Develop Reference

How to extract object orientation from video stream (Webcam) - Python

I found this guide which teaches how to refine the orientation of objects from images. I would love to know if it can and should be used to analyze the orientation of objects displayed in video streams.
The basis for the work is from the scientific publication found in this video. I want to know how they got information about the direction of the Fish's face.
Thanks,
Avishai

You will probably need library like opencv to get orientation information from the image. You can apply threshold after converting this image to grayscale and extract contour of the image. After that you need to follow something like below pattern to get orientation. Very easy, just a little bit search you can find a lot of similar examples as well.
rectangle_for_angle = cv2.minAreaRect(cntrs[0])
angle = rectangle_for_angle[-1]
rect_points = cv2.boxPoints(rectangle_for_angle)
rect_points_result = np.int0(rect_points)
#You can also draw rotated image
cv2.drawContours(image,[rect_points_result],0,(0,0,255),2)

How can you crop an SVG Image in Python?

With a program, I am producing an SVG image with dimensions of 400px x 400px. However, I would like to crop the bottom of this SVG image off, based off of a variable that dictates how much of the bottom of the image should be cropped in pixels.
This SVG image is being generated with pyCairo with surface = cairo.SVGSurface("output.svg", WIDTH, HEIGHT) and ctx = cairo.Context(surface).
Although the HEIGHT variable is a constant and isn't changed, after I perform some operations on the surface object, I would like to be able to resize it once more. I can use the Pillow Image object to crop PNGs, but it does not support SVGs.
I have also tried to open the svg file with open("output.svg"). However, if I try to read it, I am unable to and it shows up as blank, thus making it unmodifiable.
Is there any way in Python to either crop an SVG image or modify its size after it has been modified with pycairo?

The answer above is incomplete and at least for me doesn't solve the problem.
A SVG can simply be cropped (trimmed, clipped, cut) using vpype with the crop or trim and translate commands.
import vpype_cli as vp
#vp.excute("read test.svg translate 300 400 trim 30 20 write output.svg")
vpype_cli.execute("read test.svg crop 0cm 0cm 10cm 20cm write output.svg")
Playing around with the parameters should lead to the desired crop.
Took some time to find this, as most answers say it cant be done, which is ridiculous.

You cannot crop SVG like you crop PNG because in the latter you can just drop pixels, while for the former you have defined paths that can't be easily recomputed.
If you're sure there's nothing in the part you are about to "crop", you can use set_context_size to make the svg context/canvas smaller while preserving ratio and size inside.

Color in image gets dull after saving it in OpenCV

I am using opencv module to read and write the image. here is the code and below is the image i am reading and second image is after saving it on disk using cv2.imwrite().
import cv2
img = cv2.imread('originalImage.jpg')
cv2.imwrite('test.jpg',img)
It is significantly visible that colors are dull in second image. Is there any workaround to this problem or I am missing on some sort of setting parameters..?

I have done a bit of research on the point #mark raised about ICC profile. I have figured out a way to handle this in python PIL module. here is the code that worked for me. I have also learned to use PNG file format rather JPEG to do lossless conversion.
import Image
img = Image.open('originalImage.jpg')
img.save('test.jpg',icc_profile=img.info.get('icc_profile'))
I hope this will help others as well.

The difference is that the initial image (on the left in the diagram) has an attached ICC profile whereas the second one (on the right) does not.
I obtained the above image by running the ImageMagick utility called identify like this:
identify -verbose first.jpg > 1.txt
identify -verbose second.jpg > 2.txt
Then I ran the brilliant opendiff tool (which is part of macOS) like this:
opendiff [12].txt
You can extract the ICC profile from the first image also with ImageMagick like this:
convert first.jpg profile.icc

Your first input image has some icc-Profile associated in the meta-data, which is an optional attribute and most devices may not inject it in the first place. The ICC profile basically performs a sort of color correction, and the correction coefficients are calculated for each unique device during calibration.
Modern Web Browsers, Image Viewing utilities mainly take into account this ICC profile information before rendering the image onto the screen, that is the reason why there is a diff in both the images.
But Unfortunately OpenCV doesn't reads the ICC config from the meta data of the image to perform any color correction.

Automatic extract subimage with a frame

I am trying to extract a subimage from a scanned paper like this:
https://cloud.kopa.ch/index.php/s/gGZm5xeMYlPfU81
The extracted images should be georeferenced and added to a webmap service, but thats not the question here.
How can I get the frame / its pixel coordinates to crop the image?
I am also free in creating the "layout" (similar to the example), which means I could add markers to get the frame better after scanning it again.
The workflow is:
generate layout - print map - draw on the map - scan it - crop "map-frame" - georeferencing this frame - show it on a webmap
The "map-frames" are preprocessed and I know their location/extent
Has anybody an idea how to crop the (scanned) images automatically to this "map-frame"?
I have to work with python and have the packages PIL, pillow and imagemagick for the image processing
Thanks for you help!
If you need more information, don't hesitate to ask

Here's an example I adapted form the Pillow docs, check them out for any further processing that you might need to perform:
from Pillow import Image
Image.open("/path/to/image.jpg")
box = (100, 100, 400, 400)
region = im.crop(box)
Also, it might prove valuable to search Stack Overflow for this kind of operation, I'm sure it has been discussed earlier.
As for finding the actual rectangle to crop you'll have to do some form of image analysis. In it's simplest form, conceptually that could be something along these lines:
Applying an S-curve filter to a black-and-white representation of your image
Iterate over all of the pixels in the image
Keep track of horizontal and vertical lines that has sufficiently black pixel values.
Use this data to determine the bounding box of the portion of the image your interested in.
Depending on your needs you might want to look into some computer vision library instead, which are well optimized for this and similar tasks. The one that springs to mind is OpenCV which is I would guess is well optimized and documented, and there's a python module available as well.

Reading tiffs in opencv swaps top and bottom third of image

I've got a pretty strange issue. I have several tif images of astronomical objects. I'm trying to use opencv's python bindings to process them. Upon reading the image file, it appears that segments of the images are swapped or rotated. I've stripped it down to the bare minimum, and it still reproduces:
img = cv2.imread('image.tif', 0)
cv2.imwrite('image_unaltered.tif', img)
I've uploaded some samples to imgur, to show the effect. The images aren't super clear, that's the nature of preprocessed astronomical images, but you can see it:
First set:
http://imgur.com/vXzRQvS
http://imgur.com/wig99KR
Second set:
http://imgur.com/pf7tnPz
http://imgur.com/xGn9C77
The same rotated/swapped images appear if I use cv2.imShow(...) as well, so I believe it's something when I read the file. Furthermore, it persists if I save as jpg as well. Opening the original in Photoshop shows the correct image. I'm using opencv 2.4.10, on Linux Mint 17.1. If it matters, the original tifs were created with FITS liberator on windows.
Any idea what's happening here?