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Add a file with preprocessing parameters for deep learning networks

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Dmitry Kurtaev
2018-09-20 17:59:04 +03:00
parent 87b1835cb6
commit ad5898224d
12 changed files with 435 additions and 104 deletions
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samples/dnn/classification.py
+17 -22
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@@ -1,35 +1,19 @@
import cv2 as cv
import argparse
import numpy as np
import sys
from common import *
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 classification 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)')
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 (TensorFlow), .cfg (Darknet)')
parser.add_argument('--framework', choices=['caffe', 'tensorflow', 'torch', 'darknet'],
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.')
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, required=True,
help='Preprocess input image by resizing to a specific width.')
parser.add_argument('--height', type=int, required=True,
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('--backend', choices=backends, default=cv.dnn.DNN_BACKEND_DEFAULT, type=int,
help="Choose one of computation backends: "
"%d: automatically (by default), "
@@ -42,8 +26,17 @@ 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, 'classification')
parser = argparse.ArgumentParser(parents=[parser],
description='Use this script to run classification 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)
# Load names of classes
classes = None
if args.classes:
@@ -66,7 +59,9 @@ while cv.waitKey(1) < 0:
break
# Create a 4D blob from a frame.
blob = cv.dnn.blobFromImage(frame, args.scale, (args.width, args.height), args.mean, args.rgb, crop=False)
inpWidth = args.width if args.width else frame.shape[1]
inpHeight = args.height if args.height else frame.shape[0]
blob = cv.dnn.blobFromImage(frame, args.scale, (inpWidth, inpHeight), args.mean, args.rgb, crop=False)
# Run a model
net.setInput(blob)