Strange problem searching

[DanielFerreiraJorge]

Hello, I have a weird problem. I have two images, A and B (B is a bigger and with more resolution version of A). If I use ImageSignature to calculate the normalized distance between the two I get 0.314299892917, which is pretty good, showing that they are a match.

Now, here is the problem, if I add image A to elasticsearch using ses.add_image('A.jpg') and then, use ses.search_image('B.jpg'), I get no results. I tried modifying the distance_cutoff to 0.99, and got a bunch of results, BUT these results did not include the A.jpg and all the results in this scenario had a distance of at least 0.60... I KNOW image A is ther because if I ses.search_image('A.jpg') I get a perfect match.

I attached the images.

[rhsimplex]

That is strange. I'm on vacation until Apr 5, but please share any other information or if you figure out what the problem is.

Otherwise, I will look into it when I'm back.

[fabito]

Could you create a failing test case reproducing the issue and open a PR?

[ecdeveloper]

I'm noticing a similar issue. Having 2 almost identical images (will attach to that message). After indexing one of them and searching by second image - I'm getting the match, where distance is 0.43 and elasticsearch score is 4.0. I usually get similar numbers for completely different images. Thoughts?

Indexed image:

Image I search by:

[SthPhoenix]

Hi! Just stuck at same issue, but looks like I found a solution.
In my case i'm indexing photos unmodified, as taken by camera, and trying to search them providing photoshopped images (tweaked histogram and a bit changed white balance) and get distance of 0.69
BUT if I make the following:

img1 = cv2.imread(path1)
img2 = cv2.imread(path2)
sig1 = gis.generate_signature(img1)
sig2 = gis.generate_signature(img2)

I get distance of 0.09. Looks like the reason behind this bug might be different color spaces of source images, or something like that.

BTW: got exactly same issue using imagehash library: hashes of images opened by PIL are completely different, but if I open image with cv2 and than convert it to PIL it gives absolutely same hashes

[SthPhoenix]

Addition to previous post: looks like in my case of unmodified pics from camera they were always horizontal by default, and opencv just handled rotation by itself.
Details here

[rhsimplex]

Hey @SthPhoenix, does that mean you don't think it's a color space issue (i.e. is completely explained by the rotation?)

[SthPhoenix]

Yes, it seems so ) I have tried saving image in Adobe RGB without rotation, and it gives a distance of zero with a slightly edited pic in sRGB. Looks like in my case PIL for some reason doesn't handle EXIF rotation by itself.

[rhsimplex]

any suggestions, then? should we switch to cv2?

[fabito]

My 2 cents,

Since OpenCV 3.1, exif orientation is used to rotate images properly. imread should output an image rotated accordingly to the exif orientation.

In the case of color images, the decoded images (imread) will have the channels stored in B G R order.

Hope this info is useful

[SthPhoenix]

OpenCV might be a bit overkill, especially considering installation harder than PIL )
I think a code like this (SO post):

   import Image, ExifTags

    try :
        image=Image.open(os.path.join(path, fileName))
        for orientation in ExifTags.TAGS.keys() : 
            if ExifTags.TAGS[orientation]=='Orientation' : break 
        exif=dict(image._getexif().items())

        if   exif[orientation] == 3 : 
            image=image.rotate(180, expand=True)
        elif exif[orientation] == 6 : 
            image=image.rotate(270, expand=True)
        elif exif[orientation] == 8 : 
            image=image.rotate(90, expand=True)

        image.thumbnail((THUMB_WIDTH , THUMB_HIGHT), Image.ANTIALIAS)
        image.save(os.path.join(path,fileName))

    except:
        traceback.print_exc()

might help without injecting new dependencies

[rhsimplex]

Well I think you know what I'm going to ask for next, @SthPhoenix =)

[SthPhoenix]

I think I do ) Sure, I'll try to figure out how to implement this )

[SthPhoenix]

@rhsimplex, could you try generating signature for image I posted in imagehash issue?
For some reason if I am using gis.generate_signature('path/to/image.jpg') I get a weird error:

Traceback (most recent call last):
  File "/codes/ImSearch/imageMeta.py", line 153, in <module>
    s1 = gis.generate_signature(im1)
  File "/usr/local/lib/python2.7/dist-packages/goldberg.py", line 166, in generate_signature
    fix_ratio=self.fix_ratio)
  File "/usr/local/lib/python2.7/dist-packages/image_match/goldberg.py", line 287, in crop_image
    rw = np.cumsum(np.sum(np.abs(np.diff(image, axis=1)), axis=1))
  File "/usr/local/lib/python2.7/dist-packages/numpy/lib/function_base.py", line 1761, in diff
    slice1[axis] = slice(1, None)
IndexError: list assignment index out of range

Excuse me for off-topic, I'm not sure if it's an image-match issue, or something on my side, related to my environment.

[rhsimplex]

I get the same error:

In [3]: im.generate_signature('https://cloud.githubusercontent.com/assets/17834919/25771654/4aa9732c-3260-11e7-8370-8fcc62418
   ...: 677.JPG')
---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-3-01518c70844d> in <module>()
----> 1 im.generate_signature('https://cloud.githubusercontent.com/assets/17834919/25771654/4aa9732c-3260-11e7-8370-8fcc62418
677.JPG')

/home/ryan/image-match/image_match/goldberg.py in generate_signature(self, path_or_image, bytestream)
    164                                            lower_percentile=self.lower_percentile,
    165                                            upper_percentile=self.upper_percentile,
--> 166                                            fix_ratio=self.fix_ratio)
    167         else:
    168             image_limits = None

/home/ryan/image-match/image_match/goldberg.py in crop_image(image, lower_percentile, upper_percentile, fix_ratio)
    282         """
    283         # row-wise differences
--> 284         rw = np.cumsum(np.sum(np.abs(np.diff(image, axis=1)), axis=1))
    285         # column-wise differences
    286         cw = np.cumsum(np.sum(np.abs(np.diff(image, axis=0)), axis=0))

/home/ryan/image-match/env/lib/python3.5/site-packages/numpy/lib/function_base.py in diff(a, n, axis)
   1759     slice1 = [slice(None)]*nd
   1760     slice2 = [slice(None)]*nd
-> 1761     slice1[axis] = slice(1, None)
   1762     slice2[axis] = slice(None, -1)
   1763     slice1 = tuple(slice1)

IndexError: list assignment index out of range