Find image location inside larger image

Question

So, I have an image to say small.png and a larger big.png image. A small image occurs 2 times in an enlarged image, and I want to find its location.

I tried using numpy and Image but got an error

'JpegImageFile' object has no attribute '__getitem__' . I had the png format before and it gave the same error.

Is there any other module or way to do this. I am open to anyone.

Currently the code that is causing the error is

 import numpy from PIL import Image here = Image.open(r"/Users/vks/Desktop/heren.jpg") big = Image.open(r"/Users/vks/Desktop/Settingsn.jpg") print here,big herear = numpy.asarray(here) bigar = numpy.asarray(big) herearx = herear.shape[0] hereary = herear.shape[1] bigarx = bigar.shape[0] bigary = bigar.shape[1] print herearx , hereary , bigarx, bigary stopx = bigarx - herearx + 1 stopy = bigary - hereary + 1 for x in range(0, stopx): for y in range(0, stopy): x2 = x+ herearx y2 = y + hereary pic = big[y:y2, x:x2] ===> error thrown here test = pic = here if test.all(): print x,y else: print "None" 

There was another cv2 module, but it just doesn't install on my mac. pip install cv2 cannot find the package found.

Answer 1

The following code works for me:

 import numpy from PIL import Image here = Image.open(r"eye.png") big = Image.open(r"lenna.png") herear = numpy.asarray(here) bigar = numpy.asarray(big) hereary, herearx = herear.shape[:2] bigary, bigarx = bigar.shape[:2] stopx = bigarx - herearx + 1 stopy = bigary - hereary + 1 for x in range(0, stopx): for y in range(0, stopy): x2 = x + herearx y2 = y + hereary pic = bigar[y:y2, x:x2] test = (pic == herear) if test.all(): print(x,y) 

Conclusion: 312 237

Graphically:

Test Images Used

lenna.png

eye.png

Note It is important that you use a lossless image format when you create a smaller, cropped version (lossless PNG, JPEG is usually lost). If you use a lossy format, you risk that the pixel values ​​are close but not identical. The above code based on yours will only find exact pixel matches. In this regard, the OpenCV pattern matching features are a bit more flexible. This does not mean that you cannot change your code to work just as well, you could. But, as it is, the code in this answer has this limitation.

More general version

Here, as a function, this collects all the matching coordinates and returns them as a list of (x, y) tuples:

 import numpy as np from PIL import Image im_haystack = Image.open(r"lenna.png") im_needle = Image.open(r"eye.png") def find_matches(haystack, needle): arr_h = np.asarray(haystack) arr_n = np.asarray(needle) y_h, x_h = arr_h.shape[:2] y_n, x_n = arr_n.shape[:2] xstop = x_h - x_n + 1 ystop = y_h - y_n + 1 matches = [] for xmin in range(0, xstop): for ymin in range(0, ystop): xmax = xmin + x_n ymax = ymin + y_n arr_s = arr_h[ymin:ymax, xmin:xmax] # Extract subimage arr_t = (arr_s == arr_n) # Create test matrix if arr_t.all(): # Only consider exact matches matches.append((xmin,ymin)) return matches print(find_matches(im_haystack, im_needle)) 

Update:

Given the images you have provided, you will notice that the matching method is configured, it will correspond to only one of the two. The top left is the one that you cropped for the needle image, so it exactly matches. The bottom right image should match exactly the pixel for the pixel. With this implementation, even a one-difference difference in one of the color channels will lead to its ignoring.

As it turned out, the two here are changing quite a bit:

Verified Versions:

• Python 2.7.10, Numpy 1.11.1, PIL (pillow) 1.1.7
• Python 3.5.0, Numpy 1.10.4, PIL (pillow) 1.1.7