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Merge pull request #12641 from dkurt:dnn_samples_args_autofill

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Alexander Alekhin
2018-10-13 12:28:08 +00:00
parent 1be75437b7 ad5898224d
commit 0f41daeba5
12 changed files with 435 additions and 104 deletions
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samples/dnn/object_detection.py
+13 -21
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@@ -1,8 +1,8 @@
import cv2 as cv
import argparse
import sys
import numpy as np
from common import *
from tf_text_graph_common import readTextMessage
from tf_text_graph_ssd import createSSDGraph
from tf_text_graph_faster_rcnn import createFasterRCNNGraph
@@ -10,15 +10,10 @@ from tf_text_graph_faster_rcnn import createFasterRCNNGraph
backends = (cv.dnn.DNN_BACKEND_DEFAULT, cv.dnn.DNN_BACKEND_HALIDE, cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_BACKEND_OPENCV)
targets = (cv.dnn.DNN_TARGET_CPU, cv.dnn.DNN_TARGET_OPENCL, cv.dnn.DNN_TARGET_OPENCL_FP16, cv.dnn.DNN_TARGET_MYRIAD)
parser = argparse.ArgumentParser(description='Use this script to run object detection deep learning networks using OpenCV.')
parser = argparse.ArgumentParser(add_help=False)
parser.add_argument('--zoo', default=os.path.join(os.path.dirname(os.path.abspath(__file__)), 'models.yml'),
help='An optional path to file with preprocessing parameters.')
parser.add_argument('--input', help='Path to input image or video file. Skip this argument to capture frames from a camera.')
parser.add_argument('--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)')
parser.add_argument('--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)')
parser.add_argument('--out_tf_graph', default='graph.pbtxt',
help='For models from TensorFlow Object Detection API, you may '
'pass a .config file which was used for training through --config '
@@ -26,18 +21,6 @@ parser.add_argument('--out_tf_graph', default='graph.pbtxt',
parser.add_argument('--framework', choices=['caffe', 'tensorflow', 'torch', 'darknet', 'dldt'],
help='Optional name of an origin framework of the model. '
'Detect it automatically if it does not set.')
parser.add_argument('--classes', help='Optional path to a text file with names of classes to label detected objects.')
parser.add_argument('--mean', nargs='+', type=float, default=[0, 0, 0],
help='Preprocess input image by subtracting mean values. '
'Mean values should be in BGR order.')
parser.add_argument('--scale', type=float, default=1.0,
help='Preprocess input image by multiplying on a scale factor.')
parser.add_argument('--width', type=int,
help='Preprocess input image by resizing to a specific width.')
parser.add_argument('--height', type=int,
help='Preprocess input image by resizing to a specific height.')
parser.add_argument('--rgb', action='store_true',
help='Indicate that model works with RGB input images instead BGR ones.')
parser.add_argument('--thr', type=float, default=0.5, help='Confidence threshold')
parser.add_argument('--nms', type=float, default=0.4, help='Non-maximum suppression threshold')
parser.add_argument('--backend', choices=backends, default=cv.dnn.DNN_BACKEND_DEFAULT, type=int,
@@ -52,8 +35,17 @@ parser.add_argument('--target', choices=targets, default=cv.dnn.DNN_TARGET_CPU,
'%d: OpenCL, '
'%d: OpenCL fp16 (half-float precision), '
'%d: VPU' % targets)
args, _ = parser.parse_known_args()
add_preproc_args(args.zoo, parser, 'object_detection')
parser = argparse.ArgumentParser(parents=[parser],
description='Use this script to run object detection deep learning networks using OpenCV.',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
args = parser.parse_args()
args.model = findFile(args.model)
args.config = findFile(args.config)
args.classes = findFile(args.classes)
# If config specified, try to load it as TensorFlow Object Detection API's pipeline.
config = readTextMessage(args.config)
if 'model' in config: