perform_inference.py

import glob
test_images = glob.glob('images/test/*')
print(*test_images, sep="\n")

def prediction_to_bbox_data(image_path, prediction):
    class_id, confidence, xmin, ymin, xmax, ymax = prediction
    width, height = Image.open(image_path).size
    bbox_data = {'class_id': class_id,
               'height': (ymax-ymin)*height,
               'width': (xmax-xmin)*width,
               'left': xmin*width,
               'top': ymin*height}
    return bbox_data
  
  import matplotlib.pyplot as plt
runtime_client = boto3.client('sagemaker-runtime')
def get_predictions_for_img(runtime_client, endpoint_name, img_path):
    with open(img_path, 'rb') as f:
        payload = f.read()
        payload = bytearray(payload)

    response = runtime_client.invoke_endpoint(EndpointName=endpoint_name, 
                                       ContentType='application/x-image', 
                                       Body=payload)

    result = response['Body'].read()
    result = json.loads(result)
    return result


# wait until the status has changed
client.get_waiter('endpoint_in_service').wait(EndpointName=endpoint_name)
endpoint_response = client.describe_endpoint(EndpointName=endpoint_name)
status = endpoint_response['EndpointStatus']
if status != 'InService':
    raise Exception('Endpoint creation failed.')
    
for test_image in test_images:
    result = get_predictions_for_img(runtime_client, endpoint_name, test_image)
    confidence_threshold = .03
    best_n = 1
    # display the best n predictions with confidence > confidence_threshold
    predictions = [prediction for prediction in result['prediction'] if prediction[1] > confidence_threshold]
    predictions.sort(reverse=True, key = lambda x: x[1])
    bboxes = [prediction_to_bbox_data(test_image, prediction) for prediction in predictions[:best_n]]
    
    for prediction in predictions[:best_n]:
        prediction_list = sorted(prediction, reverse = True)
    for item in sorted(prediction, reverse = True):
        if item == 1.0:
            try:
                prediction_list.remove(item)
            except:
                print("")
    conf_precision3 = str(prediction_list[0])[0] + str(prediction_list[0])[1] + str(prediction_list[0])[2] + str(prediction_list[0])[3]
    
    show_annotated_image(test_image, bboxes, conf_precision3)