I'm looking for a way to execute MATLAB code in a python file without using MATLAB. This is my MATLAB code:
count = 0
for image_name in os.listdir(directory_segmentation):
if count < n_displays:
segmented_image = cv2.imread(directory_segmentation + "/" + image_name)
segmented = Image.open(directory_segmentation + "/" + image_name)
original = Image.open(directory_originals + "/" + image_name)
font = ImageFont.truetype(font_path, font_size)
d1 = ImageDraw.Draw(segmented)
masked = self.create_mask_plaque(segmented_image, color_plaque)
maxlevel = 3
**v = self.waveletdescr(masked, maxlevel)**
print(v)
count += 1
And the function that it's between ** ** it's coded in MATLAB. So I need to give some parameters to it and get back a vector. Is it possible? Thank you!!
PD. The structure of the MATLAB script looks like this:
function v = waveletdescr(im,maxlevel) ;
im = rgb2gray(im)
im = double(im);
[m,n] = size(im);
npix = m*n;
....
v = zeros( 3*maxlevel+1, 1 ); % the descriptors
end
v(end) = sum(sum( im.^2 )) / npix;
function imf = filterh(im,l)
d = size(im(end-1:-1:end-l,:))
imf = 0.5*[im; im(end-1:-1:end-l,:)];
imf = imf(1:end-l,:) + imf(l+1:end,:);
function imf = filterg(im,l)
imf = 0.5*[im; im(end-1:-1:end-l,:)];
imf = imf(l+1:end,:) - imf(1:end-l,:);
Related
So, I have written a code that hides data into a grayscale image, and can retrieve back from a grayscale image. I want be able to do this for a color image.
At the moment, I'm thinking to convert a color image to grayscale, hide the data, convert the image back to color. If that's possible.
Another stuff that I'm thinking is, for grayscale, getpixel returns a single value, while for color getpixel returns a tuple, so I also thought of just manipulating only one value of a tuple, (if this is correct).
Edit: Code, where I'm trying to get a value from a tuple of a color image. Also, sorry its not documented at all.
from PIL import Image
import numpy as np
import glob
import os
from helper import tobits
image_list = []
for filename in glob.glob('*.png'):
image_list.append(filename)
print(image_list)
#onlyforalphas
message = "he23#"*200
#print(message)
messagebi = ''.join(format(ord(x), '07b') for x in message)
#messagebi = tobits(message)
#print(messagebi)
payload = len(messagebi)
print(".........................PAYLOAD LENGTH : ", payload, "..................................................")
width = 512
height = 512
max_a = 0
min_b = 0
max_value = 0
min_value = 10000
z = 0
zi = 0
fileindex = 0
im = Image.open(image_list[0])
#print(im.histogram())
while payload > 0 and z < len(image_list):
print(".........................PAYLOAD LENGTH : ", payload, "..........................................")
print("OPENING FILE", image_list[z])
im = Image.open(image_list[z])
#print(im.histogram())
z = z + 1
hist_list = np.asarray(im.histogram())
print(im.histogram())
for i in range(len(hist_list)):
if hist_list[i] >= max_value:
max_value = hist_list[i]
max_a = i
if hist_list[i] <= min_value:
min_value = hist_list[i]
min_b = i
if payload > max_value:
print("ERROR:payload size: ", payload, " too large. Trying another image")
hist_list = np.asarray(im.histogram())
print(max_a, " ", max_value)
print(min_b, " ", min_value)
payload = payload - max_value
if payload < 0:
payload = 0
hist_list[max_a] = 0
#zi = 0
messagelength = len(messagebi)
#print(messagebi, " ", messagelength)
for i in range(width):
for j in range(height):
temp = im.getpixel((i,j))[0]
#print(temp)
if temp > max_a and temp < min_b:
im.putpixel((i,j), temp + 1)
#print(im.getpixel((i,j)), end = "")
if zi < messagelength and messagebi[zi] == '1' and temp == max_a:
im.putpixel((i,j), max_a+1)
zi = zi + 1
elif zi < messagelength and messagebi[zi] == '0' and temp == max_a:
zi = zi + 1
#print("")
#imnu = Image.fromarray(hist_list, mode='L')
print("payload size after ", fileindex, "iteration is:", payload)
filename = "output/filename" + str(fileindex) + ".png"
im.save(filename)
fileindex = fileindex + 1
print(im.histogram())
I have trained cnn model and saved parameters in five files,but when I use these params to test photos ,I meet a question like this:enter image description here
the code of load_data is:
def load_data(pag_name):``
k = 0
for filename in os.listdir(pag_name):
if (filename != '.DS_Store'):
k = k + 1
num = k
# test_per = k*4
print k
i = 0
j = 0
label = 0
train_set = numpy.empty((num, 1, 56, 56))
while (j < 1):
for filename in os.listdir(pag_name):
if (filename != '.DS_Store'):
filename = pag_name+ '/' + filename
image = Image.open(filename)
#print image.size
#print image
img_ndarray = numpy.asarray(image, dtype='float64') / 256
img_ndarray = numpy.asarray([img_ndarray])
# train_set[i] = numpy.ndarray.flatten(img_ndarray)
train_set[i] = img_ndarray
#print train_set.shape
# print filename1
# print 'label:', label
# print 'i:',i
i = i + 1
j = j + 1
def shared_dataset(data_x, borrow=True):
shared_x = theano.shared(numpy.asarray(data_x,
dtype=theano.config.floatX),
borrow=borrow)
return shared_x
train_set = shared_dataset(train_set)
print train_set.get_value(borrow=True).shape
return train_set
and the code of use_CNN is :
def use_CNN(pag_name,nkerns=[20,40,60]):
data = load_data(pag_name)
data_num = data.get_value(borrow=True).shape[0]
layer0_params,layer01_params,layer1_params,layer2_params,layer3_params = load_params()
x = T.matrix('x')
layer0_input = x.reshape((data_num,1,56,56))
layer0 = LeNetConvPoolLayer(
input=layer0_input,
params_W = layer0_params[0],
params_b = layer0_params[1],
image_shape=(data_num, 1, 56, 56),
filter_shape=(nkerns[0], 1, 5,5),
poolsize=(2, 2)`
)
I haven't meet this problem ,and I don't know where and how I change my code.
the result of this error is the params are not 4D, the params I load is 3D, like my W and b is (20,1,5,5),but I load (1,5,5),so I meet this problem.
I try to make a dataset that is similar to CIFAR10. I found this tutorial:
How to create dataset similar to cifar-10
I already can make a dataset with 1 image, but when I try to use several images I got this error:
tensorflow.python.framework.errors.InvalidArgumentError: Indices are not valid: not lexicographically sorted or containing repeats.
Can anyone help me to solve this problem?
This is my code:
from PIL import Image
import numpy as np
out =np.empty([20,7501])
for j in xrange(0, 10):
im = Image.open('%d_receipt.jpg' % j)
im = (np.array(im))
r = im[:,:,0].flatten()
g = im[:,:,1].flatten()
b = im[:,:,2].flatten()
label = [0]
out[j] = np.array(list(label) + list(r) + list(g) + list(b),np.uint8)
for i in xrange(0, 10):
im = Image.open('%d_news.jpg' % i)
im = (np.array(im))
r = im[:,:,0].flatten()
g = im[:,:,1].flatten()
b = im[:,:,2].flatten()
label = [1]
j = i + 10
out[j] = np.array(list(label) + list(r) + list(g) + list(b),np.uint8)
out.tofile("data_batch.bin")
I do it like this:
import numpy as np
import scipy.io
mat = scipy.io.loadmat('train_32x32.mat')
data = mat['X']
label = mat['y']
R_data = data[:,:,0,:]
G_data = data[:,:,1,:]
B_data = data[:,:,2,:]
R_data = np.transpose(R_data, (2,0,1))
G_data = np.transpose(G_data, (2,0,1))
B_data = np.transpose(B_data, (2,0,1))
R_data = np.reshape(R_data,(73257,32*32))
G_data = np.reshape(G_data,(73257,32*32))
B_data = np.reshape(B_data,(73257,32*32))
outdata = np.concatenate((label,R_data,G_data,B_data), axis = 1)
step = 10000
for i in range(1,6):
temp = outdata[i*step:(i+1)*step,:]
temp.tofile('SVHN_train_data_batch%d.bin' % i)
print('save data %d' % i)
Then, just put it directly in the train code of Cifar10 tensorflow example.
