Develop Reference

Why the extracted watermark is not the same as the embedded one?

I'm trying to write the code of this paper paper for a university project. the idea is to insert an invisible watermark into a grayscale image, which can be extracted later to verify the image ownership.
This is the code I wrote for the watermark embedding process :
import pywt
import numpy as np
import cv2
from PIL import Image
from math import sqrt, log10
from scipy.fftpack import dct, idct
def Get_MSB_LSB_Watermark () : #Function that separates the watermark into MSB and LSB images
MSBs = []
LSBs = []
for i in range (len(Watermark)) :
binary = '{:0>8}'.format(str(bin(Watermark[i]))[2:])
MSB = (binary[0:4])
LSB = (binary[4:])
MSB = int(MSB, 2)
LSB = int(LSB,2)
MSBs.append(MSB)
LSBs.append(LSB)
MSBs = np.array(MSBs)
LSBs = np.array(LSBs)
return MSBs.reshape(64,64), LSBs.reshape(64,64)
def split(array, nrows, ncols): #Split array into blocks of size nrows* ncols
r, h = array.shape
return (array.reshape(h//nrows, nrows, -1, ncols)
.swapaxes(1, 2)
.reshape(-1, nrows, ncols))
def unblockshaped(arr, h, w): #the inverse of the split function
n, nrows, ncols = arr.shape
return (arr.reshape(h//nrows, -1, nrows, ncols)
.swapaxes(1,2)
.reshape(h, w))
def ISVD (U,S,V): #the inverse of singular value decomposition
s = np.zeros(np.shape(U))
for i in range(4):
s[i, i] = S[i]
recon_image = U # s # V
return recon_image
def Watermark_Embedding (blocks, watermark) :
Watermarked_blocks = []
k1 = []
k2 = []
#convert the watermark to a list
w = list(np.ndarray.flatten(watermark))
for i in range (len(blocks)) :
B = blocks[i]
#Aplly singular value decoposition to the block
U, s, V = np.linalg.svd(B)
#Modify the singular values of the block
P = s[1] - s[2]
delta = abs(w[i]) - P
s[1] = s[1] + delta
if s[0] >= s[1] :
k1.append(1)
else :
k1.append(-1)
#the inverse of SVD after watermark embedding
recunstructed_B = ISVD(U, s, V)
Watermarked_blocks.append(recunstructed_B)
for j in range(len(w)):
if w[j] >= 0:
k2.append(1)
else:
k2.append(-1)
return k1,k2, np.array(Watermarked_blocks)
def apply_dct(image_array):
size = image_array[0].__len__()
all_subdct = np.empty((size, size))
for i in range (0, size, 4):
for j in range (0, size, 4):
subpixels = image_array[i:i+4, j:j+4]
subdct = dct(dct(subpixels.T, norm="ortho").T, norm="ortho")
all_subdct[i:i+4, j:j+4] = subdct
return all_subdct
def inverse_dct(all_subdct):
size = all_subdct[0].__len__()
all_subidct = np.empty((size, size))
for i in range (0, size, 4):
for j in range (0, size, 4):
subidct = idct(idct(all_subdct[i:i+4, j:j+4].T, norm="ortho").T, norm="ortho")
all_subidct[i:i+4, j:j+4] = subidct
return all_subidct
#read watermark
Watermark = Image.open('Copyright.png').convert('L')
Watermark = list(Watermark.getdata())
#Separate the watermark into LSB and MSB images
Watermark1, Watermark2 = Get_MSB_LSB_Watermark()
#Apply descrete cosine Transform on the two generated images
DCT_Watermark1 = apply_dct(Watermark1)
DCT_Watermark2 = apply_dct(Watermark2)
#read cover Image
Cover_Image = Image.open('10.png').convert('L')
#Apply 1 level descrete wavelet transform
LL1, (LH1, HL1, HH1) = pywt.dwt2(Cover_Image, 'haar')
