I want to create a basic video editing application where the user can import video clips and then use symmetry (vertical or horizontal) and offsets on their videos. How feasible is this?
For instance, consider the following image:
Right-symmetry:
Image offset to the top-left:
If that last image is confusing, basically you can think of it as the images repeating one next to the the other in a grid, infinitely, such that they're symmetric. Then, you can select a window of this grid equal to the size of the original image. Eg. the red square represents the window:
This is very feasible. Opencv can do all of this frame by frame. Although it would probably take sometime for high quality/long videos. If you want to know how to do these operations, I would open seperate questions. mirroring can for example be done by cv2.flip().
You can use the .flip () method present in the cv2 library. First enter the image with cv2.imread (path). Then to make the mirror effect you have to create a insert cv2.flip (image, 0).
Just as reported below:
image = cv2.imread(path)
mirrow = cv2.flip(image, 0)
Related
I have the a list of images (each of these image is a separate file), let's say they are some jigsaw puzzle pieces and, for each of them, I know it's position (x,y) and rotation in the complete puzzle.
How can I show the complete puzzle by stitching each of these pieces together in a single image (given that i know where to put each of them)?
I don't know if this is important but the pieces are not of regular shape (e.g. they are not squares), and they are all of different sizes
EDIT:
For the moments it seems to be working without the rotation but there is another problem, the pieces seems to not have a transparent background but rather a black one.
I have loaded them with opencv2 in the following way:
import glob
folder = './img/2/frag_eroded/'
frags = []
files = glob.glob(folder+"/*.png")
for file in files:
image = cv2.imread(file, cv2.IMREAD_UNCHANGED)
image = cv2.cvtColor(image, cv2.COLOR_BGRA2RGBA)
frags.append(image)
Example of resulting image, you can kinda see the the squares around each piece and see how the pieces overlap with their "background" that should be transparent rather then black
This depends on how you want to handle it when there's an overlapping transparent area.
Suppose all pixels are either transparent or opaque, and
Suppose each image has RGBA (4-channels including alpha),
then you can set all RGB values to zero whenever the pixel is transparent.
Then proceed to add the smaller images to a bigger canvas (initialized to be all zeros RGB). The canvas can either have an alpha layer or not, depending on your preference.
Beware the canvas is big enough to contain all of them. So the first step here would be to make a large enough matrix / opencv image.
How to add images: https://stackoverflow.com/a/68878529/19042045
Considering I have the coordinates already of the area of the image I want to do image processing on. It was already explained here using Rect but how do you do this on python OpenCV 3?
From the link you gave, it seems you don't want the output in a different image variable, given that you know the coordinates of the region you want to process. I'll assume your image processing function to be cv2.blur() so this is how it'll be:
image[y:y+height, w:w+width] = cv2.blur(image[y:y+height, w:w+width], (11,11))
Here, x & y are your ROI starting co-ordinates, and height & width are the height, width of the ROI
Hope this is what you wanted, or if it's anything different, provide more details in your question.
It would be very useful if you would provide more details and maybe some code you've tried.
From my understanding, you want to do image processing on a region of an image array only. You can do something like
foo(im[i1:i2, j1:j2, :])
Where foo is your image processing function.
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.
I am trying to add two images of different sizes using bitwise operations in OpenCV using python. I want a particular point in Image1(an image of face of a person) to coincide with a particular point in Image2(image of a spectacle frame). The particular points are not the cornermost points of the images.I know the 2 mid points of the frame glasses and the pupil of the eyes. I want the frame mid points to coincide with the pupil points of the eyes in the face. The code which I am using adds the second image's leftmost corner point to the specific point of Image1 as in Line 10, whereas i want the mid point of left glass frame to be added.
The face image can be any random image and the spectacle image is as -
I am using the code:
import cv2
import numpy as np
img_frame = cv2.imread('image1.jpg',1)
img_in = cv2.imread('face.jpg',1)
new_image = np.zeros(img_frame.shape,dtype=np.uint8)
i,j,k = img_frame.shape
for ii in range (1,i):
for jj in range (1,j):
pixel = img_frame[ii,jj]
img_in[339+ii,468+jj] = pixel
cv2.imwrite('pc2_with_frame_7.jpg',img_in)
cv2.imshow('win',img_in)
cv2.waitKey(0)
cv2.destroyWindow('win')
Any kind of help would be appreciated.
