Develop Reference

Opencv findContours in Android seems much slower than findContours in Python. Do you have any suggestion to improve algorithm speed?

it's the first time for me that I ask help here. I will try to be as precise as possible in my question.
I am trying to develop a shape detection app for Android.
I first identified the algorithm which works for my case playing with Python. Basically for each frame I do this:
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
mask = cv2.inRange(hsv, lower_color, upper_color)
contours, _ = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
for cnt in contours:
#here I filter my results
by this algorithm I am able to run the analysis realtime on videos having a frame rate of 120fps.
So I tryied to implement the same algorithm on Android Studio, doing the following for each Frame:
Imgproc.cvtColor(frameInput, tempFrame, Imgproc.COLOR_BGR2HSV);
Core.inRange(tempFrame,lowColorRoi,highColorRoi,tempFrame);
List<MatOfPoint> contours1 = new ArrayList<MatOfPoint>();
Imgproc.findContours(tempFrame /*.clone()*/, contours1, new Mat(), Imgproc.RETR_TREE, Imgproc.CHAIN_APPROX_SIMPLE);
for(MatOfPoint c : contours1){
//here I filter my results
}
and I see that only the findContour function takes 5-600ms to be performed at each iteration (I noticed that it takes also more using tempFrame.clone()), allowing more or less to run the analysis with only 2fps.
This speed is not acceptable at all of course. Do you have any suggestion about how to improve this speed? 30-40fps would be already a good target for me.
I will really appreciate any help from you all. Many thanks in advance.

I would suggest trying to do your shape analysis on a lower resolution version of the image, if that is acceptable. I often see directly proportional timing with number of pixels of the image and the number of channels of the image - so if you can halve the width and height it could be a 4 times performance improvement. If that works, likely the first thing to do is a resize, then all subsequent calls have a smaller burden.
Next, be careful using OpenCV in Java/Kotlin because there is a definite cost to marshalling over the JNI interface. You could write the majority of your code in native C++, and then make just a single call across JNI to a C++ function that handles all of the shape analysis at once.

About dlib::frontal_face_detector optimization

hi I'm making a app which detects face landmarks ( 68 point )
I'm in trouble optimizing system. I'm using HOG method to detect faces.
In, detector(cv_grayscale, face_detections, -0.2); type "dlib::frontal_face_detector& detector"
There are so many computations in there. So, android cpu cannot cover them.
So, anybody who solved this problem or relevant issues ?
bool DetectFacesHOG(vector<cv::Rect_<double> >& o_regions, const cv::Mat_<uchar>& intensity, dlib::frontal_face_detector& detector, std::vector<double>& o_confidences)
{
double scaling = 1.3;
cv::Mat_<uchar> upsampled_intensity;
cv::resize(intensity, upsampled_intensity, cv::Size((int)(intensity.cols*scaling), (int)(intensity.rows*scaling)));
dlib::cv_image<uchar> cv_grayscale(upsampled_intensity);
std::vector<dlib::full_detection> face_detections;
// millions of computation !!!!!!!!!!!!!!!!!!!!!!!!
detector(cv_grayscale, face_detections, -0.2);
....
}

Download latest opencv android SDK from here.
it contains a lot of debugged samples. One of them is face detection and it detects faces with 22 frames per second speed on my Xperia-Z5 Phone. Finally, if opencv errors cause of rotation of camera, use this code. The code is very Clear and finds best frame resolution for your Camera View. İf you also want face recognition you can download C++ modules but you must use NDK(c++). Because Android SDK won't have face.h or other modules. You can combine detecting a face from java and recognize them from c++. Don't worry about speed opencv optimizes that. Face detecting lpcascade classificer xmls works high performance. But if you want more detect use haarcascade.