#Split the LH1 and HL1 subbands into blocks of size 4*4
blocks_LH1 = split(LH1,4,4)
blocks_HL1 = split(HL1,4,4)
#Watermark Embedding in LH1 and HL1 and Keys generation
Key1, Key3, WatermarkedblocksLH1 = Watermark_Embedding(blocks_LH1,DCT_Watermark1)
Key2 ,Key4, WatermarkedblocksHL1 = Watermark_Embedding(blocks_HL1,DCT_Watermark2)
#Merge the watermzrked Blocks
reconstructed_LH1 = unblockshaped(WatermarkedblocksLH1, 256,256)
reconstructed_HL1 = unblockshaped(WatermarkedblocksHL1, 256,256)
#Apply the inverse of descrete wavelet transform to get the watermarked image
IDWT = pywt.idwt2((LL1, (reconstructed_LH1, reconstructed_HL1, HH1)), 'haar')
cv2.imwrite('Watermarked_img.png', IDWT)
This is the code I wrote for the Extraction process :
import pywt
from scipy import fftpack
import numpy as np
import cv2
from PIL import Image
import scipy
from math import sqrt, log10
from Watermark_Embedding import *
def Watermark_Extraction(blocks,key1, key2) :
Extracted_Watermark = []
for i in range(len(blocks)):
B = blocks[i]
#apply SVD on the Block
U, s, V = np.linalg.svd(B)
if key1[i] == 1 :
P = (s[1] - s[2])
Extracted_Watermark.append(P)
else :
P = (s[0] - s[2])
Extracted_Watermark.append(P)
for j in range(len(Extracted_Watermark)) :
if key2[j] == 1 :
Extracted_Watermark[j] = Extracted_Watermark[j]
else :
Extracted_Watermark[j] = - (Extracted_Watermark[j])
return np.array(Extracted_Watermark)
def Merge_W1_W2 ():
Merged_watermark = []
w1 = list(np.ndarray.flatten(IDCTW1))
w2 = list(np.ndarray.flatten(IDCTW2))
for i in range (len(w2)):
bw1 = '{:0>4}'.format((bin(int(abs(w1[i]))))[2:])
bw2 = '{:0>4}'.format((bin(int(abs(w2[i]))))[2:])
P = bw1+bw2
pixel = (int(P,2))
Merged_watermark.append(pixel)
return Merged_watermark
Watermarked_Image = Image.open('Watermarked_img.png')
LL1, (LH1, HL1, HH1) = pywt.dwt2(Watermarked_Image, 'haar')
blocks_LH1 = split(LH1,4,4)
blocks_HL1 = split(HL1,4,4)
W1 = Watermark_Extraction(blocks_LH1, Key1,Key3)
W2 = Watermark_Extraction(blocks_HL1, Key2, Key4)
W1 = W1.reshape(64,64)
W2 = W2.reshape(64,64)
IDCTW1 = inverse_dct(W1)
IDCTW2 = inverse_dct(W2)
Merged = np.array(Merge_W1_W2())
Merged = Merged.reshape(64,64)
cv2.imwrite('Extracted_Watermark.png', Merged)
The cover Image of size 512*512:
The 64*64 watermark I used
The watermarked Image :
The extracted Watermark I get:
I calculated the similarity between the two watermarks using SSIM :
from skimage.metrics import structural_similarity
original_Watermark = cv2.imread('Copyright.png')
extracted_watermark = cv2.imread('Extracted_Watermark.png')
# Convert images to grayscale
original_watermark = cv2.cvtColor(original_Watermark, cv2.COLOR_BGR2GRAY)
extracted_Watermark = cv2.cvtColor(extracted_watermark, cv2.COLOR_BGR2GRAY)
# Compute SSIM between two images
(score, diff) = structural_similarity(original_Watermark, extracted_Watermark, full=True)
print("SSIM = ", score)
I didn't apply any modification on the watermarked image and The SSIM I got is 0.8445354561524052. however the SSIM of the extracted watermark should be 0.99 according to the paper.
I don't know what's wrong with my code and I have a deadline after two days so I really need help.
thanks in advance.

There are two issues:
In Merge_W1_W2 you are using int to convert from float to int but that introduces errors for numbers where the floating point representation is not exact (e.g. 14.99999999999997); this can be fixed by using round instead.
Saving cv2.imwrite('Watermarked_img.png', IDWT) is a lossy operation because it rounds the values in IDWT to the nearest integer; if you use Watermarked_Image = IDWT then you will get back the exact same watermark image.

Quick pixel manipulation with Pillow and/or NumPy

I'm trying to improve the speed of my image manipulation as it's been too slow for actual use.
What I need to do is apply a complex transformation on the colour of every pixel on an image. The manipulation is basically apply a vector transform like T(r, g, b, a) => (r * x, g * x, b * y, a) or in layman's terms, it's a multiplication of Red and Green values by a constant, a different multiplication for Blue and keep Alpha. But I also need to manipulate it differently if the RGB colour falls under some specific colours, in those cases they must follow a dictionary/transformation table where RGB => newRGB again keeping alpha.
The algorithm would be:
for each pixel in image:
if pixel[r, g, b] in special:
return special[pixel[r, g, b]] + pixel[a]
else:
return T(pixel)
It's simple but speed has been sub-optimal. I believe there's some way using numpy vectors, but I could not find how.
Important details about the implementation:
I don't care about the original buffer/image (manipulation can be in place)
I can use wxPython, Pillow and NumPy
Order or dimension of the array is not important as long as the buffer keeps the length
The buffer is obtained from a wxPython Bitmap and special and (RG|B)_pal are transformation tables, the end result will become a wxPython Bitmap too. They're obtained like these:
# buffer
bitmap = wx.Bitmap # it's valid wxBitmap here, this is just to let you know it exists
buff = bytearray(bitmap.GetWidth() * bitmap.GetHeight() * 4)
bitmap.CopyToBuffer(buff, wx.BitmapBufferFormat_RGBA)
self.RG_mult= 0.75
self.B_mult = 0.83
self.RG_pal = []
self.B_pal = []
for i in range(0, 256):
self.RG_pal.append(int(i * self.RG_mult))
self.B_pal.append(int(i * self.B_mult))
self.special = {
# RGB: new_RGB
# Implementation specific for the fastest access
# with buffer keys are 24bit numbers, with PIL keys are tuples
}
Implementations I tried include direct buffer manipulation:
for x in range(0, bitmap.GetWidth() * bitmap.GetHeight()):
index = x * 4
r = buf[index]
g = buf[index + 1]
b = buf[index + 2]
rgb = buf[index:index + 3]
if rgb in self.special:
special = self.special[rgb]
buf[index] = special[0]
buf[index + 1] = special[1]
buf[index + 2] = special[2]
else:
buf[index] = self.RG_pal[r]
buf[index + 1] = self.RG_pal[g]
buf[index + 2] = self.B_pal[b]
Use Pillow with getdata():
pil = Image.frombuffer("RGBA", (bitmap.GetWidth(), bitmap.GetHeight()), buf)
pil_buf = []
for colour in pil.getdata():
colour_idx = colour[0:3]
if (colour_idx in self.special):
special = self.special[colour_idx]
pil_buf.append((
special[0],
special[1],
special[2],
colour[3],
))
else:
pil_buf.append((
self.RG_pal[colour[0]],
self.RG_pal[colour[1]],
self.B_pal[colour[2]],
colour[3],
))
pil.putdata(pil_buf)
buf = pil.tobytes()
Pillow with point() and getdata() (fastest I achieved, more than twice times faster than others)
pil = Image.frombuffer("RGBA", (bitmap.GetWidth(), bitmap.GetHeight()), buf)
r, g, b, a = pil.split()
r = r.point(lambda r: r * self.RG_mult)
g = g.point(lambda g: g * self.RG_mult)
b = b.point(lambda b: b * self.B_mult)
pil = Image.merge("RGBA", (r, g, b, a))
i = 0
for colour in pil.getdata():
colour_idx = colour[0:3]
if (colour_idx in self.special):
special = self.special[colour_idx]
pil.putpixel(
(i % bitmap.GetWidth(), i // bitmap.GetWidth()),
(
special[0],
special[1],
special[2],
colour[3],
)
)
i += 1
buf = pil.tobytes()
I also tried working with numpy.where but then I could not get it to work. With numpy.apply_along_axis it worked but the performance was terrible. Other tries with numpy I could not access the RGB together, only as separated bands.