Thank you.
Ok, it seems nobody else much can help so I will offer what I can...
What you are trying to do is called alpha-compositing. You can read about it here on Wikipedia and also here in the OpenCV documentation.
My tool of choice for this would be ImageMagick, which is free and has Perl, Python, C/C++ bindings as well as command-line tools. If I start with this photo (face.jpg):
and take your glasses.jpg file and convert it to a PNG with transparency, whcih looks like this:
I can run the following ImageMagick command at the Terminal
composite glasses.png face.jpg out.jpg
and I get this:
It seems that OpenCV has problems maybe with transparency, and a solution is presented here. If you want to try the masking method suggested by #ypnos in that post, I have made you the necessary input files and you can download them from my website at:
glasses.png with alpha channel
input-mask.png
I've been working on a project of recognizing a flag shown in the camera using opencv python.
I've already tried using surf, color histogram matching, and template matching. But of these 3, it does not always return the correct answer. what i want now, is what would be the best solution to this problem of mine.
Example of the template images:
Here is an example of flag shown in camera.
what to use if this is the kind of images that i want to recognize?
Update code in matchTemplate
flags=["Cambodia.jpg","Laos.jpg","Malaysia.jpg","Myanmar.jpg","Philippines.jpg","Singapore.jpg","Thailand.jpg","Vietnam.jpg","Indonesia.jpg","Brunei.jpg"]
while True:
methods = 'cv2.TM_CCOEFF_NORMED'
list_of_pics=[]
for flag in flags:
template= cv2.imread(flag,0)
img = cv2.imread('philippines2.jpg',0)
# generate Gaussian pyramid for A
G = template.copy()
gpA = [G]
for i in xrange(6):
G = cv2.pyrDown(G)
gpA.append(G)
n=0
for x in gpA:
w, h = x.shape[::-1]
method = eval(methods)#
# Apply template Match
res = cv2.matchTemplate(img,x,method)
matchVal=res[0][0]
picDict={"matchVal":matchVal,"name":flag}
list_of_pics.append(picDict)
n=n+1
newlist = sorted(list_of_pics, key=operator.itemgetter('matchVal'),reverse=True)
#print newlist
matched_image=newlist[0]['name']
print matched_image
k=cv2.waitKey(10)
if (k==27):
break
cv2.destroyAllWindows()
I don't think that you can get good results from SURF/SIFT because:
SURF/SIFT need keypoints to detect the object but in your case, you have to detect flags and most of the flags are mostly uniform and do not provide much keypoints.
In your webcam frame, you have several things rather than having only flag. Those several things also contribute to get the keypoints.
Solution: i still think that you should use matchTemplate() of opencv which you have already tried but the problem in your version is that you didn't consider the fact that matchTemplate() is not scale and orientation invariant. So, the solution is to use Gaussian pyramid and create the different size (half, one forth, double etc.) of your sample flags. After getting the same flag in 2-5 different size, you should perform the matchTemplate() between every size of flag and the webcam frame.
Strategy:
Receive the webcam frame
Load the image of a flag.
Using Gaussian pyramid, create smaller and bigger images of that flag (you don't need to store them.)
Perform matchTemplate() between the webcam frame and each size of flag.
Result = with which so ever image you get the maximum correlation value is the flag present in your webcam.
REMEMBER: matchTemplate is not scale and orientation invariant. so, if you rotate the image or make it larger/smaller in the webcam frame...you won't get the good results.
SURF cannot be applied to the images that have no corners (when gradient is mostly goes in one direction like in a striped flag). Color histogram of the whole object may not work since both of your examples have similar colors. However, if you can apply a histogram to different parts of the image it will work better.
What you need to do is to split your training image on say 4 quadrants and create 4 color histograms. The testing stage will integrate these 4 back projected histograms and check for the right spatial order of responses. Color histogram is quite robust to rotations, scaling and perspective. It changes with illumination so you need to have liberal matching thresholds. Spatial resolution from 4 quadrants will help to ameliorate this situation.
For the future I recommend studying methods in more detail to understand their applicability rather than trying them randomly.