mirror of https://github.com/opencv/opencv.git
Alexander Alekhin
6 years ago
commit
5115e5decb
17 changed files with 494 additions and 136 deletions
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#include <opencv2/core/utils/filesystem.hpp> |
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using namespace cv; |
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std::string genArgument(const std::string& argName, const std::string& help, |
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const std::string& modelName, const std::string& zooFile, |
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char key = ' ', std::string defaultVal = ""); |
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std::string genPreprocArguments(const std::string& modelName, const std::string& zooFile); |
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std::string findFile(const std::string& filename); |
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std::string genArgument(const std::string& argName, const std::string& help, |
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const std::string& modelName, const std::string& zooFile, |
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char key, std::string defaultVal) |
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{ |
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if (!modelName.empty()) |
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{ |
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FileStorage fs(zooFile, FileStorage::READ); |
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if (fs.isOpened()) |
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{ |
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FileNode node = fs[modelName]; |
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if (!node.empty()) |
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{ |
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FileNode value = node[argName]; |
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if (!value.empty()) |
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{ |
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if (value.isReal()) |
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defaultVal = format("%f", (float)value); |
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else if (value.isString()) |
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defaultVal = (std::string)value; |
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else if (value.isInt()) |
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defaultVal = format("%d", (int)value); |
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else if (value.isSeq()) |
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{ |
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for (size_t i = 0; i < value.size(); ++i) |
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{ |
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FileNode v = value[(int)i]; |
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if (v.isInt()) |
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defaultVal += format("%d ", (int)v); |
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else if (v.isReal()) |
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defaultVal += format("%f ", (float)v); |
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else |
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CV_Error(Error::StsNotImplemented, "Unexpected value format"); |
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} |
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} |
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else |
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CV_Error(Error::StsNotImplemented, "Unexpected field format"); |
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} |
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} |
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} |
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} |
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return "{ " + argName + " " + key + " | " + defaultVal + " | " + help + " }"; |
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} |
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std::string findFile(const std::string& filename) |
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{ |
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if (filename.empty() || utils::fs::exists(filename)) |
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return filename; |
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std::string extraPaths[] = {getenv("OPENCV_DNN_TEST_DATA_PATH"), |
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getenv("OPENCV_TEST_DATA_PATH")}; |
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for (int i = 0; i < 2; ++i) |
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{ |
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std::string absPath = utils::fs::join(extraPaths[i], utils::fs::join("dnn", filename)); |
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if (utils::fs::exists(absPath)) |
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return absPath; |
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} |
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CV_Error(Error::StsObjectNotFound, "File " + filename + " not found! " |
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"Please specify a path to /opencv_extra/testdata in OPENCV_DNN_TEST_DATA_PATH " |
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"environment variable or pass a full path to model."); |
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} |
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std::string genPreprocArguments(const std::string& modelName, const std::string& zooFile) |
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{ |
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return genArgument("model", "Path to a binary file of model contains trained weights. " |
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"It could be a file with extensions .caffemodel (Caffe), " |
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".pb (TensorFlow), .t7 or .net (Torch), .weights (Darknet), .bin (OpenVINO).", |
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modelName, zooFile, 'm') + |
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genArgument("config", "Path to a text file of model contains network configuration. " |
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"It could be a file with extensions .prototxt (Caffe), .pbtxt (TensorFlow), .cfg (Darknet), .xml (OpenVINO).", |
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modelName, zooFile, 'c') + |
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genArgument("mean", "Preprocess input image by subtracting mean values. Mean values should be in BGR order and delimited by spaces.", |
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modelName, zooFile) + |
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genArgument("scale", "Preprocess input image by multiplying on a scale factor.", |
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modelName, zooFile, ' ', "1.0") + |
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genArgument("width", "Preprocess input image by resizing to a specific width.", |
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modelName, zooFile, ' ', "-1") + |
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genArgument("height", "Preprocess input image by resizing to a specific height.", |
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modelName, zooFile, ' ', "-1") + |
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genArgument("rgb", "Indicate that model works with RGB input images instead BGR ones.", |
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modelName, zooFile); |
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} |
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@ -0,0 +1,108 @@ |
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import sys |
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import os |
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import cv2 as cv |
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def add_argument(zoo, parser, name, help, required=False, default=None, type=None, action=None, nargs=None): |
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if len(sys.argv) <= 1: |
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return |
