Develop Reference

CV2 changes the image

I have a following code:
import cv2 as cv
import numpy as np
im = cv.imread('outline.png', cv.IMREAD_UNCHANGED)
cv.imwrite('output.png', im)
f1 = open('outline.png', 'rb')
f2 = open('output.png', 'rb')
img1_b = b64encode(f1.read())
img2_b = b64encode(f2.read())
print(img1_b)
print(img2_b)
What is the reason that img1_b and img2_b are different? img2_b is much longer - why?.
I do not want to copy the file - I would like to process it before saving but this part of code is not included.
Both outline.png and output.png looks same after the operation.
What can I change in my code to make img2_b value same as img1_b??
I have tried PIL Image with same result.

The phenomenon you have run into is the result of data compression not being 100% rigidly defined. PNG files use DEFLATE compression, which requires a given compressed file must always decompress to the same output, but does not require that a given input must produce the same compressed file. This gives room for improvement in the compression algorithm where a more optimal compression may be found over a different type of file. It sounds like your original image was compressed using a better (or just different) algorithm than cv2 is using. In order to duplicate the exact compressed version you'll likely need the exact same implementation of compression algorithm that was used to create the original image.
If you want to ensure that the images are indeed identical, you should compare the decoded pixel values. In the name of not re-inventing the wheel, I'll refer you to this excellent blog post on the subject.
Edit: linked article wasn't loading consistently for me so I copied the code here for referencing.
import cv2
import numpy as np
original = cv2.imread("imaoriginal_golden_bridge.jpg")
duplicate = cv2.imread("images/duplicate.jpg")
# 1) Check if 2 images are equals
if original.shape == duplicate.shape:
print("The images have same size and channels")
difference = cv2.subtract(original, duplicate)
b, g, r = cv2.split(difference)
if cv2.countNonZero(b) == 0 and cv2.countNonZero(g) == 0 and cv2.countNonZero(r) == 0:
print("The images are completely Equal")

Images opened in Pillow and OpenCV are not equivelant

I downloaded a test image from Wikipedia (the tree seen below) to compare Pillow and OpenCV (using cv2) in python. Perceptually the two images appear the same, but their respective md5 hashes don't match; and if I subtract the two images the result is not even close to solid black (the image shown below the original). The original image is a JPEG. If I convert it to a PNG first, the hashes match.
The last image shows the frequency distribution of how the pixel value differences.
As Catree pointed out my subtraction was causing integer overflow. I updated to converting too dtype=int before the subtraction (to show the negative values) and then taking the absolute value before plotting the difference. Now the difference image is perceptually solid black.
This is the code I used:
from PIL import Image
import cv2
import sys
import md5
import numpy as np
def hashIm(im):
imP = np.array(Image.open(im))
# Convert to BGR and drop alpha channel if it exists
imP = imP[..., 2::-1]
# Make the array contiguous again
imP = np.array(imP)
im = cv2.imread(im)
diff = im.astype(int)-imP.astype(int)
cv2.imshow('cv2', im)
cv2.imshow('PIL', imP)
cv2.imshow('diff', np.abs(diff).astype(np.uint8))
cv2.imshow('diff_overflow', diff.astype(np.uint8))
with open('dist.csv', 'w') as outfile:
diff = im-imP
for i in range(-256, 256):
outfile.write('{},{}\n'.format(i, np.count_nonzero(diff==i)))
cv2.waitKey(0)
cv2.destroyAllWindows()
return md5.md5(im).hexdigest() + ' ' + md5.md5(imP).hexdigest()
if __name__ == '__main__':
print sys.argv[1] + '\t' + hashIm(sys.argv[1])
Frequency distribution updated to show negative values.
This is what I was seeing before I implemented the changes recommended by Catree.

