video_details.video_result.video_id))
print("Expected Video name: {}".format(
video_details.video_result.name))
print("Expected Video url: {}".format(
video_details.video_result.content_url))
else:
print("Couldn't find expected video")
if video_details.related_videos.value:
first_related_video = video_details.related_videos.value[0]
print("Related video count: {}".format(
len(video_details.related_videos.value)))
print("First related video id: {}".format(
first_related_video.video_id))
print("First related video name: {}".format(
first_related_video.name))
print("First related video content url: {}".format(
first_related_video.content_url))
else:
print("Couldn't find any related video!")
except Exception as err:
print("Encountered exception. {}".format(err))
if __name__ == "__main__":
import sys, os.path
sys.path.append(os.path.abspath(os.path.join(__file__, "..", "..", "..")))
from samples.tools import execute_samples
execute_samples(globals(), SUBSCRIPTION_KEY)
with open(os.path.join(hemlock_dir, image), mode="rb") as img_data:
trainer.create_images_from_data(project.id, img_data.read(),
[hemlock_tag.id])
cherry_dir = os.path.join(IMAGES_FOLDER, "Japanese Cherry")
for image in os.listdir(cherry_dir):
with open(os.path.join(cherry_dir, image), mode="rb") as img_data:
trainer.create_images_from_data(project.id, img_data.read(),
[cherry_tag.id])
print("Training...")
iteration = trainer.train_project(project.id)
while (iteration.status == "Training"):
iteration = trainer.get_iteration(project.id, iteration.id)
print("Training status: " + iteration.status)
time.sleep(1)
# The iteration is now trained. Name and publish this iteration to a prediciton endpoint
trainer.publish_iteration(project.id, iteration.id, PUBLISH_ITERATION_NAME,
prediction_resource_id)
print("Done!")
return project
if __name__ == "__main__":
import sys, os.path
sys.path.append(os.path.abspath(os.path.join(__file__, "..", "..", "..")))
from samples.tools import execute_samples
execute_samples(globals(), SUBSCRIPTION_KEY_ENV_NAME)
