ozgurshn

import SwiftUI
import SVGKit

struct SVGKFastImageViewSUI:UIViewRepresentable
{
    @Binding var url:URL
    @Binding var size:CGSize
    
    func makeUIView(context: Context) -> SVGKFastImageView {

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/// URL of model assuming it was installed in the same bundle as this class
//    class var urlOfModelInThisBundle : URL {
//        let bundle = Bundle(for: ObjectDetectionAccurate.self)
//        return bundle.url(forResource: "ObjectDetectionAccurate", withExtension:"mlmodelc")!
//    }
    class var urlOfModelInThisBundle: URL {
        return try! MLModel.compileModel(at: URL(fileURLWithPath:"/Users/ozgur/Library/Autosave Information/ThermDetector.playground/Resources/ObjectDetectionAccurate.mlmodel"))
    }

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func observationToRect(box:VNRectangleObservation)->CGRect
{
    let xCord = box.topLeft.x * imageView.frame.size.width
    let yCord = (1 - box.topLeft.y) * imageView.frame.size.height
    let width = (box.topRight.x - box.bottomLeft.x) * imageView.frame.size.width
    let height = (box.topLeft.y - box.bottomLeft.y) * imageView.frame.size.height
    
    return CGRect(x: xCord, y: yCord, width: width, height: height)
}

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guard let rectangle = request?.results?.first as? VNRectangleObservation else {
    guard let error = error else { return }
    print("Error: Rectangle detection failed - Vision request returned an error. \(error.localizedDescription)")
    return
}
guard let filter = CIFilter(name: "CIPerspectiveCorrection") else {
    print("Error: Rectangle detection failed - Could not create perspective correction filter.")
    return
}
let width = CGFloat(CVPixelBufferGetWidth(currentCameraImage))

ozgurshn

Scan and Colorize Your Photos terms of use
Please find the legal information and terms and conditions of the Scan and Colorize Your Photos service, here below
IMPORTANT: BY USING THE SERVICES (AS DEFINED BELOW) YOU INDICATE YOUR ACCEPTANCE OF THE FOLLOWING TERMS AND CONDITIONS. IF YOU DO NOT ACCEPT THE TERMS AND CONDITIONS SET FORTH HEREIN, YOU MUST NOT USE THE SERVICES. PLEASE READ THESE TERMS OF SERVICE CAREFULLY AS THEY CONTAIN IMPORTANT INFORMATION ABOUT YOUR LEGAL RIGHTS, REMEDIES AND OBLIGATIONS.

Welcome to Scan and Colorize Your Photos where you can scan your old photo albums!

Acceptance of Terms
The following instrument consists of the terms and conditions governing your ("you") access to and use of the  application known as "Scan and Colorize Your Photoss" and the content, features, software, related technology and services therein (collectively, the "Services"). These Scan and Colorize Your Photos Terms of Use together with the Privacy Policy (collectively, the "Terms") constitute a binding agreem

ozgurshn

##https://github.com/apple/coremltools/blob/master/examples/APIExamples.md
import coremltools
import numpy as np
import PIL.Image

# load a model whose input type is "Image"
model = coremltools.models.MLModel('path/to/the/saved/model.mlmodel')

Height = 20  # use the correct input image height
Width = 60  # use the correct input image width

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##https://heartbeat.fritz.ai/advanced-tips-for-core-ml-77c9e0231a9
from coremltools.models.neural_network import quantization_utils

def quantize_model(mlmodel, nbits, method='linear'):
    """Quantize the weights of an mlmodel to a specific number of bits.
    Args:
        mlmodel (coremltools.models.MLModel): A Core ML model
        nbits (int): the bit depth of the quantized weights.
        method (string): the quantization method.
    

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##https://heartbeat.fritz.ai/advanced-tips-for-core-ml-77c9e0231a9
from coremltools.models.neural_network import flexible_shape_utils

def make_mlmodel_flexible(spec, size_range=(100, 1920):
    """Make input and output sizes of a Core ML model flexible.
    Args:
        spec (NeuralNetwork_pb2): a Core ML neural network spec
        size_range ([Int]): a tuple containing the min and max input sizes.
    """
    size_range_spec = flexible_shape_utils.NeuralNetworkImageSizeRange()

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##https://heartbeat.fritz.ai/advanced-tips-for-core-ml-77c9e0231a9
def add_reflective_padding_and_crop(mlmodel, padding_size=20):
    """Add reflective padding and crop layers to remove edge artifcats.
    Because the convolution layers rely on 'same' padding, stylized images have
    a small ring of distortion around the outer edge. This can be eliminated
    with reflective padding on the input image. This method modifies the
    original MLModel spec to add a padding layer after the input and a crop
    layer before the output to remove the padding at the end.
    Args:
        mlmodel (coremltools.models.MLModel): an MLModel spec.

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guard let results = request.results as? [VNRectangleObservation] else {
      fatalError("Unexpected result type from VNDetectRectanglesRequest")
    }
    // Need check results only if objectsCountToDetect isn't default
    if objectsCountToDetect != defaultObjectsCount {
      guard results.count == objectsCountToDetect else {
        Logger.e("Wrong Rectangulars Results count: Founded - \(results.count) | Required - \(objectsCountToDetect)")
        return
      }
    }