def train_project(subscription_key):
trainer = CustomVisionTrainingClient(subscription_key, endpoint=ENDPOINT)
# Create a new project
print ("Creating project...")
project = trainer.create_project(SAMPLE_PROJECT_NAME)
# Make two tags in the new project
hemlock_tag = trainer.create_tag(project.id, "Hemlock")
cherry_tag = trainer.create_tag(project.id, "Japanese Cherry")
print ("Adding images...")
hemlock_dir = os.path.join(IMAGES_FOLDER, "Hemlock")
for image in os.listdir(hemlock_dir):
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. Make it the default project endpoint
trainer.update_iteration(project.id, iteration.id, is_default=True)
print ("Done!")
return project
def train_project(subscription_key):
try:
prediction_resource_id = os.environ[
PREDICTION_RESOURCE_ID_KEY_ENV_NAME]
except KeyError:
raise PredictionResourceMissingError(
"Didn't find a prediction resource to publish to. Please set the {} environment variable"
.format(PREDICTION_RESOURCE_ID_KEY_ENV_NAME))
trainer = CustomVisionTrainingClient(subscription_key, endpoint=ENDPOINT)
# Create a new project
print("Creating project...")
project = trainer.create_project(SAMPLE_PROJECT_NAME,
classification_type=Classifier.multiclass)
# Make two tags in the new project
hemlock_tag = trainer.create_tag(project.id, "Hemlock")
cherry_tag = trainer.create_tag(project.id, "Japanese Cherry")
pine_needle_tag = trainer.create_tag(project.id, "Pine Needle Leaves")
flat_leaf_tag = trainer.create_tag(project.id, "Flat Leaves")
print("Adding images...")
hemlock_dir = os.path.join(IMAGES_FOLDER, "Hemlock")
for image in os.listdir(hemlock_dir):
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, pine_needle_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, flat_leaf_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
project = trainer.create_project("ElephantNoElephant")
print("Project created")
# Make two tags in the new project
print("Creating tags")
elephant_tag = trainer.create_tag(project.id, "Elephant")
giraffe_tag = trainer.create_tag(project.id, "Giraffe")
print("Adding images...")
# Add all images in Elephant folder to your project with the tag "elephant"
IMAGES_FOLDER = os.path.join(os.path.dirname(os.path.realpath(__file__)),
"ElephantGiraffeTrainingImages")
elephant_dir = os.path.join(IMAGES_FOLDER, "Elephant")
for image in os.listdir(elephant_dir):
with open(os.path.join(elephant_dir, image), mode="rb") as img_data:
trainer.create_images_from_data(project.id, img_data.read(),
[elephant_tag.id])
print("added elephants")
# Add all images in Giraffe folder to your project with the tag "giraffe"
IMAGES_FOLDER = os.path.join(os.path.dirname(os.path.realpath(__file__)),
"ElephantGiraffeTrainingImages")
giraffe_dir = os.path.join(IMAGES_FOLDER, "Giraffe")
for image in os.listdir(giraffe_dir):
with open(os.path.join(giraffe_dir, image), mode="rb") as img_data:
trainer.create_images_from_data(project.id, img_data.read(),
[giraffe_tag.id])
print("added giraffes")
# Train the model
print("Training...")
iteration = trainer.train_project(project.id)
contents=image_contents.read(),
tag_ids=[tag_id]))
return image_list
def uploadImageList(image_list):
upload_result = trainer.create_images_from_files(project.id,
images=image_list)
if not upload_result.is_batch_successful:
print("Image batch upload failed.")
for image in upload_result.images:
print("Image status: ", image.status)
exit(-1)
for tag in tags:
tag_id = createTag(tag)
print("tag creation done with tag id {tag_id}")
# Set directory to current tag
base_image_url = image_folder + "/" + tag + "/"
photo_name_list = os.listdir(base_image_url)
for file_name in photo_name_list[0:21]:
print(file_name)
with open(base_image_url + file_name, "rb") as image_contents:
trainer.create_images_from_data(project.id,
image_contents.read(),
tag_ids=[tag_id])
print('Project ID: ' + project.id)
hardshell_jacket_tag = trainer.create_tag(project.id, "hardshell jacket")
insulated_jacket_tag = trainer.create_tag(project.id, "insulated jacket")
# In[43]:
#trainer.create_images_from_files()
import os
root = r'/data/home/team15/notebooks'
hardshell_dir = r'resize_images/hardshell_jackets'
insulated_dir = r'resize_images/insulated_jackets'
for img1 in os.listdir(os.path.join(root, hardshell_dir)):
img1 = os.path.join(root, hardshell_dir, img1)
with open(img1, 'rb') as f:
trainer.create_images_from_data(project.id, f.read(),
[hardshell_jacket_tag.id])
# In[44]:
#trainer.create_images_from_files()
for img1 in os.listdir(os.path.join(root, insulated_dir)):
img1 = os.path.join(root, insulated_dir, img1)
with open(img1, 'rb') as f:
trainer.create_images_from_data(project.id, f.read(),
[insulated_jacket_tag.id])
# In[50]:
import time
print("Training...")