I too tried to follow the tutorial you posted in the question however I couldn't get it to work so I made my own solution. It can be found on my github here: https://github.com/jdeepee/machine_learning/tree/master
The code is commented so should be easy enough to follow. I should note it iterated through a master directory containing multiple folders which contain the images.
The below snippet is what I did to adapt CIFAR-10 to GTSRB. More details here. https://github.com/hashkanna/traffic-signs/blob/master/Traffic_Signs_Recognition_binFiles.ipynb
out = {}
for i in range(5):
bin_val = (i%5) + 1
#im = Image.open(X_train[i])
#im = np.array(im)
im = X_train[i]
r = im[:,:,0].flatten()
g = im[:,:,1].flatten()
b = im[:,:,2].flatten()
label = [y_train[i]]
out[bin_val] = np.array(list(label) + list(r) + list(g) + list(b),np.uint8)
for i in range(5,len(X_train)):
bin_val = (i%5) + 1
#im = Image.open(X_train[i])
#im = np.array(im)
im = X_train[i]
r = im[:,:,0].flatten()
g = im[:,:,1].flatten()
b = im[:,:,2].flatten()
label = [y_train[i]]
new_array = np.array(list(label) + list(r) + list(g) + list(b),np.uint8)
out[bin_val] = np.append(out[bin_val], new_array, 0)
for bin_val in range(1,6):
out[bin_val].tofile("/Users/kanna/Downloads/data_batch_%s.bin"%bin_val)
I am trying to compare one image with all images of another file , get the difference percentage and print file name of the least difference percentage .... if i try to append the output differences to a list ... i get an error saying " float values cannot be iterated".... this is what i have done so far ....
from itertools import izip
import os
import numpy as np
import cv2
from matplotlib import pyplot as plt
from PIL import Image
import math
res = 0
def take_and_save_picture(im_save):
'''Take a picture and save it
Args:
im_save: filepath where the image should be stored
'''
camera_port = 0
ramp_frames = 30
cap = cv2.VideoCapture(camera_port)
def get_image():
retval, im = cap.read()
return im
for i in xrange(ramp_frames):
temp = get_image()
print("Taking image...")
# Take the actual image we want to keep
camera_capture = get_image()
#im_save_tmp = im_save + '.jpg'
im_save_tmp = im_save
# A nice feature of the imwrite method is that it will automatically choose the
# correct format based on the file extension you provide. Convenient!
cv2.imwrite(im_save_tmp, camera_capture)
# You'll want to release the camera, otherwise you won't be able to create a new
# capture object until your script exits
# del(cap)
img1 = cv2.imread(im_save_tmp, 0)
edges = cv2.Canny(img1, 100, 200)
cv2.imwrite(im_save, edges)
cv2.waitKey(0)
cv2.destroyAllWindows()
def re(path1,path2):
#path1 = raw_input("Enter the path1:")
#path2 = raw_input("Enter the path2:")
i2= Image.open(path2)
listing = os.listdir(path1)
for file in listing:
i1 = Image.open(path1 + file)
assert i1.mode == i2.mode, "Different kinds of images."
assert i1.size == i2.size, "Different sizes."
pairs = izip(i1.getdata(), i2.getdata())
if len(i1.getbands()) == 1:
# for gray-scale jpegs
dif = sum(abs(p1-p2) for p1,p2 in pairs)
else:
dif = sum(abs(c1-c2) for p1,p2 in pairs for c1,c2 in zip(p1,p2))
ncomponents = i1.size[0] * i1.size[1] * 3
res = (dif / 255.0 * 100) / ncomponents
print "Difference (percentage):", res
def main():
capture_img = "/Users/Me/Documents/python programs/New/pro.png"
#img_to_compare = "/Users/Me/Documents/python programs/compare/img2.jpg"
take_and_save_picture(capture_img)
path1 = "/Users/Me/Documents/python programs/New/numbers1/"
path2 = "/Users/Me/Documents/python programs/New/pro.png"
re(path1,path2)
if __name__ == '__main__':
main()
the output is the difference
Difference (percentage): 2.52484809028
Difference (percentage): 2.64822048611
Difference (percentage): 2.64822048611
Difference (percentage): 3.55436197917
the values that i get in "res" have to be stored in a list and the minimum value should be found and printed.... please give me some code ... totally new to python ... thank you ...
