I am only concerned about finding the outline of card in an image and isolating the outline of card from rest of the image.
This can be efficiently solved by semantic segmentation (aka dense prediction) - problem in which every pixel must be labeled with class.
In your case, you will have 2 classes: credit card and background. And you will need to have ...
I would recommend to use adaptive threshold rather than global threshold. Adaptive threshold deals better with uneven illumination. I have modified your code accordingly. You may want to experiment further with the value of block size and C value.
I have also used RETR_CCOMP and only show contours which either have a parent (index 3 != -1) or don't have any ...
This problem is known as "style transfer", and deep learning can achieve some pretty amazing results.
Here's a fantastic GitHub repo with an easy-to-use implementation of neural style transfer (usage instructions). I think you'll have better luck using a pre-built model like this rather than training your own.
If you want to train your own, this repo ...
If all of your images are similar to this one(or have a small set of possible designs), you can simply reference the location (pixel-wise) on the image where this fields are and slice it.
After slicing you can use any OCR algorithm to extract that data.
If your data has more variation than that, you can use OCR on the entire image, which is usually a slow ...
Would not it be easier to simply apply geometric computation to your triangle to get smaller triangles whose vertices can be used for the polyline.?
With Wolfram Language
You have a triangle.
shp = Triangle;
A ScalingTransform about the RegionCentroid can be performed with TransformedRegion to get smaller inner triangles....
You might want to take a look at this project FAISS.
Faiss is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM.
It has nice wrappers for you to use from Python. Check the wiki for examples on how you can integrate this ...
You could probably use Dask to do this.
Dask natively scales Python. Dask provides advanced parallelism for analytics, enabling performance at scale for the tools you love
Dask will schedule your computation so that you don't run out of memory and then provide you with the result of your computation. It supports many of the libraries in the data science ...
If you need to re-train the model to classify new faces, this will not scale well to registering new people routinely. You may also suffer from glitches in accuracy during new registrations unless the training routines are carefully monitored.
Instead, recognition systems that need to register new items typically don't re-train. They are trained on the ...
OpenCV has a module to detect faces, eyes etc. With pretrained models you may also be able to detect cars.
There are many good tutorials online. Just have a look.
So if you are happy with this, you don‘t need to know much about data science. However, if you want to go a step ...
Haar-like features are, as the name says, features. They are basically some filters, just like the CNN filters. The main difference is that CNN learns the filters by training, while Haar filters are hand designed. These filters are called Haar-like features due to their similarity with Haar wavelets.
Haar cascades are a bunch of Haar-like features arranged ...
There are two options :
Scan the images with a higher DPI. This should accentuate vertical separation between paragraphs.
Train a Deep learning model for Text Detection in scene. Examples : https://github.com/qjadud1994/CRNN-Keras and https://github.com/mvoelk/ssd_detectors
So from an architectural viewpoint you are right, both are 2D Convolutional Kernels with size of (3,3).
But there are some major differences. While cv2.filter2D(image, -1, kernel_sharpening) directly convolute the image dis2 = Conv2D(filters=64, kernel_size=3, strides=2, padding='same')(dis1) only constructs a Conv2D Layer which is part of the Graph ( ...
(0, 0) is top left.
Here an a helpful blog that goes through all the features in the output vector.
This is common in image processing. There are a few reasons that this is the convention in computer vision. Check here for a list, the accepted answer states:
This is caused in the history. Early computers had Cathode Ray Tubes (CRTs) which "draw" the ...
Updated: Measuring distance from camera to object
Perhaps you could search and find tons of materials. This is a well-established task, for example this tutorial, of more advanced methods combining object detection with distance measurements, see Object-Detection-and-Distance-Measurement, and references at the bottom. In a nutshell, when you know details ...
You don't have to blur your image before passing it to Canny.
OpenCv's implementation already includes a blurring step. So by passing a blurred image, you're effectively blurring the image twice. That will suppresses edges.
Read about openCv's implementation here.
Of course, making them smaller will cause loss of information. Since before you had N x M data points for each image to describe the content of the image, but after resizing you will have n x m (Where n and N denote the number of rows in an image, m and M denotes the number of columns in an image. Also, n <= N and/or m <= M). That is, a small number of ...
def ReadImages(Path): #path to dir with all images
LabelList = list() #Initialized empty labels list
ImageCV = list() #Initialized empty ImageCV list initialized
classes = ["nonPdr", "pdr"] #classes labels all image files startwith
#return list of image folders in main dir path/
FolderList = [f for f in os.listdir(Path) if not f....
There are multiple ways to do it. Here is one technique:
Run OCR on your image and search for your desired text . in this case it is Total.
Get the pixels of that word - run image_to_data method of ocr to get pixels.
Now you can extract the ROI using those pixels via OpenCV.
It'd be good idea to extract face segment first. In OpenCV there're predefined HaarCascades for face detection. Though, you can run into some problems using them. Photos really need to be well-exposed. They're also prone to false positives as hell.
Slightly better, but still vanilla, there's dlib.
Maybe you might try out the brightness data augmentation; in Keras you have something like:
in case you want to generate different brightness levels between 20% and 100% based on your train images. For example (taken from Deep learning for computer vision book by Jason Brownlee):
If you don't need it to work in real time you should not worry about your CPU that much.
There are a few models for face detection using Res Net 10, with portability to OpenCV, those might be enough if you the people you are trying to count are facing forward.
Else, you can use a Res Net 10, it runs up to 100 FPS on a Intel i5 7200u, which is not that big ...
Some parts of the code are missing that might contain clues (e.g. you plot method would only plot random noise, as it stands). What might be happening is that you are showing the image in matplotlib in a figure that is much smaller than the image, so matplotlib will automatically scale the image to the pixel space.
The plt.imshow() method can take an ...
Have you looked into Tesseract (and its Python wrapper/interface: pytesseract)? I don't guarantee that it will solve your problems entirely, but it offers bounding box and OCR features.
On this Tesseract site it lists possible page segmentation modes that you could play around with.
There is also this page that provides some quality improvement ...
I think this may be due to the fact that XML file is missing or the path to it is incorrect. Could you please check that? Maybe you coul try with the full path for the haarcascade_frontalface_alt.xml file.
cv2.selectROI() returns a 2D rectangle with (x, y, width, height) (see the Rect constructor - that object is created from selectROI()).
So, if you want to measure the apparent width in pixels, you need marker.
this is pretty simple approach
Firstly, can you evaluate your scripts on more images to get an idea of how well it performs? If you get an acceptable classification accuracy (or e.g. a good F1 score), then there is no need to try out a CNN!
I have no idea about the false positives
Actually if you cannot evaluate your method like that, then a CNN is ...