model.py

import mrcnn
import mrcnn.config
import mrcnn.model
import mrcnn.visualize
import cv2
import os
import numpy
import sys
import time
import codecs, json 
import mask_example
numpy.set_printoptions(threshold=sys.maxsize)
CLASS_NAMES = ['BG', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
               'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush']


class SimpleConfig(mrcnn.config.Config):
    NAME = "coco_inference"

    GPU_COUNT = 1
    IMAGES_PER_GPU = 1

    NUM_CLASSES = len(CLASS_NAMES)
model = mrcnn.model.MaskRCNN(mode="inference",
                             config=SimpleConfig(),
                             model_dir=os.getcwd())
model.load_weights(filepath="mask_rcnn_coco.h5",
                   by_name=True)

image = cv2.imread("image.jpg")
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

r = model.detect([image], verbose=0)
r = r[0]

start_time = time.time()

image_masks = json.dumps(r["masks"].tolist())
mask_example.process_image("image.jpg", image_masks)
end_time = time.time()
elapsed_time = end_time - start_time

print('Execution time of image manipulation:', elapsed_time, 'seconds')