Works, used to test all the files (45e246f4) · Commits · Elphel / image-compression

WorkingPyDemo.py

+18 −40

Original line number	Original line	Diff line number	Diff line
	@@ -14,6 +14,14 @@ folder_name = "images"
	outputlocation = ""		outputlocation = ""

	def file_extractor(dirname="images"):		def file_extractor(dirname="images"):
			"""Find all the files in the directory

			Parameters:
			dirname (str): the directory name

			Returns:
			files (list): a list of all the files in the directory
			"""
	files = os.listdir(dirname)		files = os.listdir(dirname)
	scenes = []		scenes = []
	for file in files:		for file in files:
	@@ -24,6 +32,13 @@ def file_extractor(dirname="images"):
	return scenes		return scenes

	def image_extractor(scenes):		def image_extractor(scenes):
			"""
			This function gives the list of all of the valid images
			Parameters:
			scenes (list): a list of all the files in the directory
			Returns:
			images (list): a list of all the images in the directory
			"""
	image_folder = []		image_folder = []
	for scene in scenes:		for scene in scenes:
	files = os.listdir(scene)		files = os.listdir(scene)
	@@ -34,29 +49,6 @@ def image_extractor(scenes):
	image_folder.append(os.path.join(scene, file))		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		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

	def predict_pix(tiff_image_path, difference = True):		def predict_pix(tiff_image_path, difference = True):
	"""		"""
	@@ -74,7 +66,7 @@ def predict_pix(tiff_image_path, difference = True):

	Return:		Return:
	image ndarray(512 X 640): original image		image ndarray(512 X 640): original image
	predict ndarray(325380,): predicted image excluding the boundary
	diff. ndarray(325380,): IF difference = TRUE, difference between the min and max of four neighbors exclude the boundary		diff. ndarray(325380,): IF difference = TRUE, difference between the min and max of four neighbors exclude the boundary
	ELSE: the residuals of the four nearest pixels to a fitted hyperplane		ELSE: the residuals of the four nearest pixels to a fitted hyperplane
	error ndarray(325380,): difference between the original image and predicted image		error ndarray(325380,): difference between the original image and predicted image
	@@ -140,6 +132,7 @@ class NodeTree(object):
	return self.left, self.right		return self.left, self.right

	def __str__(self):		def __str__(self):
			"Technically does not return a string, cannot be used with print"
	return self.left, self.right		return self.left, self.right


	@@ -188,15 +181,8 @@ def make_dictionary(tiff_image_path_list, num_bins=4, difference = True):
	num_bins (int): number of bins		num_bins (int): number of bins

	Return:		Return:
	huffman_encoding_list list (num_bins + 1): a list of dictionary		huffman_encoding_list list (num_bins + 1): a list of dictionary, each dictionary is a huffman encoding
	image_array ndarray (512, 640): original image
	new_error ndarray (512, 640): error that includes the boundary
	diff ndarray (510, 638): difference of min and max of the 4 neighbors
	boundary ndarray (2300,): the boundary values after subtracting the very first pixel value
	predict ndarray (325380,): the list of predicted values
	bins list (num_bins - 1,): a list of threshold to cut the bins		bins list (num_bins - 1,): a list of threshold to cut the bins
	A ndarray (3 X 3): system of equation

	"""		"""
	list_of_all_vals = []		list_of_all_vals = []
	huffman_encoding_list = []		huffman_encoding_list = []
	@@ -289,10 +275,6 @@ def huffman(tiff_image_path, num_bins=4, difference = True):
	# get the image_as_array, etc		# get the image_as_array, etc
	image_as_array, diff, error= predict_pix(tiff_image_path, difference)		image_as_array, diff, error= predict_pix(tiff_image_path, difference)

	# calculate the number of points that will go in each bin


	# sort the difference and create the bins
	bins = [21,32,48]		bins = [21,32,48]
	# get the boundary		# get the boundary
	boundary = np.hstack((image_as_array[0,:],image_as_array[-1,:],image_as_array[1:-1,0],image_as_array[1:-1,-1]))		boundary = np.hstack((image_as_array[0,:],image_as_array[-1,:],image_as_array[1:-1,0],image_as_array[1:-1,-1]))
	@@ -530,7 +512,3 @@ for i in range(len(images)):
	# encoded_string2 = bytes_to_bitstring(read_from_file(item))		# encoded_string2 = bytes_to_bitstring(read_from_file(item))
	# reconstruct_image = decoder(encoded_string2, list_dic, bins, False)		# reconstruct_image = decoder(encoded_string2, list_dic, bins, False)
	# print(np.allclose(image, reconstruct_image))		# print(np.allclose(image, reconstruct_image))
	print(np.mean(file_size_ratios))
	print(np.max(file_size_ratios))
	print(np.min(file_size_ratios))
	print(np.argmax(file_size_ratios))
	No newline at end of file

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