def tearDownClass(cls):
trainer = CustomVisionTrainingClient(api_key=TRAINING_KEY,
endpoint=ENDPOINT)
projects = trainer.get_projects()
for project in projects:
if project.name.find(PROJECT_PREFIX) == 0:
trainer.delete_project(project_id=project.id)
def tearDown(self, *args, **kwargs):
trainer = CustomVisionTrainingClient(api_key=self.training_key,
endpoint=self.endpoint)
projects = trainer.get_projects()
for project in projects:
if project.name.find(self.project_prefix) == 0:
logger.info("Deleting project %s", project.id)
trainer.delete_project(project_id=project.id)
super(CustomVisionTestCase, self).tearDown(*args, **kwargs)
trainer.publish_iteration(project.id, iteration.id, publish_iteration_name,
prediction_resource_id)
print("Done!")
# </snippet_publish>
# <snippet_test>
# Now there is a trained endpoint that can be used to make a prediction
prediction_credentials = ApiKeyCredentials(
in_headers={"Prediction-key": prediction_key})
predictor = CustomVisionPredictionClient(ENDPOINT, prediction_credentials)
with open(os.path.join(base_image_location, "Test/test_image.jpg"),
"rb") as image_contents:
results = predictor.classify_image(project.id, publish_iteration_name,
image_contents.read())
# Display the results.
for prediction in results.predictions:
print("\t" + prediction.tag_name +
": {0:.2f}%".format(prediction.probability * 100))
# </snippet_test>
# <snippet_delete>
# You cannot delete a project with published iterations, so you must first unpublish them.
print("Unpublishing project...")
trainer.unpublish_iteration(project.id, iteration.id)
print("Deleting project...")
trainer.delete_project(project.id)
# </snippet_delete>
