Make samples work with azure-cognitiveservcies-vision-customvision 2.0.0

requirements.txt

@@ -12,7 +12,7 @@ azure-cognitiveservices-search-visualsearch>=0.2.0  # sample won't work with pre
 azure-cognitiveservices-search-websearch
 azure-cognitiveservices-vision-computervision>=0.3.0  # sample won't work with previous versions
 azure-cognitiveservices-vision-contentmoderator>=1.0.0  # sample won't work with previous versions
-azure-cognitiveservices-vision-customvision>=0.4.0  # sample won't work with previous versions
+azure-cognitiveservices-vision-customvision>=2.0.0  # sample won't work with previous versions
 azure-cognitiveservices-vision-face
 azure-cognitiveservices-anomalydetector>=0.2.0 #  sample won't work with previous versions
 azure-cognitiveservices-inkrecognizer>=1.0.0b1

samples/vision/custom_vision_object_detection_sample.py

@@ -5,6 +5,7 @@
 from azure.cognitiveservices.vision.customvision.training import training_api
 from azure.cognitiveservices.vision.customvision.training.models import ImageFileCreateEntry, Region
 from azure.cognitiveservices.vision.customvision.prediction import CustomVisionPredictionClient
+from msrest.authentication import ApiKeyCredentials
 
 sys.path.append(os.path.abspath(os.path.join(__file__, "..", "..", "..")))
 
@@ -41,7 +42,8 @@ def train_project(training_key):
     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(training_key, endpoint=ENDPOINT)
+    credentials = ApiKeyCredentials(in_headers={"Training-key": training_key})
+    trainer = CustomVisionTrainingClient(ENDPOINT, credentials)
 
     # Find the object detection domain
 
@@ -139,15 +141,12 @@ def train_project(training_key):
     trainer.publish_iteration(project.id, iteration.id, PUBLISH_ITERATION_NAME, prediction_resource_id)
     print ("Done!")
 
-    # 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
 
 
 def predict_project(prediction_key, project, iteration):
-    predictor = CustomVisionPredictionClient(prediction_key, endpoint=ENDPOINT)
+    credentials = ApiKeyCredentials(in_headers={"Prediction-key": prediction_key})
+    predictor = CustomVisionPredictionClient(ENDPOINT, credentials)
 
     # Open the sample image and get back the prediction results.
     with open(os.path.join(IMAGES_FOLDER, "Test", "test_od_image.jpg"), mode="rb") as test_data:

samples/vision/custom_vision_prediction_samples.py

@@ -3,6 +3,7 @@
 
 from azure.cognitiveservices.vision.customvision.training import CustomVisionTrainingClient
 from azure.cognitiveservices.vision.customvision.prediction import CustomVisionPredictionClient
+from msrest.authentication import ApiKeyCredentials
 
 TRAINING_KEY_ENV_NAME = "CUSTOMVISION_TRAINING_KEY"
 SUBSCRIPTION_KEY_ENV_NAME = "CUSTOMVISION_PREDICTION_KEY"
@@ -26,7 +27,8 @@ def find_or_train_project():
 
     # Use the training API to find the SDK sample project created from the training example.
     from custom_vision_training_samples import train_project, SAMPLE_PROJECT_NAME
-    trainer = CustomVisionTrainingClient(training_key, endpoint=ENDPOINT)
+    credentials = ApiKeyCredentials(in_headers={"Training-key": training_key})
+    trainer = CustomVisionTrainingClient(ENDPOINT, credentials)
 
     for proj in trainer.get_projects():
         if (proj.name == SAMPLE_PROJECT_NAME):
@@ -37,8 +39,8 @@ def find_or_train_project():
 
 
 def predict_project(subscription_key):
-    predictor = CustomVisionPredictionClient(
-        subscription_key, endpoint=ENDPOINT)
+    credentials = ApiKeyCredentials(in_headers={"Prediction-key": subscription_key})
+    predictor = CustomVisionPredictionClient(ENDPOINT, credentials)
 
     # Find or train a new project to use for prediction.
     project = find_or_train_project()

samples/vision/custom_vision_training_multiclass_samples.py

@@ -3,74 +3,64 @@
 
 from azure.cognitiveservices.vision.customvision.training import CustomVisionTrainingClient
 from azure.cognitiveservices.vision.customvision.training.models import Classifier
+from msrest.authentication import ApiKeyCredentials
 
 SUBSCRIPTION_KEY_ENV_NAME = "CUSTOMVISION_TRAINING_KEY"
-PREDICTION_RESOURCE_ID_KEY_ENV_NAME = "CUSTOMVISION_PREDICTION_ID"
-
 SAMPLE_PROJECT_NAME = "Python SDK Sample"
 
 # The prediction resource can be found with your keys and is tied to the Prediction Key
-PREDICTION_RESOURCE_ID = "enter your prediction resource"
+PREDICTION_RESOURCE_ID_KEY_ENV_NAME = "CUSTOMVISION_PREDICTION_ID"
 
 PUBLISH_ITERATION_NAME = "classifyModel"
 
 ENDPOINT = "https://southcentralus.api.cognitive.microsoft.com"
 
-IMAGES_FOLDER = os.path.join(os.path.dirname(
-    os.path.realpath(__file__)), "images")
-
-class PredictionResourceMissingError(Exception):
-    pass
+IMAGES_FOLDER = os.path.join(os.path.dirname(os.path.realpath(__file__)), "images")
 
 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)
+    credentials = ApiKeyCredentials(in_headers={"Training-key": subscription_key})
+    trainer = CustomVisionTrainingClient(ENDPOINT, credentials)
 
     # Create a new project
-    print("Creating project...")
-    project = trainer.create_project(
-        SAMPLE_PROJECT_NAME, classification_type=Classifier.multiclass)
+    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")
+    pine_needle_tag =  trainer.create_tag(project.id, "Pine Needle Leaves")
     flat_leaf_tag = trainer.create_tag(project.id, "Flat Leaves")
 
-    print("Adding images...")
+    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])
+        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])
+        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...")
+    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)
+        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
-    import os.path
+    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)
+    execute_samples(globals(), SUBSCRIPTION_KEY_ENV_NAME)

samples/vision/custom_vision_training_samples.py

@@ -2,6 +2,7 @@
 import time
 
 from azure.cognitiveservices.vision.customvision.training import CustomVisionTrainingClient
+from msrest.authentication import ApiKeyCredentials
 
 SUBSCRIPTION_KEY_ENV_NAME = "CUSTOMVISION_TRAINING_KEY"
 PREDICTION_RESOURCE_ID_KEY_ENV_NAME = "CUSTOMVISION_PREDICTION_ID"
@@ -25,7 +26,8 @@ def train_project(subscription_key):
     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)
+    credentials = ApiKeyCredentials(in_headers={"Training-key": subscription_key})
+    trainer = CustomVisionTrainingClient(ENDPOINT, credentials)
 
     # Create a new project
     print("Creating project...")