| import sys | |
| import os | |
| import cv2 as cv | |
| def add_argument(zoo, parser, name, help, required=False, default=None, type=None, action=None, nargs=None): | |
| if len(sys.argv) <= 1: | |
| return | |
| modelName = sys.argv[1] | |
| if os.path.isfile(zoo): | |
| fs = cv.FileStorage(zoo, cv.FILE_STORAGE_READ) | |
| node = fs.getNode(modelName) | |
| if not node.empty(): | |
| value = node.getNode(name) | |
| if not value.empty(): | |
| if value.isReal(): | |
| default = value.real() | |
| elif value.isString(): | |
| default = value.string() | |
| elif value.isInt(): | |
| default = int(value.real()) | |
| elif value.isSeq(): | |
| default = [] | |
| for i in range(value.size()): | |
| v = value.at(i) | |
| if v.isInt(): | |
| default.append(int(v.real())) | |
| elif v.isReal(): | |
| default.append(v.real()) | |
| else: | |
| print('Unexpected value format') | |
| exit(0) | |
| else: | |
| print('Unexpected field format') | |
| exit(0) | |
| required = False | |
| if action == 'store_true': | |
| default = 1 if default == 'true' else (0 if default == 'false' else default) | |
| assert(default is None or default == 0 or default == 1) | |
| parser.add_argument('--' + name, required=required, help=help, default=bool(default), | |
| action=action) | |
| else: | |
| parser.add_argument('--' + name, required=required, help=help, default=default, | |
| action=action, nargs=nargs, type=type) | |
| def add_preproc_args(zoo, parser, sample): | |
| aliases = [] | |
| if os.path.isfile(zoo): | |
| fs = cv.FileStorage(zoo, cv.FILE_STORAGE_READ) | |
| root = fs.root() | |
| for name in root.keys(): | |
| model = root.getNode(name) | |
| if model.getNode('sample').string() == sample: | |
| aliases.append(name) | |
| parser.add_argument('alias', nargs='?', choices=aliases, | |
| help='An alias name of model to extract preprocessing parameters from models.yml file.') | |
| add_argument(zoo, parser, 'model', required=True, | |
| help='Path to a binary file of model contains trained weights. ' | |
| 'It could be a file with extensions .caffemodel (Caffe), ' | |
| '.pb (TensorFlow), .t7 or .net (Torch), .weights (Darknet), .bin (OpenVINO)') | |
| add_argument(zoo, parser, 'config', | |
| help='Path to a text file of model contains network configuration. ' | |
| 'It could be a file with extensions .prototxt (Caffe), .pbtxt or .config (TensorFlow), .cfg (Darknet), .xml (OpenVINO)') | |
| add_argument(zoo, parser, 'mean', nargs='+', type=float, default=[0, 0, 0], | |
| help='Preprocess input image by subtracting mean values. ' | |
| 'Mean values should be in BGR order.') | |
| add_argument(zoo, parser, 'scale', type=float, default=1.0, | |
| help='Preprocess input image by multiplying on a scale factor.') | |
| add_argument(zoo, parser, 'width', type=int, | |
| help='Preprocess input image by resizing to a specific width.') | |
| add_argument(zoo, parser, 'height', type=int, | |
| help='Preprocess input image by resizing to a specific height.') | |
| add_argument(zoo, parser, 'rgb', action='store_true', | |
| help='Indicate that model works with RGB input images instead BGR ones.') | |
| add_argument(zoo, parser, 'classes', | |
| help='Optional path to a text file with names of classes to label detected objects.') | |
| add_argument(zoo, parser, 'postprocessing', type=str, | |
| help='Post-processing kind depends on model topology.') | |
| add_argument(zoo, parser, 'background_label_id', type=int, default=-1, | |
| help='An index of background class in predictions. If not negative, exclude such class from list of classes.') | |
| def findFile(filename): | |
| if filename: | |
| if os.path.exists(filename): | |
| return filename | |
| fpath = cv.samples.findFile(filename, False) | |
| if fpath: | |
| return fpath | |
| samplesDataDir = os.path.join(os.path.dirname(os.path.abspath(__file__)), | |
| '..', | |
| 'data', | |
| 'dnn') | |
| if os.path.exists(os.path.join(samplesDataDir, filename)): | |
| return os.path.join(samplesDataDir, filename) | |
| for path in ['OPENCV_DNN_TEST_DATA_PATH', 'OPENCV_TEST_DATA_PATH']: | |
| try: | |
| extraPath = os.environ[path] | |
| absPath = os.path.join(extraPath, 'dnn', filename) | |
| if os.path.exists(absPath): | |
| return absPath | |
| except KeyError: | |
| pass | |
| print('File ' + filename + ' not found! Please specify a path to ' | |
| '/opencv_extra/testdata in OPENCV_DNN_TEST_DATA_PATH environment ' | |
| 'variable or pass a full path to model.') | |
| exit(0) | |