I'd like to use the DeepLearningKit for iOS. I want to use UIImage objects to be classified. The sample application only uses a float array loaded from a json file. Thus I have to create the bitmap representation of the UIImage as a float array and use this for the classify-method.
Can anybody help me on that? Is there a way to create a bitmap representation for UIImage? Moreover I have to swap the channels from RGB to BGR.
Thank you
Have added an extension to UIImage that allows setting and getting RGB(A) pixels directly - key methods:
public func setPixelColorAtPoint(point:CGPoint, color: RawColorType) -> UIImage?
func getPixelColorAtLocation(point:CGPoint)->UIColor?
where RawColorType is defined as
public typealias RawColorType = (newRedColor:UInt8, newgreenColor:UInt8, newblueColor:UInt8, newalphaValue:UInt8)
This way you should be able to convert back and forth between bitmap representation and UIImage. Wrote a blog post that gives some more context: http://deeplearningkit.org/tutorials-for-ios-os-x-and-tvos/tutorial-image-handling-in-deeplearningkit/
I've write a function to convert a image file to Caffe blob on iOS platform. You can find it here. I hope it will help you.
Code snippet:
// Convert Bitmap (channels*width*height) to Matrix (width*height*channels)
// Remove alpha channel
int input_channels = input_layer->channels();
LOG(INFO) << "image_channels:" << image_channels << " input_channels:" << input_channels;
if (input_channels == 3 && image_channels != 4) {
LOG(ERROR) << "image_channels input_channels not match.";
return false;
} else if (input_channels == 1 && image_channels != 1) {
LOG(ERROR) << "image_channels input_channels not match.";
return false;
}
float *input_data = input_layer->mutable_cpu_data();
for (size_t h = 0; h < height; h++) {
for (size_t w = 0; w < width; w++) {
for (size_t c = 0; c < input_channels; c++) {
// OpenCV use BGR instead of RGB
size_t cc = c;
if (input_channels == 3) {
cc = 2 - c;
}
// Convert uint8_t to float
input_data[c*width*height + h*width + w] =
static_cast<float>(result[h*width*image_channels + w*image_channels + cc]);
if (mean.size() == input_channels) {
input_data[c*width*height + h*width + w] -= mean[c];
}
}
}
}
