Context
I have an Android app that takes a picture, blurs the picture, removes the blur based on a mask and applies a final layer (not relevant). The last 2 steps, removing the blur based on a mask and applying a final layer is done repeatedly, each time with a new mask (150 masks).
The output get's drawn on a canvas (SurfaceView). This way the app effectively creates a view of the image with an animated blur.
Technical details & code
All of these image processing steps are achieved with RenderScript.
I'm leaving out the code for step 1, blurring the picture, since this is irrelevant for the problem I'm facing.
Step 2: removing the blur based on a mask
I have a custom kernel which takes an in Allocation as argument and holds 2 global variables, which are Allocations as well.
These 3 Allocations all get their data from bitmaps using Allocation.copyFrom(bitmap).
Step 3: applying a final layer
Here I have a custom kernel as well which takes an in Allocation as argument and holds 3 global variables, of which 1 is and Allocation and 2 are floats.
How these kernels work is irrelevant to this question but just to be sure I included some simplified snippets below.
Another thing to note is that I am following all best practices to ensure performance is at its best regarding Allocations, RenderScript and my SurfaceView.
So common mistakes such as creating a new RenderScript instance each time, not re-using Allocations when possible,.. are safe to ignore.
blurMask.rs
#pragma version(1)
#pragma rs java_package_name(com.example.rs)
#pragma rs_fp_relaxed
// Extra allocations
rs_allocation allocBlur;
rs_allocation allocBlurMask;
/*
* RenderScript kernel that performs a masked blur manipulation.
* Blur Pseudo: out = original * blurMask + blur * (1.0 - blurMask)
* -> Execute this for all channels
*/
uchar4 __attribute__((kernel)) blur_mask(uchar4 inOriginal, uint32_t x, uint32_t y) {
// Manually getting current element from the blur and mask allocations
uchar4 inBlur = rsGetElementAt_uchar4(allocBlur, x, y);
uchar4 inBlurMask = rsGetElementAt_uchar4(allocBlurMask, x, y);
// normalize to 0.0 -> 1.0
float4 inOriginalNorm = rsUnpackColor8888(inOriginal);
float4 inBlurNorm = rsUnpackColor8888(inBlur);
float4 inBlurMaskNorm = rsUnpackColor8888(inBlurMask);
inBlurNorm.rgb = inBlurNorm.rgb * 0.7 + 0.3;
float4 outNorm = inOriginalNorm;
outNorm.rgb = inOriginalNorm.rgb * inBlurMaskNorm.rgb + inBlurNorm.rgb * (1.0 - inBlurMaskNorm.rgb);
return rsPackColorTo8888(outNorm);
}
myKernel.rs
#pragma version(1)
#pragma rs java_package_name(com.example.rs)
#pragma rs_fp_relaxed
// Extra allocations
rs_allocation allocExtra;
// Randoms; Values are set from kotlin, the values below just act as a min placeholder.
float randB = 0.1f;
float randW = 0.75f;
/*
* RenderScript kernel that performs a manipulation.
*/
uchar4 __attribute__((kernel)) my_kernel(uchar4 inOriginal, uint32_t x, uint32_t y) {
// Manually getting current element from the extra allocation
uchar4 inExtra = rsGetElementAt_uchar4(allocExtra, x, y);
// normalize to 0.0 -> 1.0
float4 inOriginalNorm = rsUnpackColor8888(inOriginal);
float4 inExtraNorm = rsUnpackColor8888(inExtra);
float4 outNorm = inOriginalNorm;
if (inExtraNorm.r > 0.0) {
outNorm.rgb = inOriginalNorm.rgb * 0.7 + 0.3;
// Separate channel operation since we are using inExtraNorm.r everywhere
outNorm.r = outNorm.r * inExtraNorm.r + inOriginalNorm.r * (1.0 - inExtraNorm.r);
outNorm.g = outNorm.g * inExtraNorm.r + inOriginalNorm.g * (1.0 - inExtraNorm.r);
outNorm.b = outNorm.b * inExtraNorm.r + inOriginalNorm.b * (1.0 - inExtraNorm.r);
}
else if (inExtraNorm.g > 0.0) {
...
