Image.fromarray(average_image).convert("L").filter(ImageFilter.GaussianBlur(radius=4))))
adjusted_image = adjusted_image - np.min(adjusted_image)
adjusted_image = adjusted_image*original_image_max/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
return adjusted_image
return adjusted_image.astype(np.uint16)
def color_adjust(visual_array):
min_of_errors = np.min(visual_array)
adjusted_array = visual_array - min_of_errors
adjusted_array = adjusted_array/np.max(adjusted_array)
# print(adjusted_array)
# print(np.max(adjusted_array))
return adjusted_array
def save_new_average(quantity_of_images = 1500, channel = "10", head_dir = "/media/elphel/NVME/lwir16-proc/te0607/scenes/"):
scenes = remote_file_extractor(head_dir)
images = find_only_in_channel(remote_image_extractor(scenes),channel)
if quantity_of_images != "all":
# images = np.random.choice(images,quantity_of_images,replace = False)
images = images[:quantity_of_images]
average_image = remote_create_average(images,channel)
# average_image = average_image.astype(int)
average_savable_image = Image.fromarray(average_image)
average_savable_image.save("Average_On_Channel(" + channel + ").tiff")
def create_testable_images(images, selected_channel, quantity_of_images):
sftp_client = setup_remote_sftpclient()
images = find_only_in_channel(images, selected_channel)
image_locations = np.random.choice(len(images), quantity_of_images, replace=False)
# image_locations = np.arange(quantity_of_images) + 10000
selected_images = np.array(images)[image_locations]
# average_image = np.array(Image.open("Average_On_Channel(" + selected_channel + ").tiff"))
images_in_each_direction = 4
for i, item in enumerate(selected_images):
if image_locations[i] < images_in_each_direction:
average_image = remote_create_average(images[image_locations[i] - image_locations[i]: image_locations[i] + images_in_each_direction + image_locations[i]], selected_channel)
elif image_locations[i] > (len(images) - images_in_each_direction):
average_image = remote_create_average(images[image_locations[i] - images_in_each_direction - (len(images) - image_locations[i]): image_locations[i] + (len(images) - image_locations[i])], selected_channel)
else:
average_image = remote_create_average(images[image_locations[i] - images_in_each_direction: image_locations[i] + images_in_each_direction], selected_channel)
image = Image.open(sftp_client.open(item))
wherelastslash = item.rfind("/")
image = np.array(image)[1:]
savable_original = Image.fromarray(image)
savable_original.save("averaged_images(" + selected_channel + ")/innerfolder/original" + item[wherelastslash + 1:])
altered_image = adjust_to_original(image, average_image)
altered_image = Image.fromarray(altered_image)
# altered_image.save("averaged_images(" + selected_channel + ")/innerfolder" + item[wherelastslash:])
# average_image = Image.fromarray(average_image)
sftp_client.close()
if __name__ == "__main__":
# save_new_average(15,"11")
scenes = remote_file_extractor("/media/elphel/NVME/lwir16-proc/te0607/scenes/")
# images = remote_image_extractor(np.random.choice(scenes,10000,replace = False))
images = remote_image_extractor(scenes)
# average_image = remote_remove_noise(images,"10")
average_image = np.array(Image.open("hopefullyaverage.tiff"))
# average_savable_image = Image.fromarray(average_image)
# average_savable_image.save("hopefullyaverage.tiff")
# print(np.max(average_image))
# print(np.min(average_image))
# average_image = color_adjust(average_image)
plt.imshow(color_adjust(average_image),cmap='gray',vmin = 0, vmax=1)
plt.show()
images = find_only_in_channel(images, "11")
# average_image = np.array(Image.open("Average_On_Channel(" + "11" + ").tiff"))
create_testable_images(images,"11",10)
# plt.imshow(color_adjust(average_image),cmap='gray',vmin = 0, vmax=1)
# plt.show()
# print(len(images))
average_image = remote_create_average(images[10000 - 10: 10000 + 10], "11")
plt.imshow(color_adjust((average_image - gaussian_filter(average_image,sigma=5))),cmap='gray',vmin = 0, vmax=1)
plt.show()
sftp_client = setup_remote_sftpclient()
print(len(images))
for i, item in enumerate(images[22000:22030]):
if item[-7:-5] == "10":
print(i)
print(item)
print(images[22016])
test_image = sftp_client.open(images[22016])
# print(images[10000])
test_image = sftp_client.open(images[10000])
test_image = Image.open(test_image)
test_image = np.array(test_image)[1:]
newimage = Image.fromarray(test_image - average_image)
newimage.save("NoInterference.tiff")
# print(test_image)
# plt.imshow(color_adjust(adjust_to_original(test_image,average_image)))
# plt.show()
# newimage = Image.fromarray(test_image - average_image)
# newimage.save("NoInterference.tiff")
plt.subplot(121)
plt.imshow(color_adjust(test_image),cmap='gray',vmin = 0, vmax=1)
plt.subplot(122)
plt.imshow(color_adjust(test_image-average_image),cmap='gray',vmin = 0, vmax=1)
plt.imshow(color_adjust(adjust_to_original(test_image,average_image)),cmap='gray',vmin = 0, vmax=1)
plt.show()
sftp_client.close()
# print(np.linalg.inv(np.array([[3,0,-1],[0,3,3],[1,-3,-4]])))
# print(np.linalg.pinv(np.array([[-1,-1,1], [-1,0,1], [-1,1,1], [0,-1,1]])))
\ No newline at end of file
WorkingPyDemo.py
View file @ de772659
......@@ -74,7 +74,7 @@ def predict_pix(tiff_image_path, difference = True):
A ndarray(3 X 3): system of equation
"""
image_obj = Image.open(tiff_image_path) #Open the image and read it as an Image object
image_array = np.array(image_obj)[1:].astype(int) #Convert to an array, leaving out the first row because the first row is just housekeeping data
image_array = np.array(image_obj).astype(int) #Convert to an array, leaving out the first row because the first row is just housekeeping data
# image_array = image_array.astype(int)
# A = np.array([[3,0,-1],[0,3,3],[1,-3,-4]]) # the matrix for system of equation
Ainv = np.array([[0.5,-0.5,-0.5],[-0.5,1.83333333,1.5],[0.5,-1.5,-1.5]])
......@@ -514,7 +514,7 @@ def text_to_tiff(filename, list_dic, bins):
if __name__ == "__main__":
scenes = file_extractor(folder_name)
scenes = file_extractor("averaged_images(11)")
images = image_extractor(scenes)
newnamesforlater = []
list_dic, bins = make_dictionary(images, 4, False)
......@@ -522,7 +522,7 @@ if __name__ == "__main__":
file_sizes_old = []
# list_dic = np.load("first_dic.npy", allow_pickle="TRUE")
bins = [21,32,48]
# np.save("first_dic.npy", list_dic)
np.save("second_dic.npy", list_dic)
for i in range(len(images)):
image, new_error, diff = huffman(images[i], 4, False)
......@@ -537,18 +537,18 @@ if __name__ == "__main__":
newnamesforlater.append(newname + "_Compressed.txt")
with open(newname + "_Compressed.txt", 'wb') as f:
f.write(inletters)
# file_sizes_new.append((os.path.getsize(newname + "_Compressed.txt")))
# file_sizes_old.append((os.path.getsize(images[i])))
file_sizes_new.append((os.path.getsize(newname + "_Compressed.txt")))
file_sizes_old.append((os.path.getsize(images[i])))
# sleep(5)
# if i % 50 == 0:
# print(i)
# sleep(20)
# print(np.sum(file_sizes_new)/np.sum(file_sizes_old))
# file_sizes_new.append(os.path.getsize("first_dic.npy"))
# print(np.sum(file_sizes_new)/np.sum(file_sizes_old))
print(np.sum(file_sizes_new)/np.sum(file_sizes_old))
file_sizes_new.append(os.path.getsize("first_dic.npy"))
print(np.sum(file_sizes_new)/np.sum(file_sizes_old))
# list_dic = np.load("first_dic.npy", allow_pickle="TRUE")
bins = [21,32,48]
# os.system("ls")
# for i,item in enumerate(newnamesforlater):
# print(item)
# image, new_error, diff = huffman(images[i], 4, False)
......
second_dic.npy 0 → 100644
View file @ de772659
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