I am working with the Unity Entities Graphics package and as per the 0.7 documentation have added the HYBRID_ENTITIES_CAMERA_CONVERSION tag to scripting define symbols on windows and android builds. It works great on windows but clearly the camera is not being converted to DOTS on android.
Have I missed a step in the project settings for camera conversion on android?
Version: com.unity.entities.graphics#1.0
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XboxOneLanguage:
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I am trying to create a recyclerview that shows images like instagram tab. I have checked this Implement Asymmetrical Grid Layout Manager Like Instagram Search question but it does not seem to do the work. I tried using gridlayoutmanager but I can't make it work. How to recreate it with google's FlexboxLayoutManager or a custom layout manager? Hope you'll answer. Regards.
You can use SpannedGridLayoutManager from Nick Butcher as layout manager for your RecyclerView.
Here how it looks like in action, i used it for creating the sample layout below.
This works for me so well SpannedGridLayoutManager.
// Sample usage from your Activity/Fragment
private fun setupSpannedGridLayout() {
val manager = SpannedGridLayoutManager(
object : GridSpanLookup {
override fun getSpanInfo(position: Int): SpanInfo {
// Conditions for 2x2 items
return if (position % 6 == 0 || position % 6 == 4) {
SpanInfo(2, 2)
} else {
SpanInfo(1, 1)
}
}
},
3, // number of columns
1f // how big is default item
)
recyclerView.layoutManager = manager
adapter = GridAdapter(arrayListOf())
recyclerView.adapter = adapter
}
PS: i am using kotlin here :).
As per the above answer use SpannedGridLayoutManager.
To show grids similar to instagram use below code
var spannedGridLayoutManager = SpannedGridLayoutManager(
orientation = SpannedGridLayoutManager.Orientation.VERTICAL,
spans = 3
)
spannedGridLayoutManager.itemOrderIsStable = true
spannedGridLayoutManager.spanSizeLookup =
SpannedGridLayoutManager.SpanSizeLookup { position ->
var x = 0
if (position % 9 == 0) {
x = position / 9
}
when {
position == 1 || x % 2 == 1 || (position - 1) % 18 == 0 ->
SpanSize(2, 2)
else ->
SpanSize(1, 1)
}
}
recyclerView.layoutManager = spannedGridLayoutManager
recyclerview.adapter = searchGridAdapter
for the newest Instagram layout in 2022 you can try this:
layoutManager.spanSizeLookup = IGLayoutManager.SpanSizeLookup { position ->
// 0 1 2
// s s b
// 3 4 5 6 7 8 9 10 11
// s s s s s s s s b
// 12 13 14 15 16 17 18 19 20 21 22 23 24
// s s s s s s s s s s s s b
// 25 26 27 28 29 30 31 32 33
// s s s s s s s s b
// 34 35 36 37 38 39 40 41 42 43 44 45 46
// s s s s s s s s s s s s b
// 47 48 49 50 51 52 53 54 55
// s s s s s s s s b
// 56 57 58 59 60 61 62 63 64 65 66 67 68
// s s s s s s s s s s s s b
// 69 70 71 72 73 74 75 76 77
// s s s s s s s s b
// 78 79 80 81 82 83 84 85 86 87 88 89 90
// s s s s s s s s s s s s b
// 91 92 93 94 95 96 97 98 99
// s s s s s s s s b
// 100 101 102 103 104 105 106 107 108 109 110 111 112
// s s s s s s s s s s s s b
// 113 114 115 116 117 118 119 120 121
// s s s s s s s s b
if (position < 2) {
// ILog.debug("???", "$position is small")
bigItemFlag = 0
SpanSize(1, 1)
}
else if (position == 2) {
// ILog.debug("???", "$position is big")
bigItemFlag = 11
SpanSize(1, 2)
}
else if (position % 11 == 0 && position == bigItemFlag) {
bigItemFlag += 13
// ILog.debug("???", "$position is big, $position mod 11 is ${position % 11}")
SpanSize(1, 2)
}
else if (position % 11 == 2 && position == bigItemFlag) {
bigItemFlag += 9
// ILog.debug("???", "$position is big, $position mod 11 is ${position % 11}")
SpanSize(1, 2)
}
else {
// ILog.debug("???", "$position is small")
SpanSize(1, 1)
}
}
MediaMuxer has been driving me mad for two days & nights now :-(
The situation: I receive a H264 encoded 1280x720 video stream via UDP.
