addedBlobs.reserve(layersSize + 1);
//setup input layer names
+ std::vector<String> netInputs(net.input_size());
{
- std::vector<String> netInputs(net.input_size());
for (int inNum = 0; inNum < net.input_size(); inNum++)
{
addedBlobs.push_back(BlobNote(net.input(inNum), 0, inNum));
netInputs[inNum] = net.input(inNum);
}
- dstNet.setInputsNames(netInputs);
}
for (int li = 0; li < layersSize; li++)
if (repetitions)
name += String("_") + toString(repetitions);
+ if (type == "Input")
+ {
+ addedBlobs.push_back(BlobNote(name, 0, netInputs.size()));
+ netInputs.push_back(name);
+ continue;
+ }
+
int id = dstNet.addLayer(name, type, layerParams);
for (int inNum = 0; inNum < layer.bottom_size(); inNum++)
for (int outNum = 0; outNum < layer.top_size(); outNum++)
addOutput(layer, id, outNum);
}
+ dstNet.setInputsNames(netInputs);
addedBlobs.clear();
}
CV_DNN_REGISTER_LAYER_CLASS(MaxUnpool, MaxUnpoolLayer);
CV_DNN_REGISTER_LAYER_CLASS(Dropout, BlankLayer);
CV_DNN_REGISTER_LAYER_CLASS(Identity, BlankLayer);
+ CV_DNN_REGISTER_LAYER_CLASS(Silence, BlankLayer);
CV_DNN_REGISTER_LAYER_CLASS(Crop, CropLayer);
CV_DNN_REGISTER_LAYER_CLASS(Eltwise, EltwiseLayer);
Size kernel, Size pad, Size stride, Size dilation,
const ActivationLayer* activ, int ngroups, int nstripes )
{
- CV_Assert( input.dims == 4 && output.dims == 4 &&
- input.size[0] == output.size[0] &&
- weights.rows == output.size[1] &&
- weights.cols == (input.size[1]/ngroups)*kernel.width*kernel.height &&
- input.type() == output.type() &&
- input.type() == weights.type() &&
- input.type() == CV_32F &&
- input.isContinuous() &&
- output.isContinuous() &&
+ CV_Assert( input.dims == 4 && output.dims == 4,
+ input.size[0] == output.size[0],
+ weights.rows == output.size[1],
+ weights.cols == (input.size[1]/ngroups)*kernel.width*kernel.height,
+ input.type() == output.type(),
+ input.type() == weights.type(),
+ input.type() == CV_32F,
+ input.isContinuous(),
+ output.isContinuous(),
biasvec.size() == (size_t)output.size[1]+2);
ParallelConv p;
l->pad.width, l->stride.height, l->stride.width, l->dilation.height,
l->dilation.width, l->padMode);
- bool bias = params.get<bool>("bias_term", true);
l->numOutput = params.get<int>("num_output");
int ngroups = params.get<int>("group", 1);
l->adjustPad.width = params.get<int>("adj_w", 0);
CV_Assert(l->numOutput % ngroups == 0);
- CV_Assert((bias && l->blobs.size() == 2) || (!bias && l->blobs.size() == 1));
CV_Assert(l->adjustPad.width < l->stride.width &&
l->adjustPad.height < l->stride.height);
}
std::vector<MatShape> &outputs,
std::vector<MatShape> &internals) const
{
+ CV_Assert(blobs.size() == 1 + hasBias);
Layer::getMemoryShapes(inputs, requiredOutputs, outputs, internals);
return true;
}
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
- CV_Assert(blobs.size() == 1 + hasBias);
-
for (size_t ii = 0; ii < outputs.size(); ii++)
{
Mat &inpBlob = *inputs[ii];
normAssert(out, ref, "", l1, lInf);
}
+// https://github.com/richzhang/colorization
+TEST(Reproducibility_Colorization, Accuracy)
+{
+ const float l1 = 1e-5;
+ const float lInf = 3e-3;
+
+ Mat inp = blobFromNPY(_tf("colorization_inp.npy"));
+ Mat ref = blobFromNPY(_tf("colorization_out.npy"));
