end of day, no major findings (e426ec6e) · Commits · Elphel / image-compression

FPNprojstart.py

+34 −13

Original line number	Original line	Diff line number	Diff line
	from functools import singledispatchmethod
	from cv2 import mean
	import numpy as np		import numpy as np
	from WorkingPyDemo import *		from WorkingPyDemo import *
	from Remove_Noise import *		from Remove_Noise import *
	@@ -13,7 +11,7 @@ from sklearn.metrics import mean_squared_error

	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, "5")		# images = find_only_in_channel(images, "5")
	sftp_client = setup_remote_sftpclient()		sftp_client = setup_remote_sftpclient()
	image_array = np.array(Image.open(sftp_client.open(images[250])))		image_array = np.array(Image.open(sftp_client.open(images[250])))
	# image_array = np.array(Image.open(images[250])).astype(np.uint16)		# image_array = np.array(Image.open(images[250])).astype(np.uint16)
	@@ -52,22 +50,30 @@ def intarray_to_uint32(int_array):
	# print(repopulate_array_with_bitstring(uint16_array_to_bitstring(image_array[0]))[0:6])		# print(repopulate_array_with_bitstring(uint16_array_to_bitstring(image_array[0]))[0:6])
	frame_spacings = []		frame_spacings = []
	signaltonoise = []		signaltonoise = []
	information_array = np.zeros((len(images),3))		information_array = np.zeros((len(images),5))
	def celcius_to_kelvin(celcius):		def celcius_to_kelvin(celcius):
	return celcius + 273.15		return celcius + 273.15
			last_image = np.array(Image.open(sftp_client.open(images[0]))).astype(np.uint16)
	for i in range(len(images)):		for i in range(1,3000):
	image_array = np.array(Image.open(sftp_client.open(images[i]))).astype(np.uint16)		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])		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])		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:]))		# signaltonoise.append(1/np.std(image_array[1:]))
	information_array[i,0] = current_frame_count - frame_count_at_FFC		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] = 4096/np.std(image_array[1:])
	information_array[i,-1] = mean_squared_error(gaussian_filter(image_array[1:],sigma=.4),image_array[1:])		information_array[i,-2] = mean_squared_error(gaussian_filter(image_array[1:],sigma=.4),image_array[1:])
	# print(repopulate_array_with_bitstring(uint16_array_to_bitstring(image_array[0]))[76:77])		# print(repopulate_array_with_bitstring(uint16_array_to_bitstring(image_array[0]))[76:77])
	# print("start")		# print("start")
	information_array[i,1] = intarray_to_uint32(repopulate_array_with_bitstring(uint16_array_to_bitstring(image_array[0]))[94:96]) - \		# 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]))		# celcius_to_kelvin(intarray_to_uint32(repopulate_array_with_bitstring(uint16_array_to_bitstring(image_array[0]))[162:166]))
			information_array[i,1] = mean_squared_error(gaussian_filter(last_image[1:],sigma=.4),last_image[1:])
			# information_array[i,2] = 20*np.log10(np.std(image_array[1:]))
			if images[i][-7] == "1":
			information_array[i,2] = images[i][-7:-5]
			else:
			information_array[i,2] = images[i][-6:-5]
			# print(information_array[i,2])
			# information_array[i,3] = np.std(image_array[1:])
	# information_array[i,2] = intarray_to_uint32(repopulate_array_with_bitstring(uint16_array_to_bitstring(image_array[0]))[96:98])		# 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,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,4] = information_array[i,1] - information_array[i,2]
	@@ -75,6 +81,7 @@ for i in range(len(images)):
	# information_array[i,4] = intarray_to_uint32(repopulate_array_with_bitstring(uint16_array_to_bitstring(image_array[0]))[170:174])		# 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])		# 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)		# frame_spacings.append(current_frame_count - frame_count_at_FFC)
			last_image = np.array(Image.open(sftp_client.open(images[i]))).astype(np.uint16)
	# print(np.argmin(signaltonoise))		# print(np.argmin(signaltonoise))
	# print(np.argmax(signaltonoise))		# print(np.argmax(signaltonoise))
	# print(np.min(abs(np.array(frame_spacings))))		# print(np.min(abs(np.array(frame_spacings))))
	@@ -82,11 +89,25 @@ for i in range(len(images)):


	mask = information_array[:,0] > 0		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.DataFrame(information_array[mask],columns=["Frame_Spacing","Last_MSE","ImageName","Signal_to_Noise","MSE"])
	information_df = pd.read_csv("information_weird_array.csv")		information_df.to_csv("smaller_information_weird_array.csv")
			information_df = pd.read_csv("smaller_information_weird_array.csv",index_col=0)
	information_array = information_df.values		information_array = information_df.values
			print(information_array[0,2])
	plt.scatter(information_array[:,0],information_array[:,-1],s=1)		plt.scatter(information_array[:,0],information_array[:,-1],s=1)
			plt.legend()
			plt.xlabel("Frame_Spacing")
			plt.ylabel("Signal_to_Noise")
			plt.show()
			colors = ["red","blue","green","orange","purple","brown","pink","black","grey","cyan","magenta","yellow","white","lime","teal","olive","maroon","navy","silver","gold","indigo","violet"]
			secondmask = information_array[:,1] < 30000
			print(np.unique(information_array[:,2]))
			for g in np.unique(information_array[:,2]):
			i = np.where(information_array[:,2] == g)
			print(g)
			plt.scatter(information_array[:,1][secondmask][i],information_array[:,4][secondmask][i],s=1,label=g,color = colors[int(g)])

	plt.legend()		plt.legend()
	plt.xlabel("Frame_Spacing")		plt.xlabel("Frame_Spacing")
	plt.ylabel("Signal_to_Noise")		plt.ylabel("Signal_to_Noise")

information_weird_array.csv

+13919 −997

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smaller_information_weird_array.csv

0 → 100644

+2996 −0

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