Minko - Android light issue

I am working with minko and seem to be facing a light issue with Android.
I managed to compile for linux64, Android and html a modified code (based on the tutorials provided by Minko). I simply load and rotate 4 .obj files (the pirate one provided and 3 found on turbosquid for demo purposes only).
The correct result is viewed in the linux64 and html version but the Android one has a "redish" light thrown into it, although the binaries are being generated from the same c++ code.
Here are some pics to demonstrate the problem:
linux64 :
http://tinypic.com/r/qzm2s5/8
Android version :
http://tinypic.com/r/23mn0p3/8
(Couldn’t link the html version but it is close to the linux64 one.)
Here is the part of the code related to the light :
// create the spot light node
auto spotLightNode = scene::Node::create("spotLight");
// change the spot light position
//spotLightNode->addComponent(Transform::create(Matrix4x4::create()->lookAt(Vector3::zero(), Vector3::create(0.1f, 2.f, 0.f)))); //ok linux - html
spotLightNode->addComponent(Transform::create(Matrix4x4::create()->lookAt(Vector3::zero(), Vector3::create(0.1f, 8.f, 0.f))));
// create the point light component
auto spotLight = SpotLight::create(.15f, .4f); //ok linux and html
// update the spot light component attributes
spotLight->diffuse(4.5f); //ori - ok linux - html
// add the component to the spot light node
spotLightNode->addComponent(spotLight);
//sets a red color to our spot light
//spotLightNode->component<SpotLight>()->color()->setTo(2.0f, 1.0f, 1.0f);
// add the node to the root of the scene graph
rootNode->addChild(spotLightNode);
As you can notice the color()->setTo has been turned off and works for all except Android (clean and rebuild). Any idea what might be the source of the problem here ?
Any pointer would be much appreciated.
Thx.

Can you test it on other Android devices or with a more recent ROM and give us the result? LG-D855 (LG G3) is powered by an Adreno 330: those GPUs are known to have GLSL compiling deffects, especially with loops and/or structs like we use in Phong.fragment.glsl on the master branch.
The Phong.fragment.glsl on the dev branch has been heavily refactored to fix this (for directional lights only for now).
You could try the dev branch and a directional light and see if it fixes the issue. Be careful though: the dev branch introduces the beta 3, with some API changes. The biggest API change being the math API now using GLM, and the *.effect file format. The best way to go is simply to update your math code to use the new API, everything else should be straight forward.

Use XAML path geometries in android

I have a DrawingImage library (icons) for my Windows application development under WPF. I am new to Android development and this library has a lot of path geometries that I would like to use in my Android projects.
I searched to find a built-in way to use geometries such as "F1 M 0 0 -5.715 5 -8.48 5 -14.195 0 0 0 z m -15.0977 9.0001 0 -1 0 -7.461 5.488 4.803 -4.809 3.206 7.319 5.91 7.324 -5.91 -4.81 -3.206 5.488 -4.803 0 8.461 -7.998 6.795 -8.002 -6.795 z" directly in Android however I could not find a way.
I have run into some libraries that can display SVG images and they are OK. However I need to display my path geometries from XAML in android. Is this possible?

I found out that it is not possible to transfer my XAML vectors into Android environment. I took another way:
As far as I see, the best way to use verctor icons in Android is using font icon libraries. https://github.com/bperin/FontAwesomeAndroid is what I used.
Later I needed more icons than provided by the FontAwesome library. My search ended with http://fontastic.me/ service. You can generate your custom font collection free of charge. Then I modified source code of FontAwesomeAndroid so I am able to use my own font icon collection.

Check if image is black

I'm running a Python script under Windows which deals with 480x800 PNG images with 32-bit depth. I need to check if the given image is fully black or not. After some searching I've found that ImageMagick could help me to achieve this but unfortunately there's no manual for such task.
So a more general question is how to check if the image consists only of one color?
Edit:
My apologies for not providing all the information about the environment from beginning. The python script is executed using Android's monkeyrunner. Since it uses it's own instance of Jython (version 2.5) it's not possible to use any modules from external libraries. Inside there's a MonkeyImage class to work with screenshots taken from the device. So I adopted #eumiro's answer for it.

import Image
im = Image.load("image.png")
diff_colors = list(set(im.getdata()))
if len(diff_colors) == 1 and diff_colors[0] == (0, 0, 0):
print "all black"
EDIT as #JonClements proposes, this will be faster and stop as soon as anything else than black is found:
import Image
im = Image.load("image.png")
if all(rgb == (0,0,0) for rgb in im.getdata()):
print "all black"

I am no expert in Python but I saw that there is a PNG module that you can use.
Load the PNG and export it to an RGB(A) array.
Checking if it is totally black should then be simple. Run through the array and make sure nothing differs from 0.
I think this should work.
Out of curiosity, why would you want to check if the image is black?