Sample updates and additions for V2.0 API (#10)

Author: areddish

samples/vision/custom_vision_object_detection_sample.py

@@ -0,0 +1,137 @@
+import os
+import sys
+import time
+
+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 prediction_endpoint
+from azure.cognitiveservices.vision.customvision.prediction.prediction_endpoint import models
+
+# Replace with a valid key
+SUBSCRIPTION_KEY_ENV_NAME = "CUSTOMVISION_TRAINING_KEY"
+PREDICTION_KEY_ENV_NAME = "CUSTOMVISION_PREDICTION_KEY"
+
+# Add this directory to the path so that custom_vision_training_samples can be found
+sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), "."))
+
+IMAGES_FOLDER = os.path.join(os.path.dirname(os.path.realpath(__file__)), "images")
+
+def run_sample(subscription_key):
+    try:
+        prediction_key = os.environ[PREDICTION_KEY_ENV_NAME]
+    except KeyError:
+        raise SubscriptionKeyError("You need to set the {} env variable.".format(PREDICTION_KEY_ENV_NAME))
+
+    project, iteration = train_project(subscription_key)
+    predict_project(prediction_key, project, iteration)
+
+def train_project(training_key):
+    trainer = training_api.TrainingApi(training_key)
+
+    # 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
+
+def predict_project(prediction_key, project, iteration):
+    predictor = prediction_endpoint.PredictionEndpoint(prediction_key)
+
+    # 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:
+        results = predictor.predict_image(project.id, test_data, iteration.id)
+
+    # Display the results.
+    for prediction in results.predictions:
+        print ("\t" + prediction.tag_name + ": {0:.2f}%".format(prediction.probability * 100), prediction.bounding_box.left, prediction.bounding_box.top, prediction.bounding_box.width, prediction.bounding_box.height)
+
+
+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)

samples/vision/custom_vision_prediction_samples.py

@@ -41,7 +41,7 @@ def predict_project(subscription_key):
 
     # Display the results.
     for prediction in results.predictions:
-        print ("\t" + prediction.tag + ": {0:.2f}%".format(prediction.probability * 100))
+        print ("\t" + prediction.tag_name + ": {0:.2f}%".format(prediction.probability * 100))
 
 
 if __name__ == "__main__":