The h264 stream contains NALU 1 - slice and NALU 5 - keyframe (5 is always preceded by NALU 7 - SPS and NALU 8 - PPS).
This stream appears to be stable 30fps with at least one NALU 5 keyframe per second. Bitrate is variable but less than 4Mbps.
MediaCodec sucessfully decodes the stream and renders it in a surface view so that part works well.
But now I need to save the H.264 into a local mpg file.
I set up a MediaMuxer with all MediaFormat information that I have, and feed it with the sample data from the stream.
Each sample contains one frame (NALU 1 or 5), and the first data sent to MediaMuxer is a keyframe (NALU 5). The presentation time is calculated based on framenumber and framerate.
All involved methods are called from the same thread.
But the mpg file is never created. As you can see in the output below the data in the ByteBuffer does start with NALU headers followed by varying size of data. MediaMuxer seems to "see" frames in the data as it counts the frames.
So what is wrong here?
Minimum API is 21, and I have tested with a Samsung Galaxy S4 running stock Android 5 and a couple of devices running Lineageos Oreo and Nougat.
Here is the code to setup the MediaMuxer:
void setupMuxer(File f) throws IOException {
if (DEBUG) Log.d(TAG, "Setup Muxer: " + f.getAbsolutePath() +" can write: " + f.canWrite());
MediaFormat format = MediaFormat.createVideoFormat(MediaFormat.MIMETYPE_VIDEO_AVC, decoderWidth, decoderHeight);
format.setInteger(MediaFormat.KEY_BIT_RATE, 4000000);
format.setInteger(MediaFormat.KEY_FRAME_RATE, 29);
format.setInteger(MediaFormat.KEY_I_FRAME_INTERVAL, 1);
format.setByteBuffer("csd-0", ByteBuffer.wrap(sps)); // sps and pps have been retrieved from the stream's NAL 7/8
format.setByteBuffer("csd-1", ByteBuffer.wrap(pps));
format.setInteger(MediaFormat.KEY_MAX_INPUT_SIZE, 1920 * 1080);
muxer = new MediaMuxer(f.getPath(), MediaMuxer.OutputFormat.MUXER_OUTPUT_MPEG_4);
videoTrack = muxer.addTrack(format);
muxer.start();
}
This method is called for each (complete) NALU 1 and NALU 5:
void muxFrame(ByteBuffer buf, int frame) {
MediaCodec.BufferInfo bufferInfo = new MediaCodec.BufferInfo();
bufferInfo.offset = buf.arrayOffset();
bufferInfo.size = buf.position() - bufferInfo.offset;
bufferInfo.flags = (buf.get(4) & 0x1f) == 5 ? MediaCodec.BUFFER_FLAG_KEY_FRAME : 0;
bufferInfo.presentationTimeUs = computePresentationTime(frame);
if (DEBUG)
Log.d(TAG, "muxFrame frame: " + frame + " size: " + bufferInfo.size + " NAL: " + (buf.get(4) & 0x1f) + " Flags: " + bufferInfo.flags + " PTS: " + bufferInfo.presentationTimeUs + " content: " + BitByteUtil.toByteString(buf.array(), buf.arrayOffset(), 8));
try {
muxer.writeSampleData(videoTrack, buf, bufferInfo);
} catch (Exception e) {
Log.w(TAG, "muxer failed", e);
} finally {
}
}
private static long computePresentationTime(int frameIndex) {
return 42 + frameIndex * 1000000 / FRAME_RATE;
}
Here is my output if MediaMuxer is stopped after it has consumed 100 NALUs.
05.651 D/VideoDecoderView: Setup Muxer: /storage/emulated/0/Pictures/test.mpg can write: true
05.656 I/MPEG4Writer: limits: 4294967295/0 bytes/us, bit rate: -1 bps and the estimated moov size 3317 bytes
06.263 D/VideoDecoderView: muxFrame frame: 2 size: 7257 NAL: 5 Flags: 1 PTS: 66708 content: 0:000 1:000 2:000 3:001 4:101 5:184 6:000 7:015
06.264 I/MPEG4Writer: setStartTimestampUs: 66708
06.264 I/MPEG4Writer: Earliest track starting time: 66708
06.308 D/VideoDecoderView: muxFrame frame: 3 size: 8998 NAL: 1 Flags: 0 PTS: 100042 content: 0:000 1:000 2:000 3:001 4:065 5:224 6:034 7:020
06.342 D/VideoDecoderView: muxFrame frame: 4 size: 13664 NAL: 1 Flags: 0 PTS: 133375 content: 0:000 1:000 2:000 3:001 4:065 5:224 6:066 7:020
06.375 D/VideoDecoderView: muxFrame frame: 5 size: 13674 NAL: 1 Flags: 0 PTS: 166708 content: 0:000 1:000 2:000 3:001 4:065 5:224 6:098 7:020
06.409 D/VideoDecoderView: muxFrame frame: 6 size: 13772 NAL: 1 Flags: 0 PTS: 200042 content: 0:000 1:000 2:000 3:001 4:065 5:224 6:130 7:020
06.483 D/VideoDecoderView: muxFrame frame: 7 size: 13707 NAL: 1 Flags: 0 PTS: 233375 content: 0:000 1:000 2:000 3:001 4:065 5:224 6:162 7:020
06.520 D/VideoDecoderView: muxFrame frame: 8 size: 13778 NAL: 1 Flags: 0 PTS: 266708 content: 0:000 1:000 2:000 3:001 4:065 5:224 6:194 7:020
06.555 D/VideoDecoderView: muxFrame frame: 9 size: 13743 NAL: 1 Flags: 0 PTS: 300042 content: 0:000 1:000 2:000 3:001 4:065 5:224 6:226 7:020
06.575 D/VideoDecoderView: muxFrame frame: 10 size: 7338 NAL: 5 Flags: 1 PTS: 333375 content: 0:000 1:000 2:000 3:001 4:101 5:184 6:000 7:015
06.593 D/VideoDecoderView: muxFrame frame: 11 size: 9059 NAL: 1 Flags: 0 PTS: 366708 content: 0:000 1:000 2:000 3:001 4:065 5:224 6:034 7:020
06.618 D/VideoDecoderView: muxFrame frame: 12 size: 13587 NAL: 1 Flags: 0 PTS: 400042 content: 0:000 1:000 2:000 3:001 4:065 5:224 6:066 7:020
06.644 D/VideoDecoderView: muxFrame frame: 13 size: 13650 NAL: 1 Flags: 0 PTS: 433375 content: 0:000 1:000 2:000 3:001 4:065 5:224 6:098 7:020
06.671 D/VideoDecoderView: muxFrame frame: 14 size: 13797 NAL: 1 Flags: 0 PTS: 466708 content: 0:000 1:000 2:000 3:001 4:065 5:224 6:130 7:020
.... [snip]
09.620 D/VideoDecoderView: muxFrame frame: 97 size: 7212 NAL: 5 Flags: 1 PTS: 3233375 content: 0:000 1:000 2:000 3:001 4:101 5:184 6:000 7:015
09.661 D/VideoDecoderView: muxFrame frame: 98 size: 8814 NAL: 1 Flags: 0 PTS: 3266708 content: 0:000 1:000 2:000 3:001 4:065 5:224 6:034 7:020
09.692 D/VideoDecoderView: muxFrame frame: 99 size: 13566 NAL: 1 Flags: 0 PTS: 3300042 content: 0:000 1:000 2:000 3:001 4:065 5:224 6:066 7:020
09.737 D/VideoDecoderView: muxFrame frame: 100 size: 13733 NAL: 1 Flags: 0 PTS: 3333375 content: 0:000 1:000 2:000 3:001 4:065 5:224 6:098 7:020
09.771 D/VideoDecoderView: muxFrame frame: 101 size: 13771 NAL: 1 Flags: 0 PTS: 3366708 content: 0:000 1:000 2:000 3:001 4:065 5:224 6:130 7:020
09.775 D/MPEG4Writer: Video track stopping. Stop source
09.775 I/MPEG4Writer: Received total/0-length (100/1) buffers and encoded 100 frames. - Video
09.775 D/MPEG4Writer: Video track source stopping
09.775 D/MPEG4Writer: Video track source stopped
09.775 D/MPEG4Writer: Video track stopped. Stop source
09.775 D/MPEG4Writer: Stopping writer thread
09.776 D/MPEG4Writer: 0 chunks are written in the last batch
09.779 D/MPEG4Writer: Writer thread stopped
09.780 I/MPEG4Writer: Ajust the moov start time from 66708 us -> 66708 us
09.780 D/MPEG4Writer: Video track stopping. Stop source
#greeble31: You are right. The first log entry clearly states "Pictures" and not "Videos".
I spent hours looking at this problem without noticing a simple cut&paste mistake in my preferences keys. How stupid is that!!?!
Note to myself: Coding two days & nights in a row is not heroic but just plain stupid.
My android app passes in an OpenGL texture2D to my OpenCL kernel, however the pixels values being read by my kernel are out of bounds (>255).
I create my OpenGL texture like this:
GLES20.glGenTextures ( 2, targetTex, 0 );
GLES20.glBindTexture(GLES20.GL_TEXTURE_2D, targetTex[0]);
GLES20.glTexParameteri(GLES20.GL_TEXTURE_2D, GLES20.GL_TEXTURE_MIN_FILTER, GLES20.GL_LINEAR);
GLES20.glTexParameteri(GLES20.GL_TEXTURE_2D, GLES20.GL_TEXTURE_MAG_FILTER, GLES20.GL_LINEAR);
GLES20.glTexImage2D(GLES20.GL_TEXTURE_2D, 0, GLES20.GL_RGBA, image_width, image_height, 0, GLES20.GL_RGBA, GLES20.GL_UNSIGNED_BYTE, null);
GLES20.glBindTexture(GLES20.GL_TEXTURE_2D, 0);
The texture is then rendered to by binding it with a FBO:
targetFramebuffer = IntBuffer.allocate(1);
GLES20.glGenFramebuffers(1, targetFramebuffer);
GLES20.glBindFramebuffer(GLES20.GL_FRAMEBUFFER, targetFramebuffer.get(0));
GLES20.glFramebufferTexture2D(GLES20.GL_FRAMEBUFFER, GLES20.GL_COLOR_ATTACHMENT0, GLES20.GL_TEXTURE_2D, targetTex[0], 0);
GLES20.glBindFramebuffer(GLES20.GL_FRAMEBUFFER, 0);
I create the cl memory object like so:
mem_images[0] = clCreateFromGLTexture2D(m_clContext, CL_MEM_READ_ONLY, GL_TEXTURE_2D, 0, in_tex, &err);
and this is my OpenCL kernel:
const sampler_t sampler = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_NONE | CLK_FILTER_NEAREST;
kernel void transfer_data(__read_only image2d_t input_image, __global float* debug) {
int2 pos;
uint4 pixel;
for (pos.y = get_global_id(1); pos.y < HEIGHT; pos.y += get_global_size(1)) {
for (pos.x = get_global_id(0); pos.x < WIDTH; pos.x += get_global_size(0)) {
pixel = read_imageui(input_image, sampler, pos);
debug[(pos.x + pos.y*WIDTH)*NUM_CHANNELS + 0] = pixel.x;
debug[(pos.x + pos.y*WIDTH)*NUM_CHANNELS + 1] = pixel.y;
debug[(pos.x + pos.y*WIDTH)*NUM_CHANNELS + 2] = pixel.z;
}
}
}
This is how I am enqueing the kernel:
local2Dsize[0] = 4;
local2Dsize[1] = 4;
global2Dsize[0] = clamp(image_width, 0, max_work_items[0]);
global2Dsize[1] = clamp(image_height, 0, max_work_items[1]);
global2Dsize[0] = ceil((float)global2Dsize[0]/(float)local2Dsize[0])*local2Dsize[0];
global2Dsize[1] = ceil((float)global2Dsize[1]/(float)local2Dsize[1])*local2Dsize[1];
twoDlocal_sizes["transfer_data"] = local2Dsize;
twoDglobal_sizes["transfer_data"] = global2Dsize;
kernels["transfer_data"] = clCreateKernel(m_program, "transfer_data", &err);
err = clSetKernelArg(kernels["transfer_data"], 0, sizeof(cl_mem), &mem_images[0]);
err |= clSetKernelArg(kernels["transfer_data"], 1, sizeof(cl_mem), &mems["image"]);
err = clEnqueueAcquireGLObjects(m_queue, 1, &mem_images[0], 0, 0, 0);
CHECK_ERROR_OCL(err, "acquiring GL objects", return false);
err = clEnqueueNDRangeKernel(m_queue, kernels["transfer_data"], 2, NULL, twoDglobal_sizes["transfer_data"], twoDlocal_sizes["transfer_data"], 0, NULL, NULL);
err = clFinish(m_queue);
err = clEnqueueReleaseGLObjects(m_queue, 1, &mem_images[0], 0, 0, 0);
CHECK_ERROR_OCL(err, "releasing GL objects", return false);
Now back on host when I print out these pixel values (from the array debug), they are way out of bounds and I don't understand why that is the case.
If you need more insight:
The overall aim of my project is to obtain camera frames in form of an OpenGL texture, process them using OpenCL and render the output back to the screen.
However the texture obtained from android camera can only be bound to GL_TEXTURE_EXTERNAL_OES (http://developer.android.com/reference/android/graphics/SurfaceTexture.html), and this is not a valid texture to create an OpenCL memory object from.
Therefore I am rendering the camera output to a GL_TEXTURE_2D and passing that to OpenCL.
I am sure that the pixels are being rendered to the texture correctly, because when I display the texture on the screen (without any OpenCL involved) it displays the image properly.
I did some debugging by creating a texture (as opposed to getting data from the camera) and passing that to opencl. So these are the mappings I get:
0 -> 0
1 -> 7172
2 -> 8196
3 -> 8710
4 -> 9220
5 -> 9477
6 -> 9734
7 -> 9991
8 -> 10244
9 -> 10372
10 -> 10501
11 -> 10629
12 -> 10758
13 -> 10886
14 -> 11015
15 -> 11143
16 -> 11268
17 -> 11332
18 -> 11396
19 -> 11460
20 -> 11525
21 -> 11589
22 -> 11653
23 -> 11717
24 -> 11782
25 -> 11846
26 -> 11910
27 -> 11974
28 -> 12039
29 -> 12103
30 -> 12167
31 -> 12231
32 -> 12292
33 -> 12324
34 -> 12356
35 -> 12388
36 -> 12420
37 -> 12452
38 -> 12484
39 -> 12516
40 -> 12549
41 -> 12581
42 -> 12613
43 -> 12645
44 -> 12677
45 -> 12709
46 -> 12741
47 -> 12773
48 -> 12806
49 -> 12838
50 -> 12870
51 -> 12902
52 -> 12934
53 -> 12966
54 -> 12998
55 -> 13030
56 -> 13063
57 -> 13095
58 -> 13127
59 -> 13159
60 -> 13191
61 -> 13223
62 -> 13255
63 -> 13287
64 -> 13316
65 -> 13332
66 -> 13348
67 -> 13364
68 -> 13380
69 -> 13396
70 -> 13412
71 -> 13428
72 -> 13444
73 -> 13460
74 -> 13476
75 -> 13492
76 -> 13508
77 -> 13524
78 -> 13540
79 -> 13556
80 -> 13573
81 -> 13589
82 -> 13605
83 -> 13621
84 -> 13637
85 -> 13653
86 -> 13669
87 -> 13685
88 -> 13701
89 -> 13717
90 -> 13733
91 -> 13749
92 -> 13765
93 -> 13781
94 -> 13797
95 -> 13813
96 -> 13830
97 -> 13846
98 -> 13862
99 -> 13878
100 -> 13894
101 -> 13910
102 -> 13926
103 -> 13942
104 -> 13958
105 -> 13974
106 -> 13990
107 -> 14006
108 -> 14022
109 -> 14038
110 -> 14054
111 -> 14070
112 -> 14087
113 -> 14103
114 -> 14119
115 -> 14135
116 -> 14151
117 -> 14167
118 -> 14183
119 -> 14199
120 -> 14215
121 -> 14231
122 -> 14247
123 -> 14263
124 -> 14279
125 -> 14295
126 -> 14311
127 -> 14327
128 -> 14340
129 -> 14348
130 -> 14356
131 -> 14364
132 -> 14372
133 -> 14380
134 -> 14388
135 -> 14396
136 -> 14404
137 -> 14412
138 -> 14420
139 -> 14428
140 -> 14436
141 -> 14444
142 -> 14452
143 -> 14460
144 -> 14468
145 -> 14476
146 -> 14484
147 -> 14492
148 -> 14500
149 -> 14508
150 -> 14516
151 -> 14524
152 -> 14532
153 -> 14540
154 -> 14548
155 -> 14556
156 -> 14564
157 -> 14572
158 -> 14580
159 -> 14588
160 -> 14597
161 -> 14605
162 -> 14613
163 -> 14621
164 -> 14629
165 -> 14637
