Commit e505d52d authored by Bryce Hepner's avatar Bryce Hepner

trying to find a good metric

parent 2bc3f4c7
...@@ -90,7 +90,7 @@ def color_adjust(visual_array): ...@@ -90,7 +90,7 @@ def color_adjust(visual_array):
if __name__ == "__main__": if __name__ == "__main__":
scenes = file_extractor("averaged_images(11)") scenes = file_extractor("averaged_images(11)")
images = image_extractor(scenes) images = image_extractor(scenes)
list_dic = np.load("second_dic.npy", allow_pickle="TRUE") list_dic = np.load("second_dict.npy", allow_pickle="TRUE")
bins = [21,32,48] bins = [21,32,48]
print(images) print(images)
encoded_string2 = bytes_to_bitstring(read_from_file(images[0][:-5] + "_Compressed.txt")) encoded_string2 = bytes_to_bitstring(read_from_file(images[0][:-5] + "_Compressed.txt"))
......
from functools import singledispatchmethod
from cv2 import mean
import numpy as np
from WorkingPyDemo import *
from Remove_Noise import *
import pandas as pd
import statsmodels.api as sm
from matplotlib import pyplot as plt
from sklearn.metrics import mean_squared_error
# scenes = file_extractor("images")
# images = image_extractor(scenes)
# print(images)
scenes = remote_file_extractor("/media/elphel/NVME/lwir16-proc/te0607/scenes/")
images = remote_image_extractor(scenes)
# images = find_only_in_channel(images, "11")
sftp_client = setup_remote_sftpclient()
image_array = np.array(Image.open(sftp_client.open(images[250])))
# image_array = np.array(Image.open(images[250])).astype(np.uint16)
# print(image_array[0])
# print(images[0:5])
# print(image_array[0,0].dtype)
thebitstring = bytes_to_bitstring(image_array[0])
frame = thebitstring[84*8:88*8]
# print(int(frame,2))
otherframe = thebitstring[88*8:92*8]
# print(int(otherframe,2))
# print(image_array[0].astype(np.uint16))
# print(image_array[0,42])
# print(bin(image_array[0,0])[2:].zfill(16))
# for i in range(len(images)):
# image_array = np.array(Image.open(images[i]))
# print(image_array[0,91] - image_array[0,90])
# print(np.array(Image.open(images[100]))[0])
def uint16_array_to_bitstring(array):
bitstring = ""
for i in range(len(array)):
bitstring += bin(array[i])[2:].zfill(16)
return bitstring
def repopulate_array_with_bitstring(bitstring):
array = []
for i in range(0,len(bitstring),8):
array.append(int(bitstring[i:i+8],2))
return np.array(array)
def intarray_to_uint32(int_array):
bitstring = ""
# print(int_array)
for i in range(len(int_array)):
bitstring += bin(int_array[i])[2:].zfill(8)
# print(bitstring)
return int(bitstring,2)
# print(repopulate_array_with_bitstring(uint16_array_to_bitstring(image_array[0]))[0:6])
frame_spacings = []
signaltonoise = []
information_array = np.zeros((len(images),3))
def celcius_to_kelvin(celcius):
return celcius + 273.15
for i in range(len(images)):
image_array = np.array(Image.open(sftp_client.open(images[i]))).astype(np.uint16)
current_frame_count = intarray_to_uint32(repopulate_array_with_bitstring(uint16_array_to_bitstring(image_array[0]))[84:88])
frame_count_at_FFC = intarray_to_uint32(repopulate_array_with_bitstring(uint16_array_to_bitstring(image_array[0]))[88:92])
# signaltonoise.append(1/np.std(image_array[1:]))
information_array[i,0] = current_frame_count - frame_count_at_FFC
# information_array[i,-1] = 1/np.std(image_array[1:])
information_array[i,-1] = mean_squared_error(image_array[1:],gaussian_filter(image_array[1:],sigma=.3))
# print(repopulate_array_with_bitstring(uint16_array_to_bitstring(image_array[0]))[76:77])
# print("start")
information_array[i,1] = intarray_to_uint32(repopulate_array_with_bitstring(uint16_array_to_bitstring(image_array[0]))[94:96]) - \
celcius_to_kelvin(intarray_to_uint32(repopulate_array_with_bitstring(uint16_array_to_bitstring(image_array[0]))[162:166]))
# information_array[i,2] = intarray_to_uint32(repopulate_array_with_bitstring(uint16_array_to_bitstring(image_array[0]))[96:98])
# information_array[i,2] = celcius_to_kelvin(intarray_to_uint32(repopulate_array_with_bitstring(uint16_array_to_bitstring(image_array[0]))[162:166]))
# information_array[i,4] = information_array[i,1] - information_array[i,2]
# information_array[i,5] = information_array[i,1] - information_array[i,3]
# information_array[i,4] = intarray_to_uint32(repopulate_array_with_bitstring(uint16_array_to_bitstring(image_array[0]))[170:174])
# information_array[i,5] = intarray_to_uint32(repopulate_array_with_bitstring(uint16_array_to_bitstring(image_array[0]))[174:178])
# frame_spacings.append(current_frame_count - frame_count_at_FFC)
# print(np.argmin(signaltonoise))
# print(np.argmax(signaltonoise))
# print(np.min(abs(np.array(frame_spacings))))
# print("middle")
mask = information_array[:,0] > 0
information_df = pd.DataFrame(information_array[mask],columns=["Frame_Spacing","Curr_Temp_Diff","Signal_to_Noise"])
information_df.to_csv("information_weird_array.csv")
# information_df = pd.read_csv("information_array.csv")
information_array = information_df.values
plt.scatter(information_array[:,0],information_array[:,-1],s=1)
plt.legend()
plt.xlabel("Frame_Spacing")
plt.ylabel("Signal_to_Noise")
plt.show()
print(np.max(information_array[:,0]))
print(information_array[-5:-1,0])
X = sm.add_constant(information_df.drop(["Signal_to_Noise"],axis=1))
y = information_df["Signal_to_Noise"]
end_result = sm.OLS(y,X).fit()
print(end_result.summary())
sftp_client.close()
...@@ -30,7 +30,7 @@ ...@@ -30,7 +30,7 @@
"source": [ "source": [
"scenes = file_extractor(\"images\")\n", "scenes = file_extractor(\"images\")\n",
"images = image_extractor(scenes)\n", "images = image_extractor(scenes)\n",
"print(len(images)) #\n", "print(len(images))\n",
"newnamesforlater = []\n", "newnamesforlater = []\n",
"file_sizes_new = [] \n", "file_sizes_new = [] \n",
"file_sizes_old = []\n", "file_sizes_old = []\n",
......
