├── .gitignore
├── Dockerfile
├── Gopkg.lock
├── Gopkg.toml
├── README.md
├── assets
    ├── demo-1.png
    └── demo-2.png
├── cmd
    ├── classify.go
    ├── root.go
    └── server.go
├── data
    └── labels.json
├── main.go
├── notes
    └── docker.md
├── package-lock.json
├── package.json
├── public
    └── index.html
├── requirements.txt
├── tensorcv
    ├── data_util.go
    ├── handler.go
    ├── model.go
    ├── route.go
    └── tensor_util.go
├── tf_models
    ├── create_mnist_model.py
    └── create_resnet_model.py
├── tsconfig.json
├── ui
    ├── components
    │   ├── bar_chart.tsx
    │   └── enums.ts
    ├── index.scss
    ├── index.tsx
    └── util.ts
└── webpack.config.js


/.gitignore:
--------------------------------------------------------------------------------
1 | environment/
2 | node_modules/
3 | .vscode/
4 | vendor/
5 | tf_models/resnet_50_model
6 | tf_models/mnist_model
7 | public/index.bundle.js


--------------------------------------------------------------------------------
/Dockerfile:
--------------------------------------------------------------------------------
 1 | # Build from golang base image
 2 | FROM golang:1.10
 3 | 
 4 | LABEL authors="Calvin Feng"
 5 | 
 6 | COPY . /go/src/tensorgo
 7 | WORKDIR /go/src/tensorgo
 8 | 
 9 | EXPOSE 3000
10 | 
11 | ENV TF_TYPE "cpu"
12 | ENV TARGET_DIRECTORY "/usr/local"
13 | 
14 | # Install TensorFlow C library
15 | RUN curl -L \
16 |    https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-linux-x86_64-1.6.0.tar.gz | \
17 |    tar -C ${TARGET_DIRECTORY} -xz
18 | RUN ldconfig
19 | 
20 | # Hide some warnings
21 | ENV TF_CPP_MIN_LOG_LEVEL 2
22 | 
23 | # Install Python.
24 | RUN \
25 |   apt-get update && \
26 |   apt-get install -y python python-dev python-pip python-virtualenv && \
27 |   rm -rf /var/lib/apt/lists/*
28 | 
29 | RUN pip install -r requirements.txt
30 | 
31 | # Create the residual neural network model
32 | RUN python ./tf_models/create_resnet_model.py
33 | 
34 | # Install Go dependencies
35 | RUN go get -u github.com/golang/dep/cmd/dep
36 | RUN go install
37 | 
38 | # Install Node
39 | RUN curl -sL https://deb.nodesource.com/setup_6.x | bash - && apt-get install -y nodejs
40 | RUN npm install
41 | RUN npm run build
42 | 
43 | CMD tensorgo server


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/Gopkg.lock:
--------------------------------------------------------------------------------
 1 | # This file is autogenerated, do not edit; changes may be undone by the next 'dep ensure'.
 2 | 
 3 | 
 4 | [[projects]]
 5 |   name = "github.com/gorilla/context"
 6 |   packages = ["."]
 7 |   revision = "08b5f424b9271eedf6f9f0ce86cb9396ed337a42"
 8 |   version = "v1.1.1"
 9 | 
10 | [[projects]]
11 |   name = "github.com/gorilla/mux"
12 |   packages = ["."]
13 |   revision = "e3702bed27f0d39777b0b37b664b6280e8ef8fbf"
14 |   version = "v1.6.2"
15 | 
16 | [[projects]]
17 |   name = "github.com/inconshreveable/mousetrap"
18 |   packages = ["."]
19 |   revision = "76626ae9c91c4f2a10f34cad8ce83ea42c93bb75"
20 |   version = "v1.0"
21 | 
22 | [[projects]]
23 |   name = "github.com/spf13/cobra"
24 |   packages = ["."]
25 |   revision = "ef82de70bb3f60c65fb8eebacbb2d122ef517385"
26 |   version = "v0.0.3"
27 | 
28 | [[projects]]
29 |   name = "github.com/spf13/pflag"
30 |   packages = ["."]
31 |   revision = "583c0c0531f06d5278b7d917446061adc344b5cd"
32 |   version = "v1.0.1"
33 | 
34 | [[projects]]
35 |   name = "github.com/tensorflow/tensorflow"
36 |   packages = ["tensorflow/go","tensorflow/go/op"]
37 |   revision = "93bc2e2072e0daccbcff7a90d397b704a9e8f778"
38 |   version = "v1.8.0"
39 | 
40 | [solve-meta]
41 |   analyzer-name = "dep"
42 |   analyzer-version = 1
43 |   inputs-digest = "91e770bf9d239204227044b623f47180dd772e5e78ed7e5d216786047cb9f9b3"
44 |   solver-name = "gps-cdcl"
45 |   solver-version = 1
46 | 


--------------------------------------------------------------------------------
/Gopkg.toml:
--------------------------------------------------------------------------------
 1 | 
 2 | # Gopkg.toml example
 3 | #
 4 | # Refer to https://github.com/golang/dep/blob/master/docs/Gopkg.toml.md
 5 | # for detailed Gopkg.toml documentation.
 6 | #
 7 | # required = ["github.com/user/thing/cmd/thing"]
 8 | # ignored = ["github.com/user/project/pkgX", "bitbucket.org/user/project/pkgA/pkgY"]
 9 | #
10 | # [[constraint]]
11 | #   name = "github.com/user/project"
12 | #   version = "1.0.0"
13 | #
14 | # [[constraint]]
15 | #   name = "github.com/user/project2"
16 | #   branch = "dev"
17 | #   source = "github.com/myfork/project2"
18 | #
19 | # [[override]]
20 | #  name = "github.com/x/y"
21 | #  version = "2.4.0"
22 | 
23 | 
24 | [[constraint]]
25 |   name = "github.com/gorilla/mux"
26 |   version = "1.6.2"
27 | 
28 | [[constraint]]
29 |   name = "github.com/spf13/cobra"
30 |   version = "0.0.3"
31 | 
32 | [[constraint]]
33 |   name = "github.com/tensorflow/tensorflow"
34 |   version = "1.8.0"
35 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
  1 | # Image Classification - TensorFlow in Go
  2 | ## What is it?
  3 | It's a simple application that allows you to upload an image and performs classification on it.
  4 | 
  5 | First, user clicks upload and select an image:
  6 | ![demo-1](./assets/demo-1.png)
  7 | 
  8 | Then the server will run the image through a residual neural network with 50 layers and compute an
  9 | answer:
 10 | 
 11 | ![demo-2](./assets/demo-2.png)
 12 | 
 13 | ## Run With Docker
 14 | ### Build The Image
 15 | The easiest way to run this project is through docker. First, build the image.
 16 | 
 17 |     docker build -t tensorgo .
 18 | 
 19 | And then run the image in a container and publish port to 3000
 20 | 
 21 |     docker run --publish 3000:3000 tensorgo
 22 | 
 23 | Now you can see the app on `localhost:3000`!
 24 | 
 25 | ### Didn't work?
 26 | If you are experiencing error regarding model failed to load or complaint from TensorFlow about 
 27 | `cannot find model`, then that means you probably need to run the Python script to build the model
 28 | first and then build Docker image.
 29 | 
 30 | Create a virtual environment and install requirements using `pip`:
 31 | 
 32 |     virtualenv environment
 33 |     source environment/bin/activate
 34 |     pip install -r requirements.txt
 35 | 
 36 | Now navigate to `tf_models/` folder and run one of the Python scripts:
 37 | 
 38 |     python create_resnet_model.py
 39 | 
 40 | Now build again:
 41 | 
 42 |     docker build -t tensorgo .
 43 | 
 44 | ## Run Without Docker
 45 | ### Prerequisites
 46 | * Go 1.8+
 47 | * Node 6+
 48 | * Python 2.7+
 49 | 
 50 | ### Install TensorFlow C binding
 51 | [Source](https://www.tensorflow.org/install/install_c): Execute the following shell command
 52 | ```
 53 |  TF_TYPE="cpu" # Change to "gpu" for GPU support
 54 |  OS="linux" # Change to "darwin" for macOS
 55 |  TARGET_DIRECTORY="/usr/local"
 56 |  curl -L \
 57 |    "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.8.0.tar.gz" |
 58 |    sudo tar -C $TARGET_DIRECTORY -xz
 59 | ```
 60 | 
 61 | Then configure the linker
 62 | ```
 63 | sudo ldconfig
 64 | ```
 65 | 
 66 | ### Install Project Dependencies
 67 | Once TensorFlow is installed, next is to install Go dependency
 68 | ```
 69 | dep ensure
 70 | ```
 71 | 
 72 | Then compile Go source code
 73 | ```
 74 | go install
 75 | ```
 76 | 
 77 | Install node modules for building the frontend
 78 | ```
 79 | npm install
 80 | ```
 81 | 
 82 | Then build it
 83 | ```
 84 | npm run build
 85 | ```
 86 | 
 87 | ### Compile TensorFlow Model
 88 | Create a virtual environment and install requirements using `pip`:
 89 | 
 90 |     virtualenv environment
 91 |     source environment/bin/activate
 92 |     pip install -r requirements.txt
 93 | 
 94 | Now navigate to `tf_models/` folder and run one of the Python scripts:
 95 | 
 96 |     python create_resnet_model.py
 97 | 
 98 | Now you are ready to go.
 99 | 
100 | ## Start
101 | Simply do
102 | 
103 |     tensorgo start
104 | 


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/assets/demo-1.png:
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https://raw.githubusercontent.com/calvinfeng/tensorgo/3d9cd55b3870f811a367adfbcbbdbd3265314f21/assets/demo-1.png


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/assets/demo-2.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/calvinfeng/tensorgo/3d9cd55b3870f811a367adfbcbbdbd3265314f21/assets/demo-2.png


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/cmd/classify.go:
--------------------------------------------------------------------------------
 1 | package cmd
 2 | 
 3 | import (
 4 | 	"errors"
 5 | 	"fmt"
 6 | 	"strings"
 7 | 	"tensorgo/tensorcv"
 8 | 
 9 | 	"github.com/spf13/cobra"
10 | )
11 | 
12 | // Image file extensions
13 | const (
14 | 	PNG  = "png"
15 | 	JPG  = "jpg"
16 | 	JPEG = "jpeg"
17 | )
18 | 
19 | func classify(cmd *cobra.Command, args []string) error {
20 | 	imgPath, err := cmd.Flags().GetString("img")
21 | 	if err != nil {
22 | 		return err
23 | 	}
24 | 
25 | 	if imgPath == "" {
26 | 		return errors.New("please specify a valid image file location")
27 | 	}
28 | 
29 | 	splitPath := strings.Split(imgPath, ".")
30 | 
31 | 	var imgType string
32 | 	switch splitPath[len(splitPath)-1] {
33 | 	case PNG:
34 | 		imgType = "png"
35 | 	case JPEG:
36 | 		imgType = "jpeg"
37 | 	case JPG:
38 | 		imgType = "jpeg"
39 | 	default:
40 | 		return fmt.Errorf("%s is not a valid image", imgPath)
41 | 	}
42 | 
43 | 	tensor, err := tensorcv.GetTensorFromImagePath(imgPath, imgType, 3)
44 | 	if err != nil {
45 | 		return err
46 | 	}
47 | 
48 | 	fmt.Println("Image tensor is loaded:", tensor.Shape())
49 | 
50 | 	softmaxScore := tensorcv.RunResNetModel(tensor, ResNet)
51 | 	if softmaxScore == nil {
52 | 		return fmt.Errorf("unexpected problem occurred when resnet model is run, score is nil")
53 | 	}
54 | 
55 | 	// Softmax score is of shape (N, 1000), since N is 1 here, so we will use the 0 indexed item.
56 | 	score := softmaxScore[0]
57 | 	top := make(map[int]bool)
58 | 
59 | 	// Pick top 5, using the lazy way instead of writing a quick select...
60 | 	results := make([]string, 0, 5)
61 | 	for i := 0; i < 5; i++ {
62 | 		classIdx, _ := tensorcv.ArgMax(score, top)
63 | 		results = append(results, labels[classIdx])
64 | 	}
65 | 
66 | 	fmt.Printf("Top 5 classifications: %s\n", results)
67 | 	return nil
68 | }
69 | 


--------------------------------------------------------------------------------
/cmd/root.go:
--------------------------------------------------------------------------------
 1 | // Author(s): Calvin Feng
 2 | 
 3 | package cmd
 4 | 
 5 | import (
 6 | 	"fmt"
 7 | 	"os"
 8 | 	"tensorgo/tensorcv"
 9 | 
10 | 	"github.com/spf13/cobra"
11 | )
12 | 
13 | // Model Path
14 | const (
15 | 	ResNet = "./tf_models/resnet_50_model"
16 | )
17 | 
18 | var labels map[int]string
19 | 
20 | func init() {
21 | 	l, err := tensorcv.LoadLabels("./data/labels.json")
22 | 	if err != nil {
23 | 		panic(err)
24 | 	}
25 | 
26 | 	labels = l
27 | }
28 | 
29 | var rootCommand = &cobra.Command{
30 | 	Use:   "tensor_go",
31 | 	Short: "image recognition in Go using tensorflow and keras models",
32 | }
33 | 
34 | var serverCommand = &cobra.Command{
35 | 	Use:     "server",
36 | 	Short:   "start http server and listen for requests",
37 | 	Aliases: []string{"s"},
38 | 	RunE:    server,
39 | }
40 | 
41 | var classifyCommand = &cobra.Command{
42 | 	Use:     "classify",
43 | 	Short:   "classify a specified png or jpeg image",
44 | 	Aliases: []string{"c"},
45 | 	RunE:    classify,
46 | }
47 | 
48 | // Execute will activate the root command.
49 | func Execute() {
50 | 	classifyCommand.Flags().String("img", "", "image path for the image you wish to classify")
51 | 
52 | 	rootCommand.AddCommand(serverCommand, classifyCommand)
53 | 	if err := rootCommand.Execute(); err != nil {
54 | 		fmt.Println(err)
55 | 		os.Exit(1)
56 | 	}
57 | }
58 | 


--------------------------------------------------------------------------------
/cmd/server.go:
--------------------------------------------------------------------------------
 1 | package cmd
 2 | 
 3 | import (
 4 | 	"fmt"
 5 | 	"net/http"
 6 | 	"tensorgo/tensorcv"
 7 | 
 8 | 	"github.com/spf13/cobra"
 9 | )
10 | 
11 | func server(cmd *cobra.Command, args []string) error {
12 | 	port := ":3000"
13 | 
14 | 	if len(args) > 0 {
15 | 		port = fmt.Sprintf(":%s", args[0])
16 | 	}
17 | 
18 | 	server := &http.Server{
19 | 		Addr:    port,
20 | 		Handler: tensorcv.LoadRoutes(labels, ResNet),
21 | 	}
22 | 
23 | 	fmt.Printf("server is listening and serving on %s\n", port)
24 | 	if err := server.ListenAndServe(); err != nil {
25 | 		return err
26 | 	}
27 | 
28 | 	return nil
29 | }
30 | 


--------------------------------------------------------------------------------
/data/labels.json:
--------------------------------------------------------------------------------
   1 | {
   2 |     "0": "tench, Tinca tinca",
   3 |     "1": "goldfish, Carassius auratus",
   4 |     "2": "great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias",
   5 |     "3": "tiger shark, Galeocerdo cuvieri",
   6 |     "4": "hammerhead, hammerhead shark",
   7 |     "5": "electric ray, crampfish, numbfish, torpedo",
   8 |     "6": "stingray",
   9 |     "7": "cock",
  10 |     "8": "hen",
  11 |     "9": "ostrich, Struthio camelus",
  12 |     "10": "brambling, Fringilla montifringilla",
  13 |     "11": "goldfinch, Carduelis carduelis",
  14 |     "12": "house finch, linnet, Carpodacus mexicanus",
  15 |     "13": "junco, snowbird",
  16 |     "14": "indigo bunting, indigo finch, indigo bird, Passerina cyanea",
  17 |     "15": "robin, American robin, Turdus migratorius",
  18 |     "16": "bulbul",
  19 |     "17": "jay",
  20 |     "18": "magpie",
  21 |     "19": "chickadee",
  22 |     "20": "water ouzel, dipper",
  23 |     "21": "kite",
  24 |     "22": "bald eagle, American eagle, Haliaeetus leucocephalus",
  25 |     "23": "vulture",
  26 |     "24": "great grey owl, great gray owl, Strix nebulosa",
  27 |     "25": "European fire salamander, Salamandra salamandra",
  28 |     "26": "common newt, Triturus vulgaris",
  29 |     "27": "eft",
  30 |     "28": "spotted salamander, Ambystoma maculatum",
  31 |     "29": "axolotl, mud puppy, Ambystoma mexicanum",
  32 |     "30": "bullfrog, Rana catesbeiana",
  33 |     "31": "tree frog, tree-frog",
  34 |     "32": "tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui",
  35 |     "33": "loggerhead, loggerhead turtle, Caretta caretta",
  36 |     "34": "leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea",
  37 |     "35": "mud turtle",
  38 |     "36": "terrapin",
  39 |     "37": "box turtle, box tortoise",
  40 |     "38": "banded gecko",
  41 |     "39": "common iguana, iguana, Iguana iguana",
  42 |     "40": "American chameleon, anole, Anolis carolinensis",
  43 |     "41": "whiptail, whiptail lizard",
  44 |     "42": "agama",
  45 |     "43": "frilled lizard, Chlamydosaurus kingi",
  46 |     "44": "alligator lizard",
  47 |     "45": "Gila monster, Heloderma suspectum",
  48 |     "46": "green lizard, Lacerta viridis",
  49 |     "47": "African chameleon, Chamaeleo chamaeleon",
  50 |     "48": "Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis",
  51 |     "49": "African crocodile, Nile crocodile, Crocodylus niloticus",
  52 |     "50": "American alligator, Alligator mississipiensis",
  53 |     "51": "triceratops",
  54 |     "52": "thunder snake, worm snake, Carphophis amoenus",
  55 |     "53": "ringneck snake, ring-necked snake, ring snake",
  56 |     "54": "hognose snake, puff adder, sand viper",
  57 |     "55": "green snake, grass snake",
  58 |     "56": "king snake, kingsnake",
  59 |     "57": "garter snake, grass snake",
  60 |     "58": "water snake",
  61 |     "59": "vine snake",
  62 |     "60": "night snake, Hypsiglena torquata",
  63 |     "61": "boa constrictor, Constrictor constrictor",
  64 |     "62": "rock python, rock snake, Python sebae",
  65 |     "63": "Indian cobra, Naja naja",
  66 |     "64": "green mamba",
  67 |     "65": "sea snake",
  68 |     "66": "horned viper, cerastes, sand viper, horned asp, Cerastes cornutus",
  69 |     "67": "diamondback, diamondback rattlesnake, Crotalus adamanteus",
  70 |     "68": "sidewinder, horned rattlesnake, Crotalus cerastes",
  71 |     "69": "trilobite",
  72 |     "70": "harvestman, daddy longlegs, Phalangium opilio",
  73 |     "71": "scorpion",
  74 |     "72": "black and gold garden spider, Argiope aurantia",
  75 |     "73": "barn spider, Araneus cavaticus",
  76 |     "74": "garden spider, Aranea diademata",
  77 |     "75": "black widow, Latrodectus mactans",
  78 |     "76": "tarantula",
  79 |     "77": "wolf spider, hunting spider",
  80 |     "78": "tick",
  81 |     "79": "centipede",
  82 |     "80": "black grouse",
  83 |     "81": "ptarmigan",
  84 |     "82": "ruffed grouse, partridge, Bonasa umbellus",
  85 |     "83": "prairie chicken, prairie grouse, prairie fowl",
  86 |     "84": "peacock",
  87 |     "85": "quail",
  88 |     "86": "partridge",
  89 |     "87": "African grey, African gray, Psittacus erithacus",
  90 |     "88": "macaw",
  91 |     "89": "sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita",
  92 |     "90": "lorikeet",
  93 |     "91": "coucal",
  94 |     "92": "bee eater",
  95 |     "93": "hornbill",
  96 |     "94": "hummingbird",
  97 |     "95": "jacamar",
  98 |     "96": "toucan",
  99 |     "97": "drake",
 100 |     "98": "red-breasted merganser, Mergus serrator",
 101 |     "99": "goose",
 102 |     "100": "black swan, Cygnus atratus",
 103 |     "101": "tusker",
 104 |     "102": "echidna, spiny anteater, anteater",
 105 |     "103": "platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus",
 106 |     "104": "wallaby, brush kangaroo",
 107 |     "105": "koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus",
 108 |     "106": "wombat",
 109 |     "107": "jellyfish",
 110 |     "108": "sea anemone, anemone",
 111 |     "109": "brain coral",
 112 |     "110": "flatworm, platyhelminth",
 113 |     "111": "nematode, nematode worm, roundworm",
 114 |     "112": "conch",
 115 |     "113": "snail",
 116 |     "114": "slug",
 117 |     "115": "sea slug, nudibranch",
 118 |     "116": "chiton, coat-of-mail shell, sea cradle, polyplacophore",
 119 |     "117": "chambered nautilus, pearly nautilus, nautilus",
 120 |     "118": "Dungeness crab, Cancer magister",
 121 |     "119": "rock crab, Cancer irroratus",
 122 |     "120": "fiddler crab",
 123 |     "121": "king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica",
 124 |     "122": "American lobster, Northern lobster, Maine lobster, Homarus americanus",
 125 |     "123": "spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish",
 126 |     "124": "crayfish, crawfish, crawdad, crawdaddy",
 127 |     "125": "hermit crab",
 128 |     "126": "isopod",
 129 |     "127": "white stork, Ciconia ciconia",
 130 |     "128": "black stork, Ciconia nigra",
 131 |     "129": "spoonbill",
 132 |     "130": "flamingo",
 133 |     "131": "little blue heron, Egretta caerulea",
 134 |     "132": "American egret, great white heron, Egretta albus",
 135 |     "133": "bittern",
 136 |     "134": "crane",
 137 |     "135": "limpkin, Aramus pictus",
 138 |     "136": "European gallinule, Porphyrio porphyrio",
 139 |     "137": "American coot, marsh hen, mud hen, water hen, Fulica americana",
 140 |     "138": "bustard",
 141 |     "139": "ruddy turnstone, Arenaria interpres",
 142 |     "140": "red-backed sandpiper, dunlin, Erolia alpina",
 143 |     "141": "redshank, Tringa totanus",
 144 |     "142": "dowitcher",
 145 |     "143": "oystercatcher, oyster catcher",
 146 |     "144": "pelican",
 147 |     "145": "king penguin, Aptenodytes patagonica",
 148 |     "146": "albatross, mollymawk",
 149 |     "147": "grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus",
 150 |     "148": "killer whale, killer, orca, grampus, sea wolf, Orcinus orca",
 151 |     "149": "dugong, Dugong dugon",
 152 |     "150": "sea lion",
 153 |     "151": "Chihuahua",
 154 |     "152": "Japanese spaniel",
 155 |     "153": "Maltese dog, Maltese terrier, Maltese",
 156 |     "154": "Pekinese, Pekingese, Peke",
 157 |     "155": "Shih-Tzu",
 158 |     "156": "Blenheim spaniel",
 159 |     "157": "papillon",
 160 |     "158": "toy terrier",
 161 |     "159": "Rhodesian ridgeback",
 162 |     "160": "Afghan hound, Afghan",
 163 |     "161": "basset, basset hound",
 164 |     "162": "beagle",
 165 |     "163": "bloodhound, sleuthhound",
 166 |     "164": "bluetick",
 167 |     "165": "black-and-tan coonhound",
 168 |     "166": "Walker hound, Walker foxhound",
 169 |     "167": "English foxhound",
 170 |     "168": "redbone",
 171 |     "169": "borzoi, Russian wolfhound",
 172 |     "170": "Irish wolfhound",
 173 |     "171": "Italian greyhound",
 174 |     "172": "whippet",
 175 |     "173": "Ibizan hound, Ibizan Podenco",
 176 |     "174": "Norwegian elkhound, elkhound",
 177 |     "175": "otterhound, otter hound",
 178 |     "176": "Saluki, gazelle hound",
 179 |     "177": "Scottish deerhound, deerhound",
 180 |     "178": "Weimaraner",
 181 |     "179": "Staffordshire bullterrier, Staffordshire bull terrier",
 182 |     "180": "American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier",
 183 |     "181": "Bedlington terrier",
 184 |     "182": "Border terrier",
 185 |     "183": "Kerry blue terrier",
 186 |     "184": "Irish terrier",
 187 |     "185": "Norfolk terrier",
 188 |     "186": "Norwich terrier",
 189 |     "187": "Yorkshire terrier",
 190 |     "188": "wire-haired fox terrier",
 191 |     "189": "Lakeland terrier",
 192 |     "190": "Sealyham terrier, Sealyham",
 193 |     "191": "Airedale, Airedale terrier",
 194 |     "192": "cairn, cairn terrier",
 195 |     "193": "Australian terrier",
 196 |     "194": "Dandie Dinmont, Dandie Dinmont terrier",
 197 |     "195": "Boston bull, Boston terrier",
 198 |     "196": "miniature schnauzer",
 199 |     "197": "giant schnauzer",
 200 |     "198": "standard schnauzer",
 201 |     "199": "Scotch terrier, Scottish terrier, Scottie",
 202 |     "200": "Tibetan terrier, chrysanthemum dog",
 203 |     "201": "silky terrier, Sydney silky",
 204 |     "202": "soft-coated wheaten terrier",
 205 |     "203": "West Highland white terrier",
 206 |     "204": "Lhasa, Lhasa apso",
 207 |     "205": "flat-coated retriever",
 208 |     "206": "curly-coated retriever",
 209 |     "207": "golden retriever",
 210 |     "208": "Labrador retriever",
 211 |     "209": "Chesapeake Bay retriever",
 212 |     "210": "German short-haired pointer",
 213 |     "211": "vizsla, Hungarian pointer",
 214 |     "212": "English setter",
 215 |     "213": "Irish setter, red setter",
 216 |     "214": "Gordon setter",
 217 |     "215": "Brittany spaniel",
 218 |     "216": "clumber, clumber spaniel",
 219 |     "217": "English springer, English springer spaniel",
 220 |     "218": "Welsh springer spaniel",
 221 |     "219": "cocker spaniel, English cocker spaniel, cocker",
 222 |     "220": "Sussex spaniel",
 223 |     "221": "Irish water spaniel",
 224 |     "222": "kuvasz",
 225 |     "223": "schipperke",
 226 |     "224": "groenendael",
 227 |     "225": "malinois",
 228 |     "226": "briard",
 229 |     "227": "kelpie",
 230 |     "228": "komondor",
 231 |     "229": "Old English sheepdog, bobtail",
 232 |     "230": "Shetland sheepdog, Shetland sheep dog, Shetland",
 233 |     "231": "collie",
 234 |     "232": "Border collie",
 235 |     "233": "Bouvier des Flandres, Bouviers des Flandres",
 236 |     "234": "Rottweiler",
 237 |     "235": "German shepherd, German shepherd dog, German police dog, alsatian",
 238 |     "236": "Doberman, Doberman pinscher",
 239 |     "237": "miniature pinscher",
 240 |     "238": "Greater Swiss Mountain dog",
 241 |     "239": "Bernese mountain dog",
 242 |     "240": "Appenzeller",
 243 |     "241": "EntleBucher",
 244 |     "242": "boxer",
 245 |     "243": "bull mastiff",
 246 |     "244": "Tibetan mastiff",
 247 |     "245": "French bulldog",
 248 |     "246": "Great Dane",
 249 |     "247": "Saint Bernard, St Bernard",
 250 |     "248": "Eskimo dog, husky",
 251 |     "249": "malamute, malemute, Alaskan malamute",
 252 |     "250": "Siberian husky",
 253 |     "251": "dalmatian, coach dog, carriage dog",
 254 |     "252": "affenpinscher, monkey pinscher, monkey dog",
 255 |     "253": "basenji",
 256 |     "254": "pug, pug-dog",
 257 |     "255": "Leonberg",
 258 |     "256": "Newfoundland, Newfoundland dog",
 259 |     "257": "Great Pyrenees",
 260 |     "258": "Samoyed, Samoyede",
 261 |     "259": "Pomeranian",
 262 |     "260": "chow, chow chow",
 263 |     "261": "keeshond",
 264 |     "262": "Brabancon griffon",
 265 |     "263": "Pembroke, Pembroke Welsh corgi",
 266 |     "264": "Cardigan, Cardigan Welsh corgi",
 267 |     "265": "toy poodle",
 268 |     "266": "miniature poodle",
 269 |     "267": "standard poodle",
 270 |     "268": "Mexican hairless",
 271 |     "269": "timber wolf, grey wolf, gray wolf, Canis lupus",
 272 |     "270": "white wolf, Arctic wolf, Canis lupus tundrarum",
 273 |     "271": "red wolf, maned wolf, Canis rufus, Canis niger",
 274 |     "272": "coyote, prairie wolf, brush wolf, Canis latrans",
 275 |     "273": "dingo, warrigal, warragal, Canis dingo",
 276 |     "274": "dhole, Cuon alpinus",
 277 |     "275": "African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus",
 278 |     "276": "hyena, hyaena",
 279 |     "277": "red fox, Vulpes vulpes",
 280 |     "278": "kit fox, Vulpes macrotis",
 281 |     "279": "Arctic fox, white fox, Alopex lagopus",
 282 |     "280": "grey fox, gray fox, Urocyon cinereoargenteus",
 283 |     "281": "tabby, tabby cat",
 284 |     "282": "tiger cat",
 285 |     "283": "Persian cat",
 286 |     "284": "Siamese cat, Siamese",
 287 |     "285": "Egyptian cat",
 288 |     "286": "cougar, puma, catamount, mountain lion, painter, panther, Felis concolor",
 289 |     "287": "lynx, catamount",
 290 |     "288": "leopard, Panthera pardus",
 291 |     "289": "snow leopard, ounce, Panthera uncia",
 292 |     "290": "jaguar, panther, Panthera onca, Felis onca",
 293 |     "291": "lion, king of beasts, Panthera leo",
 294 |     "292": "tiger, Panthera tigris",
 295 |     "293": "cheetah, chetah, Acinonyx jubatus",
 296 |     "294": "brown bear, bruin, Ursus arctos",
 297 |     "295": "American black bear, black bear, Ursus americanus, Euarctos americanus",
 298 |     "296": "ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus",
 299 |     "297": "sloth bear, Melursus ursinus, Ursus ursinus",
 300 |     "298": "mongoose",
 301 |     "299": "meerkat, mierkat",
 302 |     "300": "tiger beetle",
 303 |     "301": "ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle",
 304 |     "302": "ground beetle, carabid beetle",
 305 |     "303": "long-horned beetle, longicorn, longicorn beetle",
 306 |     "304": "leaf beetle, chrysomelid",
 307 |     "305": "dung beetle",
 308 |     "306": "rhinoceros beetle",
 309 |     "307": "weevil",
 310 |     "308": "fly",
 311 |     "309": "bee",
 312 |     "310": "ant, emmet, pismire",
 313 |     "311": "grasshopper, hopper",
 314 |     "312": "cricket",
 315 |     "313": "walking stick, walkingstick, stick insect",
 316 |     "314": "cockroach, roach",
 317 |     "315": "mantis, mantid",
 318 |     "316": "cicada, cicala",
 319 |     "317": "leafhopper",
 320 |     "318": "lacewing, lacewing fly",
 321 |     "319": "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk",
 322 |     "320": "damselfly",
 323 |     "321": "admiral",
 324 |     "322": "ringlet, ringlet butterfly",
 325 |     "323": "monarch, monarch butterfly, milkweed butterfly, Danaus plexippus",
 326 |     "324": "cabbage butterfly",
 327 |     "325": "sulphur butterfly, sulfur butterfly",
 328 |     "326": "lycaenid, lycaenid butterfly",
 329 |     "327": "starfish, sea star",
 330 |     "328": "sea urchin",
 331 |     "329": "sea cucumber, holothurian",
 332 |     "330": "wood rabbit, cottontail, cottontail rabbit",
 333 |     "331": "hare",
 334 |     "332": "Angora, Angora rabbit",
 335 |     "333": "hamster",
 336 |     "334": "porcupine, hedgehog",
 337 |     "335": "fox squirrel, eastern fox squirrel, Sciurus niger",
 338 |     "336": "marmot",
 339 |     "337": "beaver",
 340 |     "338": "guinea pig, Cavia cobaya",
 341 |     "339": "sorrel",
 342 |     "340": "zebra",
 343 |     "341": "hog, pig, grunter, squealer, Sus scrofa",
 344 |     "342": "wild boar, boar, Sus scrofa",
 345 |     "343": "warthog",
 346 |     "344": "hippopotamus, hippo, river horse, Hippopotamus amphibius",
 347 |     "345": "ox",
 348 |     "346": "water buffalo, water ox, Asiatic buffalo, Bubalus bubalis",
 349 |     "347": "bison",
 350 |     "348": "ram, tup",
 351 |     "349": "bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis",
 352 |     "350": "ibex, Capra ibex",
 353 |     "351": "hartebeest",
 354 |     "352": "impala, Aepyceros melampus",
 355 |     "353": "gazelle",
 356 |     "354": "Arabian camel, dromedary, Camelus dromedarius",
 357 |     "355": "llama",
 358 |     "356": "weasel",
 359 |     "357": "mink",
 360 |     "358": "polecat, fitch, foulmart, foumart, Mustela putorius",
 361 |     "359": "black-footed ferret, ferret, Mustela nigripes",
 362 |     "360": "otter",
 363 |     "361": "skunk, polecat, wood pussy",
 364 |     "362": "badger",
 365 |     "363": "armadillo",
 366 |     "364": "three-toed sloth, ai, Bradypus tridactylus",
 367 |     "365": "orangutan, orang, orangutang, Pongo pygmaeus",
 368 |     "366": "gorilla, Gorilla gorilla",
 369 |     "367": "chimpanzee, chimp, Pan troglodytes",
 370 |     "368": "gibbon, Hylobates lar",
 371 |     "369": "siamang, Hylobates syndactylus, Symphalangus syndactylus",
 372 |     "370": "guenon, guenon monkey",
 373 |     "371": "patas, hussar monkey, Erythrocebus patas",
 374 |     "372": "baboon",
 375 |     "373": "macaque",
 376 |     "374": "langur",
 377 |     "375": "colobus, colobus monkey",
 378 |     "376": "proboscis monkey, Nasalis larvatus",
 379 |     "377": "marmoset",
 380 |     "378": "capuchin, ringtail, Cebus capucinus",
 381 |     "379": "howler monkey, howler",
 382 |     "380": "titi, titi monkey",
 383 |     "381": "spider monkey, Ateles geoffroyi",
 384 |     "382": "squirrel monkey, Saimiri sciureus",
 385 |     "383": "Madagascar cat, ring-tailed lemur, Lemur catta",
 386 |     "384": "indri, indris, Indri indri, Indri brevicaudatus",
 387 |     "385": "Indian elephant, Elephas maximus",
 388 |     "386": "African elephant, Loxodonta africana",
 389 |     "387": "lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens",
 390 |     "388": "giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca",
 391 |     "389": "barracouta, snoek",
 392 |     "390": "eel",
 393 |     "391": "coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch",
 394 |     "392": "rock beauty, Holocanthus tricolor",
 395 |     "393": "anemone fish",
 396 |     "394": "sturgeon",
 397 |     "395": "gar, garfish, garpike, billfish, Lepisosteus osseus",
 398 |     "396": "lionfish",
 399 |     "397": "puffer, pufferfish, blowfish, globefish",
 400 |     "398": "abacus",
 401 |     "399": "abaya",
 402 |     "400": "academic gown, academic robe, judge's robe",
 403 |     "401": "accordion, piano accordion, squeeze box",
 404 |     "402": "acoustic guitar",
 405 |     "403": "aircraft carrier, carrier, flattop, attack aircraft carrier",
 406 |     "404": "airliner",
 407 |     "405": "airship, dirigible",
 408 |     "406": "altar",
 409 |     "407": "ambulance",
 410 |     "408": "amphibian, amphibious vehicle",
 411 |     "409": "analog clock",
 412 |     "410": "apiary, bee house",
 413 |     "411": "apron",
 414 |     "412": "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin",
 415 |     "413": "assault rifle, assault gun",
 416 |     "414": "backpack, back pack, knapsack, packsack, rucksack, haversack",
 417 |     "415": "bakery, bakeshop, bakehouse",
 418 |     "416": "balance beam, beam",
 419 |     "417": "balloon",
 420 |     "418": "ballpoint, ballpoint pen, ballpen, Biro",
 421 |     "419": "Band Aid",
 422 |     "420": "banjo",
 423 |     "421": "bannister, banister, balustrade, balusters, handrail",
 424 |     "422": "barbell",
 425 |     "423": "barber chair",
 426 |     "424": "barbershop",
 427 |     "425": "barn",
 428 |     "426": "barometer",
 429 |     "427": "barrel, cask",
 430 |     "428": "barrow, garden cart, lawn cart, wheelbarrow",
 431 |     "429": "baseball",
 432 |     "430": "basketball",
 433 |     "431": "bassinet",
 434 |     "432": "bassoon",
 435 |     "433": "bathing cap, swimming cap",
 436 |     "434": "bath towel",
 437 |     "435": "bathtub, bathing tub, bath, tub",
 438 |     "436": "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon",
 439 |     "437": "beacon, lighthouse, beacon light, pharos",
 440 |     "438": "beaker",
 441 |     "439": "bearskin, busby, shako",
 442 |     "440": "beer bottle",
 443 |     "441": "beer glass",
 444 |     "442": "bell cote, bell cot",
 445 |     "443": "bib",
 446 |     "444": "bicycle-built-for-two, tandem bicycle, tandem",
 447 |     "445": "bikini, two-piece",
 448 |     "446": "binder, ring-binder",
 449 |     "447": "binoculars, field glasses, opera glasses",
 450 |     "448": "birdhouse",
 451 |     "449": "boathouse",
 452 |     "450": "bobsled, bobsleigh, bob",
 453 |     "451": "bolo tie, bolo, bola tie, bola",
 454 |     "452": "bonnet, poke bonnet",
 455 |     "453": "bookcase",
 456 |     "454": "bookshop, bookstore, bookstall",
 457 |     "455": "bottlecap",
 458 |     "456": "bow",
 459 |     "457": "bow tie, bow-tie, bowtie",
 460 |     "458": "brass, memorial tablet, plaque",
 461 |     "459": "brassiere, bra, bandeau",
 462 |     "460": "breakwater, groin, groyne, mole, bulwark, seawall, jetty",
 463 |     "461": "breastplate, aegis, egis",
 464 |     "462": "broom",
 465 |     "463": "bucket, pail",
 466 |     "464": "buckle",
 467 |     "465": "bulletproof vest",
 468 |     "466": "bullet train, bullet",
 469 |     "467": "butcher shop, meat market",
 470 |     "468": "cab, hack, taxi, taxicab",
 471 |     "469": "caldron, cauldron",
 472 |     "470": "candle, taper, wax light",
 473 |     "471": "cannon",
 474 |     "472": "canoe",
 475 |     "473": "can opener, tin opener",
 476 |     "474": "cardigan",
 477 |     "475": "car mirror",
 478 |     "476": "carousel, carrousel, merry-go-round, roundabout, whirligig",
 479 |     "477": "carpenter's kit, tool kit",
 480 |     "478": "carton",
 481 |     "479": "car wheel",
 482 |     "480": "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM",
 483 |     "481": "cassette",
 484 |     "482": "cassette player",
 485 |     "483": "castle",
 486 |     "484": "catamaran",
 487 |     "485": "CD player",
 488 |     "486": "cello, violoncello",
 489 |     "487": "cellular telephone, cellular phone, cellphone, cell, mobile phone",
 490 |     "488": "chain",
 491 |     "489": "chainlink fence",
 492 |     "490": "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour",
 493 |     "491": "chain saw, chainsaw",
 494 |     "492": "chest",
 495 |     "493": "chiffonier, commode",
 496 |     "494": "chime, bell, gong",
 497 |     "495": "china cabinet, china closet",
 498 |     "496": "Christmas stocking",
 499 |     "497": "church, church building",
 500 |     "498": "cinema, movie theater, movie theatre, movie house, picture palace",
 501 |     "499": "cleaver, meat cleaver, chopper",
 502 |     "500": "cliff dwelling",
 503 |     "501": "cloak",
 504 |     "502": "clog, geta, patten, sabot",
 505 |     "503": "cocktail shaker",
 506 |     "504": "coffee mug",
 507 |     "505": "coffeepot",
 508 |     "506": "coil, spiral, volute, whorl, helix",
 509 |     "507": "combination lock",
 510 |     "508": "computer keyboard, keypad",
 511 |     "509": "confectionery, confectionary, candy store",
 512 |     "510": "container ship, containership, container vessel",
 513 |     "511": "convertible",
 514 |     "512": "corkscrew, bottle screw",
 515 |     "513": "cornet, horn, trumpet, trump",
 516 |     "514": "cowboy boot",
 517 |     "515": "cowboy hat, ten-gallon hat",
 518 |     "516": "cradle",
 519 |     "517": "crane",
 520 |     "518": "crash helmet",
 521 |     "519": "crate",
 522 |     "520": "crib, cot",
 523 |     "521": "Crock Pot",
 524 |     "522": "croquet ball",
 525 |     "523": "crutch",
 526 |     "524": "cuirass",
 527 |     "525": "dam, dike, dyke",
 528 |     "526": "desk",
 529 |     "527": "desktop computer",
 530 |     "528": "dial telephone, dial phone",
 531 |     "529": "diaper, nappy, napkin",
 532 |     "530": "digital clock",
 533 |     "531": "digital watch",
 534 |     "532": "dining table, board",
 535 |     "533": "dishrag, dishcloth",
 536 |     "534": "dishwasher, dish washer, dishwashing machine",
 537 |     "535": "disk brake, disc brake",
 538 |     "536": "dock, dockage, docking facility",
 539 |     "537": "dogsled, dog sled, dog sleigh",
 540 |     "538": "dome",
 541 |     "539": "doormat, welcome mat",
 542 |     "540": "drilling platform, offshore rig",
 543 |     "541": "drum, membranophone, tympan",
 544 |     "542": "drumstick",
 545 |     "543": "dumbbell",
 546 |     "544": "Dutch oven",
 547 |     "545": "electric fan, blower",
 548 |     "546": "electric guitar",
 549 |     "547": "electric locomotive",
 550 |     "548": "entertainment center",
 551 |     "549": "envelope",
 552 |     "550": "espresso maker",
 553 |     "551": "face powder",
 554 |     "552": "feather boa, boa",
 555 |     "553": "file, file cabinet, filing cabinet",
 556 |     "554": "fireboat",
 557 |     "555": "fire engine, fire truck",
 558 |     "556": "fire screen, fireguard",
 559 |     "557": "flagpole, flagstaff",
 560 |     "558": "flute, transverse flute",
 561 |     "559": "folding chair",
 562 |     "560": "football helmet",
 563 |     "561": "forklift",
 564 |     "562": "fountain",
 565 |     "563": "fountain pen",
 566 |     "564": "four-poster",
 567 |     "565": "freight car",
 568 |     "566": "French horn, horn",
 569 |     "567": "frying pan, frypan, skillet",
 570 |     "568": "fur coat",
 571 |     "569": "garbage truck, dustcart",
 572 |     "570": "gasmask, respirator, gas helmet",
 573 |     "571": "gas pump, gasoline pump, petrol pump, island dispenser",
 574 |     "572": "goblet",
 575 |     "573": "go-kart",
 576 |     "574": "golf ball",
 577 |     "575": "golfcart, golf cart",
 578 |     "576": "gondola",
 579 |     "577": "gong, tam-tam",
 580 |     "578": "gown",
 581 |     "579": "grand piano, grand",
 582 |     "580": "greenhouse, nursery, glasshouse",
 583 |     "581": "grille, radiator grille",
 584 |     "582": "grocery store, grocery, food market, market",
 585 |     "583": "guillotine",
 586 |     "584": "hair slide",
 587 |     "585": "hair spray",
 588 |     "586": "half track",
 589 |     "587": "hammer",
 590 |     "588": "hamper",
 591 |     "589": "hand blower, blow dryer, blow drier, hair dryer, hair drier",
 592 |     "590": "hand-held computer, hand-held microcomputer",
 593 |     "591": "handkerchief, hankie, hanky, hankey",
 594 |     "592": "hard disc, hard disk, fixed disk",
 595 |     "593": "harmonica, mouth organ, harp, mouth harp",
 596 |     "594": "harp",
 597 |     "595": "harvester, reaper",
 598 |     "596": "hatchet",
 599 |     "597": "holster",
 600 |     "598": "home theater, home theatre",
 601 |     "599": "honeycomb",
 602 |     "600": "hook, claw",
 603 |     "601": "hoopskirt, crinoline",
 604 |     "602": "horizontal bar, high bar",
 605 |     "603": "horse cart, horse-cart",
 606 |     "604": "hourglass",
 607 |     "605": "iPod",
 608 |     "606": "iron, smoothing iron",
 609 |     "607": "jack-o'-lantern",
 610 |     "608": "jean, blue jean, denim",
 611 |     "609": "jeep, landrover",
 612 |     "610": "jersey, T-shirt, tee shirt",
 613 |     "611": "jigsaw puzzle",
 614 |     "612": "jinrikisha, ricksha, rickshaw",
 615 |     "613": "joystick",
 616 |     "614": "kimono",
 617 |     "615": "knee pad",
 618 |     "616": "knot",
 619 |     "617": "lab coat, laboratory coat",
 620 |     "618": "ladle",
 621 |     "619": "lampshade, lamp shade",
 622 |     "620": "laptop, laptop computer",
 623 |     "621": "lawn mower, mower",
 624 |     "622": "lens cap, lens cover",
 625 |     "623": "letter opener, paper knife, paperknife",
 626 |     "624": "library",
 627 |     "625": "lifeboat",
 628 |     "626": "lighter, light, igniter, ignitor",
 629 |     "627": "limousine, limo",
 630 |     "628": "liner, ocean liner",
 631 |     "629": "lipstick, lip rouge",
 632 |     "630": "Loafer",
 633 |     "631": "lotion",
 634 |     "632": "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system",
 635 |     "633": "loupe, jeweler's loupe",
 636 |     "634": "lumbermill, sawmill",
 637 |     "635": "magnetic compass",
 638 |     "636": "mailbag, postbag",
 639 |     "637": "mailbox, letter box",
 640 |     "638": "maillot",
 641 |     "639": "maillot, tank suit",
 642 |     "640": "manhole cover",
 643 |     "641": "maraca",
 644 |     "642": "marimba, xylophone",
 645 |     "643": "mask",
 646 |     "644": "matchstick",
 647 |     "645": "maypole",
 648 |     "646": "maze, labyrinth",
 649 |     "647": "measuring cup",
 650 |     "648": "medicine chest, medicine cabinet",
 651 |     "649": "megalith, megalithic structure",
 652 |     "650": "microphone, mike",
 653 |     "651": "microwave, microwave oven",
 654 |     "652": "military uniform",
 655 |     "653": "milk can",
 656 |     "654": "minibus",
 657 |     "655": "miniskirt, mini",
 658 |     "656": "minivan",
 659 |     "657": "missile",
 660 |     "658": "mitten",
 661 |     "659": "mixing bowl",
 662 |     "660": "mobile home, manufactured home",
 663 |     "661": "Model T",
 664 |     "662": "modem",
 665 |     "663": "monastery",
 666 |     "664": "monitor",
 667 |     "665": "moped",
 668 |     "666": "mortar",
 669 |     "667": "mortarboard",
 670 |     "668": "mosque",
 671 |     "669": "mosquito net",
 672 |     "670": "motor scooter, scooter",
 673 |     "671": "mountain bike, all-terrain bike, off-roader",
 674 |     "672": "mountain tent",
 675 |     "673": "mouse, computer mouse",
 676 |     "674": "mousetrap",
 677 |     "675": "moving van",
 678 |     "676": "muzzle",
 679 |     "677": "nail",
 680 |     "678": "neck brace",
 681 |     "679": "necklace",
 682 |     "680": "nipple",
 683 |     "681": "notebook, notebook computer",
 684 |     "682": "obelisk",
 685 |     "683": "oboe, hautboy, hautbois",
 686 |     "684": "ocarina, sweet potato",
 687 |     "685": "odometer, hodometer, mileometer, milometer",
 688 |     "686": "oil filter",
 689 |     "687": "organ, pipe organ",
 690 |     "688": "oscilloscope, scope, cathode-ray oscilloscope, CRO",
 691 |     "689": "overskirt",
 692 |     "690": "oxcart",
 693 |     "691": "oxygen mask",
 694 |     "692": "packet",
 695 |     "693": "paddle, boat paddle",
 696 |     "694": "paddlewheel, paddle wheel",
 697 |     "695": "padlock",
 698 |     "696": "paintbrush",
 699 |     "697": "pajama, pyjama, pj's, jammies",
 700 |     "698": "palace",
 701 |     "699": "panpipe, pandean pipe, syrinx",
 702 |     "700": "paper towel",
 703 |     "701": "parachute, chute",
 704 |     "702": "parallel bars, bars",
 705 |     "703": "park bench",
 706 |     "704": "parking meter",
 707 |     "705": "passenger car, coach, carriage",
 708 |     "706": "patio, terrace",
 709 |     "707": "pay-phone, pay-station",
 710 |     "708": "pedestal, plinth, footstall",
 711 |     "709": "pencil box, pencil case",
 712 |     "710": "pencil sharpener",
 713 |     "711": "perfume, essence",
 714 |     "712": "Petri dish",
 715 |     "713": "photocopier",
 716 |     "714": "pick, plectrum, plectron",
 717 |     "715": "pickelhaube",
 718 |     "716": "picket fence, paling",
 719 |     "717": "pickup, pickup truck",
 720 |     "718": "pier",
 721 |     "719": "piggy bank, penny bank",
 722 |     "720": "pill bottle",
 723 |     "721": "pillow",
 724 |     "722": "ping-pong ball",
 725 |     "723": "pinwheel",
 726 |     "724": "pirate, pirate ship",
 727 |     "725": "pitcher, ewer",
 728 |     "726": "plane, carpenter's plane, woodworking plane",
 729 |     "727": "planetarium",
 730 |     "728": "plastic bag",
 731 |     "729": "plate rack",
 732 |     "730": "plow, plough",
 733 |     "731": "plunger, plumber's helper",
 734 |     "732": "Polaroid camera, Polaroid Land camera",
 735 |     "733": "pole",
 736 |     "734": "police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria",
 737 |     "735": "poncho",
 738 |     "736": "pool table, billiard table, snooker table",
 739 |     "737": "pop bottle, soda bottle",
 740 |     "738": "pot, flowerpot",
 741 |     "739": "potter's wheel",
 742 |     "740": "power drill",
 743 |     "741": "prayer rug, prayer mat",
 744 |     "742": "printer",
 745 |     "743": "prison, prison house",
 746 |     "744": "projectile, missile",
 747 |     "745": "projector",
 748 |     "746": "puck, hockey puck",
 749 |     "747": "punching bag, punch bag, punching ball, punchball",
 750 |     "748": "purse",
 751 |     "749": "quill, quill pen",
 752 |     "750": "quilt, comforter, comfort, puff",
 753 |     "751": "racer, race car, racing car",
 754 |     "752": "racket, racquet",
 755 |     "753": "radiator",
 756 |     "754": "radio, wireless",
 757 |     "755": "radio telescope, radio reflector",
 758 |     "756": "rain barrel",
 759 |     "757": "recreational vehicle, RV, R.V.",
 760 |     "758": "reel",
 761 |     "759": "reflex camera",
 762 |     "760": "refrigerator, icebox",
 763 |     "761": "remote control, remote",
 764 |     "762": "restaurant, eating house, eating place, eatery",
 765 |     "763": "revolver, six-gun, six-shooter",
 766 |     "764": "rifle",
 767 |     "765": "rocking chair, rocker",
 768 |     "766": "rotisserie",
 769 |     "767": "rubber eraser, rubber, pencil eraser",
 770 |     "768": "rugby ball",
 771 |     "769": "rule, ruler",
 772 |     "770": "running shoe",
 773 |     "771": "safe",
 774 |     "772": "safety pin",
 775 |     "773": "saltshaker, salt shaker",
 776 |     "774": "sandal",
 777 |     "775": "sarong",
 778 |     "776": "sax, saxophone",
 779 |     "777": "scabbard",
 780 |     "778": "scale, weighing machine",
 781 |     "779": "school bus",
 782 |     "780": "schooner",
 783 |     "781": "scoreboard",
 784 |     "782": "screen, CRT screen",
 785 |     "783": "screw",
 786 |     "784": "screwdriver",
 787 |     "785": "seat belt, seatbelt",
 788 |     "786": "sewing machine",
 789 |     "787": "shield, buckler",
 790 |     "788": "shoe shop, shoe-shop, shoe store",
 791 |     "789": "shoji",
 792 |     "790": "shopping basket",
 793 |     "791": "shopping cart",
 794 |     "792": "shovel",
 795 |     "793": "shower cap",
 796 |     "794": "shower curtain",
 797 |     "795": "ski",
 798 |     "796": "ski mask",
 799 |     "797": "sleeping bag",
 800 |     "798": "slide rule, slipstick",
 801 |     "799": "sliding door",
 802 |     "800": "slot, one-armed bandit",
 803 |     "801": "snorkel",
 804 |     "802": "snowmobile",
 805 |     "803": "snowplow, snowplough",
 806 |     "804": "soap dispenser",
 807 |     "805": "soccer ball",
 808 |     "806": "sock",
 809 |     "807": "solar dish, solar collector, solar furnace",
 810 |     "808": "sombrero",
 811 |     "809": "soup bowl",
 812 |     "810": "space bar",
 813 |     "811": "space heater",
 814 |     "812": "space shuttle",
 815 |     "813": "spatula",
 816 |     "814": "speedboat",
 817 |     "815": "spider web, spider's web",
 818 |     "816": "spindle",
 819 |     "817": "sports car, sport car",
 820 |     "818": "spotlight, spot",
 821 |     "819": "stage",
 822 |     "820": "steam locomotive",
 823 |     "821": "steel arch bridge",
 824 |     "822": "steel drum",
 825 |     "823": "stethoscope",
 826 |     "824": "stole",
 827 |     "825": "stone wall",
 828 |     "826": "stopwatch, stop watch",
 829 |     "827": "stove",
 830 |     "828": "strainer",
 831 |     "829": "streetcar, tram, tramcar, trolley, trolley car",
 832 |     "830": "stretcher",
 833 |     "831": "studio couch, day bed",
 834 |     "832": "stupa, tope",
 835 |     "833": "submarine, pigboat, sub, U-boat",
 836 |     "834": "suit, suit of clothes",
 837 |     "835": "sundial",
 838 |     "836": "sunglass",
 839 |     "837": "sunglasses, dark glasses, shades",
 840 |     "838": "sunscreen, sunblock, sun blocker",
