def suite_result(self, suite, result, **kwargs):
"""
Extended from parent with JSON export functionality
:param suite: default testrunner test suite
:param result: StoreResultsTestRunner instance
"""
result.results['results'] = self.get_results_summary(
results=result.results)
with open(settings.PROJECT_DIR('') + '/test_results.json',
'w') as outfile:
json.dump(result.results, outfile, sort_keys=True, indent=4)
t = get_template('test/test_results.html')
c = Context(result.results)
with open(settings.PROJECT_DIR('') + '/test_results.html',
'w') as outfile:
outfile.write(t.render(c))
super(ConcreteDiscoverRunner,
self).suite_result(suite, result, **kwargs)
def download_pdf(data: pd.DataFrame, file_name='output'):
data_html = data.to_html(index=False)
try:
data_pdf = pdf.from_string(data_html, False)
except OSError:
env = Environment(loader=FileSystemLoader(settings.PROJECT_DIR('templates')))
template = env.get_template('pdf_export.html')
template_vars = {"title": file_name.capitalize(),
"table": data_html}
data_pdf = HTML(string=template.render(template_vars)).write_pdf()
response = HttpResponse(content_type='application/pdf')
response['Content-Disposition'] = 'attachment; filename="{}.{}"'.format(file_name, 'pdf')
response.write(data_pdf)
return response
def process_path(file_path, imf):
if file_path:
if file_path.startswith(settings.MEDIA_URL):
file_path = file_path[1:]
file_path = settings.PROJECT_DIR('../{0}'.format(file_path))
files[field_name] = (imf.name, open(file_path, 'rb'))
import numpy as np
import os
from muses.naive_classification.definitions_os import classes
from collections import OrderedDict
from keras.models import model_from_json
from PIL import Image
from django.conf import settings
LOADED_MODELS = {}
json_path = settings.PROJECT_DIR(
os.path.join(
'..',
'..',
'..',
'src',
'muses',
'naive_classification',
'model_architecture.json',
))
def predict_image_paths(image_paths, model_path, target_size=(128, 128)):
"""Use a trained classifier to predict the class probabilities of a list of images
Returns most likely class and its probability
:param image_paths: list of path(s) to the image(s)
:param model_path: path to the pre-trained model
:param target_size:
:type image_paths: list
:return:
