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
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)
while (iteration.status != "Completed"):
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!")
input()
# Now there is a trained endpoint that can be used to make a prediction
# look at the code in MakePrediction.py to see how you send a new image to the trained model to get a prediction
def train_project(training_key):
trainer = CustomVisionTrainingClient(training_key, endpoint=ENDPOINT)
# Find the object detection domain
obj_detection_domain = next(domain for domain in trainer.get_domains()
if domain.type == "ObjectDetection")
# Create a new project
print("Creating project...")
project = trainer.create_project("My Detection Project",
domain_id=obj_detection_domain.id)
# Make two tags in the new project
fork_tag = trainer.create_tag(project.id, "fork")
scissors_tag = trainer.create_tag(project.id, "scissors")
fork_image_regions = {
"fork_1": [0.145833328, 0.3509314, 0.5894608, 0.238562092],
"fork_2": [0.294117659, 0.216944471, 0.534313738, 0.5980392],
"fork_3": [0.09191177, 0.0682516545, 0.757352948, 0.6143791],
"fork_4": [0.254901975, 0.185898721, 0.5232843, 0.594771266],
"fork_5": [0.2365196, 0.128709182, 0.5845588, 0.71405226],
"fork_6": [0.115196079, 0.133611143, 0.676470637, 0.6993464],
"fork_7": [0.164215669, 0.31008172, 0.767156839, 0.410130739],
"fork_8": [0.118872553, 0.318251669, 0.817401946, 0.225490168],
"fork_9": [0.18259804, 0.2136765, 0.6335784, 0.643790841],
"fork_10": [0.05269608, 0.282303959, 0.8088235, 0.452614367],
"fork_11": [0.05759804, 0.0894935, 0.9007353, 0.3251634],
"fork_12": [0.3345588, 0.07315363, 0.375, 0.9150327],
"fork_13": [0.269607842, 0.194068655, 0.4093137, 0.6732026],
"fork_14": [0.143382356, 0.218578458, 0.7977941, 0.295751631],
"fork_15": [0.19240196, 0.0633497, 0.5710784, 0.8398692],
"fork_16": [0.140931368, 0.480016381, 0.6838235, 0.240196079],
"fork_17": [0.305147052, 0.2512582, 0.4791667, 0.5408496],
"fork_18": [0.234068632, 0.445702642, 0.6127451, 0.344771236],
"fork_19": [0.219362751, 0.141781077, 0.5919118, 0.6683006],
"fork_20": [0.180147052, 0.239820287, 0.6887255, 0.235294119]
}
scissors_image_regions = {
"scissors_1": [0.4007353, 0.194068655, 0.259803921, 0.6617647],
"scissors_2": [0.426470578, 0.185898721, 0.172794119, 0.5539216],
"scissors_3": [0.289215684, 0.259428144, 0.403186262, 0.421568632],
"scissors_4": [0.343137264, 0.105833367, 0.332107842, 0.8055556],
"scissors_5": [0.3125, 0.09766343, 0.435049027, 0.71405226],
"scissors_6": [0.379901975, 0.24308826, 0.32107842, 0.5718954],
"scissors_7": [0.341911763, 0.20714055, 0.3137255, 0.6356209],
"scissors_8": [0.231617644, 0.08459154, 0.504901946, 0.8480392],
"scissors_9": [0.170343131, 0.332957536, 0.767156839, 0.403594762],
"scissors_10": [0.204656869, 0.120539248, 0.5245098, 0.743464053],
"scissors_11": [0.05514706, 0.159754932, 0.799019635, 0.730392158],
"scissors_12": [0.265931368, 0.169558853, 0.5061275, 0.606209159],
"scissors_13": [0.241421565, 0.184264734, 0.448529422, 0.6830065],
"scissors_14": [0.05759804, 0.05027781, 0.75, 0.882352948],
"scissors_15": [0.191176474, 0.169558853, 0.6936275, 0.6748366],
"scissors_16": [0.1004902, 0.279036, 0.6911765, 0.477124184],
"scissors_17": [0.2720588, 0.131977156, 0.4987745, 0.6911765],
"scissors_18": [0.180147052, 0.112369314, 0.6262255, 0.6666667],
"scissors_19": [0.333333343, 0.0274019931, 0.443627447, 0.852941155],
"scissors_20": [0.158088237, 0.04047389, 0.6691176, 0.843137264]
}
# Go through the data table above and create the images
print("Adding images...")
tagged_images_with_regions = []
for file_name in fork_image_regions.keys():
x, y, w, h = fork_image_regions[file_name]
regions = [
Region(tag_id=fork_tag.id, left=x, top=y, width=w, height=h)
]
with open(os.path.join(IMAGES_FOLDER, "fork", file_name + ".jpg"),
mode="rb") as image_contents:
tagged_images_with_regions.append(
ImageFileCreateEntry(name=file_name,
contents=image_contents.read(),
regions=regions))
for file_name in scissors_image_regions.keys():
x, y, w, h = scissors_image_regions[file_name]
regions = [
Region(tag_id=scissors_tag.id, left=x, top=y, width=w, height=h)
]
with open(os.path.join(IMAGES_FOLDER, "scissors", file_name + ".jpg"),
mode="rb") as image_contents:
tagged_images_with_regions.append(
ImageFileCreateEntry(name=file_name,
contents=image_contents.read(),
regions=regions))
trainer.create_images_from_files(project.id,
images=tagged_images_with_regions)
print("Training...")
iteration = trainer.train_project(project.id)
while (iteration.status != "Completed"):
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, iteration
with open(file_name, mode="rb") as image_contents:
coach_Rives.create_images_from_files(legoProject.id, [
ImageFileCreateEntry(
name=file_name, contents=image_contents.read(), tag_ids=[city.id])
])
file_name = "Images/city/racecar.jpg"
with open(file_name, mode="rb") as image_contents:
coach_Rives.create_images_from_files(legoProject.id, [
ImageFileCreateEntry(
name=file_name, contents=image_contents.read(), tag_ids=[city.id])
])
file_name = "Images/city/snowmobile.jpg"
with open(file_name, mode="rb") as image_contents:
coach_Rives.create_images_from_files(legoProject.id, [
ImageFileCreateEntry(
name=file_name, contents=image_contents.read(), tag_ids=[city.id])
])
# Fotoğrafları çeşitli tag'ler ile ilişkilendirdiğimize göre öğretimi başlatabiliriz
print("lego fotoğraflarım için eğitim başlıyor")
iteration = coach_Rives.train_project(legoProject.id)
while (iteration.status != "Completed"):
iteration = coach_Rives.get_iteration(legoProject.id, iteration.id)
print("Durum..." + iteration.status)
coach_Rives.update_iteration(legoProject.id, iteration.id, is_default=True)
print("Eğitim tamamlandı...")