Pure Numpy Version
This first optimization relies on the fact, that one probably has way less special colors than pixels. I use numpy to do all the inner loops. This works well with images of up to 1MP. If You have multiple images I'd recommend the parallel approach.
Let's define a test case:
import requests
from io import BytesIO
from PIL import Image
import numpy as np
# Load some image, so we have the same
response = requests.get("https://upload.wikimedia.org/wikipedia/commons/4/41/Rick_Astley_Dallas.jpg")
# Make areas of known color
img = Image.open(BytesIO(response.content)).rotate(10, expand=True).rotate(-10,expand=True, fillcolor=(255,255,255)).convert('RGBA')
print("height: %d, width: %d (%.2f MP)"%(img.height, img.width, img.width*img.height/10e6))
height: 5034, width: 5792 (2.92 MP)
Define our special colors
specials = {
(4,1,6):(255,255,255),
(0, 0, 0):(255, 0, 255),
(255, 255, 255):(0, 255, 0)
}
Algorithm
def transform_map(img, specials, R_factor, G_factor, B_factor):
# Your transform
def transform(x, a):
a *= x
return a.clip(0, 255).astype(np.uint8)
# Convert to array
img_array = np.asarray(img)
# Extract channels
R = img_array.T[0]
G = img_array.T[1]
B = img_array.T[2]
A = img_array.T[3]
# Find Special colors
# First, calculate a uniqe hash
color_hashes = (R + 2**8 * G + 2**16 * B)
# Find inidices of special colors
special_idxs = []
for k, v in specials.items():
key_arr = np.array(list(k))
val_arr = np.array(list(v))
spec_hash = key_arr[0] + 2**8 * key_arr[1] + 2**16 * key_arr[2]
special_idxs.append(
{
'mask': np.where(np.isin(color_hashes, spec_hash)),
'value': val_arr
}
)
# Apply transform to whole image
R = transform(R, R_factor)
G = transform(G, G_factor)
B = transform(B, B_factor)
# Replace values where special colors were found
for idx in special_idxs:
R[idx['mask']] = idx['value'][0]
G[idx['mask']] = idx['value'][1]
B[idx['mask']] = idx['value'][2]
return Image.fromarray(np.array([R,G,B,A]).T, mode='RGBA')
And finally some bench marks on a Intel Core i5-6300U # 2.40GHz
import time
times = []
for i in range(10):
t0 = time.time()
# Test
transform_map(img, specials, 1.2, .9, 1.2)
#
t1 = time.time()
times.append(t1-t0)
np.round(times, 2)
print('average run time: %.2f +/-%.2f'%(np.mean(times), np.std(times)))
average run time: 9.72 +/-0.91
EDIT Parallelization
With the same setup as above, we can get a 2x speed increase on large images. (Small ones are faster without numba)
from numba import njit, prange
from numba.core import types
from numba.typed import Dict
# Map dict of special colors or transform over array of pixel values
#njit(parallel=True, locals={'px_hash': types.uint32})
def check_and_transform(img_array, d, T):
#Save Shape for later
shape = img_array.shape
# Flatten image for 1-d iteration
img_array_flat = img_array.reshape(-1,3).copy()
N = img_array_flat.shape[0]
# Replace or map
for i in prange(N):
px_hash = np.uint32(0)
px_hash += img_array_flat[i,0]
px_hash += types.uint32(2**8) * img_array_flat[i,1]
px_hash += types.uint32(2**16) * img_array_flat[i,2]
try:
img_array_flat[i] = d[px_hash]
except Exception:
img_array_flat[i] = (img_array_flat[i] * T).astype(np.uint8)
# return image
return img_array_flat.reshape(shape)
# Wrapper for function above
def map_or_transform_jit(image: Image, specials: dict, T: np.ndarray):
# assemble numba typed dict
d = Dict.empty(
key_type=types.uint32,
value_type=types.uint8[:],
)
for k, v in specials.items():
k = types.uint32(k[0] + 2**8 * k[1] + 2**16 * k[2])
v = np.array(v, dtype=np.uint8)
d[k] = v
# get rgb channels
img_arr = np.array(img)
rgb = img_arr[:,:,:3].copy()
img_shape = img_arr.shape
# apply map
rgb = check_and_transform(rgb, d, T)
# set color channels
img_arr[:,:,:3] = rgb
return Image.fromarray(img_arr, mode='RGBA')
# Benchmark
import time
times = []
for i in range(10):
t0 = time.time()
# Test
test_img = map_or_transform_jit(img, specials, np.array([1, .5, .5]))
#
t1 = time.time()
times.append(t1-t0)
np.round(times, 2)
print('average run time: %.2f +/- %.2f'%(np.mean(times), np.std(times)))
test_img
average run time: 3.76 +/- 0.08

