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from detection import ml_detection, ml_utils
import base64
import json
import logging
logger = logging.getLogger()
logger.setLevel(logging.INFO)
# Find ML model type based on string request
def get_model_type(query_string):
# Default ml model type
if query_string == "":
model_type = "facebook/detr-resnet-50"
# Assess query string value
elif "detr" in query_string:
model_type = "facebook/" + query_string
elif "yolos" in query_string:
model_type = "hustvl/" + query_string
else:
raise Exception('Incorrect model type.')
return model_type
# Run detection pipeline: load ML model, perform object detection and return json object
def detection_pipeline(model_type, image_bytes):
# Load correct ML model
processor, model = ml_detection.load_model(model_type)
# Perform object detection
results = ml_detection.object_detection(processor, model, image_bytes)
# Convert dictionary of tensors to JSON object
result_json_dict = ml_utils.convert_tensor_dict_to_json(results)
return result_json_dict
def lambda_handler(event, context):
# logger.info(f"API event: {event}")
try:
# Retrieve model type
model_query = event.get('model', '')
model_type = get_model_type(model_query)
logger.info(f"Model query: {model_query}")
logger.info(f"Model type: {model_type}")
# Decode the base64-encoded image data from the event
image_data = event['body']
if event['isBase64Encoded']:
image_data = base64.b64decode(image_data)
# Run detection pipeline
result_dict = detection_pipeline(model_type, image_data)
logger.info(f"API Results: {result_dict}")
return {
'statusCode': 200,
'headers': {
'Content-Type': 'application/json'
},
'body': json.dumps(result_dict),
}
except Exception as e:
logger.info(f"API Error: {str(e)}")
return {
'statusCode': 500,
'headers': {
'Content-Type': 'application/json'
},
'body': json.dumps({'error': str(e)}),
} |