├── FlowerClassifier.xcodeproj
├── project.xcworkspace
│ └── contents.xcworkspacedata
├── xcuserdata
│ └── vamshikrishna.xcuserdatad
│ │ └── xcschemes
│ │ └── xcschememanagement.plist
└── project.pbxproj
├── FlowerClassifier
├── Info.plist
├── Base.lproj
│ ├── LaunchScreen.storyboard
│ └── Main.storyboard
├── Assets.xcassets
│ └── AppIcon.appiconset
│ │ └── Contents.json
├── AppDelegate.swift
└── ViewController.swift
└── README.md
/FlowerClassifier.xcodeproj/project.xcworkspace/contents.xcworkspacedata:
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/FlowerClassifier.xcodeproj/xcuserdata/vamshikrishna.xcuserdatad/xcschemes/xcschememanagement.plist:
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2 |
3 |
4 |
5 | SchemeUserState
6 |
7 | FlowerClassifier.xcscheme
8 |
9 | orderHint
10 | 5
11 |
12 |
13 |
14 |
15 |
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/FlowerClassifier/Info.plist:
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1 |
2 |
3 |
4 |
5 | CFBundleDevelopmentRegion
6 | $(DEVELOPMENT_LANGUAGE)
7 | CFBundleExecutable
8 | $(EXECUTABLE_NAME)
9 | CFBundleIdentifier
10 | $(PRODUCT_BUNDLE_IDENTIFIER)
11 | CFBundleInfoDictionaryVersion
12 | 6.0
13 | CFBundleName
14 | $(PRODUCT_NAME)
15 | CFBundlePackageType
16 | APPL
17 | CFBundleShortVersionString
18 | 1.0
19 | CFBundleVersion
20 | 1
21 | LSRequiresIPhoneOS
22 |
23 | UILaunchStoryboardName
24 | LaunchScreen
25 | UIMainStoryboardFile
26 | Main
27 | NSCameraUsageDescription
28 | Need Camera Access
29 | UIRequiredDeviceCapabilities
30 |
31 | armv7
32 |
33 | UISupportedInterfaceOrientations
34 |
35 | UIInterfaceOrientationPortrait
36 | UIInterfaceOrientationLandscapeLeft
37 | UIInterfaceOrientationLandscapeRight
38 |
39 | UISupportedInterfaceOrientations~ipad
40 |
41 | UIInterfaceOrientationPortrait
42 | UIInterfaceOrientationPortraitUpsideDown
43 | UIInterfaceOrientationLandscapeLeft
44 | UIInterfaceOrientationLandscapeRight
45 |
46 |
47 |
48 |
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/FlowerClassifier/Base.lproj/LaunchScreen.storyboard:
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/FlowerClassifier/Assets.xcassets/AppIcon.appiconset/Contents.json:
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1 | {
2 | "images" : [
3 | {
4 | "idiom" : "iphone",
5 | "size" : "20x20",
6 | "scale" : "2x"
7 | },
8 | {
9 | "idiom" : "iphone",
10 | "size" : "20x20",
11 | "scale" : "3x"
12 | },
13 | {
14 | "idiom" : "iphone",
15 | "size" : "29x29",
16 | "scale" : "2x"
17 | },
18 | {
19 | "idiom" : "iphone",
20 | "size" : "29x29",
21 | "scale" : "3x"
22 | },
23 | {
24 | "idiom" : "iphone",
25 | "size" : "40x40",
26 | "scale" : "2x"
27 | },
28 | {
29 | "idiom" : "iphone",
30 | "size" : "40x40",
31 | "scale" : "3x"
32 | },
33 | {
34 | "idiom" : "iphone",
35 | "size" : "60x60",
36 | "scale" : "2x"
37 | },
38 | {
39 | "idiom" : "iphone",
40 | "size" : "60x60",
41 | "scale" : "3x"
42 | },
43 | {
44 | "idiom" : "ipad",
45 | "size" : "20x20",
46 | "scale" : "1x"
47 | },
48 | {
49 | "idiom" : "ipad",
50 | "size" : "20x20",
51 | "scale" : "2x"
52 | },
53 | {
54 | "idiom" : "ipad",
55 | "size" : "29x29",
56 | "scale" : "1x"
57 | },
58 | {
59 | "idiom" : "ipad",
60 | "size" : "29x29",
61 | "scale" : "2x"
62 | },
63 | {
64 | "idiom" : "ipad",
65 | "size" : "40x40",
66 | "scale" : "1x"
67 | },
68 | {
69 | "idiom" : "ipad",
70 | "size" : "40x40",
71 | "scale" : "2x"
72 | },
73 | {
74 | "idiom" : "ipad",
75 | "size" : "76x76",
76 | "scale" : "1x"
77 | },
78 | {
79 | "idiom" : "ipad",
80 | "size" : "76x76",
81 | "scale" : "2x"
82 | },
83 | {
84 | "idiom" : "ipad",
85 | "size" : "83.5x83.5",
86 | "scale" : "2x"
87 | }
88 | ],
89 | "info" : {
90 | "version" : 1,
91 | "author" : "xcode"
92 | }
93 | }
--------------------------------------------------------------------------------
/FlowerClassifier/AppDelegate.swift:
--------------------------------------------------------------------------------
1 | //
2 | // AppDelegate.swift
3 | // FlowerClassifier
4 | //
5 | // Created by Vamshi Krishna on 08/12/17.