}
return rsPackColorTo8888(outNorm);
}
Problem
So the app works great on a range of devices, even on low-end devices. I manually cap the FPS at 15, but when I remove this cap, I get results ranging from 15-20 on low-end devices to 35-40 on high-end devices.
The Samsung Galaxy S8 is where my problem occurs. For some reason I only manage to get around 10 FPS. If I use adb to force RenderScript to use CPU instead:
adb shell setprop debug.rs.default-CPU-driver 1
I get around 12-15 FPS, but obviously I want it to run on the GPU.
An important, weird thing I noticed
If I trigger a touch event, no matter where (even out of the app), the performance dramatically increases to around 35-40 FPS. If I lift my finger from the screen again, FPS drops back to 10 FPS.
NOTE: drawing the result on the SurfaceView can be excluded as an impacting factor since the results are the same with just the computation in RenderScript without drawing the actual result.
Questions
So I have more than one question really:
What could be the reason behind the low performance?
Why would a touch event improve this performance so dramatically?
How could I solve or work around this issue?
I am kind of stuck with this problem, and I know there are so many questions about it on stack overflow but in my case. Nothing gives the expected result.
The Context:
Am using Android OpenCV along with Tesseract so I can read the MRZ area in the passport. When the camera is started I pass the input frame to an AsyncTask, the frame is processed, the MRZ area is extracted succesfully, I pass the extracted MRZ area to a function prepareForOCR(inputImage) that takes the MRZ area as gray Mat and Will output a bitmap with the thresholded image that I will pass to Tesseract.
The problem:
The problem is while thresholding the Image, I use adaptive thresholding with blockSize = 13 and C = 15, but the result given is not always the same depending on the lighting of the image and the conditions in general from which the frame is taken.
What I have tried:
First I am resizing the image to a specific size (871,108) so the input image is always the same and not dependant on which phone is used.
After resizing, I try with different BlockSize and C values
//toOcr contains the extracted MRZ area
Bitmap toOCRBitmap = Bitmap.createBitmap(bitmap);
Mat inputFrame = new Mat();
Mat toOcr = new Mat();
Utils.bitmapToMat(toOCRBitmap, inputFrame);
Imgproc.cvtColor(inputFrame, inputFrame, Imgproc.COLOR_BGR2GRAY);
TesseractResult lastResult = null;
for (int B = 11; B < 70; B++) {
for (int C = 11; C < 70; C++){
if (IsPrime(B) && IsPrime(C)){
Imgproc.adaptiveThreshold(inputFrame, toOcr, 255, Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C, Imgproc.THRESH_BINARY, B ,C);
Bitmap toOcrBitmap = OpenCVHelper.getBitmap(toOcr);
TesseractResult result = TesseractInstance.extractFrame(toOcrBitmap, "ocrba");
if (result.getMeanConfidence()> 70) {
if (MrzParser.tryParse(result.getText())){
Log.d("Main2Activity", "Best result with " + B + " : " + C);
return result;
}
}
}
}
}
Using the code below, the thresholded result image is a black on white image which gives a confidence greater than 70, I can't really post the whole image for privacy reasons, but here's a clipped one and a dummy password one.
Using the MrzParser.tryParse function which adds checks for the character position and its validity within the MRZ, am able to correct some occurences like a name containing a 8 instead of B, and get a good result but it takes so much time, which is normal because am thresholding almost 255 images in the loop, adding to that the Tesseract call.
I already tried getting a list of C and B values which occurs the most but the results are different.
The question:
Is there a way to define a C and blocksize value so that it s always giving the same result, maybe adding more OpenCV calls so The input image like increasing contrast and so on, I searched the web for 2 weeks now I can't find a viable solution, this is the only one that is giving accurate results
You can use a clustering algorithm to cluster the pixels based on color. The characters are dark and there is a good contrast in the MRZ region, so a clustering method will most probably give you a good segmentation if you apply it to the MRZ region.
Here I demonstrate it with MRZ regions obtained from sample images that can be found on the internet.
I use color images, apply some smoothing, convert to Lab color space, then cluster the a, b channel data using kmeans (k=2). The code is in python but you can easily adapt it to java. Due to the randomized nature of the kmeans algorithm, the segmented characters will have label 0 or 1. You can easily sort it out by inspecting cluster centers. The cluster-center corresponding to characters should have a dark value in the color space you are using.
I just used the Lab color space here. You can use RGB, HSV or even GRAY and see which one is better for you.
After segmenting like this, I think you can even find good values for B and C of your adaptive-threshold using the properties of the stroke width of the characters (if you think the adaptive-threshold gives a better quality output).
import cv2
import numpy as np
im = cv2.imread('mrz1.png')
# convert to Lab
lab = cv2.cvtColor(cv2.GaussianBlur(im, (3, 3), 1), cv2.COLOR_BGR2Lab)
im32f = np.array(im[:, :, 1:3], dtype=np.float32)
k = 2 # 2 clusters
term_crit = (cv2.TERM_CRITERIA_EPS, 30, 0.1)
ret, labels, centers = cv2.kmeans(im32f.reshape([im.shape[0]*im.shape[1], -1]),
k, None, term_crit, 10, 0)
# segmented image
labels = labels.reshape([im.shape[0], im.shape[1]]) * 255
Some results:
I looking for some advices about recognition of three handwritten shapes - circles, diamonds and rectangles. I tried diffrent aproaches but they failed so maybe you could point me in another, better direction.
What I tried:
1) Simple algorithm based on dot product between points of handwritten shape and ideal shape. It works not so bad at recognition of rectangle, but failed on circles and diamonds. The problem is that dot product of the circle and diamond is quite similiar even for ideal shapes.
2) Same aproach but using Dynamic Time Warping as measure of simililarity. Similiar problems.
3) Neural networks. I tried few aproaches - giving points data to neural networks (Feedforward and Kohonen) or giving rasterized image. For Kohonen it allways classified all the data (event the sample used to train) into the same category. Feedforward with points was better (but on the same level as aproach 1 and 2) and with rasterized image it was very slow (I needs at least size^2 input neurons and for small sized of raster circle is indistinguishable even for me ;) ) and also without success. I think is because all of this shapes are closed figures? I am not big specialist of ANN (had 1 semester course of them) so maybe I am using them wrong?
4) Saving the shape as Freeman Chain Code and using some algorithms for computing similarity. I though that in FCC the shapes will be realy diffrent from each other. No success here (but I havent explorer this path very deeply).
I am building app for Android with this but I think the language is irrelevant here.
Here's some working code for a shape classifier. http://jsfiddle.net/R3ns3/ I pulled the threshold numbers (*Threshold variables in the code) out of the ether, so of course they can be tweaked for better results.
I use the bounding box, average point in a sub-section, angle between points, polar angle from bounding box center, and corner recognition. It can classify hand drawn rectangles, diamonds, and circles. The code records points while the mouse button is down and tries to classify when you stop drawing.
HTML
<canvas id="draw" width="300" height="300" style="position:absolute; top:0px; left:0p; margin:0; padding:0; width:300px; height:300px; border:2px solid blue;"></canvas>
JS
var state = {
width: 300,
height: 300,
pointRadius: 2,
cornerThreshold: 125,
circleThreshold: 145,
rectangleThreshold: 45,
diamondThreshold: 135,
canvas: document.getElementById("draw"),
ctx: document.getElementById("draw").getContext("2d"),
drawing: false,
points: [],
getCorners: function(angles, pts) {
var list = pts || this.points;
var corners = [];
for(var i=0; i<angles.length; i++) {
if(angles[i] <= this.cornerThreshold) {
corners.push(list[(i + 1) % list.length]);
}
}
return corners;
},
draw: function(color, pts) {
var list = pts||this.points;
this.ctx.fillStyle = color;
for(var i=0; i<list.length; i++) {
this.ctx.beginPath();
this.ctx.arc(list[i].x, list[i].y, this.pointRadius, 0, Math.PI * 2, false);
this.ctx.fill();
