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073488896e
Improved and refactored text detection sample in dnn module #25326 Clean up samples: #25006 This pull requests merges and simplifies different text detection samples in dnn module of opencv in to one file. An option has been provided to choose the detection model from EAST or DB ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [x] There is a reference to the original bug report and related work - [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [x] The feature is well documented and sample code can be built with the project CMake
188 lines
8.6 KiB
Python
188 lines
8.6 KiB
Python
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, alias=None):
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if alias is not None:
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modelName = alias
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elif len(sys.argv) > 1:
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modelName = sys.argv[1]
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else:
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return
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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 "sha1" in name:
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prefix = name.replace("sha1", "")
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value = node.getNode(prefix + "load_info")
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value = value.getNode(name)
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if "download_sha" in name:
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prefix = name.replace("download_sha", "")
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value = node.getNode(prefix + "load_info")
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value = value.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, alias=None, prefix=""):
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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(prefix+'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, prefix+'model',
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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), .weights (Darknet), .bin (OpenVINO)', alias=alias)
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add_argument(zoo, parser, prefix+'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)', alias=alias)
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add_argument(zoo, parser, prefix+'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.', alias=alias)
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add_argument(zoo, parser, prefix+'std', nargs='+', type=float, default=[0, 0, 0],
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help='Preprocess input image by dividing on a standard deviation.', alias=alias)
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add_argument(zoo, parser, prefix+'scale', type=float, default=1.0,
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help='Preprocess input image by multiplying on a scale factor.', alias=alias)
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add_argument(zoo, parser, prefix+'width', type=int,
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help='Preprocess input image by resizing to a specific width.', alias=alias)
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add_argument(zoo, parser, prefix+'height', type=int,
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help='Preprocess input image by resizing to a specific height.', alias=alias)
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add_argument(zoo, parser, prefix+'rgb', action='store_true',
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help='Indicate that model works with RGB input images instead BGR ones.', alias=alias)
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add_argument(zoo, parser, prefix+'labels',
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help='Optional path to a text file with names of labels to label detected objects.', alias=alias)
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add_argument(zoo, parser, prefix+'postprocessing', type=str,
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help='Post-processing kind depends on model topology.', alias=alias)
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add_argument(zoo, parser, prefix+'background_label_id', type=int, default=-1,
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help='An index of background class in predictions. If not negative, exclude such class from list of classes.', alias=alias)
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add_argument(zoo, parser, prefix+'sha1', type=str,
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help='Optional path to hashsum of downloaded model to be loaded from models.yml', alias=alias)
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add_argument(zoo, parser, prefix+'download_sha', type=str,
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help='Optional path to hashsum of downloaded model to be loaded from models.yml', alias=alias)
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def findModel(filename, sha1):
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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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fpath = cv.samples.findFile(filename, False)
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if fpath:
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return fpath
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if os.getenv('OPENCV_DOWNLOAD_CACHE_DIR') is None:
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print('[WARN] Please specify a path to model download directory in OPENCV_DOWNLOAD_CACHE_DIR environment variable.')
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return findFile(filename)
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if os.path.exists(os.path.join(os.environ['OPENCV_DOWNLOAD_CACHE_DIR'], sha1, filename)):
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return os.path.join(os.environ['OPENCV_DOWNLOAD_CACHE_DIR'], sha1, filename)
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if os.path.exists(os.path.join(os.environ['OPENCV_DOWNLOAD_CACHE_DIR'], filename)):
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return os.path.join(os.environ['OPENCV_DOWNLOAD_CACHE_DIR'], filename)
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raise FileNotFoundError('File ' + filename + ' not found! Please specify a path to '
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'model download directory in OPENCV_DOWNLOAD_CACHE_DIR '
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'environment variable or pass a full path to ' + filename)
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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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fpath = cv.samples.findFile(filename, False)
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if fpath:
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return fpath
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if os.getenv('OPENCV_SAMPLES_DATA_PATH') is None:
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print('[WARN] Please specify a path to `/samples/data` in OPENCV_SAMPLES_DATA_PATH environment variable.')
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exit(0)
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if os.path.exists(os.path.join(os.environ['OPENCV_SAMPLES_DATA_PATH'], filename)):
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return os.path.join(os.environ['OPENCV_SAMPLES_DATA_PATH'], filename)
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for path in ['OPENCV_DNN_TEST_DATA_PATH', 'OPENCV_TEST_DATA_PATH', 'OPENCV_SAMPLES_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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raise FileNotFoundError(
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'File ' + filename + ' not found! Please specify the path to '
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'/opencv/samples/data in the OPENCV_SAMPLES_DATA_PATH environment variable, '
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'or specify the path to opencv_extra/testdata in the OPENCV_DNN_TEST_DATA_PATH environment variable, '
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'or specify the path to the model download cache directory in the OPENCV_DOWNLOAD_CACHE_DIR environment variable, '
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'or pass the full path to ' + filename + '.'
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)
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def get_backend_id(backend_name):
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backend_ids = {
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"default": cv.dnn.DNN_BACKEND_DEFAULT,
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"openvino": cv.dnn.DNN_BACKEND_INFERENCE_ENGINE,
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"opencv": cv.dnn.DNN_BACKEND_OPENCV,
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"vkcom": cv.dnn.DNN_BACKEND_VKCOM,
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"cuda": cv.dnn.DNN_BACKEND_CUDA
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}
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if backend_name not in backend_ids:
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raise ValueError(f"Invalid backend name: {backend_name}")
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return backend_ids[backend_name]
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def get_target_id(target_name):
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target_ids = {
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"cpu": cv.dnn.DNN_TARGET_CPU,
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"opencl": cv.dnn.DNN_TARGET_OPENCL,
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"opencl_fp16": cv.dnn.DNN_TARGET_OPENCL_FP16,
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"ncs2_vpu": cv.dnn.DNN_TARGET_MYRIAD,
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"hddl_vpu": cv.dnn.DNN_TARGET_HDDL,
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"vulkan": cv.dnn.DNN_TARGET_VULKAN,
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"cuda": cv.dnn.DNN_TARGET_CUDA,
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"cuda_fp16": cv.dnn.DNN_TARGET_CUDA_FP16
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}
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if target_name not in target_ids:
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raise ValueError(f"Invalid target name: {target_name}")
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return target_ids[target_name] |