6 | // Copyright © 2017 Vamshi Krishna. All rights reserved.
7 | //
8 |
9 | import UIKit
10 |
11 | @UIApplicationMain
12 | class AppDelegate: UIResponder, UIApplicationDelegate {
13 |
14 | var window: UIWindow?
15 |
16 |
17 | func application(_ application: UIApplication, didFinishLaunchingWithOptions launchOptions: [UIApplicationLaunchOptionsKey: Any]?) -> Bool {
18 | // Override point for customization after application launch.
19 | return true
20 | }
21 |
22 | func applicationWillResignActive(_ application: UIApplication) {
23 | // Sent when the application is about to move from active to inactive state. This can occur for certain types of temporary interruptions (such as an incoming phone call or SMS message) or when the user quits the application and it begins the transition to the background state.
24 | // Use this method to pause ongoing tasks, disable timers, and invalidate graphics rendering callbacks. Games should use this method to pause the game.
25 | }
26 |
27 | func applicationDidEnterBackground(_ application: UIApplication) {
28 | // Use this method to release shared resources, save user data, invalidate timers, and store enough application state information to restore your application to its current state in case it is terminated later.
29 | // If your application supports background execution, this method is called instead of applicationWillTerminate: when the user quits.
30 | }
31 |
32 | func applicationWillEnterForeground(_ application: UIApplication) {
33 | // Called as part of the transition from the background to the active state; here you can undo many of the changes made on entering the background.
34 | }
35 |
36 | func applicationDidBecomeActive(_ application: UIApplication) {
37 | // Restart any tasks that were paused (or not yet started) while the application was inactive. If the application was previously in the background, optionally refresh the user interface.
38 | }
39 |
40 | func applicationWillTerminate(_ application: UIApplication) {
41 | // Called when the application is about to terminate. Save data if appropriate. See also applicationDidEnterBackground:.
42 | }
43 |
44 |
45 | }
46 |
47 |
--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 | # CaffeToCoreML
2 | Hey'll!
3 | There are a lot of tutorials/ open source projects on how to use **Vision** and **CoreML** frameworks for **Object Detection** in real world using iOS apps using **.mlmodels** given by Apple. But seldom in reality, do we get a **.mlmodel** available suiting our use case.
4 |
5 | **CAFFE** is one of the
6 | most famous Deep Learning frameworks used to train ML models (more details here: http://caffe.berkeleyvision.org)
7 |
8 | Here, I took up a **Caffe model for the Oxford 102 flower dataset**, which was converted to **CoreML model** using **coremltools python package**.
9 |
10 | `oxford102.caffemodel` can be downloaded at https://drive.google.com/uc?export=download&confirm=PXEh&id=0B0HbJVlOlJ3SVVNyMDQwR3FRYWc . Please not that along with `oxford102.caffemodel`, we will also need `flower-labels.txt` and `deploy.prototxt` files in order to successfully **convert .caffemodel to .mlmodel**
11 |
12 | Also note that this conversion from `.caffemodel to .mlmodel` is only possible with `**Python2.7**`
13 |
14 |
15 |
16 | Once downloaded the above mentioned three files, here are the steps used for conversion:
17 |
18 | 1) Put all the three downloaded files in a folder, say FlowerClassifier.
19 | 2) Add a python file, say convert-script.py in the same folder.
20 | 3) Add the script below to your convert-script.py file.
21 |
22 | ```
23 | import coremltools
24 |
25 | caffe_model = ('oxford102.caffemodel', 'deploy.prototxt')
26 | labels = 'flower-labels.txt'
27 |
28 | coreml_model = coremltools.converters.caffe.convert(
29 | caffe_model,
30 | class_labels = labels,
31 | image_input_names = 'data'
32 | )
33 |
34 | coreml_model.save('FlowerClassifier.mlmodel')
35 | ```
36 |
37 | 4) Now, launch Terminal in your Mac, activate Python2.7 using command ```source python27/bin/activate```
38 |
39 | 5) Then cd into your folder (***FlowerClassifier***)
40 | 6) Then run the script using command ```python convert-script.py```.
41 |
42 | Voila! 2-3 minutes later you will have your FlowerClassifier.ml model present in your FlowerClassifier folder.
43 |
44 | For those who want to skip the conversion part and directly use FlowerClassifier.mlmodel , you can download it here (https://drive.google.com/file/d/0B1ghKa_MYL6meDBHT2NaZGxkNzQ/view) . Please note that I have not uploaded this model in my project owing to size constraints.
45 |
46 | Just download the .mlmodel, drag and drop it into your project directory and test the ap on different flowers in real world.