...@@ -211,7 +211,7 @@ def save_testable_images(images, selected_channel, quantity_of_images): ...@@ -211,7 +211,7 @@ def save_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) + 5000 image_locations = np.arange(quantity_of_images) + 10000
selected_images = np.array(images)[image_locations] selected_images = np.array(images)[image_locations]
...@@ -270,7 +270,7 @@ if __name__ == "__main__": ...@@ -270,7 +270,7 @@ if __name__ == "__main__":
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"))
# save_testable_images(images,"11",3) save_testable_images(images,"11",5)
# 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()
...@@ -287,27 +287,27 @@ if __name__ == "__main__": ...@@ -287,27 +287,27 @@ if __name__ == "__main__":
# print(test_image) # print(test_image)
plt.subplot(121) plt.subplot(121)
plt.imshow(color_adjust(adjust_to_original(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.subplot(122) # plt.subplot(122)
# plt.show() # plt.show()
# print(gaussian_kernel) # print(gaussian_kernel)
little_more_blurred = convolve2d(np.pad(test_image,2, mode = 'edge'),gaussian_kernel, "valid") # little_more_blurred = convolve2d(np.pad(test_image,2, mode = 'edge'),gaussian_kernel, "valid")
# print(little_more_blurred) # print(little_more_blurred)
print(little_more_blurred.shape) # print(little_more_blurred.shape)
# print(little_inverter(gaussian_kernel)@gaussian_kernel) # print(little_inverter(gaussian_kernel)@gaussian_kernel)
altered_image = Image.fromarray(little_more_blurred.astype(np.uint16)) # altered_image = Image.fromarray(little_more_blurred.astype(np.uint16))
altered_image.save("averaged_images(" + "11" + ")/innerfolder" + "/testable.tiff") # altered_image.save("averaged_images(" + "11" + ")/innerfolder" + "/testable.tiff")
plt.imshow(color_adjust(little_more_blurred[3:-3,3:-3]),cmap='gray',vmin = 0, vmax=1) # plt.imshow(color_adjust(little_more_blurred[3:-3,3:-3]),cmap='gray',vmin = 0, vmax=1)
# plt.show() # plt.show()
# renewed_array = # renewed_array =
# H = fft(kernel) # H = fft(kernel)
# deconvolved = np.real(ifft(fft(signal)*np.conj(H)/(H*np.conj(H) + lambd**2))) # deconvolved = np.real(ifft(fft(signal)*np.conj(H)/(H*np.conj(H) + lambd**2)))
back_to_normal = wiener(little_more_blurred,gaussian_kernel,.02,clip=False) # back_to_normal = wiener(little_more_blurred,gaussian_kernel,.02,clip=False)
# print(back_to_normal) # print(back_to_normal)
# print(back_to_normal.shape) # print(back_to_normal.shape)
# plt.imshow(color_adjust(back_to_normal), cmap='gray',vmin = 0, vmax=1) # plt.imshow(color_adjust(back_to_normal), cmap='gray',vmin = 0, vmax=1)
plt.show() # plt.show()
# newimage = Image.fromarray(test_image - average_image) # newimage = Image.fromarray(test_image - average_image)
# newimage.save("NoInterference.tiff") # newimage.save("NoInterference.tiff")
...@@ -320,6 +320,6 @@ if __name__ == "__main__": ...@@ -320,6 +320,6 @@ if __name__ == "__main__":
# print(np.linalg.inv(np.array([[3,0,-1],[0,3,3],[1,-3,-4]]))) # 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]]))) # print(np.linalg.pinv(np.array([[-1,-1,1], [-1,0,1], [-1,1,1], [0,-1,1]])))
last_image = Image.open("NoInterference.tiff") # last_image = Image.open("NoInterference.tiff")
# plt.imshow(color_adjust(np.array(last_image)),cmap='gray',vmin = 0, vmax=1) # plt.imshow(color_adjust(np.array(last_image)),cmap='gray',vmin = 0, vmax=1)
# plt.show() # plt.show()
This diff is collapsed.
No preview for this file type
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment