I am currently testing options for depth measurement with the smartphone and wanted to create a depth image initially for testing. I am using the Camera2Basic example as a basis for this. (https://github.com/android/camera-samples/tree/main/Camera2Basic) Using Depth16 I get a relatively sharp "depth image" back. But the millimetres are not correct. They are in a range around from 3600mm to 5000mm for an object like a wall that is about 500mm or 800mm away from the camera.
But what puzzles me the most is that the image does not transmit any information in the dark. If Android is really targeting the ToF sensor for DEPTH16, it shouldn't be affected in the dark, should it? Or do I have to use AR-Core or Huawei's HMS core to get a real ToF image?
I am using a Huawei P30 Pro and the code for extracting the depth information looks like this. And yes performance wise it is bullshit but it is only for testing purposes:)
private Map<String, PixelData> parseDepth16IntoDistanceMap(Image image) {
Map<String, PixelData> map = new HashMap();
Image.Plane plane = image.getPlanes()[0];
// using asShortBuffer() like in the documentation leads to a wrong format (for me) but does not help getting better values
ByteBuffer depthBuffer = plane.getBuffer().order(ByteOrder.nativeOrder());
int stride = plane.getRowStride();
int offset = 0;
int i = 0;
for (short y = 0; y < image.getHeight(); y++) {
for (short x = 0; x < image.getWidth(); x++) {
short depthSample = depthBuffer.getShort( (y / 2) * stride + x);
short depthSampleShort = (short) depthSample;
short depthRange = (short) (depthSampleShort & 0x1FFF);
short depthConfidence = (short) ((depthSampleShort >> 13) & 0x7);
float depthPercentage = depthConfidence == 0 ? 1.f : (depthConfidence - 1) / 7.f;
maxz = depthRange;
sum = sum + depthRange;
numPoints++;
listOfRanges.add((float) depthRange);
if (depthRange < minz && depthRange > 0) {
minz = depthRange;
}
map.put(x + "_" + y, new PixelData(x, y, depthRange, depthPercentage));
i++;
}
}
return map;
}
In any case, it would help a lot to know if you can get the data this way at all, so I know if I'm already doing something fundamentally wrong. Otherwise I will change to one of the ar systems. Either way, many thanks for your efforts
If you want to extract a depth map where you can see the distance to an object you might use ARCORE Depth API.
https://developers.google.com/ar/develop/java/depth/overview
Or you can follow the codelab where shows you how to get the data in millimeters.
https://codelabs.developers.google.com/codelabs/arcore-depth#0
i have set up an app using OpenCV framework, to detect an image marker with the camera, and then react if the image on the screen matches the marker.
So far i have gotten quite a way by following:
https://medium.com/#dwayneforde/image-recognition-on-ios-with-swift-and-opencv-b5cf0667b79
And have also used his code, to implement the image-matching-function. Everything is set up, and i should be ready to go... but... The app crashes when it try to run the application.
The problem lies where i check for matches. I use this code as instructed in the tutorial:
- (void)processImage:(cv::Mat &)img {
cv::Mat gimg;
// Convert incoming img to greyscale to match template
cv::cvtColor(img, gimg, CV_BGR2GRAY);
// Use check for matches with a certain threshold to help with scaling and angles
cv::Mat res(img.rows-gtpl.rows+1, gtpl.cols-gtpl.cols+1, CV_32FC1);
cv::matchTemplate(gimg, gtpl, res, CV_TM_CCOEFF_NORMED);
cv::threshold(res, res, 0.5, 1., CV_THRESH_TOZERO);
double minval, maxval, threshold = 0.9;
cv::Point minloc, maxloc;
cv::minMaxLoc(res, &minval, &maxval, &minloc, &maxloc);
// If it's a good enough match
if (maxval >= threshold)
{
// Draw a rectangle for confirmation
cv::rectangle(img, maxloc, cv::Point(maxloc.x + gtpl.cols, maxloc.y + gtpl.rows), CV_RGB(0,255,0), 2);
cv::floodFill(res, maxloc, cv::Scalar(0), 0, cv::Scalar(.1), cv::Scalar(1.));
// Call our delegates callback method
[delegate matchedItem];
}
}
The problem seems to be with the line:
cv::matchTemplate(gimg, gtpl, res, CV_TM_CCOEFF_NORMED);
And i get this error-message:
OpenCV(3.4.1) Error: Assertion failed (corrsize.height <= img.rows + templ.rows - 1 && corrsize.width <= img.cols + templ.cols - 1) in crossCorr, file /Volumes/build-storage/build/master_iOS-mac/opencv/modules/imgproc/src/templmatch.cpp, line 589
libc++abi.dylib: terminating with uncaught exception of type cv::Exception: OpenCV(3.4.1) /Volumes/build-storage/build/master_iOS-mac/opencv/modules/imgproc/src/templmatch.cpp:589: error: (-215) corrsize.height <= img.rows + templ.rows - 1 && corrsize.width <= img.cols + templ.cols - 1 in function crossCorr
Can anyone see what i am doing wrong?
I extra data from an image and save as int data. I want to convert back to an JPEG image. Thank You.
for j in 1 ..< dataCount!-1 {
var z = ImageArray![j]
if ( z > 0 ){
data[j] =z
}
else if (z < 0){
var y = 256 + z
data[j] = y
}
i += 1
}
*** need to convert int to byte ****** but don't know how to do that.
byteArray.writeToFile("/Users/picpath/picture.jpg", atomically: true )
to convert image into bytes you can use the following line of code.
var nsdata = UIImageJPEGRepresentation(data[j], 1)
The function takes two parameters , first parameter should be an image and the second parameter which is a float is used to define the compression ratio. by default we use "1"
This will convert image into bytes. Hopefully this will help you
Cheers
This is a continuous question of https://stackoverflow.com/a/21074207/3269910
the solution seems fine c0.0.851.315
but the c0.0 values are pointing to 0x0 position of the images
what i want is the image with offset_y . if an offset_y: 20,
ie, i want the same size and part of image, what i am seeing in my page timeline in a api call.
Is it possible to do this ?
Note: if i change c0.0 the zero value to some other then it is pointing to pixels but offset_y is % i think.
Thanks for your help.
I had discussion with Facebook Platform Team;
They are not ready to share the manual calculation to us.
and yes, it is possible to calculate value of c0.XXXX manually using your API offset_y value.
Here is my PHP code.
$fb_page_cover_json = json_decode( file_get_contents( 'https://graph.facebook.com/11111111?fields=cover'));
$fb_cover_image_url = $fb_page_cover_json->cover->source;
$image_info = getimagesize($fb_cover_image_url);
$image_width = $image_info[0];
$image_height = $image_info[1];
echo getTop($fb_page_cover_json->cover->offset_y,$image_width,$image_height);
function getTop($offset_y,$image_width,$image_height) {
$cover_w = 851;
$cover_h = 315;
$img_w = $image_width;
$img_h = $image_height;
$real_img_h = ($cover_w * $img_h / $img_w) - $cover_h;
$result = ($real_img_h * $offset_y / 100 * -1);
return floor($result);
}
The method getTop will return the c0.XXX value.
Hope this helps some one.
I'm using PIL to process uploaded images. Unfortunately, I'm having trouble with color conversion from CMYK to RGB, as the resulting images tone and contrast changes.
I'd suspect that it's only doing direct number transformations. Does PIL, or anything built on top of it, have an Adobian dummy-proof consume embedded profile, convert to destination, preserve numbers tool I can use for conversion?
In all my healthy ignorance and inexperience, this sort of jumped at me and it's got me in a pinch. I'd really like to get this done without engaging any intricacies of color spaces, transformations and the necessary math for both at this point.
Though I've never previously used it, I'm also disposed at using ImageMagick for this processing step if anyone has experience that it can perform it in a gracious manner.
So it didn't take me long to run into other people mentioning Little CMS, being the most popular open source solution for color management. I ended snooping around for Python bindings, found the old pyCMS and some ostensible notions about PIL supporting Little CMS.
Indeed, there is support for Little CMS, it's mentioned in a whole whopping one-liner:
CMS support: littleCMS (1.1.5 or later is recommended).
The documentation contains no references, no topical guides, Google didn't crawl out anything, their mailing list is closed... but digging through the source there's a PIL.ImageCms module that's well documented and get's the job done. Hope this saves someone from a messy internet excavation.
Goes off getting himself a cookie...
it's 2019 and things have changed. Your problem is significantly more complex than it may appear at first sight. The problem is, CMYK to RGB and RGB to CMYK is not a simple there and back. If e.g. you open an image in Photoshop and convert it there, this conversion has 2 additional parameters: source color profile and destination color profile. These change things greatly! For a typical use case, you would assume Adobe RGB 1998 on the RGB side and say Coated FOGRA 39 on the CMYK side. These two additional pieces of information clarify to the converter how to deal with the colors on input and output. What you need next is a transformation mechanism, Little CMS is in deed a great tool for this. It is MIT licensed and (after looking for solutions myself for a considerable time), I would recommend the following setup if you indeed do need a python way to transform colors:
Python 3.X (necessary because of littlecms)
pip install littlecms
pip install Pillow
In littlecms' /tests folder you will find a great set of examples. I would allow myself a particular adaptation of one test. Before you get the code, please let me tell you something about those color profiles. On Windows, as is my case, you will find a set of files with an .icc extension in the folder C:\Windows\System32\spool\drivers\color where Windows stores it's color profiles. You can download other profiles from sites like https://www.adobe.com/support/downloads/iccprofiles/iccprofiles_win.html and install them on Windows simply by double-clicking the corresponding .icc file. The example I provide depends on such profile files, which Little CMS uses to do those magic color transforms. I work as a semi-professional graphics designer and needed to be able to convert colors from CMYK to RGB and vice versa for certain scripts that manipulate objects in InDesign. My setup is RGB: Adobe RGB 1998 and CMYK: Coated FOGRA 39 (these settings were recommended by most book printers I get my books printed at). The aforementioned color profiles generated very similar results for me to the same transforms made by Photoshop and InDesign. Still, be warned, the colors are slightly (by around 1%) off in comparison to what PS and Id will give you for the same inputs. I am trying to figure out why...
The little program:
import littlecms as lc
from PIL import Image
def rgb2cmykColor(rgb, psrc='C:\\Windows\\System32\\spool\\drivers\\color\\AdobeRGB1998.icc', pdst='C:\\Windows\\System32\\spool\\drivers\\color\\CoatedFOGRA39.icc') :
ctxt = lc.cmsCreateContext(None, None)
white = lc.cmsD50_xyY() # Set white point for D50
dst_profile = lc.cmsOpenProfileFromFile(pdst, 'r')
src_profile = lc.cmsOpenProfileFromFile(psrc, 'r') # cmsCreate_sRGBProfile()
transform = lc.cmsCreateTransform(src_profile, lc.TYPE_RGB_8, dst_profile, lc.TYPE_CMYK_8,
lc.INTENT_RELATIVE_COLORIMETRIC, lc.cmsFLAGS_NOCACHE)
n_pixels = 1
in_comps = 3
out_comps = 4
rgb_in = lc.uint8Array(in_comps * n_pixels)
cmyk_out = lc.uint8Array(out_comps * n_pixels)
for i in range(in_comps):
rgb_in[i] = rgb[i]
lc.cmsDoTransform(transform, rgb_in, cmyk_out, n_pixels)
cmyk = tuple(cmyk_out[i] for i in range(out_comps * n_pixels))
return cmyk
def cmyk2rgbColor(cmyk, psrc='C:\\Windows\\System32\\spool\\drivers\\color\\CoatedFOGRA39.icc', pdst='C:\\Windows\\System32\\spool\\drivers\\color\\AdobeRGB1998.icc') :
ctxt = lc.cmsCreateContext(None, None)
white = lc.cmsD50_xyY() # Set white point for D50
dst_profile = lc.cmsOpenProfileFromFile(pdst, 'r')
src_profile = lc.cmsOpenProfileFromFile(psrc, 'r') # cmsCreate_sRGBProfile()
transform = lc.cmsCreateTransform(src_profile, lc.TYPE_CMYK_8, dst_profile, lc.TYPE_RGB_8,
lc.INTENT_RELATIVE_COLORIMETRIC, lc.cmsFLAGS_NOCACHE)
n_pixels = 1
in_comps = 4
out_comps = 3
cmyk_in = lc.uint8Array(in_comps * n_pixels)
rgb_out = lc.uint8Array(out_comps * n_pixels)
for i in range(in_comps):
cmyk_in[i] = cmyk[i]
lc.cmsDoTransform(transform, cmyk_in, rgb_out, n_pixels)
rgb = tuple(rgb_out[i] for i in range(out_comps * n_pixels))
return rgb
def rgb2cmykImage(PILImage, psrc='C:\\Windows\\System32\\spool\\drivers\\color\\AdobeRGB1998.icc', pdst='C:\\Windows\\System32\\spool\\drivers\\color\\CoatedFOGRA39.icc') :
ctxt = lc.cmsCreateContext(None, None)
white = lc.cmsD50_xyY() # Set white point for D50
dst_profile = lc.cmsOpenProfileFromFile(pdst, 'r')
src_profile = lc.cmsOpenProfileFromFile(psrc, 'r')
transform = lc.cmsCreateTransform(src_profile, lc.TYPE_RGB_8, dst_profile, lc.TYPE_CMYK_8,
lc.INTENT_RELATIVE_COLORIMETRIC, lc.cmsFLAGS_NOCACHE)
n_pixels = PILImage.size[0]
in_comps = 3
out_comps = 4
n_rows = 16
rgb_in = lc.uint8Array(in_comps * n_pixels * n_rows)
cmyk_out = lc.uint8Array(out_comps * n_pixels * n_rows)
outImage = Image.new('CMYK', PILImage.size, 'white')
in_row = Image.new('RGB', (PILImage.size[0], n_rows), 'white')
out_row = Image.new('CMYK', (PILImage.size[0], n_rows), 'white')
out_b = bytearray(n_pixels * n_rows * out_comps)
row = 0
while row < PILImage.size[1] :
in_row.paste(PILImage, (0, -row))
data_in = in_row.tobytes('raw')
j = in_comps * n_pixels * n_rows
for i in range(j):
rgb_in[i] = data_in[i]
lc.cmsDoTransform(transform, rgb_in, cmyk_out, n_pixels * n_rows)
for j in cmyk_out :
out_b[j] = cmyk_out[j]
out_row = Image.frombytes('CMYK', in_row.size, bytes(out_b))
outImage.paste(out_row, (0, row))
row += n_rows
return outImage
def cmyk2rgbImage(PILImage, psrc='C:\\Windows\\System32\\spool\\drivers\\color\\CoatedFOGRA39.icc', pdst='C:\\Windows\\System32\\spool\\drivers\\color\\AdobeRGB1998.icc') :
ctxt = lc.cmsCreateContext(None, None)
white = lc.cmsD50_xyY() # Set white point for D50
dst_profile = lc.cmsOpenProfileFromFile(pdst, 'r')
src_profile = lc.cmsOpenProfileFromFile(psrc, 'r')
transform = lc.cmsCreateTransform(src_profile, lc.TYPE_CMYK_8, dst_profile, lc.TYPE_RGB_8,
lc.INTENT_RELATIVE_COLORIMETRIC, lc.cmsFLAGS_NOCACHE)
n_pixels = PILImage.size[0]
in_comps = 4
out_comps = 3
n_rows = 16
cmyk_in = lc.uint8Array(in_comps * n_pixels * n_rows)
rgb_out = lc.uint8Array(out_comps * n_pixels * n_rows)
outImage = Image.new('RGB', PILImage.size, 'white')
in_row = Image.new('CMYK', (PILImage.size[0], n_rows), 'white')
out_row = Image.new('RGB', (PILImage.size[0], n_rows), 'white')
out_b = bytearray(n_pixels * n_rows * out_comps)
row = 0
while row < PILImage.size[1] :
in_row.paste(PILImage, (0, -row))
data_in = in_row.tobytes('raw')
j = in_comps * n_pixels * n_rows
for i in range(j):
cmyk_in[i] = data_in[i]
lc.cmsDoTransform(transform, cmyk_in, rgb_out, n_pixels * n_rows)
for j in rgb_out :
out_b[j] = rgb_out[j]
out_row = Image.frombytes('RGB', in_row.size, bytes(out_b))
outImage.paste(out_row, (0, row))
row += n_rows
return outImage
Something to note for anyone implementing this: you probably want to take the uint8 CMYK values (0-255) and round them into the range 0-100 to better match most color pickers and uses of these values. See my code here: https://gist.github.com/mattdesl/ecf305c2f2b20672d682153a7ed0f133