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Refactoring

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This commit is contained in:
Liubov Batanina 2020-01-22 10:52:40 +03:00
parent fada959b4b
commit 832ca0734d
3 changed files with 29 additions and 31 deletions
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2
modules/dnn/include/opencv2/dnn/all_layers.hpp
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@ -250,7 +250,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
std::vector<size_t> pads_begin, pads_end;
CV_DEPRECATED_EXTERNAL Size kernel, stride, pad;
CV_DEPRECATED_EXTERNAL int pad_l, pad_t, pad_r, pad_b;
CV_DEPRECATED_EXTERNAL bool globalPooling;
CV_DEPRECATED_EXTERNAL bool globalPooling; //!< Flag is true if at least one of the axes is global pooled.
std::vector<bool> isGlobalPooling;
bool computeMaxIdx;
String padMode;

20
modules/dnn/src/layers/pooling_layer.cpp
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@ -97,7 +97,7 @@ public:
CV_Error(Error::StsBadArg, "Unknown pooling type \"" + pool + "\"");
getPoolingKernelParams(params, kernel_size, isGlobalPooling, pads_begin, pads_end, strides, padMode);
globalPooling = std::accumulate(isGlobalPooling.begin(), isGlobalPooling.end(), 0) == 3;
globalPooling = isGlobalPooling[0] || isGlobalPooling[1] || isGlobalPooling[2];
if (kernel_size.size() == 2) {
kernel = Size(kernel_size[1], kernel_size[0]);
stride = Size(strides[1], strides[0]);
@ -149,18 +149,16 @@ public:
out.push_back(outputs[0].size[i]);
}
if (kernel_size.size() > inp.size()) {
kernel_size.erase(kernel_size.begin());
}
kernel_size.resize(out.size());
for (int i = 0; i < inp.size(); i++)
{
if (globalPooling) {
std::vector<size_t> finalKernel;
for (int i = 0; i < inp.size(); i++) {
int idx = isGlobalPooling.size() - inp.size() + i;
if (isGlobalPooling[idx])
kernel_size[i] = inp[i];
finalKernel.push_back(isGlobalPooling[idx] ? inp[i] : kernel_size[idx]);
}
kernel = Size(kernel_size.back(), kernel_size[kernel_size.size() - 2]);
kernel_size = finalKernel;
kernel = Size(kernel_size[1], kernel_size[0]);
}
getConvPoolPaddings(inp, kernel_size, strides, padMode, pads_begin, pads_end);
if (pads_begin.size() == 2) {

36
samples/dnn/human_parsing.py
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@ -6,23 +6,6 @@ import argparse
backends = (cv.dnn.DNN_BACKEND_DEFAULT, 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 human parsing using JPPNet',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--input', '-i', help='Path to input image. Skip this argument to capture frames from a camera.')
parser.add_argument('--model', '-m', required=True, help='Path to pb model.')
parser.add_argument('--backend', choices=backends, default=cv.dnn.DNN_BACKEND_DEFAULT, type=int,
help="Choose one of computation backends: "
"%d: automatically (by default), "
"%d: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
"%d: OpenCV implementation" % backends)
parser.add_argument('--target', choices=targets, default=cv.dnn.DNN_TARGET_CPU, type=int,
help='Choose one of target computation devices: '
'%d: CPU target (by default), '
'%d: OpenCL, '
'%d: OpenCL fp16 (half-float precision), '
'%d: VPU' % targets)
args, _ = parser.parse_known_args()
# To get pre-trained model download https://drive.google.com/file/d/1BFVXgeln-bek8TCbRjN6utPAgRE0LJZg/view
# For correct convert .meta to .pb model download original repository https://github.com/Engineering-Course/LIP_JPPNet
# Change script evaluate_parsing_JPPNet-s2.py for human parsing
@ -147,7 +130,7 @@ def decode_labels(gray_image):
return segm
def parse_human(image_path, model_path, backend, target):
def parse_human(image_path, model_path, backend=cv.dnn.DNN_BACKEND_OPENCV, target=cv.dnn.DNN_TARGET_CPU):
"""
Prepare input for execution, run net and postprocess output to parse human.
:param image_path: path to input image
@ -164,6 +147,23 @@ def parse_human(image_path, model_path, backend, target):
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Use this script to run human parsing using JPPNet',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--input', '-i', help='Path to input image. Skip this argument to capture frames from a camera.')
parser.add_argument('--model', '-m', required=True, help='Path to pb model.')
parser.add_argument('--backend', choices=backends, default=cv.dnn.DNN_BACKEND_DEFAULT, type=int,
help="Choose one of computation backends: "
"%d: automatically (by default), "
"%d: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
"%d: OpenCV implementation" % backends)
parser.add_argument('--target', choices=targets, default=cv.dnn.DNN_TARGET_CPU, type=int,
help='Choose one of target computation devices: '
'%d: CPU target (by default), '
'%d: OpenCL, '
'%d: OpenCL fp16 (half-float precision), '
'%d: VPU' % targets)
args, _ = parser.parse_known_args()
output = parse_human(args.input, args.model, args.backend, args.target)
winName = 'Deep learning human parsing in OpenCV'
cv.namedWindow(winName, cv.WINDOW_AUTOSIZE)