Add a sample for Multiclass training (#12)

* Add a sample for Multiclass training

* Update call to assign arg

Author: areddish

samples/vision/custom_vision_training_multiclass_samples.py

@@ -0,0 +1,53 @@
+import os
+import time
+
+from azure.cognitiveservices.vision.customvision.training import training_api
+from azure.cognitiveservices.vision.customvision.training.models import Classifier
+
+SUBSCRIPTION_KEY_ENV_NAME = "CUSTOMVISION_TRAINING_KEY"
+SAMPLE_PROJECT_NAME = "Python SDK Sample"
+
+IMAGES_FOLDER = os.path.join(os.path.dirname(os.path.realpath(__file__)), "images")
+
+def train_project(subscription_key):
+
+    trainer = training_api.TrainingApi(subscription_key)
+
+    # 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. Make it the default project endpoint
+    trainer.update_iteration(project.id, iteration.id, is_default=True)
+    print ("Done!")
+    return project
+
+if __name__ == "__main__":
+    import sys, os.path
+    sys.path.append(os.path.abspath(os.path.join(__file__, "..", "..")))
+    from tools import execute_samples
+    execute_samples(globals(), SUBSCRIPTION_KEY_ENV_NAME)