def run_prog(): parser = OptionParser() parser.add_option("-m", "--model-file", action="store", default="svm_model.pkl") parser.add_option("-f", "--false-pos-file", action="store", default="false_pos.pkl") parser.add_option("-n", "--num-images", action="store", type=int, default=-1) parser.add_option("-s", "--signed", action="store_true", default=False) parser.add_option("-r", "--random-window", action="store_true", default=False) (options, args) = parser.parse_args() print "Loading model from file ", options.model_file model_file = open(options.model_file, "rb") scaler, svc = pickle.load(model_file) model_file.close() print "Model loaded" imgs = DirectoryImagesLoader(args[0]) imgs.randomize() false_pos_cells = [] if options.num_images != -1: stop_at = options.num_images else: stop_at = len(imgs) for i in range(stop_at): print "Extracting false positives on image", i, "of", stop_at img = imgs.get_image(i) window_hits, detected_humans = detect_humans(img, svc, scaler, options.signed, debug=False, extract_humans_to=false_pos_cells) print len(detected_humans), "humans found" #ax.add_patch(matplotlib.patches.Rectangle((y_win_hits * cellsize, x_win_hits * cellsize), window_pixel_shape[1], window_pixel_shape[0], ec='red', facecolor='none', hatch="/")) f = open(options.false_pos_file, 'wb') pickle.dump(false_pos_cells, f) f.close()
from randWindowExtractor import randWindowExtractor import cPickle as pickle if __name__ == "__main__": parser = OptionParser() parser.add_option("-s", "--signed", action="store_true", default=False) parser.add_option("-r", "--random-window", action="store_true", default=False) (options, args) = parser.parse_args() if len(args) < 2: raise "Must provide image directory and file to save to" imgs = DirectoryImagesLoader(args[0]) hogs = [] for i in range(len(imgs)): print "Running HOG on image", i, "of", len(imgs) img = imgs.get_image(i) if options.random_window: #for i in range(10): win = randWindowExtractor(img) _, normcells = HOG.HOG(win, options.signed) hogs.append(normcells.flatten()) else: