def test_compare_color_diff(self):
imgA = open_img("./testImage/TestOrig.jpg")
imgB = open_img("./testImage/TestImage.png")
imgA_avg = blockify(imgA)
imgB_avg = blockify(imgB)
result = diff(imgA_avg, imgB_avg)
self.assertNotEqual(result, 1)
def test_compare_grey_similar(self):
imgA = open_img(u"./testImage/[クール教信者] 小林さんちのメイドラゴン 01_pg4.jpg")
imgB = open_img(u"./testImage/[クール教信者] 小林さんちのメイドラゴン 01_pg4_eng.jpg")
imgA_avg = blockify(imgA)
imgB_avg = blockify(imgB)
result = diff(imgA_avg, imgB_avg)
self.assertNotEqual(result, 1)
self.assertGreater(result, .95)
def test_compare_color_similar(self):
imgA = open_img("./testImage/TestOrig.jpg")
imgB = open_img("./testImage/TestSimilar.jpg")
imgA_avg = blockify(imgA)
imgB_avg = blockify(imgB)
result = diff(imgA_avg, imgB_avg)
self.assertNotEqual(result, 1)
self.assertGreater(result, .85)
def __eval_image(self, block_list):
""" evaluate differences between each image to another
If evaluation result is higher or equal to desired
percentage, it is a match
Parameter
---------
block_list - list of Tuple of image path and
its averaged blocks
Returns
---------
A list of tuples of matching image with its maching image
in a form of (imageA, imageB, result in int)"""
compare_result = []
block_list_len = len(block_list)
if block_list_len < 4:
next_not_touch_index = 1
for imageA in block_list:
for index in range(next_not_touch_index, block_list_len):
imageB = block_list[index]
result = float(block.diff(imageA[1], imageB[1]))
if result >= self.fuzzy_percentage:
result = "%.2f" % result
compare_result.append((imageA[0], imageB[0], result))
# to prevent images of being compare to itself and already
# compared images in previous loops
next_not_touch_index += 1
else:
jobs = []
procs = 4
queue = Queue()
block_split = self.__split_list(block_list, 2)
# prepare processes
# The first part of the split image list will be compared to the first part and second
# part with two processes
# the second part work in the same way
for i in range(procs):
index = i % 2
process = Process(target=self.__eval_image_process,
args=(block_split[i // 2],
block_split[i % 2], queue))
jobs.append(process)
# start process and wait for processes to end
for j in jobs:
j.start()
for j in jobs:
compare_result.extend(queue.get())
for j in jobs:
j.join()
return compare_result
def __eval_image_process(self, from_list, compare_list, queue):
""" single process loop for evaluating image matches"""
result_list = []
for imageA in from_list:
for imageB in compare_list:
if imageA[0] is not imageB[0]:
result = float(block.diff(imageA[1], imageB[1]))
if result >= self.fuzzy_percentage:
result = "%.2f" % result
result_list.append((imageA[0], imageB[0], result))
queue.put(result_list)