}
},
classify: function() {
// get bounding box
var left = this.width, right = 0,
top = this.height, bottom = 0;
for(var i=0; i<this.points.length; i++) {
var pt = this.points[i];
if(left > pt.x) left = pt.x;
if(right < pt.x) right = pt.x;
if(top > pt.y) top = pt.y;
if(bottom < pt.y) bottom = pt.y;
}
var center = {x: (left+right)/2, y: (top+bottom)/2};
this.draw("#00f", [
{x: left, y: top},
{x: right, y: top},
{x: left, y: bottom},
{x: right, y: bottom},
]);
// find average point in each sector (9 sectors)
var sects = [
{x:0,y:0,c:0},{x:0,y:0,c:0},{x:0,y:0,c:0},
{x:0,y:0,c:0},{x:0,y:0,c:0},{x:0,y:0,c:0},
{x:0,y:0,c:0},{x:0,y:0,c:0},{x:0,y:0,c:0}
];
var x3 = (right + (1/(right-left)) - left) / 3;
var y3 = (bottom + (1/(bottom-top)) - top) / 3;
for(var i=0; i<this.points.length; i++) {
var pt = this.points[i];
var sx = Math.floor((pt.x - left) / x3);
var sy = Math.floor((pt.y - top) / y3);
var idx = sy * 3 + sx;
sects[idx].x += pt.x;
sects[idx].y += pt.y;
sects[idx].c ++;
if(sx == 1 && sy == 1) {
return "UNKNOWN";
}
}
// get the significant points (clockwise)
var sigPts = [];
var clk = [0, 1, 2, 5, 8, 7, 6, 3]
for(var i=0; i<clk.length; i++) {
var pt = sects[clk[i]];
if(pt.c > 0) {
sigPts.push({x: pt.x / pt.c, y: pt.y / pt.c});
} else {
return "UNKNOWN";
}
}
this.draw("#0f0", sigPts);
// find angle between consecutive 3 points
var angles = [];
for(var i=0; i<sigPts.length; i++) {
var a = sigPts[i],
b = sigPts[(i + 1) % sigPts.length],
c = sigPts[(i + 2) % sigPts.length],
ab = Math.sqrt(Math.pow(b.x-a.x,2)+Math.pow(b.y-a.y,2)),
bc = Math.sqrt(Math.pow(b.x-c.x,2)+ Math.pow(b.y-c.y,2)),
ac = Math.sqrt(Math.pow(c.x-a.x,2)+ Math.pow(c.y-a.y,2)),
deg = Math.floor(Math.acos((bc*bc+ab*ab-ac*ac)/(2*bc*ab)) * 180 / Math.PI);
angles.push(deg);
}
console.log(angles);
var corners = this.getCorners(angles, sigPts);
// get polar angle of corners
for(var i=0; i<corners.length; i++) {
corners[i].t = Math.floor(Math.atan2(corners[i].y - center.y, corners[i].x - center.x) * 180 / Math.PI);
}
console.log(corners);
// whats the shape ?
if(corners.length <= 1) { // circle
return "CIRCLE";
} else if(corners.length == 2) { // circle || diamond
// difference of polar angles
var diff = Math.abs((corners[0].t - corners[1].t + 180) % 360 - 180);
console.log(diff);
if(diff <= this.circleThreshold) {
return "CIRCLE";
} else {
return "DIAMOND";
}
} else if(corners.length == 4) { // rectangle || diamond
// sum of polar angles of corners
var sum = Math.abs(corners[0].t + corners[1].t + corners[2].t + corners[3].t);
console.log(sum);
if(sum <= this.rectangleThreshold) {
return "RECTANGLE";
} else if(sum >= this.diamondThreshold) {
return "DIAMOND";
} else {
return "UNKNOWN";
}
} else {
alert("draw neater please");
return "UNKNOWN";
}
}
};
state.canvas.addEventListener("mousedown", (function(e) {
if(!this.drawing) {
this.ctx.clearRect(0, 0, 300, 300);
this.points = [];
this.drawing = true;
console.log("drawing start");
}
}).bind(state), false);
state.canvas.addEventListener("mouseup", (function(e) {
this.drawing = false;
console.log("drawing stop");
this.draw("#f00");
alert(this.classify());
}).bind(state), false);
state.canvas.addEventListener("mousemove", (function(e) {
if(this.drawing) {
var x = e.pageX, y = e.pageY;
this.points.push({"x": x, "y": y});
this.ctx.fillStyle = "#000";
this.ctx.fillRect(x-2, y-2, 4, 4);
}
}).bind(state), false);
Given the possible variation in handwritten inputs I would suggest that a neural network approach is the way to go; you will find it difficult or impossible to accurately model these classes by hand. LastCoder's attempt works to a degree, but it does not cope with much variation or have promise for high accuracy if worked on further - this kind of hand-engineered approach was abandoned a very long time ago.
