import sys, os from lib.feature import HogFeature, HogSizeFeature import config if __name__ == "__main__": if len(sys.argv) < 1: print "USAGE: python dump_data_labels.py" sys.exit(0) feature_type = config.FEATURE_TYPE feature = None if feature_type == "HogFeature": feature = HogFeature() else: feature = HogSizeFeature() # dump dataset data, labels = feature.create_data_labels( class_dict = config.CATEGORY_CLASS_DICT, limit_num = config.LIMIT_NUM, pkl_data_label_path = config.PKL_DATA_LABEL_FILE, pkl_params_path = config.PKL_PARAMS_FILE)
classifier_file = config.PKL_CLASSIFIER_FILE params_file = config.PKL_PARAMS_FILE # load data for estimator try: clf = joblib.load(classifier_file) (mu, sigma) = joblib.load(params_file) print clf except Exception, e: raise e else: pass if feature_type == "HogFeature": feature = HogFeature(mu, sigma) else: feature = HogSizeFeature(mu, sigma) print "classify for label %d with %s"%(label, classifier_file) cnt = 0 failure = 0 if os.path.isdir(src): for filename in os.listdir(src): path = os.path.join(src, filename) img = cv2.imread(path, cv2.CV_LOAD_IMAGE_GRAYSCALE) if img is not None: feature_value = feature.calc(img) label_predict = int(clf.predict(feature_value)) cnt += 1