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)
def compare_images(request, pk1, pk2):
import numpy as np
image1 = get_image(pk1, None, request)
image2 = get_image(pk2, None, request)
images = [image1, image2]
# Get image: collection: [map_type] names no longer than ~125 characters
image1_custom_name = format_image_collection_names(
image_name=image1.name,
collection_name=image1.collection.name,
map_type=image1.map_type,
total_length=125)
image2_custom_name = format_image_collection_names(
image_name=image2.name,
collection_name=image2.collection.name,
map_type=image2.map_type,
total_length=125)
image_names = [image1_custom_name, image2_custom_name]
# Create custom links for the visualization
custom = {
"IMAGE_1_LINK": "/images/%s" % (image1.pk),
"IMAGE_2_LINK": "/images/%s" % (image2.pk)
}
# Load image vectors from npy files
image_vector1 = np.load(image1.reduced_representation.file)
image_vector2 = np.load(image2.reduced_representation.file)
# Load atlas pickle, containing vectors of atlas labels, colors, and values for same voxel dimension (4mm)
neurovault_root = os.path.dirname(
os.path.dirname(os.path.realpath(neurovault.__file__)))
atlas_pkl_path = os.path.join(neurovault_root,
'neurovault/static/atlas/atlas_mni_4mm.pkl')
atlas = joblib.load(atlas_pkl_path)
# Load the atlas svg, so we don't need to dynamically generate it
atlas_svg = os.path.join(neurovault_root,
'neurovault/static/atlas/atlas_mni_2mm_svg.pkl')
atlas_svg = joblib.load(atlas_svg)
# Generate html for similarity search, do not specify atlas
html_snippet, _ = scatterplot.scatterplot_compare_vector(
image_vector1=image_vector1,
image_vector2=image_vector2,
image_names=image_names,
atlas_vector=atlas["atlas_vector"],
atlas_labels=atlas["atlas_labels"],
atlas_colors=atlas["atlas_colors"],
corr_type="pearson",
subsample_every=10, # subsample every 10th voxel
custom=custom,
remove_scripts="D3_MIN_JS")
# Add atlas svg to the image, and prepare html for rendering
html = [h.replace("[coronal]", atlas_svg) for h in html_snippet]
html = [
h.strip("\n").replace("[axial]", "").replace("[sagittal]", "")
for h in html
]
context = {'html': html}
# Determine if either image is thresholded
threshold_status = np.array(
[image_names[i] for i in range(0, 2) if images[i].is_thresholded])
if len(threshold_status) > 0:
warnings = list()
for i in range(0, len(image_names)):
warnings.append(
'Warning: Thresholded image: %s (%.4g%% of voxels are zeros),'
% (image_names[i], images[i].perc_bad_voxels))
context["warnings"] = warnings
return render(request, 'statmaps/compare_images.html', context)
def compare_images(request,pk1,pk2):
import numpy as np
image1 = get_image(pk1,None,request)
image2 = get_image(pk2,None,request)
images = [image1,image2]
# Get image: collection: [map_type] names no longer than ~125 characters
image1_custom_name = format_image_collection_names(image_name=image1.name,
collection_name=image1.collection.name,
map_type=image1.map_type,total_length=125)
image2_custom_name = format_image_collection_names(image_name=image2.name,
collection_name=image2.collection.name,
map_type=image2.map_type,total_length=125)
image_names = [image1_custom_name,image2_custom_name]
# Create custom links for the visualization
custom = {
"IMAGE_1_LINK":"/images/%s" % (image1.pk),
"IMAGE_2_LINK":"/images/%s" % (image2.pk)
}
# create reduced representation in case it's not there
if not image1.reduced_representation:
image1 = save_resampled_transformation_single(image1.id) # cannot run this async
if not image2.reduced_representation:
image2 = save_resampled_transformation_single(image1.id) # cannot run this async
# Load image vectors from npy files
image_vector1 = np.load(image1.reduced_representation.file)
image_vector2 = np.load(image2.reduced_representation.file)
# Load atlas pickle, containing vectors of atlas labels, colors, and values for same voxel dimension (4mm)
this_path = os.path.abspath(os.path.dirname(__file__))
atlas_pkl_path = os.path.join(this_path, 'static/atlas/atlas_mni_4mm.pkl')
atlas = joblib.load(atlas_pkl_path)
# Load the atlas svg, so we don't need to dynamically generate it
atlas_svg = os.path.join(this_path, 'static/atlas/atlas_mni_2mm_svg.pkl')
atlas_svg = joblib.load(atlas_svg)
# Generate html for similarity search, do not specify atlas
html_snippet, _ = scatterplot.scatterplot_compare_vector(image_vector1=image_vector1,
image_vector2=image_vector2,
image_names=image_names,
atlas_vector=atlas["atlas_vector"],
atlas_labels=atlas["atlas_labels"],
atlas_colors=atlas["atlas_colors"],
corr_type="pearson",
subsample_every=10, # subsample every 10th voxel
custom=custom,
remove_scripts="D3_MIN_JS",
width=1000)
# Add atlas svg to the image, and prepare html for rendering
html = [h.replace("[coronal]",atlas_svg) for h in html_snippet]
html = [h.strip("\n").replace("[axial]","").replace("[sagittal]","") for h in html]
context = {'html': html}
# Determine if either image is thresholded
threshold_status = np.array([image_names[i] for i in range(0,2) if images[i].is_thresholded])
if len(threshold_status) > 0:
warnings = list()
for i in range(0,len(image_names)):
warnings.append('Warning: Thresholded image: %s (%.4g%% of voxels are zeros),' %(image_names[i],images[i].perc_bad_voxels))
context["warnings"] = warnings
return render(request, 'statmaps/compare_images.html', context)