+ Mat kernel = blobFromNPY(_tf("colorization_pts_in_hull.npy"));
+
+ const string proto = findDataFile("dnn/colorization_deploy_v2.prototxt", false);
+ const string model = findDataFile("dnn/colorization_release_v2.caffemodel", false);
+ Net net = readNetFromCaffe(proto, model);
+
+ net.getLayer(net.getLayerId("class8_ab"))->blobs.push_back(kernel);
+ net.getLayer(net.getLayerId("conv8_313_rh"))->blobs.push_back(Mat(1, 313, CV_32F, 2.606));
+
+ net.setInput(inp);
+ Mat out = net.forward();
+
+ normAssert(out, ref, "", l1, lInf);
+}
+
}
--- /dev/null
+# Script is based on https://github.com/richzhang/colorization/colorize.py
+import numpy as np
+import argparse
+import cv2 as cv
+
+def parse_args():
+ parser = argparse.ArgumentParser(description='iColor: deep interactive colorization')
+ parser.add_argument('--input', help='Path to image or video. Skip to capture frames from camera')
+ parser.add_argument('--prototxt', help='Path to colorization_deploy_v2.prototxt', default='./models/colorization_release_v2.prototxt')
+ parser.add_argument('--caffemodel', help='Path to colorization_release_v2.caffemodel', default='./models/colorization_release_v2.caffemodel')
+ parser.add_argument('--kernel', help='Path to pts_in_hull.npy', default='./resources/pts_in_hull.npy')
+
+ args = parser.parse_args()
+ return args
+
+if __name__ == '__main__':
+ W_in = 224
+ H_in = 224
+ imshowSize = (640, 480)
+
+ args = parse_args()
+
+ # Select desired model
+ net = cv.dnn.readNetFromCaffe(args.prototxt, args.caffemodel)
+
+ pts_in_hull = np.load(args.kernel) # load cluster centers
+
+ # populate cluster centers as 1x1 convolution kernel
+ pts_in_hull = pts_in_hull.transpose().reshape(2, 313, 1, 1)
+ net.getLayer(long(net.getLayerId('class8_ab'))).blobs = [pts_in_hull.astype(np.float32)]
+ net.getLayer(long(net.getLayerId('conv8_313_rh'))).blobs = [np.full([1, 313], 2.606, np.float32)]
+
+ if args.input:
+ cap = cv.VideoCapture(args.input)
+ else:
+ cap = cv.VideoCapture(0)
+
+ while cv.waitKey(1) < 0:
+ hasFrame, frame = cap.read()
+ if not hasFrame:
+ cv.waitKey()
+ break
+
+ img_rgb = (frame[:,:,[2, 1, 0]] * 1.0 / 255).astype(np.float32)
+
+ img_lab = cv.cvtColor(img_rgb, cv.COLOR_RGB2Lab)
+ img_l = img_lab[:,:,0] # pull out L channel
+ (H_orig,W_orig) = img_rgb.shape[:2] # original image size
+
+ # resize image to network input size
+ img_rs = cv.resize(img_rgb, (W_in, H_in)) # resize image to network input size
+ img_lab_rs = cv.cvtColor(img_rs, cv.COLOR_RGB2Lab)
+ img_l_rs = img_lab_rs[:,:,0]
+ img_l_rs -= 50 # subtract 50 for mean-centering
+
+ net.setInput(cv.dnn.blobFromImage(img_l_rs))
+ ab_dec = net.forward('class8_ab')[0,:,:,:].transpose((1,2,0)) # this is our result
+
+ (H_out,W_out) = ab_dec.shape[:2]
+ ab_dec_us = cv.resize(ab_dec, (W_orig, H_orig))
+ img_lab_out = np.concatenate((img_l[:,:,np.newaxis],ab_dec_us),axis=2) # concatenate with original image L
+ img_bgr_out = np.clip(cv.cvtColor(img_lab_out, cv.COLOR_Lab2BGR), 0, 1)
+
+ frame = cv.resize(frame, imshowSize)
+ cv.imshow('origin', frame)
+ cv.imshow('gray', cv.cvtColor(frame, cv.COLOR_RGB2GRAY))
+ cv.imshow('colorized', cv.resize(img_bgr_out, imshowSize))