The original image is a JPEG.
JPEG decoding can produce different results depending on the libjpeg version, compiler optimization, platform, etc.
Check which version of libjpeg Pillow and OpenCV are using.
See this answer for more information: JPEG images have different pixel values across multiple devices or here.
BTW, (im-imP) produces uint8 overflow (there is no way to have such a high amount of large pixel differences without seeing it in your frequency chart). Try to cast to int type before doing your frequency computation.

RGB Values Being Returned by PIL don't match RGB color

I'm attempting to make a reasonably simple code that will be able to read the size of an image and return all the RGB values. I'm using PIL on Python 2.7, and my code goes like this:
import os, sys
from PIL import Image
img = Image.open('C:/image.png')
pixels = img.load()
print(pixels[0, 1])
now this code was actually gotten off of this site as a way to read a gif file. I'm trying to get the code to print out an RGB tuple (in this case (55, 55, 55)) but all it gives me is a small sequence of unrelated numbers, usually containing 34.
I have tried many other examples of code, whether from here or not, but it doesn't seem to work. Is it something wrong with the .png format? Do I need to further code in the rgb part? I'm happy for any help.

My guess is that your image file is using pre-multiplied alpha values. The 8 values you see are pretty close to 55*34/255 (where 34 is the alpha channel value).
PIL uses the mode "RGBa" (with a little a) to indicate when it's using premultiplied alpha. You may be able to tell PIL to covert the to normal "RGBA", where the pixels will have roughly the values you expect:
img = Image.open('C:/image.png').convert("RGBA")
Note that if your image isn't supposed to be partly transparent at all, you may have larger issues going on. We can't help you with that without knowing more about your image.

Freeimage plugin mirrors RGB arrays if saving in 16-bit

I am working with 2D floating-point numpy arrays and saving them as .png files with high precision (see this question for how I came to this point). To do this I use the freeimage plugin, as in that linked question.
This creates a weird behaviour where the images are flipped (both left-right and up-down) if saved to 16-bit. This behaviour happens only for RGB or RGBA images, not for greyscale images. Here is some example code:
from skimage import io, img_as_uint, img_as_ubyte
im = np.random.uniform(size=(256, 256))
im[:128, :128] = 1
im = img_as_ubyte(im)
io.use_plugin('freeimage')
io.imsave('test_1.png', im)
creates the following picture:
when I try to save this in 16 bit, I get the same result (albeit taking 99kb instead of 50, so I know the bitdepth is working).
Now do the same as an RGB image:
im = np.random.uniform(size=(256, 256, 3))
im[:128, :128] = 1
im = img_as_ubyte(im)
io.use_plugin('freeimage')
io.imsave('test_1.png', im)
The 8-bit result is:
but doing the following
im = img_as_uint(im)
io.use_plugin('freeimage')
io.imsave('test_1.png', im)
gives me
This happens if the array contains an alpha level too.
It can be fixed by including
im = np.fliplr(np.flipud(im))
before saving. However, it seems to me this is pretty weird behaviour and not very desirable. Any idea why this is happening or whether it is intended? As far as I could see it's not documented.

How to read raw png from an array in python opencv?

I'm streaming a png image from my iPhone to my MacBook over tcp. The MacBook code is from http://docs.python.org/library/socketserver.html#requesthandler-objects. How can the image be converted for use with OpenCV? A png was selected because they are efficient, but other formats could be used.
I wrote a test program that reads the rawImage from a file, but not sure how to convert it:
# Read rawImage from a file, but in reality will have it from TCPServer
f = open('frame.png', "rb")
rawImage = f.read()
f.close()
# Not sure how to convert rawImage
npImage = np.array(rawImage)
matImage = cv2.imdecode(rawImage, 1)
#show it
cv.NamedWindow('display')
cv.MoveWindow('display', 10, 10)
cv.ShowImage('display', matImage)
cv. WaitKey(0)

