Python Imaging Library - Revision history

Admin: /* Image Averaging for Time Lapses: Many Images */

2014-01-19T00:47:09Z

Image Averaging for Time Lapses: Many Images

← Older revision		Revision as of 00:47, 19 January 2014
Line 163:		Line 163:

	</source>		</source>



			Here is the video with the smoothing applied:

			{{#ev:vimeo\|84492003}}

			Compare this to the video without:

			{{#ev:vimeo\|84490917}}

			Dunno if there's a difference, or if this effect needs to be exaggerated more by making the blended image last for two frames instead of one.

	<!--		<!--
	~~{{#ev:vimeo\|}}~~

	{{#ev:vimeo\|}}		{{#ev:vimeo\|}}

Admin: /* Image Averaging for Time Lapses: Many Images */

2014-01-19T00:42:30Z

Image Averaging for Time Lapses: Many Images

← Older revision		Revision as of 00:42, 19 January 2014
Line 164:		Line 164:
	</source>		</source>

	<~~pre>~~		<!--
			{{#ev:vimeo\|}}

			{{#ev:vimeo\|}}

	{{#ev:vimeo\|10792824}}		{{#ev:vimeo\|10792824}}
	~~</pre~~>		-->

Admin at 00:05, 19 January 2014

2014-01-19T00:05:20Z

← Older revision		Revision as of 00:05, 19 January 2014
Line 164:		Line 164:
	</source>		</source>

	~~https://vimeo.com/10792824~~		<pre>

	{{#ev:vimeo\|10792824}}		{{#ev:vimeo\|10792824}}
			</pre>

Admin: /* Image Averaging for Time Lapses: Many Images */

2014-01-18T23:52:16Z

Image Averaging for Time Lapses: Many Images

← Older revision		Revision as of 23:52, 18 January 2014
Line 163:		Line 163:

	</source>		</source>

			https://vimeo.com/10792824

			{{#ev:vimeo\|10792824}}

Admin: /* Image Averaging for Time Lapses: Many Images */

2014-01-18T23:42:30Z

Image Averaging for Time Lapses: Many Images

← Older revision		Revision as of 23:42, 18 January 2014
Line 110:		Line 110:
	allfiles=os.listdir(os.getcwd())		allfiles=os.listdir(os.getcwd())
	imlist=[filename for filename in allfiles if filename[-4:] in [".JPG"]]		imlist=[filename for filename in allfiles if filename[-4:] in [".JPG"]]
	N=len(imlist)		Nlist = len(imlist)

	~~Nnewimages~~ = ~~len(range(1,N))~~		ii = 0
			jj = 0

	~~# Assuming all images are the same size~~, ~~get dimensions of first image~~		for ii in range(1,Nlist):
	~~w,h=Image.open(imlist[0]~~)~~.size~~

	# ~~Create a numpy array of floats to store the average (assume RGB images)~~		###################################
	~~arr=numpy.zeros((h,w,3),numpy.float)~~		# Step 0: Prep

	~~# Round values in array and cast as 8~~-~~bit integer~~		impair = [imlist[ii-1],imlist[ii]]
	~~arr~~=~~numpy.array~~(~~numpy.round(arr),dtype=numpy.uint8~~)		N=len(impair)

	~~ii = 0~~		# Assuming all images are the same size, get dimensions of first image
	jj = 0		w,h=Image.open(impair[1]).size

	~~for ii in range~~(1,N):		# Create a numpy array of floats to store the average (assume RGB images)
			arr=numpy.zeros((h,w,3),numpy.float)

	##################################		##################################
	# Step 1: Save first picture		# Step 1: Save first picture

	~~call(["cp",imlist[ii-1],~~"processed_%d.jpg"%(jj)])		outname = "processed_%d.jpg"%(jj)
			call(["cp",impair[0],outname])
	jj = jj + 1		jj = jj + 1
	~~print jj~~

	~~impair~~ = ~~[imlist[ii]~~,~~imlist~~[~~ii-1]~~]		print outname,"=",impair[0]

	##################################		##################################
Line 142:		Line 143:
	# Build up average pixel intensities, casting each image as an array of floats		# Build up average pixel intensities, casting each image as an array of floats
	for im in impair:		for im in impair:
	imarr=numpy.array(Image.open(im),dtype=numpy.float)		imarr = numpy.array(Image.open(im),dtype=numpy.float)
	arr=arr+imarr/N		arr = arr + imarr/N

			# Round values in array and cast as 8-bit integer
			arr=numpy.array(numpy.round(arr),dtype=numpy.uint8)

	# Generate, save and preview final image		# Generate, save and preview final image
	out=Image.fromarray(arr,mode="RGB")		outname = "processed_%d.jpg"%(jj)
	~~out~~.~~save~~(~~"processed_%d.jpg"%(jj)~~)		out=Image.fromarray(arr,mode="RGB").rotate(90)
	#out.~~show~~()		out.save(outname)
	jj = jj + 1		jj = jj + 1
	print jj,"..."		print outname,"=",impair[0],"+",impair[1],"..."

	##################################		##################################
	# Step 3: Save last picture		# Step 3: Save last picture

	call(["cp",imlist[N],"processed_%d.jpg"%(jj)])		call(["cp",imlist[N-1],"processed_%d.jpg"%(jj)])
	jj = jj + 1		jj = jj + 1

	</source>		</source>

Admin: /* Image Averaging for Time Lapses: Many Images */

2014-01-18T23:19:47Z

Image Averaging for Time Lapses: Many Images

← Older revision		Revision as of 23:19, 18 January 2014
Line 104:		Line 104:
	This looks for a set of images with the extension <code>.JPG</code>. It then blends them together, and renames the files so they are in-sequence images, with the first image placed first, the blended image placed second, and the third image placed third.		This looks for a set of images with the extension <code>.JPG</code>. It then blends them together, and renames the files so they are in-sequence images, with the first image placed first, the blended image placed second, and the third image placed third.