47 |
48 | ***Final project looks like this:***
49 |
50 |
51 |
52 |
53 |
54 |
55 |
56 |
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/FlowerClassifier/ViewController.swift:
--------------------------------------------------------------------------------
1 | //
2 | // ViewController.swift
3 | // FlowerClassifier
4 | //
5 | // Created by Vamshi Krishna on 08/12/17.
6 | // Copyright © 2017 Vamshi Krishna. All rights reserved.
7 | //
8 |
9 | import UIKit
10 | import CoreML
11 | import Vision
12 | import SwiftyJSON
13 | import Alamofire
14 | import SDWebImage
15 | import ColorThiefSwift
16 |
17 | class ViewController: UIViewController, UIImagePickerControllerDelegate, UINavigationControllerDelegate {
18 |
19 | let wikipediaURl = "https://en.wikipedia.org/w/api.php"
20 | var pickedImage : UIImage?
21 | @IBOutlet weak var imageView: UIImageView!
22 | @IBOutlet weak var infoLabel: UILabel!
23 |
24 | let imagePicker = UIImagePickerController()
25 |
26 | override func viewDidLoad() {
27 | super.viewDidLoad()
28 | imagePicker.delegate = self
29 | imagePicker.sourceType = .photoLibrary
30 | imagePicker.allowsEditing = true
31 | }
32 |
33 | func imagePickerController(_ picker: UIImagePickerController, didFinishPickingMediaWithInfo info: [String : Any]) {
34 |
35 |
36 |
37 | if let userPickedImage = info[UIImagePickerControllerEditedImage] as? UIImage {
38 |
39 | guard let ciImage = CIImage(image: userPickedImage) else {
40 | fatalError("Could not convert image to CIImage.")
41 | }
42 |
43 | pickedImage = userPickedImage
44 |
45 |
46 | detect(flowerImage: ciImage)
47 | }
48 |
49 | imagePicker.dismiss(animated: true, completion: nil)
50 |
51 |
52 |
53 | }
54 |
55 | func detect(flowerImage: CIImage) {
56 |
57 |
58 |
59 | guard let model = try? VNCoreMLModel(for: FlowerClassifier().model) else {
60 | fatalError("Can't load model")
61 | }
62 |
63 | let request = VNCoreMLRequest(model: model) { (request, error) in
64 | guard let result = request.results?.first as? VNClassificationObservation else {
65 | fatalError("Could not complete classfication")
66 | }
67 |
68 | self.navigationItem.title = result.identifier.capitalized
69 |
70 | self.requestInfo(flowerName: result.identifier)
71 |
72 | }
73 |
74 | let handler = VNImageRequestHandler(ciImage: flowerImage)
75 |
76 | do {
77 | try handler.perform([request])
78 | }
79 | catch {
80 | print(error)
81 | }
82 |
83 |
84 | }
85 |
86 | func requestInfo(flowerName: String) {
87 | let parameters : [String:String] = ["format" : "json", "action" : "query", "prop" : "extracts|pageimages", "exintro" : "", "explaintext" : "", "titles" : flowerName, "redirects" : "1", "pithumbsize" : "500", "indexpageids" : ""]
88 |
89 | Alamofire.request(wikipediaURl, method: .get, parameters: parameters).responseJSON { (response) in
90 | if response.result.isSuccess {
91 | let flowerJSON : JSON = JSON(response.result.value!)
92 |
93 | let pageid = flowerJSON["query"]["pageids"][0].stringValue
94 |
95 | let flowerDescription = flowerJSON["query"]["pages"][pageid]["extract"].stringValue
96 | let flowerImageURL = flowerJSON["query"]["pages"][pageid]["thumbnail"]["source"].stringValue
97 | self.infoLabel.text = flowerDescription
98 |
99 | self.imageView.sd_setImage(with: URL(string: flowerImageURL), completed: { (image, error, cache, url) in
100 |
101 | if let currentImage = self.imageView.image {
102 |
103 | guard let dominantColor = ColorThief.getColor(from: currentImage) else {
104 | fatalError("Can't get dominant color")
105 | }
106 |
107 |
108 | DispatchQueue.main.async {
109 | self.navigationController?.navigationBar.isTranslucent = true
110 | self.navigationController?.navigationBar.barTintColor = dominantColor.makeUIColor()
111 |
112 |
113 | }
114 | } else {
115 | self.imageView.image = self.pickedImage
116 | self.infoLabel.text = "Could not get information on flower from Wikipedia."
117 | }
118 |
119 | })
120 |
121 | }
122 | else {
123 | print("Error \(String(describing: response.result.error))")
124 | self.infoLabel.text = "Connection Issues"
125 |
126 |
127 |
128 | }
129 | }
130 | }
131 |
132 |
133 |
134 | @IBAction func cameraTapped(_ sender: Any) {
135 |
136 | self.present(self.imagePicker, animated: true, completion: nil)
137 |
138 | }
139 | }
140 |
141 |
--------------------------------------------------------------------------------
/FlowerClassifier/Base.lproj/Main.storyboard:
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