State-of-the-art results in handwritten character classification these days is typically achieved with convolutional neural networks (CNNs). Given that you have only 3 classes the problem should be easier than digit or character classification, although from experience with the MNIST handwritten digit dataset, I expect that your circles, squares and diamonds may occasionally end up being difficult for even humans to distinguish.
So, if it were up to me I would use a CNN. I would input binary images taken from the drawing area to the first layer of the network. These may require some preprocessing. If the drawn shapes cover a very small area of the input space you may benefit from bulking them up (i.e. increasing line thickness) so as to make the shapes more invariant to small differences. It may also be beneficial to centre the shape in the image, although the pooling step might alleviate the need for this.
I would also point out that the more training data the better. One is often faced with a trade-off between increasing the size of one's dataset and improving one's model. Synthesising more examples (e.g. by skewing, rotating, shifting, stretching, etc) or spending a few hours drawing shapes may provide more of a benefit than you could get in the same time attempting to improve your model.
Good luck with your app!
A linear Hough transform of the square or the diamond ought to be easy to recognize. They will both produce four point masses. The square's will be in pairs at zero and 90 degrees with the same y-coordinates for both pairs; in other words, a rectangle. The diamond will be at two other angles corresponding to how skinny the diamond is, e.g. 45 and 135 or else 60 and 120.
For the circle you need a circular Hough transform, and it will produce a single bright point cluster in 3d (x,y,r) Hough space.
Both linear and circular Hough transforms are implemented in OpenCV, and it's possible to run OpenCV on Android. These implementations include thresholding to identify lines and circles. See pg. 329 and pg. 331 of the documentation here.
If you are not familiar with Hough transforms, the Wikipedia page is not bad.
Another algorithm you may find interesting and perhaps useful is given in this paper about polygon similarity. I implemented it many years ago, and it's still around here. If you can convert the figures to loops of vectors, this algorithm could compare them against patterns, and the similarity metric would show goodness of match. The algorithm ignores rotational orientation, so if your definition of square and diamond is with respect to the axes of the drawing surface, you will have to modify the algorithm a bit to differentiate these cases.
What you have here is a fairly standard clasification task, in an arguably vision domain.
You could do this several ways, but the best way isn't known, and can sometimes depend on fine details of the problem.
So, this isn't an answer, per se, but there is a website - Kaggle.com that runs competition for classifications. One of the sample/experiemental tasks they list is reading single hand written numeric digits. That is close enough to this problem, that the same methods are almost certainly going to apply fairly well.
I suggest you go to https://www.kaggle.com/c/digit-recognizer and look around.
But if that is too vague, I can tell you from my reading of it, and playing with that problem space, that Random Forests are a better basic starting place than Neural networks.
In this case (your 3 simple objects) you could try RanSaC-fitting for ellipse (getting the circle) and lines (getting the sides of the rectangle or diamond) - on each connected object if there are several objects to classify at the same time. Based on the actual setting (expected size, etc.) the RanSaC-parameters (how close must a point be to count as voter, how many voters you need at minimun) must be tuned. When you have found a line with RanSaC-fitting, remove the points "close" to it and go for the next line. The angles of the lines should make a distinction between diamand and rectangle easy.
A very simple approach optimized for classifying exactly these 3 objects could be the following:
compute the center of gravity of an object to classify
then compute the distances of the center to the object points as a function on the angle (from 0 to 2 pi).
classify the resulting graph based on the smoothness and/or variance and the position and height of the local maxima and minima (maybe after smoothing the graph).
I propose a way to do it in following steps : -
Take convex hull of the image (consider the shapes being convex)
divide into segments using clustering algorithms
Try to fit a curves or straight line to it and measure & threshold using training set which can be used for classifications
For your application try to divide into 4 clusters .
once you classify clusters as line or curves you can use the info to derive whether curve is circle,rectangle or diamond
I think the answers that are already in place are good, but perhaps a better way of thinking about it is that you should try to break the problem into meaningful pieces.
If possible avoid the problem entirely. For instance if you are recognizing gestures, just analyze the gestures in real time. With gestures you can provide feedback to the user as to how your program interpreted their gesture and the user will change what they are doing appropriately.