	<source>		<source lang="python">
	import os, numpy, Image		import os, numpy, Image
	from subprocess import call		from subprocess import call
Line 113:		Line 113:

	Nnewimages = len(range(1,N))		Nnewimages = len(range(1,N))

	~~jj = 0~~

	# Assuming all images are the same size, get dimensions of first image		# Assuming all images are the same size, get dimensions of first image
Line 124:		Line 122:
	# Round values in array and cast as 8-bit integer		# Round values in array and cast as 8-bit integer
	arr=numpy.array(numpy.round(arr),dtype=numpy.uint8)		arr=numpy.array(numpy.round(arr),dtype=numpy.uint8)

			ii = 0
			jj = 0

	for ii in range(1,N):		for ii in range(1,N):
Line 130:		Line 131:
	# Step 1: Save first picture		# Step 1: Save first picture

	call(["cp",imlist[0],"processed_%d.jpg"%(jj)])		call(["cp",imlist[ii-1],"processed_%d.jpg"%(jj)])
	jj = jj + 1		jj = jj + 1
	print jj		print jj
Line 149:		Line 150:
	#out.show()		#out.show()
	jj = jj + 1		jj = jj + 1
	print jj		print jj,"..."

			##################################
			# Step 3: Save last picture

			call(["cp",imlist[N],"processed_%d.jpg"%(jj)])
			jj = jj + 1

	</source>		</source>

Admin: /* Image Averaging for Time Lapses: Many Images */

2014-01-18T23:15:53Z

Image Averaging for Time Lapses: Many Images

← Older revision		Revision as of 23:15, 18 January 2014
Line 150:		Line 150:
	jj = jj + 1		jj = jj + 1
	print jj		print jj

	~~##################################~~
	~~# Step 3: Save second picture~~

	~~call(["cp",imlist[1],"processed_%d.jpg"%(jj)])~~
	~~jj = jj + 1~~
	~~print jj,"..."~~


	</source>		</source>

Admin: /* Image Averaging for Time Lapses: Many Images */

2014-01-18T22:45:31Z

Image Averaging for Time Lapses: Many Images

← Older revision		Revision as of 22:45, 18 January 2014
Line 99:		Line 99:

	Having multiple images, and adding an average of each pair, is slightly more involved than the two-image case.		Having multiple images, and adding an average of each pair, is slightly more involved than the two-image case.

			For my application, I'm interested in stitching a large number of images together, with average images in between each.

			This looks for a set of images with the extension <code>.JPG</code>. It then blends them together, and renames the files so they are in-sequence images, with the first image placed first, the blended image placed second, and the third image placed third.

			<source>
			import os, numpy, Image
			from subprocess import call

			allfiles=os.listdir(os.getcwd())
			imlist=[filename for filename in allfiles if filename[-4:] in [".JPG"]]
			N=len(imlist)

			Nnewimages = len(range(1,N))

			jj = 0

			# Assuming all images are the same size, get dimensions of first image
			w,h=Image.open(imlist[0]).size

			# Create a numpy array of floats to store the average (assume RGB images)
			arr=numpy.zeros((h,w,3),numpy.float)

			# Round values in array and cast as 8-bit integer
			arr=numpy.array(numpy.round(arr),dtype=numpy.uint8)

			for ii in range(1,N):

			##################################
			# Step 1: Save first picture

			call(["cp",imlist[0],"processed_%d.jpg"%(jj)])
			jj = jj + 1
			print jj

			impair = [imlist[ii],imlist[ii-1]]

			##################################
			# Step 2: Save average picture

			# Build up average pixel intensities, casting each image as an array of floats
			for im in impair:
			imarr=numpy.array(Image.open(im),dtype=numpy.float)
			arr=arr+imarr/N

			# Generate, save and preview final image
			out=Image.fromarray(arr,mode="RGB")
			out.save("processed_%d.jpg"%(jj))
			#out.show()
			jj = jj + 1
			print jj

			##################################
			# Step 3: Save second picture

			call(["cp",imlist[1],"processed_%d.jpg"%(jj)])
			jj = jj + 1
			print jj,"..."


			</source>

Admin: /* Image Averaging for Time Lapses */

2014-01-18T10:24:57Z

Image Averaging for Time Lapses

@@ Line 46: / Line 46: @@
-===Image Averaging for Time Lapses===
+===Image Averaging for Time Lapses: 2 Images===
 Why I want to take the average of two images:
@@ Line 61: / Line 61: @@
 [[Image:ClocktowerAvgAB.jpg|400px]]

Admin: /* Image Averaging for Time Lapses */

2014-01-18T10:19:27Z

Image Averaging for Time Lapses

← Older revision		Revision as of 10:19, 18 January 2014
Line 56:		Line 56:
	The average of		The average of

	[[Image:ClocktowerA.jpg]]		[[Image:ClocktowerA.jpg\|350px]] and [[Image:ClocktowerB.jpg\|350px]]

	and

	[[Image:ClocktowerB.jpg]]

	is		is

	[[Image:ClocktowerAvgAB.jpg]]		[[Image:ClocktowerAvgAB.jpg\|400px]]