How to detect object on images?

Question

I need python solution.

I have 40-60 images (Happy Holiday set). I need to detect object on all these images.

I don't know object size, form, location on image, I don't have any object template. I know only one thing: this object is present in almost all images. I called it UFO.

Example:

As seen in example, from image to image everything changes except UFO. After detection I need to get:

X coordinate of the top left corner

Y coordinate of the top left corner

width of blue object region (i marked region on example as red rectangle)

height of blue object region

Answer 1

I create a second answer instead of extending my first answer even more. I use the same approach, but on your new examples. The only difference is: I use a set of fixed thresholds instead of determining it automatically. If you can play around with it, this should suffice.

import numpy as np
import PIL
import matplotlib.pyplot as plt
import glob

filenames = glob.glob("14767594/*.jpg")
images = [np.asarray(PIL.Image.open(fn)) for fn in filenames]
sample_images = np.concatenate([image.reshape(1,image.shape[0], image.shape[1],image.shape[2]) 
                            for image in images], axis=0)
                                                        
plt.figure(1)
for i in range(sample_images.shape[0]):
    plt.subplot(2,2,i+1)
    plt.imshow(sample_images[i,...])
    plt.axis("off")
plt.subplots_adjust(0,0,1,1,0,0)

# determine per-pixel variablility, std() over all images
variability = sample_images.std(axis=0).sum(axis=2)

# show image of these variabilities
plt.figure(2)
plt.imshow(variability, cmap=plt.cm.gray, interpolation="nearest", origin="lower")

# determine bounding box
thresholds = [5,10,20]
colors = ["r","b","g"]
for threshold, color in zip(thresholds, colors): #variability.mean()
    non_empty_columns = np.where(variability.min(axis=0)<threshold)[0]
    non_empty_rows = np.where(variability.min(axis=1)<threshold)[0]
    boundingBox = (min(non_empty_rows), max(non_empty_rows), min(non_empty_columns), max(non_empty_columns))
    
    # plot and print boundingBox
    bb = boundingBox
    plt.plot([bb[2], bb[3], bb[3], bb[2], bb[2]],
             [bb[0], bb[0],bb[1], bb[1], bb[0]],
             "%s-"%color, 
             label="threshold %s" % threshold)
    print boundingBox

plt.xlim(0,variability.shape[1])
plt.ylim(variability.shape[0],0)
plt.legend()

plt.show()

Produced plots:

Your requirements are closely related to ERP in cognitive neuroscience. The more input images you have, the better this approach will work as the signal-to-noise ratio increases.