ImageFilter | phonegap plugin for creating instagram style filters | Plugin library
kandi X-RAY | ImageFilter Summary
kandi X-RAY | ImageFilter Summary
ImageFilter is a phonegap / Cordova plugin that allows you to create instagram style filters to images and save them out as High-res versions.
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Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of ImageFilter
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ImageFilter Examples and Code Snippets
Community Discussions
Trending Discussions on ImageFilter
QUESTION
I tried 5 different implementations of the Sobel operator in Python, one of which I implemented myself, and the results are radically different.
My questions is similar to this one, but there are still differences I don't understand with the other implementations.
Is there any agreed on definition of the Sobel operator, and is it always synonymous to "image gradient"?
Even the definition of the Sobel kernel is different from source to source, according to Wikipedia it is [[1, 0, -1],[2, 0, -2],[1, 0, -1]]
, but according to other sources it is [[-1, 0, 1],[-2, 0, 2],[-1, 0, 1]]
.
Here is my code where I tried the different techniques:
...ANSWER
Answered 2021-Jun-15 at 14:22according to wikipedia it's [[1, 0, -1],[2, 0, -2],[1, 0, 1]] but according to other sources it's [[-1, 0, 1],[-2, 0, 2],[-1, 0, 1]]
Both are used for detecting vertical edges. Difference here is how these kernels mark "left" and "right" edges.
For simplicity sake lets consider 1D example, and let array be
[0, 0, 255, 255, 255]
then if we calculate using padding then
- kernel
[2, 0, -2]
gives[0, -510, -510, 0, 0]
- kernel
[-2, 0, 2]
gives[0, 510, 510, 0, 0]
As you can see abrupt increase in value was marked with negative values by first kernel and positive values by second. Note that is is relevant only if you need to discriminate left vs right edges, when you want just to find vertical edges, you might use any of these 2 aboves and then get absolute value.
QUESTION
ANSWER
Answered 2021-Jun-02 at 12:34i have tried but its not the best
QUESTION
I'm trying to use the Pillow with some test data that I'm generating in black and white, pixel by pixel. I'd like to use this data to test out my one pixel box blur. I've got greyscale pixels stored in row-wise 3x3 matrix like this:
...ANSWER
Answered 2021-May-18 at 11:34I understand your expected output, but let's check the reality first:
QUESTION
ANSWER
Answered 2021-Apr-03 at 16:14As documentation says:
The filter will be applied to all the area within its parent or ancestor widget's clip. If there's no clip, the filter will be applied to the full screen.
So I had to wrap BackdropFilter with ClipRect
QUESTION
I'm using a showDialog function in my app. The themeData is not passed from my parent widget to my showDialog. Can anyone tell me why it's not working? I have to specify the theme is working everywhere else but not in my showDialog. I'm using provider to change the theme accordingly to dark and light. Anyways, in the code just below the showDialog it works.
...ANSWER
Answered 2021-Mar-30 at 13:25Using a Theme like this declare theme in MaterialApp
QUESTION
I am trying to extract the subsection area of an image based on the border of a colored box on the image (see below).
I want to extract the area of the image within the yellow box..
For reference, I am extracting this image from a PDF using pdfplumber's im.draw_rect
function, which requires ImageMagick and Ghostscript. I have looked everywhere I can for a solution to this problem, and while Mark Setchell's answer to the question Python: How to cut out an area with specific color from image (OpenCV, Numpy) has come close, I'm getting some unexpected errors.
Here is what I have tried so far:
...ANSWER
Answered 2021-Mar-29 at 08:31As Mark already pointed out in the comments, the yellow rectangle doesn't have the RGB value of [247, 213, 83]
. ImageJ, for example, returns plain yellow [255, 255, 0]
. So, using this value might already help.
Nevertheless, to overcome those uncertainties regarding definitive RGB values, maybe also varying across platforms, software, and so on, I'd suggest to use color thresholding using the HSV color space, which also works using Pillow, cf. modes.
You only need to pay attention to the proper value ranges: The hue channel, for example, has values in the range of [0 ... 360]
(degree), which are mapped to a full 8-bit, unsigned integer, i.e. to the range of [0 ... 255]
. Likewise, saturation and value are mapped from [0 ... 100]
(percent) to [0 ... 255]
.
The remainder is to find proper ranges for hue, saturation, and value (e.g. using some HSV color picker), and NumPy's boolean array indexing to mask yellow-ish areas in the given image.
For the final cropping, you could add some additional border to get rid of the yellow border itself.
Finally, here's some code:
QUESTION
So I asked this question and tried the ProcessPoolExecutor approach. I used the decorator suggested the following way:
Running Image Manipulation in run_in_executor. Adapting to multiprocessing
...ANSWER
Answered 2021-Mar-22 at 13:51Decorators typically produce wrapped functions that aren't easy to pickle (serialize) because they contain hidden state. When dealing with multiprocessing, you should avoid decorators and send ordinary global functions to run_in_executor
. For example, you could re-write your executor
decorator into a utility function:
QUESTION
ANSWER
Answered 2021-Mar-19 at 21:19You need to know the followings for solving the current problem:
Image is to small for accurate prediction. Therefore I suggest to up-scale and get the binary mask
-
- Convert image to HSV color-space
-
- Get the binary mask
-
- Up-scale the binary mask
-
- Inverse the binary mask
Result:
OCR result will be (python tesseract 0.3.7):
QUESTION
I am trying to train a model on a data set which does not fit in my RAM.
Therefore I am using a data generator which inherits from tensorflow.keras.utils.Sequence
as shown below.
This is working. However because I am doing processing on the images my training is CPU bound. When looking in GPU-Z my GPU is only at 10-20% but one of my CPU Cores is at its max.
To solve this I am trying to run the generator in parallel on all my 16 cores. However when I set use_multiprocessing=True
in the fit() function the program freezes. And using workers=8
does not speed up the process just produces batches in uneven intervals.
ex.:
batch 1-8 is processed immediately than there is some delay and than batch 9-16 is processed.
The code below shows what I am trying to do.
...ANSWER
Answered 2021-Mar-12 at 09:53In the end I needed to make the Data generator use multi processing. To do this, the arrays needed to be stored in shared memory and than used in the sub processes.
QUESTION
I have my back-end server
in express(node.js)
and all apis
is running on this server
. I also have file-upload
mechanism for file-upload
api
using multer
. For file uploading i have created a middleware
and in my helper
controller
i have this
ANSWER
Answered 2021-Mar-08 at 13:35Multer provide memory options by which without storing file in local system, we can convert it into buffer and read the content.
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