ImageAI: Error when implement ObjectDetection as API in flask

I have another solution to solve this problem. Recently, I find a param which is thread_safe, it can share the model with thread by using k.session.graph in source code. We need to set it to True. So we can use the threading actually in flask or in other place with threading. And my before code can change like the following:

from flask import Flask, Response, jsonify
app = Flask(__name__)
import os
from imageai.Detection import ObjectDetection
import time
import json

execution_path = os.getcwd()
st = time.time()
detector = ObjectDetection()
detector.setModelTypeAsRetinaNet()
detector.setModelPath(os.path.join(execution_path, "model", "resnet50_coco_best_v2.0.1.h5"))
# detector.setModelTypeAsTinyYOLOv3()
# detector.setModelPath(os.path.join(execution_path, "model", "yolo-tiny.h5"))
detector.loadModel()
# detector.loadModel(detection_speed="fastest")
print(f'Init Timer: {time.time()-st}')

@app.route('/detect/<pic_name>')
def boat_detection(pic_name):
    st = time.time()
    results = getDetections(pic_name)
    print(f'Sum Timer: {time.time()-st}')

    msg = {}
    for i, result in enumerate(results, 1):
        result['percentage_probability'] = float(result['percentage_probability'])
        result['box_points'] = list(result['box_points'])
        for index in range(len(result['box_points'])):
            result['box_points'][index] = int(result['box_points'][index])
        result['box_points'] = tuple(result['box_points'])
        msg[str(i)] = json.dumps(result)
    return jsonify(msg)


def getDetections(file_name):
    start = time.time()

    image_folder = os.path.join(execution_path, 'data\\ship2\\')
    output_folder = os.path.join(execution_path, 'data\\output\\')

    st1 = time.time()
    image_file = os.path.join(image_folder, file_name)
    new_image_file = os.path.join(output_folder, file_name)
    print(image_file, "-->", new_image_file)
    if not os.path.exists(image_file):
        print("not exist.")
        return

    # global detector
    custom_objects = detector.CustomObjects(boat=True)
    detections = detector.detectCustomObjectsFromImage(
        custom_objects=custom_objects,
        input_image=image_file,
        output_image_path=new_image_file,
        minimum_percentage_probability=30,
        thread_safe=True)  # **notice this param**
    print(f'[Info]识别到 boat{len(detections)}艘')
    for eachObject in detections:
        print(eachObject.items())

    end = time.time()
    print(f'Excute Timer: {end-st1}')
    print ("耗时: ",end-start)
    return detections

if __name__ == '__main__':
    app.run(threaded=True) # we can use the threading to detect

the most important points is set the thread_safe=True. If you do that, you will find we can load model just one time and can use in many threads.

I had the same problem a week ago. and I had solved it by setting the flask run in just one thread. the following is my example:

from flask import Flask, Response, jsonify
app = Flask(__name__)
import os
from imageai.Detection import ObjectDetection
import time
import json

execution_path = os.getcwd()
st = time.time()
detector = ObjectDetection()
detector.setModelTypeAsRetinaNet()
detector.setModelPath(os.path.join(execution_path, "model", "resnet50_coco_best_v2.0.1.h5"))
# detector.setModelTypeAsTinyYOLOv3()
# detector.setModelPath(os.path.join(execution_path, "model", "yolo-tiny.h5"))
detector.loadModel()
# detector.loadModel(detection_speed="fastest")
print(f'Init Timer: {time.time()-st}')

@app.route('/detect/<pic_name>')
def boat_detection(pic_name):
    st = time.time()
    results = getDetections(pic_name)
    print(f'Sum Timer: {time.time()-st}')

    msg = {}
    for i, result in enumerate(results, 1):
        result['percentage_probability'] = float(result['percentage_probability'])
        result['box_points'] = list(result['box_points'])
        for index in range(len(result['box_points'])):
            result['box_points'][index] = int(result['box_points'][index])
        result['box_points'] = tuple(result['box_points'])
        msg[str(i)] = json.dumps(result)
    return jsonify(msg)


def getDetections(file_name):
    start = time.time()

    image_folder = os.path.join(execution_path, 'data\\ship2\\')
    output_folder = os.path.join(execution_path, 'data\\output\\')

    st1 = time.time()
    image_file = os.path.join(image_folder, file_name)
    new_image_file = os.path.join(output_folder, file_name)
    print(image_file, "-->", new_image_file)
    if not os.path.exists(image_file):
        print("not exist.")
        return

    # global detector
    custom_objects = detector.CustomObjects(boat=True)
    detections = detector.detectCustomObjectsFromImage(
        custom_objects=custom_objects,
        input_image=image_file,
        output_image_path=new_image_file,
        minimum_percentage_probability=30)
    print(f'[Info]识别到 boat{len(detections)}艘')
    for eachObject in detections:
        print(eachObject.items())

    end = time.time()
    print(f'Excute Timer: {end-st1}')
    print ("耗时: ",end-start)
    return detections

if __name__ == '__main__':
    app.run(threaded=False) # ban the Threaded mode

the most important points is set the app.run(threaded=False). my packages version: Flask==1.1.1 h5py==2.9.0 Keras==2.2.4 opencv-python==4.1.0.25 tensorflow==1.14.0 imageai==2.1.0