This is my current Flask code that works fine, it receives a POST request with the image from the client, runs it through the model (based on this GH: https://github.com/matterport/Mask_RCNN), and sends a masked image back to the client.
However, it is loading the model from the Configuration file and loading the weights for each request, which takes ages. I want to load the model on server startup and the weights and pass that to the index function. I have tried the solutions from other questions, but with no luck. I wonder if it's because I am loading a model, and then weights, rather than just loading a single h5 model file?
How do I load a file on initialization in a flask application Run code after flask application has started
Flask app:
from flask import Flask, jsonify, request
import base64
import cv2
import numpy as np
from Configuration import create_model
app = Flask(__name__)
@app.route('/', methods=['GET', 'POST'])
def index():
if request.method == "POST":
# Load the image sent from the client
imagefile = request.files['image'].read() # Type: bytes
jpg_as_np = np.frombuffer(imagefile, dtype=np.uint8) # Convert to numpy array
img = cv2.imdecode(jpg_as_np, flags=1) # Decode from numpy array to opencv object - This is an array
### Enter OpenCV/Tensorflow below ###
model = create_model()
image = img[..., ::-1]
# Detect objects
r = model.detect([image], verbose=0)[0]
REDACTED VISUALIATION CODE
### ###
string = base64.b64encode(cv2.imencode('.jpg', masked_image)[1]).decode() # Convert back to b64 string ready for json.
return jsonify({"count": str(r["masks"].shape[2]), 'image': string})
if __name__ == "__main__":
app.run()
Configuration:
def create_model():
device = "/cpu:0"
weights_path = "weights.h5"
with tf.device(device):
model = modellib.MaskRCNN(mode="inference", model_dir=weights_path, config=InferenceConfig())
model.load_weights(weights_path, by_name=True)
print("Weights Loaded")
return model
I solved this using the before_first_request decorator. Below is the general structure:
app = Flask(__name__)
@app.before_first_request
def before_first_request_func():
MOODEL WEIGHT LOADING CODE
return model
@app.route('/', methods=['POST'])
def index():
if request.method == "POST":
REDACTED LOADING CODE
# Detect objects
r = model.detect([image], verbose=0)[0]
REDACTED VISUALISATION CODE
string = base64.b64encode(cv2.imencode('.jpg', masked_image)[1]).decode() # Convert back to b64 string ready for json.
return jsonify({"count": str(r["masks"].shape[2]), 'image': string})
if __name__ == "__main__":
app.run()
model is stored in memory and can be referenced inside the detection function later on. It is usable for each POST request and does not need to be reloaded.