166 -> 14645
167 -> 14653
168 -> 14661
169 -> 14669
170 -> 14677
171 -> 14685
172 -> 14693
173 -> 14701
174 -> 14709
175 -> 14717
176 -> 14725
177 -> 14733
178 -> 14741
179 -> 14749
180 -> 14757
181 -> 14765
182 -> 14773
183 -> 14781
184 -> 14789
185 -> 14797
186 -> 14805
187 -> 14813
188 -> 14821
189 -> 14829
190 -> 14837
191 -> 14845
192 -> 14854
193 -> 14862
194 -> 14870
195 -> 14878
196 -> 14886
197 -> 14894
198 -> 14902
199 -> 14910
200 -> 14918
201 -> 14926
202 -> 14934
203 -> 14942
204 -> 14950
205 -> 14958
206 -> 14966
207 -> 14974
208 -> 14982
209 -> 14990
210 -> 14998
211 -> 15006
212 -> 15014
213 -> 15022
214 -> 15030
215 -> 15038
216 -> 15046
217 -> 15054
218 -> 15062
219 -> 15070
220 -> 15078
221 -> 15086
222 -> 15094
223 -> 15102
224 -> 15111
225 -> 15119
226 -> 15127
227 -> 15135
228 -> 15143
229 -> 15151
230 -> 15159
231 -> 15167
232 -> 15175
233 -> 15183
234 -> 15191
235 -> 15199
236 -> 15207
237 -> 15215
238 -> 15223
239 -> 15231
240 -> 15239
241 -> 15247
242 -> 15255
243 -> 15263
244 -> 15271
245 -> 15279
246 -> 15287
247 -> 15295
248 -> 15303
249 -> 15311
250 -> 15319
251 -> 15327
252 -> 15335
253 -> 15343
254 -> 15351
255 -> 15359
On the left is the colour value that I input in the OpenGL texture and on the left is the corresponding value I get when I read the values in OpenCL.
The GL_UNSIGNED_BYTE texture is being mapped to OpenCL as CL_UNORM_INT8. You need to use read_imagef to read from these images rather than read_imageui. The values you are seeing when you use read_imageui are the raw bits of the internal floating point format.
You are not acquiring and releasing the GL objects before accessing them. This causes the kernel not to read to data in the GL internal buffer but a local CL copy of it.
Proper code: (BTW you should check those "err" values for errors)
local2Dsize[0] = 4;
local2Dsize[1] = 4;
global2Dsize[0] = clamp(image_width, 0, max_work_items[0]);
global2Dsize[1] = clamp(image_height, 0, max_work_items[1]);
global2Dsize[0] = ceil((float)global2Dsize[0]/(float)local2Dsize[0])*local2Dsize[0];
global2Dsize[1] = ceil((float)global2Dsize[1]/(float)local2Dsize[1])*local2Dsize[1];
twoDlocal_sizes["transfer_data"] = local2Dsize;
twoDglobal_sizes["transfer_data"] = global2Dsize;
kernels["transfer_data"] = clCreateKernel(m_program, "transfer_data", &err);
err = clSetKernelArg(kernels["transfer_data"], 0, sizeof(cl_mem), &mem_images[0]);
err |= clSetKernelArg(kernels["transfer_data"], 1, sizeof(cl_mem), &mems["image"]);
err = clEnqueueAcquireGLObjects(m_queue, 1, mem_images, NULL, NULL, NULL);
err = clEnqueueNDRangeKernel(m_queue, kernels["transfer_data"], 2, NULL, twoDglobal_sizes["transfer_data"], twoDlocal_sizes["transfer_data"], 0, NULL, NULL);
err = clEnqueueReleaseGLObjects (m_queue, 1, mem_images, NULL, NULL, NULL);
err = clFinish(m_queue);
Okay so I think it's a bug in the OpenCL implementation.
