Computer Vision News - October 2021

3 Summary 11 Creatingamulti-object tracking model... Using TensorFlow backend. Configuration files We'll be using a model trained on the MS-COCO dataset. The configurations of this model are in the CocoConfig class in coco.py . For inferencing, modify the configurations a bit to fit the task. To do so, sub-class the CocoConfig class and override the attributes you need to change. import os import sys import random import math import numpy as np import skimage.io import matplotlib import matplotlib.pyplot as plt # Root directory of the project ROOT_DIR = os.path.abspath("../") # Import Mask RCNN sys.path.append(ROOT_DIR) from mrcnn import utils import mrcnn.model as modellib from mrcnn import visualize # Import COCO config sys.path.append(os.path.join(ROOT_DIR, "samples/coco/")) import coco %matplotlib inline # Directory to save logs and trained model MODEL_DIR = os.path.join(ROOT_DIR, "logs") # Local path to trained weights file COCO_MODEL_PATH = os.path.join(ROOT_DIR, "mask_rcnn_coco.h5") # Download COCO trained weights from Releases if needed if not os.path.exists(COCO_MODEL_PATH): utils.download_trained_weights(COCO_MODEL_PATH) # Directory of images to run detection on IMAGE_DIR = os.path.join(ROOT_DIR, "images")