Elaborating very large array in Python

I open a TIFF LAB image and return a big numpy array (4928x3264x3 float64) using python with this function:
def readTIFFLAB(filename):
"""Read TIFF LAB and retur a float matrix
read 16 bit (2 byte) each time without any multiprocessing
about 260 sec"""
import numpy as np
....
....
# Data read
# Matrix creation
dim = (int(ImageLength), int(ImageWidth), int(SamplePerPixel))
Image = np.empty(dim, np.float64)
contatore = 0
for address in range(0, len(StripOffsets)):
offset = StripOffsets[address]
f.seek(offset)
for lung in range(0, (StripByteCounts[address]/SamplePerPixel/2)):
v = np.array(f.read(2))
v.dtype = np.uint16
v1 = np.array(f.read(2))
v1.dtype = np.int16
v2 = np.array(f.read(2))
v2.dtype = np.int16
v = np.array([v/65535.0*100])
v1 = np.array([v1/32768.0*128])
v2 = np.array([v2/32768.0*128])
v = np.append(v, [v1, v2])
riga = contatore // ImageWidth
colonna = contatore % ImageWidth
# print(contatore, riga, colonna)
Image[riga, colonna, :] = v
contatore += 1
return(Image)
but this routine need about 270 second to do all the work and return a numpy array.
I try to use multiprocessing but is not possible to share an array or to use queue to pass it and sharedmem is not usable in windows system (at home I use openSuse but at work I must use windows).
Someone could help me to reduce the elaboration time? I read about threadind, to write some part in C language but I don’t understand what the best (and easier) solution,...I’m a food technologist not a real programmer :-)
Thanks

Wow, your method is really slow indeed, try tifffile library, you can find it here. That library will open your file very fast, then you just need to make the proper conversion, here's the simple usage:
import numpy as np
import tifffile
from skimage import color
import time
import matplotlib.pyplot as plt
def convert_to_tifflab(image):
# divide the color channel
L = image[:, :, 0]
a = image[:, :, 1]
b = image[:, :, 2]
# correct interpretation of a/b channel
a.dtype = np.int16
b.dtype = np.int16
# scale the result
L = L / 65535.0 * 100
a = a / 32768.0 * 128
b = b / 32768.0 * 128
# join the result
lab = np.dstack([L, a, b])
# view the image
start = time.time()
rgb = color.lab2rgb(lab)
print "Lab2Rgb: {0}".format(time.time() - start)
return rgb
if __name__ == "__main__":
filename = '/home/cilladani1/FERRERO/Immagini Digi Eye/Test Lettura CIELAB/TestLetturaCIELAB (LAB).tif'
start = time.time()
I = tifffile.imread(filename)
end = time.time()
print "Image fetching: {0}".format(end - start)
rgb = convert_to_tifflab(I)
print "Image conversion: {0}".format(time.time() - end)
plt.imshow(rgb)
plt.show()
The benchmark gives this data:
Image fetching: 0.0929999351501
Lab2Rgb: 12.9520001411
Image conversion: 13.5920000076
As you can see the bottleneck in this case is lab2rgb, which converts from xyz to rgb space. I'd recommend you to report an issue to the author of tifffile requesting the feature to read your fileformat, I'm sure he'll be able to speed up directly the C code.