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modelName = sys.argv[1] |
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if os.path.isfile(zoo): |
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fs = cv.FileStorage(zoo, cv.FILE_STORAGE_READ) |
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node = fs.getNode(modelName) |
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if not node.empty(): |
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value = node.getNode(name) |
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if not value.empty(): |
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if value.isReal(): |
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default = value.real() |
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elif value.isString(): |
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default = value.string() |
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elif value.isInt(): |
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default = int(value.real()) |
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elif value.isSeq(): |
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default = [] |
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for i in range(value.size()): |
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v = value.at(i) |
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if v.isInt(): |
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default.append(int(v.real())) |
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elif v.isReal(): |
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default.append(v.real()) |
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else: |
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print('Unexpected value format') |
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exit(0) |
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else: |
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print('Unexpected field format') |
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exit(0) |
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required = False |
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if action == 'store_true': |
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default = 1 if default == 'true' else (0 if default == 'false' else default) |
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assert(default is None or default == 0 or default == 1) |
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parser.add_argument('--' + name, required=required, help=help, default=bool(default), |
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action=action) |
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else: |
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parser.add_argument('--' + name, required=required, help=help, default=default, |
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action=action, nargs=nargs, type=type) |
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def add_preproc_args(zoo, parser, sample): |
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aliases = [] |
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if os.path.isfile(zoo): |
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fs = cv.FileStorage(zoo, cv.FILE_STORAGE_READ) |
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root = fs.root() |
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for name in root.keys(): |
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model = root.getNode(name) |
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if model.getNode('sample').string() == sample: |
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aliases.append(name) |
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parser.add_argument('alias', nargs='?', choices=aliases, |
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help='An alias name of model to extract preprocessing parameters from models.yml file.') |
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add_argument(zoo, parser, 'model', required=True, |
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help='Path to a binary file of model contains trained weights. ' |
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'It could be a file with extensions .caffemodel (Caffe), ' |
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'.pb (TensorFlow), .t7 or .net (Torch), .weights (Darknet), .bin (OpenVINO)') |
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add_argument(zoo, parser, 'config', |
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help='Path to a text file of model contains network configuration. ' |
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'It could be a file with extensions .prototxt (Caffe), .pbtxt or .config (TensorFlow), .cfg (Darknet), .xml (OpenVINO)') |
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add_argument(zoo, parser, 'mean', nargs='+', type=float, default=[0, 0, 0], |
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help='Preprocess input image by subtracting mean values. ' |
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'Mean values should be in BGR order.') |
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add_argument(zoo, parser, 'scale', type=float, default=1.0, |
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help='Preprocess input image by multiplying on a scale factor.') |
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add_argument(zoo, parser, 'width', type=int, |
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help='Preprocess input image by resizing to a specific width.') |
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add_argument(zoo, parser, 'height', type=int, |
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help='Preprocess input image by resizing to a specific height.') |
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add_argument(zoo, parser, 'rgb', action='store_true', |
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help='Indicate that model works with RGB input images instead BGR ones.') |
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add_argument(zoo, parser, 'classes', |
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help='Optional path to a text file with names of classes to label detected objects.') |
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def findFile(filename): |
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if filename: |
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if os.path.exists(filename): |
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return filename |
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samplesDataDir = os.path.join(os.path.dirname(os.path.abspath(__file__)), |
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'..', |
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'data', |
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'dnn') |
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if os.path.exists(os.path.join(samplesDataDir, filename)): |
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return os.path.join(samplesDataDir, filename) |
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for path in ['OPENCV_DNN_TEST_DATA_PATH', 'OPENCV_TEST_DATA_PATH']: |
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try: |
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extraPath = os.environ[path] |
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absPath = os.path.join(extraPath, 'dnn', filename) |
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if os.path.exists(absPath): |
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return absPath |
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except KeyError: |
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pass |
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print('File ' + filename + ' not found! Please specify a path to ' |
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'/opencv_extra/testdata in OPENCV_DNN_TEST_DATA_PATH environment ' |
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'variable or pass a full path to model.') |
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exit(0) |
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@ -0,0 +1,117 @@ |
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%YAML:1.0 |
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################################################################################ |
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# Object detection models. |
