Smooth out the code

nlpserver.py

@@ -13,12 +13,18 @@
 #app.config['var1'] = 'test'
 app.config["YOLO_MODEL_NAME"] = "yolov8m.pt"
 app.config["MOBILENET_MODEL_NAME"] = "mobilenet_v3_small.tflite"
+app.config["EFFICIENTDET_DETECT_MODEL_NAME"] = "efficientdet_lite2.tflite"
+app.config["MOBILENET_DETECT_MODEL_NAME"] = "ssd_mobilenet_v2.tflite"
 for variable, value in os.environ.items():
 	if variable == "YOLO_MODEL_NAME":
 		# Can be set via Docker ENV
 		app.config["YOLO_MODEL_NAME"] = value
 	if variable == "MOBILENET_MODEL_NAME":	
 		app.config["MOBILENET_MODEL_NAME"] = value
+	if variable == "EFFICIENTDET_MODEL_NAME":	
+		app.config["EFFICIENTDET_MODEL_NAME"] = value	
+	if variable == "MOBILENET_DETECT_MODEL_NAME":	
+		app.config["MOBILENET_DETECT_MODEL_NAME"] = value		
 
 default_data = {}
 default_data['web64'] = {
@@ -574,7 +580,7 @@ def loadImage(url, size = 640):
 		try:
 			response = requests.get(url)
 			response.raise_for_status()
-		except requests.exceptions.HTTPError as err:
+		except requests.exceptions.RequestException as err:
 			data['error'] =  err.strerror
 			return jsonify(data)
 
@@ -631,12 +637,13 @@ def loadImage(url, size = 640):
 		if hasattr(object_detect_result, "obb") and object_detect_result.obb is not None:  # Access the .obb attribute instead of .boxes
 			print('An obb model')
 			data['yolo']['objects'] = json.loads(object_detect_result.tojson(True))
-		elif hasattr(object_detect_result, "boxes")  and object_detect_result.boxes is not None:
+		elif hasattr(object_detect_result, "boxes") and object_detect_result.boxes is not None and object_detect_result.probs is not None:
 			print('Not an obb model')
-			data['yolo']['objects'] = json.loads(object_detect_result.tojson(True))
+			if type(object_detect_result) != 'NoneType':
+				data['yolo']['objects'] = json.loads(object_detect_result.tojson(True))
 		else:
 			data['error'] = 'No features detected'
-			data['yolo']['objects'] = json.loads(object_detect_result.tojson(True))
+			data['yolo']['objects'] = []
 
 	data['yolo']['modelinfo'] = {'train_args': model.ckpt["train_args"], 'date': model.ckpt["date"], 'version': model.ckpt["version"]} 
 
@@ -674,22 +681,24 @@ def loadImage(url, size = 480):
 		try:
 			response = requests.get(url)
 			response.raise_for_status()
-		except requests.exceptions.HTTPError as err:
+		except requests.exceptions.RequestException as err:
 			data['error'] =  err.strerror
 			return jsonify(data)
 
 		img_bytes = BytesIO(response.content)
 		img = Image.open(img_bytes)
 		img = img.convert('RGB')
-		img = img.resize((size,size), Image.NEAREST)
+		img.thumbnail((size,size), Image.NEAREST)
 		# Media pipe uses a different format than YOLO, img here is PIL
 		img = np.asarray(img)
 		return img
 
 	data = dict(default_data)
 	data['message'] = "mobilenet -  Parameters: 'iiif_image_url"
 	data['mobilenet'] = {}
+	data['efficientdet'] = {}
 	params = {}
+	objects = []
 
 
 	if request.method == 'GET':
@@ -709,40 +718,56 @@ def loadImage(url, size = 480):
 		return jsonify(data)
 	try:
 		# Create options for Image Embedder
-		base_options = python.BaseOptions(model_asset_path='models/mobilenet/' + app.config["MOBILENET_MODEL_NAME"])
+		base_options_embedder = python.BaseOptions(model_asset_path='models/mobilenet/' + app.config["MOBILENET_MODEL_NAME"])
+		base_options_detected = python.BaseOptions(model_asset_path='models/mobilenet/' + app.config["MOBILENET_DETECT_MODEL_NAME"])
 		l2_normalize = True #@param {type:"boolean"}
 		quantize = True #@param {type:"boolean"}
-		options = vision.ImageEmbedderOptions(
-    	base_options=base_options, l2_normalize=l2_normalize, quantize=quantize)
+		options_embedder = vision.ImageEmbedderOptions(base_options=base_options_embedder, l2_normalize=l2_normalize, quantize=quantize)
+		options_detector = vision.ObjectDetectorOptions(base_options=base_options_detected, score_threshold=0.5)
+
+
 
 
 # Create Image Embedder
-		with vision.ImageEmbedder.create_from_options(options) as embedder:
+		with vision.ImageEmbedder.create_from_options(options_embedder) as embedder:
 
   		# Format images for MediaPipe
-			img = loadImage(params['iiif_image_url'], 480)
+			img = loadImage(params['iiif_image_url'], 640)
 			image = mp.Image(image_format=mp.ImageFormat.SRGB, data=img)
 			embedding_result = embedder.embed(image)
+			with vision.ObjectDetector.create_from_options(options_detector) as detector:
+				detector_results = detector.detect(image)
 
 	except ValueError:
 		data['error'] = 'models/mobilenet/' + app.config["MOBILENET_MODEL_NAME"] + ' not found'
 		return jsonify(data)
 
-
+	if not detector_results.detections:
+		objects = []
+	else:
+		# make the coordinates percentage based.
+		for ml_result_index in range(len(detector_results.detections)):
+			detector_results.detections[ml_result_index].bounding_box.origin_x = detector_results.detections[ml_result_index].bounding_box.origin_x/image.width
+			detector_results.detections[ml_result_index].bounding_box.origin_y = detector_results.detections[ml_result_index].bounding_box.origin_y/image.height
+			detector_results.detections[ml_result_index].bounding_box.width = detector_results.detections[ml_result_index].bounding_box.width/image.width
+			detector_results.detections[ml_result_index].bounding_box.height = detector_results.detections[ml_result_index].bounding_box.width/image.height
+		objects = detector_results.detections
 	vector = embedding_result.embeddings[0].embedding
-	print(embedding_result.embeddings[0].embedding.shape[0])
+	# print(embedding_result.embeddings[0].embedding.shape[0])
 	# Vector size for this layer (inumlayers - 1) is 1024
 	# This "should" return a Unit Vector so we can use "dot_product" in Solr
 	X_l1 = preprocessing.normalize([vector], norm='l1')
 	# see https://nightlies.apache.org/solr/draft-guides/solr-reference-guide-antora/solr/10_0/query-guide/dense-vector-search.html
 	data['mobilenet']['vector'] = X_l1[0].tolist()
+
+	data['mobilenet']['objects'] = objects
 	data['message'] = 'done'
 	return jsonify(data)
 
 # @app.route("/tester", methods=['GET', 'POST'])
 # def tester():
 # 	return render_template('form.html')
 
-app.run(host='0.0.0.0', port=6400, debug=False)
+app.run(host='0.0.0.0', port=6401, debug=False)