def __gen_info_filename(i, performance): return SVM_MODEL_FILE + str(i) + "-" + ("{0:.2f}".format(performance["performance"]["TPR/FPR"])) + ".info.json" """ MAIN """ if __name__ == "__main__": # init args = parse_arguments() DATASET_DIR = args.d cats = [0, 1] ncats = len(cats) # generate and prepare folders algo.__try_mkdirs(DATASET_DIR) algo.__try_mkdirs(TRAININGSET_DIR) algo.__try_mkdirs(VALIDATIONSET_DIR) algo.__try_mkdirs(TMP_DIR_TRAINING) algo.__try_mkdirs(TMP_DIR_VALIDATION) algo.__clear_dir(DATASET_DIR) algo.__clear_dir(TRAININGSET_DIR) algo.__clear_dir(VALIDATIONSET_DIR) # do training performances = [] # keep track of different parameter performances # iterate over different patch_sizes for patch_size in HYPERPARAMETERS_OPTIONS["patch_size"]:
DATASET_DIR = '../data/patches/test' TMP_DIR = '../data/tmp/test/' IMG_BBOX=(11.60339,48.17708,11.61304,48.18326) ; IMG_SIZE=(1500, 1000) ; IMG_NAME="dopA" # between Grasmeier and Crailsheimerstr. SATELLITE_IMG_VISUALIZATION_INPUT="dopA/dop-annotated.png" SATELLITE_IMG_VISUALIZATION_OUTPUT="../data/dopA-predictions.png" #IMG_BBOX =(11.59221,48.17038,11.61233,48.18380) ; IMG_SIZE=(2000, 2000) ; SATELLITE_IMG_TMP="dopB.png" # bigger as above. #SATELLITE_IMG_VISUALIZATION_INPUT="dopB-annotated.png" #SATELLITE_IMG_VISUALIZATION_OUTPUT="dopB-predictions.png" #important: a must be smaller than c, b must be smaller then d if (__name__ == "__main__"): # init algo.__try_mkdirs(DATASET_DIR) algo.__clear_dir(DATASET_DIR) algo.__try_mkdirs(TMP_DIR) algo.__clear_dir(TMP_DIR) with open(HYPERPARAMETERS_FILE, "r") as f: params = json.loads(f.read()) # generate patches print "---------------------" print "## generating patches from '" + IMG_NAME + "' (" + str(IMG_SIZE[0])+"x"+str(IMG_SIZE[1]) + "; " + str(IMG_BBOX) + ")" patch_generator.generate_patches(IMG_BBOX, IMG_SIZE, patch_size=params['hyperparameters']['patch_size'], offset_steps=params['hyperparameters']['patch_offset'], target_folder=DATASET_DIR,