run_ship_model.py

# example of inference with a pre-trained coco model
from keras.preprocessing.image import load_img
from keras.preprocessing.image import img_to_array
from mrcnn.config import Config
from mrcnn.model import MaskRCNN
from matplotlib import pyplot
from matplotlib.patches import Rectangle


# draw an image with detected objects
def draw_image_with_boxes(filename, boxes_list):
    # load the image
    data = pyplot.imread(filename)
    # plot the image
    pyplot.imshow(data)
    # get the context for drawing boxes
    ax = pyplot.gca()
    # plot each box
    for box in boxes_list:
        # get coordinates
        y1, x1, y2, x2 = box
        # calculate width and height of the box
        width, height = x2 - x1, y2 - y1
        # create the shape
        rect = Rectangle((x1, y1), width, height, fill=False, color='red')
        # draw the box
        ax.add_patch(rect)
    # show the plot
    pyplot.show()


# define the test configuration
class TestConfig(Config):
    NAME = "test"
    GPU_COUNT = 1
    IMAGES_PER_GPU = 1
    NUM_CLASSES = 2


# define the model
rcnn = MaskRCNN(mode='inference', model_dir='./', config=TestConfig())
# load coco model weights
rcnn.load_weights('../asdc_gpu20210614T1843/mask_rcnn_asdc_gpu_0002.h5', by_name=True)
# load photograph
image_path = 'images/ships/0fb27144a.jpg'
img = load_img(image_path)
img = img_to_array(img)
# make prediction
results = rcnn.detect([img], verbose=0)
# visualize the results
draw_image_with_boxes(image_path, results[0]['rois'])