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################################################################################ |
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# OpenCV's face detection network |
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opencv_fd: |
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model: "opencv_face_detector.caffemodel" |
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config: "opencv_face_detector.prototxt" |
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mean: [104, 177, 123] |
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scale: 1.0 |
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width: 300 |
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height: 300 |
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rgb: false |
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sample: "object_detection" |
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# YOLO object detection family from Darknet (https://pjreddie.com/darknet/yolo/) |
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# Might be used for all YOLOv2, TinyYolov2 and YOLOv3 |
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yolo: |
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model: "yolov3.weights" |
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config: "yolov3.cfg" |
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mean: [0, 0, 0] |
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scale: 0.00392 |
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width: 416 |
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height: 416 |
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rgb: true |
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classes: "object_detection_classes_yolov3.txt" |
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sample: "object_detection" |
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tiny-yolo-voc: |
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model: "tiny-yolo-voc.weights" |
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config: "tiny-yolo-voc.cfg" |
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mean: [0, 0, 0] |
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scale: 0.00392 |
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width: 416 |
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height: 416 |
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rgb: true |
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classes: "object_detection_classes_pascal_voc.txt" |
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sample: "object_detection" |
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# Caffe implementation of SSD model from https://github.com/chuanqi305/MobileNet-SSD |
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ssd_caffe: |
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model: "MobileNetSSD_deploy.caffemodel" |
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config: "MobileNetSSD_deploy.prototxt" |
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mean: [127.5, 127.5, 127.5] |
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scale: 0.007843 |
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width: 300 |
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height: 300 |
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rgb: false |
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classes: "object_detection_classes_pascal_voc.txt" |
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sample: "object_detection" |
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# TensorFlow implementation of SSD model from https://github.com/tensorflow/models/tree/master/research/object_detection |
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ssd_tf: |
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model: "ssd_mobilenet_v1_coco_2017_11_17.pb" |
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config: "ssd_mobilenet_v1_coco_2017_11_17.pbtxt" |
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mean: [0, 0, 0] |
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scale: 1.0 |
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width: 300 |
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height: 300 |
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rgb: true |
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classes: "object_detection_classes_coco.txt" |
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sample: "object_detection" |
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# TensorFlow implementation of Faster-RCNN model from https://github.com/tensorflow/models/tree/master/research/object_detection |
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faster_rcnn_tf: |
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model: "faster_rcnn_inception_v2_coco_2018_01_28.pb" |
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config: "faster_rcnn_inception_v2_coco_2018_01_28.pbtxt" |
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mean: [0, 0, 0] |
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scale: 1.0 |
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width: 800 |
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height: 600 |
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rgb: true |
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sample: "object_detection" |
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################################################################################ |
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# Image classification models. |
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################################################################################ |
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# SqueezeNet v1.1 from https://github.com/DeepScale/SqueezeNet |
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squeezenet: |
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model: "squeezenet_v1.1.caffemodel" |
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config: "squeezenet_v1.1.prototxt" |
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mean: [0, 0, 0] |
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scale: 1.0 |
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width: 227 |
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height: 227 |
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rgb: false |
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classes: "classification_classes_ILSVRC2012.txt" |
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sample: "classification" |
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################################################################################ |
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# Semantic segmentation models. |
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################################################################################ |
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# ENet road scene segmentation network from https://github.com/e-lab/ENet-training |
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# Works fine for different input sizes. |
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enet: |
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model: "Enet-model-best.net" |
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mean: [0, 0, 0] |
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scale: 0.00392 |
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width: 512 |
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height: 256 |
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rgb: true |
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classes: "enet-classes.txt" |
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sample: "segmentation" |
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fcn8s: |
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model: "fcn8s-heavy-pascal.caffemodel" |
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config: "fcn8s-heavy-pascal.prototxt" |
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mean: [0, 0, 0] |
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scale: 1.0 |
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width: 500 |
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height: 500 |
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rgb: false |
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sample: "segmentation" |
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