Clean up the image in question. Before you do anything come up with an algorithm to try to select what the correct thing is you are trying to analyze. Also use an appropriate filter (convolution perhaps) to remove image artifacts before you begin the process.
Once you have figured out what the thing is you are going to analyze then analyze it and return a score, one for circle, one for noise, one for line, and the last for pyramid.
Repeat this step with the next viable candidate until you come up with the best candidate that is not noise.
I suspect you will find that you don't need a complicated algorithm to find circle, line, pyramid but that it is more so about structuring your code appropriately.
If I was you I'll use already available Image Processing libraries like "AForge".
Take A look at this sample article:
http://www.aforgenet.com/articles/shape_checker
I have a jar on github that can help if you are willing to unpack it and obey the apache license. You can try to recreate it in any other language as well.
Its an edge detector. The best step from there could be to:
find the corners (median of 90 degrees)
find mean median and maximum radius
find skew/angle from horizontal
have a decision agent decide what the shape is
Play around with it and find what you want.
My jar is open to the public at this address. It is not yet production ready but can help.
Just thought I could help. If anyone wants to be a part of the project, please do.
I did this recently with identifying circles (bone centers) in medical images.
Note: Steps 1-2 are if you are grabbing from an image.
Psuedo Code Steps
Step 1. Highlight the Edges
edges = edge_map(of the source image) (using edge detector(s))
(laymens: show the lines/edges--make them searchable)
Step 2. Trace each unique edge
I would (use a nearest neighbor search 9x9 or 25x25) to identify / follow / trace each edge, collecting each point into the list (they become neighbors), and taking note of the gradient at each point.
This step produces: a set of edges.
(where one edge/curve/line = list of [point_gradient_data_structure]s
(laymens: Collect a set of points along the edge in the image)
Step 3. Analyze Each Edge('s points and gradient data)
For each edge,
if the gradient similar for a given region/set of neighbors (a run of points along an edge), then we have a straight line.
If the gradient is changing gradually, we have a curve.
Each region/run of points that is a straight line or a curve, has a mean (center) and other gradient statistics.
Step 4. Detect Objects
We can use the summary information from Step 3 to build conclusions about diamonds, circles, or squares. (i.e. 4 straight lines, that have end points near each other with proper gradients is a diamond or square. One (or more) curves with sufficient points/gradients (with a common focal point) makes a complete circle).
Note: Using an image pyramid can improve algorithm performance, both in terms of results and speed.
This technique (Steps 1-4) would get the job done for well defined shapes, and also could detect shapes that are drawn less than perfectly, and could handle slightly disconnected lines (if needed).
Note: With some machine learning techniques (mentioned by other posters), it could be helpful/important to have good "classifiers" to basically break the problem down into smaller parts/components, so then a decider further down the chain could use to better understand/"see" the objects. I think machine learning might be a little heavy-handed for this question, but still could produce reasonable results. PCA(face detection) could potentially work too.
I am writing an Android application that must paint determined parts of a loaded bitmap image according to received events.
I need to paint (or change the current color) of a single part of a bitmap image, without changing the rest of the image.
Let's say I have a car, which is divided by many parts: door, windows, wheels, etc.
Each time an event (received from the network) arrives, I need to change the color of that particular part with the color specified by the event data.
What would be the best technique to achieve that?
I first thought on FloodFill, as suggested on many threads in SO, but given that the messages are received quite fast (several per second) I fear it would drag performance down, as it seem to be very CPU intensive algorithm.
I also thought about having multiple segments of the same image, each colored with a different color and show the right one at the right time, but the car has at least 10 different parts and each one could be painted with 4-6 colors, so I would end up with dozens of images and that would be impractical to handle, not to mention the waste of memory.
So, is there any other approach?
The fastest way to do it is with a shader. You'll need to use OpenGL ES 2 for that (some Androids only support ES 1). You'll need a temporary bitmap the same size as the image you want to change. Set it as the target. In the shader, retrieve a pixel from the sampler which is bound to the image you want to change. If it's within a small tolerance of the colour you want to change, set gl_FragColor to the new colour, otherwise just set gl_FragColor to the colour you retrieved from the sampler. You'll need to pass the desired colour and the new colour into the shader as vec4s with al_set_shader_float_vector. The fastest way to do this is to keep 2 bitmaps and swap between them as the "main one" that you're using each time a colour changes.
