import progressbar import argparse import pickle import random import cv2 # construct the argument parser and parse the arguments ap = argparse.ArgumentParser() ap.add_argument("-c", "--conf", required=True, help="path to the configuration file") args = vars(ap.parse_args()) # load the configuration file and initialize the data list conf = conf.Conf(args["conf"]) data = [] # load the classifier, then initialize the Histogram of Oriented Gradients descriptor # and the object detector model = pickle.loads(open(conf["classifier_path"], "rb").read()) hog = hog.HOG(orientations=conf["orientations"], pixelsPerCell=tuple(conf["pixels_per_cell"]), cellsPerBlock=tuple(conf["cells_per_block"]), normalise=conf["normalize"]) od = objectdetector.ObjectDetector(model, hog) # grab the set of distraction paths and randomly sample them dstPaths = list(paths.list_images(conf["image_distractions"])) dstPaths = random.sample(dstPaths, 50)
from skimage import feature from pyimagesearch.utils import dataset from pyimagesearch.utils import conf from imutils import paths from scipy import io import numpy as np import progressbar import argparse import random import cv2 ap = argparse.ArgumentParser() ap.add_argument('-c', '--conf', required=True, help='path to config file') args = ap.parse_args() conf = conf.Conf(args.conf) # hog = HOG data = [] labels = [] # feature.hog(image, orientations=conf["orientations"], pixels_per_cell=tuple(conf["pixels_per_cell"]), # cells_per_block=tuple(conf["cells_per_block"]), transform_sqrt=conf["normalize"]) # select ground-truth images and select a percentage of them for training trnPaths = list(paths.list_images(conf["image_dataset"])) trnPaths = random.sample(trnPaths, int(len(trnPaths) * conf["percent_gt_images"])) print("[INFO] describing training ROIs...") # set up the progress bar widgets = ["Extracting: ", progressbar.Percentage(), " ", progressbar.Bar(), " ", progressbar.ETA()]