def test_thresholded_image_comparison(self):
# There should be no comparisons for a thresholded image
print "testing comparisons for thresholded images"
assert_equal(count_existing_comparisons(self.pk3), 0)
# There should be no comparisons for an atlas
print "testing comparisons for atlases"
assert_equal(count_existing_comparisons(self.pk1), 0)
# There should be no comparisons for statistical map because no other statistical maps
print "testing comparisons for statistical maps"
assert_equal(count_existing_comparisons(self.pk2), 0)
# Add another statistical map
image_path = os.path.join(self.app_path,
'test_data/statmaps/motor_lips.nii.gz')
image4 = save_statmap_form(image_path=image_path,
collection=self.comparisonCollection4)
self.pk4 = image4.id
# There should STILL be no comparisons for a thresholded image
print "testing comparisons for thresholded images"
assert_equal(count_existing_comparisons(self.pk3), 0)
# There should STILL be no comparisons for an of the atlas
print "testing comparisons for atlases"
assert_equal(count_existing_comparisons(self.pk1), 0)
# There should now be one comparison for each statistical map, two total
print "testing comparisons for statistical maps"
print Comparison.objects.all()
assert_equal(count_existing_comparisons(self.pk2), 1)
assert_equal(count_existing_comparisons(self.pk4), 1)
# This is the call that find_similar users to get images
comparisons = get_existing_comparisons(self.pk4)
for comp in comparisons:
pk1 = comp.image1.pk
pk2 = comp.image2.pk
im1 = Image.objects.get(pk=pk1)
im2 = Image.objects.get(pk=pk2)
assert_equal(im1.is_thresholded, False)
assert_equal(im2.is_thresholded, False)
def test_thresholded_image_comparison(self):
# There should be no comparisons for a thresholded image
print "testing comparisons for thresholded images"
assert_equal(0,count_existing_comparisons(self.pk3))
# There should be no comparisons for an atlas
print "testing comparisons for atlases"
assert_equal(0,count_existing_comparisons(self.pk1))
# There should be no comparisons for statistical map because no other statistical maps
print "testing comparisons for statistical maps"
assert_equal(0,count_existing_comparisons(self.pk2))
# Add another statistical map
image_path = os.path.join(self.app_path,'test_data/statmaps/motor_lips.nii.gz')
image4 = save_statmap_form(image_path=image_path,collection = self.comparisonCollection)
self.pk4 = image4.id
# There should STILL be no comparisons for a thresholded image
print "testing comparisons for thresholded images"
assert_equal(0,count_existing_comparisons(self.pk3))
# There should STILL be no comparisons for an of the atlas
print "testing comparisons for atlases"
assert_equal(0,count_existing_comparisons(self.pk1))
# There should now be one comparison for each statistical map, two total
print "testing comparisons for statistical maps"
print Comparison.objects.all()
assert_equal(1,count_existing_comparisons(self.pk2))
assert_equal(1,count_existing_comparisons(self.pk4))
assert_equal(1,count_existing_comparisons())
# This is the call that find_similar users to get images
comparisons = get_existing_comparisons(self.pk4)
for comp in comparisons:
pk1=comp.image1.pk
pk2=comp.image2.pk
im1 = Image.objects.get(pk=pk1)
im2 = Image.objects.get(pk=pk2)
assert_equal(im1.is_thresholded,False)
assert_equal(im2.is_thresholded,False)
times = []
for iter in range(0,1000):
start = time.time()
number_comparisons = count_existing_comparisons()
end = time.time()
times.append(end-start)
time_log["count_existing_comparisons_all"] = np.mean(times)
print "count_existing_comparisons for all images: %s" %(np.mean(times))
# Get existing comparisons
times = []
for iter in range(0,1000):
start = time.time()
comparisons = get_existing_comparisons(pk1=8)
end = time.time()
times.append(end-start)
time_log["get_existing_comparisons_single"] = np.mean(times)
print "get_existing_comparisons for single image: %s" %(np.mean(times))
times = []
for iter in range(0,1000):
start = time.time()
comparisons = get_existing_comparisons()
end = time.time()
times.append(end-start)
time_log["get_existing_comparisons_all"] = np.mean(times)
print "get_existing_comparisons for all images: %s" %(np.mean(times))
def find_similar(request, pk):
image1 = get_image(pk, None, request)
pk = int(pk)
# Search only enabled if the image is not thresholded
if image1.is_thresholded == False:
# Count the number of comparisons that we have to determine max that we can return
number_comparisons = count_existing_comparisons(pk)
max_results = 100
if number_comparisons < 100:
max_results = number_comparisons
# Get only # max_results similarity calculations for this image, and ids of other images
comparisons = get_existing_comparisons(pk).extra(select={
"abs_score":
"abs(similarity_score)"
}).order_by("-abs_score")[0:max_results] # "-" indicates descending
images = [image1]
scores = [1] # pearsonr
for comp in comparisons:
# pick the image we are comparing with
image = [
image for image in [comp.image1, comp.image2] if image.id != pk
][0]
if hasattr(image, "map_type") and image.thumbnail:
images.append(image)
scores.append(comp.similarity_score)
# We will need lists of image ids, png paths, query id, query path, tags, names, scores
image_ids = [image.pk for image in images]
png_img_paths = [image.get_thumbnail_url() for image in images]
tags = [[str(image.map_type)] for image in images]
# The top text will be the collection name, the bottom text the image name
bottom_text = ["%s" % (image.name) for image in images]
top_text = ["%s" % (image.collection.name) for image in images]
compare_url = "/images/compare" # format will be prefix/[query_id]/[other_id]
image_url = "/images" # format will be prefix/[other_id]
image_title = format_image_collection_names(
image_name=image1.name,
collection_name=image1.collection.name,
map_type=image1.map_type,
total_length=50)
# Here is the query image
query_png = image1.thumbnail.url
# Do similarity search and return html to put in page, specify 100 max results, take absolute value of scores
html_snippet = search.similarity_search(image_scores=scores,
tags=tags,
png_paths=png_img_paths,
button_url=compare_url,
image_url=image_url,
query_png=query_png,
query_id=pk,
top_text=top_text,
image_ids=image_ids,
bottom_text=bottom_text,
max_results=max_results,
absolute_value=True)
html = [h.strip("\n") for h in html_snippet]
# Get the number of images still processing
images_processing = count_processing_comparisons(pk)
context = {
'html': html,
'images_processing': images_processing,
'image_title': image_title,
'image_url': '/images/%s' % (image1.pk)
}
return render(request, 'statmaps/compare_search.html', context)
else:
error_message = "Image comparison is not enabled for thresholded images."
context = {'error_message': error_message}
return render(request, 'statmaps/error_message.html', context)
def find_similar(request,pk):
image1 = get_image(pk,None,request)
pk = int(pk)
# Search only enabled if the image is not thresholded
if image1.is_thresholded == False:
# Count the number of comparisons that we have to determine max that we can return
# TODO: optimize this slow query
#number_comparisons = count_existing_comparisons(pk)
max_results = 100
#if number_comparisons < 100:
# max_results = number_comparisons
# Get only # max_results similarity calculations for this image, and ids of other images
comparisons = get_existing_comparisons(pk).extra(select={"abs_score": "abs(similarity_score)"}).order_by("-abs_score")[0:max_results] # "-" indicates descending
images = [image1]
scores = [1] # pearsonr
for comp in comparisons:
# pick the image we are comparing with
image = [image for image in [comp.image1, comp.image2] if image.id != pk][0]
if hasattr(image, "map_type") and image.thumbnail:
images.append(image)
scores.append(comp.similarity_score)
# We will need lists of image ids, png paths, query id, query path, tags, names, scores
image_ids = [image.pk for image in images]
png_img_paths = [image.get_thumbnail_url() for image in images]
tags = [[str(image.map_type)] for image in images]
# The top text will be the collection name, the bottom text the image name
bottom_text = ["%s" % (image.name) for image in images]
top_text = ["%s" % (image.collection.name) for image in images]
compare_url = "/images/compare" # format will be prefix/[query_id]/[other_id]
image_url = "/images" # format will be prefix/[other_id]
image_title = format_image_collection_names(image_name=image1.name,
collection_name=image1.collection.name,
map_type=image1.map_type,total_length=50)
# Here is the query image
query_png = image1.thumbnail.url
# Do similarity search and return html to put in page, specify 100 max results, take absolute value of scores
html_snippet = search.similarity_search(image_scores=scores,tags=tags,png_paths=png_img_paths,
button_url=compare_url,image_url=image_url,query_png=query_png,
query_id=pk,top_text=top_text,image_ids=image_ids,
bottom_text=bottom_text,max_results=max_results,absolute_value=True,
remove_scripts=["BOOTSTRAP","BOOTSTRAP_MIN"],container_width=1200)
html = [h.strip("\n") for h in html_snippet]
# Get the number of images still processing
# TODO: improve performance of this calculation
# images_processing = count_processing_comparisons(pk)
context = {'html': html,
#'images_processing':images_processing,
'image_title':image_title, 'image_url': '/images/%s' % (image1.pk) }
return render(request, 'statmaps/compare_search.html', context)
else:
error_message = "Image comparison is not enabled for thresholded images."
context = {'error_message': error_message}
return render(request, 'statmaps/error_message.html', context)
print "count_existing_comparisons for single image: %s" % (np.mean(times))
times = []
for iter in range(0, 1000):
start = time.time()
number_comparisons = count_existing_comparisons()
end = time.time()
times.append(end - start)
time_log["count_existing_comparisons_all"] = np.mean(times)
print "count_existing_comparisons for all images: %s" % (np.mean(times))
# Get existing comparisons
times = []
for iter in range(0, 1000):
start = time.time()
comparisons = get_existing_comparisons(pk1=8)
end = time.time()
times.append(end - start)
time_log["get_existing_comparisons_single"] = np.mean(times)
print "get_existing_comparisons for single image: %s" % (np.mean(times))
times = []
for iter in range(0, 1000):
start = time.time()
comparisons = get_existing_comparisons()
end = time.time()
times.append(end - start)
time_log["get_existing_comparisons_all"] = np.mean(times)
print "get_existing_comparisons for all images: %s" % (np.mean(times))
# Count images processing
