8
Korzystanie z samouczka znalezione w https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html, trenowałem model Keras rozpoznać różnicę między kotami i psami.Konwersja przeszkolony Keras modela klasyfikacji coreml i zintegrować w iOS11
'''
Directory structure:
data/
train/
dogs/
dog001.jpg
dog002.jpg
...
cats/
cat001.jpg
cat002.jpg
...
validation/
dogs/
dog001.jpg
dog002.jpg
...
cats/
cat001.jpg
cat002.jpg
...
'''
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras import backend as K
from PIL import Image
import numpy as np
# dimensions of our images.
img_width, img_height = 150, 150
train_data_dir = 'data/train'
validation_data_dir = 'data/validation'
nb_train_samples = 2000
nb_validation_samples = 800
epochs = 50
batch_size = 16
if K.image_data_format() == 'channels_first':
input_shape = (3, img_width, img_height)
else:
input_shape = (img_width, img_height, 3)
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=input_shape))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
# this is the augmentation configuration we will use for training
train_datagen = ImageDataGenerator(
rescale=1./255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
# this is the augmentation configuration we will use for testing:
# only rescaling
test_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
train_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='binary')
validation_generator = test_datagen.flow_from_directory(
validation_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='binary')
model.fit_generator(
train_generator,
steps_per_epoch=nb_train_samples // batch_size,
epochs=epochs,
validation_data=validation_generator,
validation_steps=nb_validation_samples // batch_size)
model.save('first_try.h5')
Używanie coremltools documentation jako przewodnik, Próbowałem konwersji mojego modelu do coreml formacie:
import coremltools
import h5py
coreml_model = coremltools.converters.keras.convert('first_try.h5',input_names='image',output_names='class',image_input_names = 'image',class_labels = ['cat', 'dog'], is_bgr=True)
coreml_model.save('cats_dogs.mlmodel')
Kiedy import modelu w XCode i uruchomić go z następującego kodu (który współpracuje z z resnet50 i inceptionv3 modele znajdujące się na stronie internetowej firmy Apple), kod snippet guard let prediction = try? model.prediction(image: pixelBuffer!) else {print("error!") return } wydruki „błąd!” a fragment kodu textView.text = "I think this is a \(String(describing: prediction.classLabel))." nigdy nie zostanie osiągnięty.
import UIKit
import Vision
import CoreML
class ViewController: UIViewController, UINavigationControllerDelegate {
//MARK: - Properties
@IBOutlet weak var imageView: UIImageView!
@IBOutlet weak var textView: UITextView!
let imagePicker = UIImagePickerController()
//MARK: - ViewController
override func viewDidLoad() {
super .viewDidLoad()
self.imagePicker.delegate = self
}
@IBAction func openImagePicker(_ sender: Any) {
imagePicker.allowsEditing = false
imagePicker.sourceType = .photoLibrary
present(imagePicker, animated: true, completion: nil)
}
@IBAction func camera(_ sender: Any) {
if !UIImagePickerController.isSourceTypeAvailable(.camera) {
return
}
let cameraPicker = UIImagePickerController()
cameraPicker.delegate = self
cameraPicker.sourceType = .camera
cameraPicker.allowsEditing = false
present(cameraPicker, animated: true)
}
}
extension ViewController: UIImagePickerControllerDelegate {
func imagePickerControllerDidCancel(_ picker: UIImagePickerController) {
dismiss(animated: true, completion: nil)
}
func imagePickerController(_ picker: UIImagePickerController, didFinishPickingMediaWithInfo info: [String : Any]) {
picker.dismiss(animated: true)
textView.text = "Analyzing Image..."
guard let image = info["UIImagePickerControllerOriginalImage"] as? UIImage else {
return
}
UIGraphicsBeginImageContextWithOptions(CGSize(width: 150, height: 150), true, 2.0)
image.draw(in: CGRect(x: 0, y: 0, width: 150, height: 150))
let newImage = UIGraphicsGetImageFromCurrentImageContext()!