#Andy Rosenblum's works, and it might be the best solution if using the outdated cv python API (vs. cv2).
However, because this question is equally interesting for users of the latest versions, I suggest the following solution. The sample code below may be better than the accepted solution because:
It is compatible with newer OpenCV python API (cv2 vs. cv). This solution is tested under opencv 3.0 and python 3.0. I believe only trivial modifications would be required for opencv 2.x and/or python 2.7x.
Fewer imports. This can all be done with numpy and opencv directly, no need for StringIO and PIL.
Here is how I create an opencv image decoded directly from a file object, or from a byte buffer read from a file object.
import cv2
import numpy as np
#read the data from the file
with open(somefile, 'rb') as infile:
buf = infile.read()
#use numpy to construct an array from the bytes
x = np.fromstring(buf, dtype='uint8')
#decode the array into an image
img = cv2.imdecode(x, cv2.IMREAD_UNCHANGED)
#show it
cv2.imshow("some window", img)
cv2.waitKey(0)
Note that in opencv 3.0, the naming convention for the various constants/flags changed, so if using opencv 2.x, you will need to change the flag cv2.IMREAD_UNCHANGED. This code sample also assumes you are loading in a standard 8-bit image, but if not, you can play with the dtype='...' flag in np.fromstring.

another way,
also in the case of a reading an actual file this will work for a unicode path (tested on windows)
with open(image_full_path, 'rb') as img_stream:
file_bytes = numpy.asarray(bytearray(img_stream.read()), dtype=numpy.uint8)
img_data_ndarray = cv2.imdecode(file_bytes, cv2.CV_LOAD_IMAGE_UNCHANGED)
img_data_cvmat = cv.fromarray(img_data_ndarray) # convert to old cvmat if needed

I figured it out:
# Read rawImage from a file, but in reality will have it from TCPServer
f = open('frame.png', "rb")
rawImage = f.read()
f.close()
# Convert rawImage to Mat
pilImage = Image.open(StringIO(rawImage));
npImage = np.array(pilImage)
matImage = cv.fromarray(npImage)
#show it
cv.NamedWindow('display')
cv.MoveWindow('display', 10, 10)
cv.ShowImage('display', matImage)
cv. WaitKey(0)

This works for me (these days):
import cv2
import numpy as np
data = open('016e263c726a.raw').read()
x = np.frombuffer(data, dtype='uint8').reshape(2048,2448)
cv2.imshow('x',x); cv2.waitKey(); cv2.destroyAllWindows()
But it reads a RAW image saved without any specific format.

(Your question seems to be tagged objective-c but you ask for Python and so is your example, so I'll use that.)
My first post on Stack Overflow!
The cv.LoadImageM method seems to be what you are looking for.
http://opencv.willowgarage.com/documentation/python/reading_and_writing_images_and_video.html
Example use:
http://opencv.willowgarage.com/wiki/PythonInterface/
LoadImage(filename, iscolor=CV_LOAD_IMAGE_COLOR) → None
Loads an image from a file as an IplImage.
Parameters:
filename (str) – Name of file to be loaded.
iscolor (int) –
Specific color type of the loaded image:
CV_LOAD_IMAGE_COLOR the loaded image is forced to be a 3-channel color image
CV_LOAD_IMAGE_GRAYSCALE the loaded image is forced to be grayscale
CV_LOAD_IMAGE_UNCHANGED the loaded image will be loaded as is.
The function cvLoadImage loads an image from the specified file and
returns the pointer to the loaded image. Currently the following file
formats are supported:
Windows bitmaps - BMP, DIB
JPEG files - JPEG, JPG, JPE
Portable Network Graphics - PNG
Portable image format - PBM, PGM, PPM
Sun rasters - SR, RAS
TIFF files - TIFF, TIF
Note that in the current implementation the alpha channel, if any, is
stripped from the output image, e.g. 4-channel RGBA image will be
loaded as RGB.

When you have to load from file, this simple solution does the job (tested with opencv-python-3.2.0.6):
import cv2
img = cv2.imread(somefile)