After doing what BPL suggest me I modify the result array as follow:
# divide the color channel
L = I[:, :, 0]
a = I[:, :, 1]
b = I[:, :, 2]
# correct interpretation of a/b channel
a.dtype = np.int16
b.dtype = np.int16
# scale the result
L = L / 65535.0 * 100
a = a / 32768.0 * 128
b = b / 32768.0 * 128
# join the result
lab = np.dstack([L, a, b])
# view the image
from skimage import color
rgb = color.lab2rgb(lab)
plt.imshow(rgb)
So now is easier to read TIFF LAB image.
Thank BPL

index error in my python program

This is a program for face recognition using pca logic. Everything went fine except for the index error that came up at the end of the program.
When I run the code I get an index error at the fourth last line of my program.
distances.append((dist, y[i]))
IndexError: list index out of range
can anyone just help in this. I am newbie into python, so am I not so expert in solving.
Here is my code :
from sklearn.decomposition import RandomizedPCA
import numpy as np
import glob
import cv2
import math
import os.path
import string
#function to get ID from filename
def ID_from_filename(filename):
part = string.split(filename, '/')
return part[1].replace("s", "")
#function to convert image to right format
def prepare_image(filename):
img_color = cv2.imread(filename)
img_gray = cv2.cvtColor(img_color, cv2.cv.CV_RGB2GRAY)
img_gray = cv2.equalizeHist(img_gray)
return img_gray.flat
IMG_RES = 92 * 112 # img resolution
NUM_EIGENFACES = 10 # images per train person
NUM_TRAINIMAGES = 110 # total images in training set
#loading training set from folder train_faces
folders = glob.glob('train_faces/*')
# Create an array with flattened images X
# and an array with ID of the people on each image y
X = np.zeros([NUM_TRAINIMAGES, IMG_RES], dtype='int8')
y = []
# Populate training array with flattened imags from subfolders of
train_faces and names
c = 0
for x, folder in enumerate(folders):
train_faces = glob.glob(folder + '/*')
for i, face in enumerate(train_faces):
X[c,:] = prepare_image(face)
y.append(ID_from_filename(face))
c = c + 1
# perform principal component analysis on the images
pca = RandomizedPCA(n_components=NUM_EIGENFACES, whiten=True).fit(X)
X_pca = pca.transform(X)
# load test faces (usually one), located in folder test_faces
test_faces = glob.glob('test_faces/*')
# Create an array with flattened images X
X = np.zeros([len(test_faces), IMG_RES], dtype='int8')
# Populate test array with flattened imags from subfolders of train_faces
for i, face in enumerate(test_faces):
X[i,:] = prepare_image(face)
# run through test images (usually one)
for j, ref_pca in enumerate(pca.transform(X)):
distances = []
# Calculate euclidian distance from test image to each of the known
images and save distances
for i, test_pca in enumerate(X_pca):
dist = math.sqrt(sum([diff**2 for diff in (ref_pca - test_pca)]))
distances.append((dist, y[i]))
found_ID = min(distances)[1]
print "Identified (result: "+ str(found_ID) +" - dist - " +
str(min(distances)[0]) + ")"

Your i in the loop below goes up to the length of X_pca - 1
for i, test_pca in enumerate(X_pca):
dist = math.sqrt(sum([diff**2 for diff in (ref_pca - test_pca)]))
distances.append((dist, y[i]))
However, your y is not built to have that length necessarily:
for x, folder in enumerate(folders):
train_faces = glob.glob(folder + '/*')
for i, face in enumerate(train_faces):
X[c,:] = prepare_image(face)
y.append(ID_from_filename(face))
So you are using an index i which is greater than the bounds of your list y.