UIGraphicsEndImageContext()
let attrs = [kCVPixelBufferCGImageCompatibilityKey: kCFBooleanTrue, kCVPixelBufferCGBitmapContextCompatibilityKey: kCFBooleanTrue] as CFDictionary
var pixelBuffer : CVPixelBuffer?
let status = CVPixelBufferCreate(kCFAllocatorDefault, Int(newImage.size.width), Int(newImage.size.height), kCVPixelFormatType_32ARGB, attrs, &pixelBuffer)
guard (status == kCVReturnSuccess) else {
return
}
CVPixelBufferLockBaseAddress(pixelBuffer!, CVPixelBufferLockFlags(rawValue: 0))
let pixelData = CVPixelBufferGetBaseAddress(pixelBuffer!)
let rgbColorSpace = CGColorSpaceCreateDeviceRGB()
let context = CGContext(data: pixelData, width: Int(newImage.size.width), height: Int(newImage.size.height), bitsPerComponent: 8, bytesPerRow: CVPixelBufferGetBytesPerRow(pixelBuffer!), space: rgbColorSpace, bitmapInfo: CGImageAlphaInfo.noneSkipFirst.rawValue) //3
context?.translateBy(x: 0, y: newImage.size.height)
context?.scaleBy(x: 1.0, y: -1.0)
UIGraphicsPushContext(context!)
newImage.draw(in: CGRect(x: 0, y: 0, width: newImage.size.width, height: newImage.size.height))
UIGraphicsPopContext()
CVPixelBufferUnlockBaseAddress(pixelBuffer!, CVPixelBufferLockFlags(rawValue: 0))
imageView.image = newImage
guard let prediction = try? model.prediction(image: pixelBuffer!) else {
print("error!")
return
}
textView.text = "I think this is a \(String(describing: prediction.classLabel))."
}
}
XCode Error Implementing Custom Keras-CoreML model
Przeszukałem StackOverflow intensywnie, aby rozwiązać ten problem. Pomoc rozwiązaniem tego problemu byłoby bardzo mile widziane!
============================================== ============================= EDYCJA # 1:
Używanie "print (try! Model.prediction (image: pixelBuffer!) as Any) "Napisałem następujący błąd:
2017-07-13 15: 33: 49.034967-0400 cats_dogs [60441: 1198094] Błąd krytyczny:" spróbuj! " Wyrażenie nieoczekiwanie zgłosił błąd: Error Code com.apple.CoreML Domain = = 0 „Wymiary warstwy«wyjście»nie jest taki sam rozmiar jak liczba etykiet klasy” UserInfo = {NSLocalizedDescription = Wymiary "wyjścia" warstwy nie mają tego samego rozmiaru, co liczba etykiet klas.}:
Nie wiem, co oznacza "Rozmiar" warstwy "nie ma tego samego rozmiaru co liczba etykiet klas " znaczy.
============================================== =============================
Edit # 2:
jest to kod używam do konwersji Model do formatu .mlmodel
import coremltools
import h5py
output_labels = ['cat','dog']
coreml_model = coremltools.converters.keras.convert('first_try.h5',input_names='image',image_input_names = 'image',class_labels = output_labels, is_bgr=False)
coreml_model.author = ''
coreml_model.short_description = 'Model to classify images as either cats or dogs'
coreml_model.input_description['image'] = 'Image of a cat or dog'
print coreml_model
coreml_model.save('cats_dogs.mlmodel')
to wyjście terminala:
0 : conv2d_1_input, <keras.engine.topology.InputLayer object at 0x1194c6c50>
1 : conv2d_1, <keras.layers.convolutional.Conv2D object at 0x1194c6c90>
2 : activation_1, <keras.layers.core.Activation object at 0x119515b90>
3 : max_pooling2d_1, <keras.layers.pooling.MaxPooling2D object at 0x119501e50>
4 : conv2d_2, <keras.layers.convolutional.Conv2D object at 0x119520cd0>
5 : activation_2, <keras.layers.core.Activation object at 0x1194e8150>
6 : max_pooling2d_2, <keras.layers.pooling.MaxPooling2D object at 0x11955cc50>
7 : conv2d_3, <keras.layers.convolutional.Conv2D object at 0x11955ce50>
8 : activation_3, <keras.layers.core.Activation object at 0x11954d9d0>
9 : max_pooling2d_3, <keras.layers.pooling.MaxPooling2D object at 0x119594cd0>
10 : flatten_1, <keras.layers.core.Flatten object at 0x1195a08d0>
11 : dense_1, <keras.layers.core.Dense object at 0x119579f10>
12 : activation_4, <keras.layers.core.Activation object at 0x1195c94d0>
13 : dense_2, <keras.layers.core.Dense object at 0x1195ea450>
14 : activation_5, <keras.layers.core.Activation object at 0x119614b10>
input {
name: "image"
shortDescription: "Image of a cat or dog"
type {
imageType {
width: 150
height: 150
colorSpace: RGB
}
}
}
output {
name: "output1"
type {
dictionaryType {
stringKeyType {
}
}
}
}
output {
name: "classLabel"
type {
stringType {
}
}
}
predictedFeatureName: "classLabel"
predictedProbabilitiesName: "output1"
metadata {
shortDescription: "Model to classify images as either cats or dogs"
author: ""
}