If you can't use a shader, then you'll have to lock the bitmap and replace the colour. Use al_lock_bitmap to lock it, then you can use al_get_pixel and al_put_pixel to change colours. Then al_unlock_bitmap when you're done. You can also avoid using al_get_pixel/al_put_pixel and access the memory manually which will be faster. If you lock the bitmap with the format ALLEGRO_PIXEL_FORMAT_ABGR_8888_LE then the memory is laid out like so:
int w = al_get_bitmap_width(bitmap);
int h = al_get_bitmap_height(bitmap);
for (int y = 0; y < h; y++) {
unsigned char *p = locked_region->data + locked_region->pitch * y;
for (int x = 0; x < w; x++) {
unsigned char r = p[0];
unsigned char g = p[1];
unsigned char b = p[2];
unsigned char a = p[3];
/* change r, g, b, a here if they match */
p[0] = r;
p[1] = g;
p[2] = b;
p[3] = a;
p += 4;
}
}
It's recommended that you lock the image in the format it was created in. That means pick an easy one like the one I mentioned, or else the inner part of the loop gets more complicated. The ABGR_8888 part of the pixel format describes the layout of the data. ABGR tells the order of the components. If you were to read a pixel into a single storage unit (an int in this case but it works the same with a short) then the bit pattern would be AAAAAAAABBBBBBBBGGGGGGGGRRRRRRRR. However, when you're reading a byte at a time, most machine are little endian so that means the small end comes first. That's why in my sample code p[0] is red. The 8888 part tells how many bits per component.
So I'm trying to understand how I can properly use hardware acceleration (when available) in a custom View that is persistently animating. This is the basic premise of my onDraw():
canvas.drawColor(mBackgroundColor);
for (Layer layer : mLayers) {
canvas.save();
canvas.translate(layer.x, layer.y);
//Draw that number of images in a grid, offset by -1
for (int i = -1; i < layer.xCount - 1; i++) {
for (int j = -1; j < layer.yCount - 1; j++) {
canvas.drawBitmap(layer.bitmap, layer.w * i, layer.h * j, null);
}
}
//If the layer's x has moved past its width, reset back to a seamless position
layer.x += ((difference * layer.xSpeed) / 1000f);
float xOverlap = layer.x % layer.w;
if (xOverlap > 0) {
layer.x = xOverlap;
}
//If the layer's y has moved past its height, reset back to a seamless position
layer.y += ((difference * layer.ySpeed) / 1000f);
float yOverlap = layer.y % layer.h;
if (yOverlap > 0) {
layer.y = yOverlap;
}
canvas.restore();
}
//Redraw the view
ViewCompat.postInvalidateOnAnimation(this);
I'm enabling hardware layers in onAttachedToWindow() and disabling them in onDetachedFromWindow(), but I'm trying to understand whether or not I'm actually using it. Essentially, the i/j loop that calls drawBitmap() never changes; the only thing that changes is the Canvas translation. Is the Bitmap automatically saved to the GPU as a texture behind the scenes, or is there something I need to do manually to do so?
On what view(s) are you setting View.LAYER_TYPE_HARDWARE exactly? If you are setting a hardware layer on the view that contains the drawing code shown above, you are causing the system to do a lot more work than necessary. Since you are only drawing bitmaps you don't need to do anything here. If you call Canvas.drawBitmap() the framework will cache the resulting OpenGL texture on your behalf.
You could however optimize your code a little more. Instead of calling drawBitmap(), you could use child views. If you move these children using the offset*() methods (or setX()/setY()) the framework will apply further optimizations to avoid calling the draw() methods again.
In general, hardware layers should be set on views that are expensive to draw and whose content won't change often (so pretty much the opposite of what you're doing :)
You can use Android's Tracer for OpenGL ES to see if your view issue OpenGL commands.
From developer.android.com
Tracer is a tool for analyzing OpenGL for Embedded Systems (ES) code in your Android application. The tool allows you to capture OpenGL ES commands and frame by frame images to help you understand how your graphics commands are being executed.
There is also a tutorial about Android Performance Study by Romain Guy which describes its use almost step by step.