Averaging over multipage TIFF pages in Python

What would be the fastest/memory efficient way to get average over many frames of 16-bit TIFF image as numpy array?
What I came up so far is the code below. To my surprise, method2 was faster than method1.
But, for profiling never assume, test it! So, I want to test more.
Worth trying Wand? I did not include here because after imstalling ImageMagick-6.8.9-Q16 and MAGICK_HOME env var it still does not import... Any other library for multipage tiff in Python? GDAL maybe little too much for this.
(edit) I included libtiff. Still method2 fastest and quite memory efficient.
from time import time
#import cv2 ## no multi page tiff support
import numpy as np
from PIL import Image
#from scipy.misc import imread ## no multi page tiff support
import tifffile # http://www.lfd.uci.edu/~gohlke/code/tifffile.py.html
from libtiff import TIFF # https://code.google.com/p/pylibtiff/
fp = r"path/2/1000frames-timelapse-image.tif"
def method1(fp):
'''
using tifffile.py by Christoph (Version: 2014.02.05)
(http://www.lfd.uci.edu/~gohlke/code/tifffile.py.html)
'''
with tifffile.TIFFfile(fp) as imfile:
return imfile.asarray().mean(axis=0)
def method2(fp):
'primitive peak memory friendly way with tifffile.py'
with tifffile.TIFFfile(fp) as imfile:
nframe, h, w = imfile.series[0]['shape']
temp = np.zeros( (h,w), dtype=np.float64 )
for n in range(nframe):
curframe = imfile.asarray(n)
temp += curframe
return (temp / nframe)
def method3(fp):
' like method2 but using pillow 2.3.0 '
im = Image.open(fp)
w, h = im.size
temp = np.zeros( (h,w), dtype=np.float64 )
n = 0
while True:
curframe = np.array(im.getdata()).reshape(h,w)
temp += curframe
n += 1
try:
im.seek(n)
except:
break
return (temp / n)
def method4(fp):
'''
https://code.google.com/p/pylibtiff/
documentaion seems out dated.
'''
tif = TIFF.open(fp)
header = tif.info()
meta = dict() # extracting meta
for l in header.splitlines():
if l:
if l.find(':')>0:
parts = l.split(':')
key = parts[0]
value = ':'.join(parts[1:])
elif l.find('=')>0:
key, value =l.split('=')
meta[key] = value
nframes = int(meta['frames'])
h = int(meta['ImageLength'])
w = int(meta['ImageWidth'])
temp = np.zeros( (h,w), dtype=np.float64 )
for frame in tif.iter_images():
temp += frame
return (temp / nframes)
t0 = time()
avgimg1 = method1(fp)
print time() - t0
# 1.17-1.33 s
t0 = time()
avgimg2 = method2(fp)
print time() - t0
# 0.90-1.53 s usually faster than method1 by 20%
t0 = time()
avgimg3 = method3(fp)
print time() - t0
# 21 s
t0 = time()
avgimg4 = method4(fp)
print time() - t0
# 1.96 - 2.21 s # may not be accurate. I got warning for every frame with the tiff file I tested.
np.testing.assert_allclose(avgimg1, avgimg2)
np.testing.assert_allclose(avgimg1, avgimg3)
np.testing.assert_allclose(avgimg1, avgimg4)

Simple logic would make me bet my money on method 1 or 3, since method 2 and 4 have for-loops in them. For-loops Always make your code go slower if you have more input.
I would definitely go for method 1: neat, clear to read...
To be really sure, just test them I would say. If you don't feel like testing, I would go for method one.
Kind regards,