With a bit of help from Wolfram Alpha, I created a function to reverse the above mappings and obtain values in the range of 0 and 255.
float GL_to_CL(uint val) {
if (val >= 14340) return round(0.1245790*val - 1658.44); //>=128
if (val >= 13316) return round(0.0622869*val - 765.408); //>=64
if (val >= 12292) return round(0.0311424*val - 350.800); //>=32
if (val >= 11268) return round(0.0155702*val - 159.443); //>=16
float v = (float) val;
return round(0.0000000000000125922*pow(v,4.f) - 0.00000000026729*pow(v,3.f) + 0.00000198135*pow(v,2.f) - 0.00496681*v - 0.0000808829);
}
So the GL_to_CL() is a combination of 4 linear functions and a Quartic function.
I tried creating just a single function using polynomial interpolation, however the degree of the polynomial was too large and therefore more computationally expensive to solve than the combination of the 5 functions proposed above.
Another alternate is to use a 15k sized array to achieve constant time, however that would require me uploading roughly 15k Bytes to GPU's global memory.
Considering I am using the kernels to do image processing, I would be slightly pushing it.
Also accessing from global memory in OpenCL is often more expensive than performing some simple calculations.
I am trying to measure CPU utilization for each core (in %) in a Micromax C4 device. I am unable to find the cpu_utilization file in /sys/devices/system/cpu/cpu0/cpufreq.
Does anyone have any idea how to find the cpu_utilization file in C4, or any relevant file which would provide percent CPU utilization for each core?
Waiting for response.
Any input from anyone???
I am not sure if there is any file which will be able to directly report the percentage CPU utilization but you can use /proc/stat to calculate the CPU utilization.
adb shell cat /proc/stat gives the following output
cpu 22251 758 19056 1170320 2786 0 243
cpu0 13628 401 14782 275769 1645 0 237
cpu1 4712 147 2394 295111 622 0 5
cpu2 2451 116 1149 298977 291 0 1
cpu3 1460 94 731 300463 228 0 0
intr 724661 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 184059 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 337 0 0 0 0 0 0 0 0 0 0 0 14401 0 0 21902 0 0 6 0 0 0 0 0 0 0 0 0 0 48850 175 0 0 0 0 0 0 0 0 0 0 0 0 2901 16819 5708 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8879 0 31124 0 0 0 0 5431 0 579 0 5 0 0 0 0 0 15043 0 0 0 0 0 6644 328 13085 0 6779 0 70695 1279 47348 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 6290 0 890 0 0 0 0 6 0 0 0 0 0 0 0 0 16846 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10519 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3158 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 9 0 0 0 0 72 0 4 0 0 0 0 0 10 0 0 0 0 0 0 0 0 0 5487 0 0 0 0 0 0 0 0 8818 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6245 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2476 0 0 0 28 0 0 0 0 5 22 39 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 4 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ctxt 1806341
btime 1378286111
processes 8249
procs_running 3
procs_blocked 0
softirq 323561 207 115027 207 2024 207 207 56793 19244 45 129600
first five lines are of interest. The first line represents the total time spent by all of the cores in different operating modes since boot and it is the aggregate of lines (cpu0/cpu1/cpu2/cpu3). The lines after the first correspond to the time spents in different modes by individual cores.
The meanings of the columns are as follows, from left to right:
user: normal processes executing in user mode
nice: niced processes executing in user mode
system: processes executing in kernel mode
idle: twiddling thumbs
iowait: waiting for I/O to complete
irq: servicing interrupts
softirq: servicing softirqs
Now CPU Utilization since boot = (user + nice + system +iowait + irq + softirq) / (user + nice + system +iowait + irq + softirq + idle)
Check this aswell: http://www.linuxhowtos.org/System/procstat.htm