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()
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:
_, normcells = HOG.HOG(img, options.signed)
hogs.append(normcells.flatten())
#sPickle.s_dump_elt(normcells, f)
f = open(args[1], 'wb')
pickle.dump(hogs, f)