You're code must be like this:
#######
list_dif = []
def re(path1,path2):
#path1 = raw_input("Enter the path1:")
#path2 = raw_input("Enter the path2:")
i2= Image.open(path2)
listing = os.listdir(path1)
for file in listing:
i1 = Image.open(path1 + file)
assert i1.mode == i2.mode, "Different kinds of images."
assert i1.size == i2.size, "Different sizes."
pairs = izip(i1.getdata(), i2.getdata())
if len(i1.getbands()) == 1:
# for gray-scale jpegs
dif = sum(abs(p1-p2) for p1,p2 in pairs)
else:
dif = sum(abs(c1-c2) for p1,p2 in pairs for c1,c2 in zip(p1,p2))
ncomponents = i1.size[0] * i1.size[1] * 3
#######
for n in range(ncomponents):
res = (dif / 255.0 * 100) / (ncomponents + 1)
list_dif.append(res)
print "Difference (percentage):", list_dif
Something like this?
def re(path1,path2):
#path1 = raw_input("Enter the path1:")
#path2 = raw_input("Enter the path2:")
i2= Image.open(path2)
listing = os.listdir(path1)
res = []
for file in listing:
i1 = Image.open(path1 + file)
assert i1.mode == i2.mode, "Different kinds of images."
assert i1.size == i2.size, "Different sizes."
pairs = izip(i1.getdata(), i2.getdata())
if len(i1.getbands()) == 1:
# for gray-scale jpegs
dif = sum(abs(p1-p2) for p1,p2 in pairs)
else:
dif = sum(abs(c1-c2) for p1,p2 in pairs for c1,c2 in zip(p1,p2))
ncomponents = i1.size[0] * i1.size[1] * 3
res.append((dif / 255.0 * 100) / ncomponents)
print "Difference (percentage):", res
minimum = min(res) # Find minimum value in res
print(minimum)
I am trying to resample some tiff files from 2000*2000 to 500*500.
I have created a function and I tried for one file and it worked nicely. Now I want to apply it for all the available file I have.
I want to write the output of the function and I have written the code based on my knowledge and I receive error on the writing out_file. I have copied the both function and main code for your consideration. The main code just read the tif files according to their naming and applies the function. I would be thankful if sb could guide me where my mistake is.
#*********function********************
def ResampleImage(infile):
fp = open(infile, "rb")
p = ImageFile.Parser()
while 1:
s = fp.read()
if not s:
break
p.feed(s)
img = p.close()
basewidth = 500
wpercent = (basewidth / float(img.size[0]))
hsize = int((float(img.size[1]) * float(wpercent)))
outfile=img.resize((basewidth, hsize), PIL.Image.ANTIALIAS)
return outfile
#********* main code********
import os,sys
import ImageResizeF
import PIL
from PIL import Image
from PIL import Image,ImageFile
tpath = 'e:/.../resampling_test/all_tiles/'
tifext = '.tif'
east_start = 32511616
north_start = 5400756
ilist = range (0,14)
jlist = range (0,11)
north = north_start
ee = ',4_'
en = ',2'
for i in ilist:
east = east_start
north = north_start + i * 400
snorth = str (north)
for j in jlist:
east = east_start + j * 400
seast = str (east)
infile = tpath + seast + ee + snorth + en + tifext
output = tpath + seast + ee + snorth + en + '_res'+tifext
out_file = ImageResizeF.ResampleImage(infile)
out_file.write (output)
out_file.close ()
Your error is probably related to what you are returning from ImageResizeF.ResampleImage, is it a file handle? Otherwise you are doing it wrong because you cannot close() something which is not a file handle. You should do the whole file processing inside the function or return an image object, for example:
def process_image(image):
"Processes the image"
image.resize((x, y), Image.ANTIALIAS) # or whatever you are doing to the image
return image
image = Image.open('infile.tiff')
proc_image = process_image(image)
proc_image.save('outfile.tiff')