 841 |     "839": "suspension bridge",
 842 |     "840": "swab, swob, mop",
 843 |     "841": "sweatshirt",
 844 |     "842": "swimming trunks, bathing trunks",
 845 |     "843": "swing",
 846 |     "844": "switch, electric switch, electrical switch",
 847 |     "845": "syringe",
 848 |     "846": "table lamp",
 849 |     "847": "tank, army tank, armored combat vehicle, armoured combat vehicle",
 850 |     "848": "tape player",
 851 |     "849": "teapot",
 852 |     "850": "teddy, teddy bear",
 853 |     "851": "television, television system",
 854 |     "852": "tennis ball",
 855 |     "853": "thatch, thatched roof",
 856 |     "854": "theater curtain, theatre curtain",
 857 |     "855": "thimble",
 858 |     "856": "thresher, thrasher, threshing machine",
 859 |     "857": "throne",
 860 |     "858": "tile roof",
 861 |     "859": "toaster",
 862 |     "860": "tobacco shop, tobacconist shop, tobacconist",
 863 |     "861": "toilet seat",
 864 |     "862": "torch",
 865 |     "863": "totem pole",
 866 |     "864": "tow truck, tow car, wrecker",
 867 |     "865": "toyshop",
 868 |     "866": "tractor",
 869 |     "867": "trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi",
 870 |     "868": "tray",
 871 |     "869": "trench coat",
 872 |     "870": "tricycle, trike, velocipede",
 873 |     "871": "trimaran",
 874 |     "872": "tripod",
 875 |     "873": "triumphal arch",
 876 |     "874": "trolleybus, trolley coach, trackless trolley",
 877 |     "875": "trombone",
 878 |     "876": "tub, vat",
 879 |     "877": "turnstile",
 880 |     "878": "typewriter keyboard",
 881 |     "879": "umbrella",
 882 |     "880": "unicycle, monocycle",
 883 |     "881": "upright, upright piano",
 884 |     "882": "vacuum, vacuum cleaner",
 885 |     "883": "vase",
 886 |     "884": "vault",
 887 |     "885": "velvet",
 888 |     "886": "vending machine",
 889 |     "887": "vestment",
 890 |     "888": "viaduct",
 891 |     "889": "violin, fiddle",
 892 |     "890": "volleyball",
 893 |     "891": "waffle iron",
 894 |     "892": "wall clock",
 895 |     "893": "wallet, billfold, notecase, pocketbook",
 896 |     "894": "wardrobe, closet, press",
 897 |     "895": "warplane, military plane",
 898 |     "896": "washbasin, handbasin, washbowl, lavabo, wash-hand basin",
 899 |     "897": "washer, automatic washer, washing machine",
 900 |     "898": "water bottle",
 901 |     "899": "water jug",
 902 |     "900": "water tower",
 903 |     "901": "whiskey jug",
 904 |     "902": "whistle",
 905 |     "903": "wig",
 906 |     "904": "window screen",
 907 |     "905": "window shade",
 908 |     "906": "Windsor tie",
 909 |     "907": "wine bottle",
 910 |     "908": "wing",
 911 |     "909": "wok",
 912 |     "910": "wooden spoon",
 913 |     "911": "wool, woolen, woollen",
 914 |     "912": "worm fence, snake fence, snake-rail fence, Virginia fence",
 915 |     "913": "wreck",
 916 |     "914": "yawl",
 917 |     "915": "yurt",
 918 |     "916": "web site, website, internet site, site",
 919 |     "917": "comic book",
 920 |     "918": "crossword puzzle, crossword",
 921 |     "919": "street sign",
 922 |     "920": "traffic light, traffic signal, stoplight",
 923 |     "921": "book jacket, dust cover, dust jacket, dust wrapper",
 924 |     "922": "menu",
 925 |     "923": "plate",
 926 |     "924": "guacamole",
 927 |     "925": "consomme",
 928 |     "926": "hot pot, hotpot",
 929 |     "927": "trifle",
 930 |     "928": "ice cream, icecream",
 931 |     "929": "ice lolly, lolly, lollipop, popsicle",
 932 |     "930": "French loaf",
 933 |     "931": "bagel, beigel",
 934 |     "932": "pretzel",
 935 |     "933": "cheeseburger",
 936 |     "934": "hotdog, hot dog, red hot",
 937 |     "935": "mashed potato",
 938 |     "936": "head cabbage",
 939 |     "937": "broccoli",
 940 |     "938": "cauliflower",
 941 |     "939": "zucchini, courgette",
 942 |     "940": "spaghetti squash",
 943 |     "941": "acorn squash",
 944 |     "942": "butternut squash",
 945 |     "943": "cucumber, cuke",
 946 |     "944": "artichoke, globe artichoke",
 947 |     "945": "bell pepper",
 948 |     "946": "cardoon",
 949 |     "947": "mushroom",
 950 |     "948": "Granny Smith",
 951 |     "949": "strawberry",
 952 |     "950": "orange",
 953 |     "951": "lemon",
 954 |     "952": "fig",
 955 |     "953": "pineapple, ananas",
 956 |     "954": "banana",
 957 |     "955": "jackfruit, jak, jack",
 958 |     "956": "custard apple",
 959 |     "957": "pomegranate",
 960 |     "958": "hay",
 961 |     "959": "carbonara",
 962 |     "960": "chocolate sauce, chocolate syrup",
 963 |     "961": "dough",
 964 |     "962": "meat loaf, meatloaf",
 965 |     "963": "pizza, pizza pie",
 966 |     "964": "potpie",
 967 |     "965": "burrito",
 968 |     "966": "red wine",
 969 |     "967": "espresso",
 970 |     "968": "cup",
 971 |     "969": "eggnog",
 972 |     "970": "alp",
 973 |     "971": "bubble",
 974 |     "972": "cliff, drop, drop-off",
 975 |     "973": "coral reef",
 976 |     "974": "geyser",
 977 |     "975": "lakeside, lakeshore",
 978 |     "976": "promontory, headland, head, foreland",
 979 |     "977": "sandbar, sand bar",
 980 |     "978": "seashore, coast, seacoast, sea-coast",
 981 |     "979": "valley, vale",
 982 |     "980": "volcano",
 983 |     "981": "ballplayer, baseball player",
 984 |     "982": "groom, bridegroom",
 985 |     "983": "scuba diver",
 986 |     "984": "rapeseed",
 987 |     "985": "daisy",
 988 |     "986": "yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum",
 989 |     "987": "corn",
 990 |     "988": "acorn",
 991 |     "989": "hip, rose hip, rosehip",
 992 |     "990": "buckeye, horse chestnut, conker",
 993 |     "991": "coral fungus",
 994 |     "992": "agaric",
 995 |     "993": "gyromitra",
 996 |     "994": "stinkhorn, carrion fungus",
 997 |     "995": "earthstar",
 998 |     "996": "hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa",
 999 |     "997": "bolete",
1000 |     "998": "ear, spike, capitulum",
1001 |     "999": "toilet tissue, toilet paper, bathroom tissue"
1002 | }


--------------------------------------------------------------------------------
/main.go:
--------------------------------------------------------------------------------
 1 | // Author(s): Calvin Feng
 2 | 
 3 | package main
 4 | 
 5 | import "tensorgo/cmd"
 6 | 
 7 | func main() {
 8 | 	cmd.Execute()
 9 | }
10 | 


--------------------------------------------------------------------------------
/notes/docker.md:
--------------------------------------------------------------------------------
  1 | # Docker
  2 | ## Basic Commands
  3 | The pull command fetches an image from Docker registry, much like `npm install`.
  4 | ```
  5 | docker pull busybox
  6 | ```
  7 | 
  8 | Use `images` command to see a list of all images on your system.
  9 | ```
 10 | docker images
 11 | ```
 12 | 
 13 | Use `run` to run a container based on the provided image.
 14 | ```
 15 | docker run busybox
 16 | ```
 17 | 
 18 | Use `ps` to see all the containers that are currently running.
 19 | ```
 20 | docker ps
 21 | ```
 22 | 
 23 | Attach `-a` to show all the containers that ran.
 24 | ```
 25 | docker ps -a
 26 | ```
 27 | 
 28 | We can SSH into a docker container using a `-it` flag
 29 | ```
 30 | docker run -it busybox sh
 31 | ```
 32 | 
 33 | We can remove containers using `rm`, even those who have already exited.
 34 | ```
 35 | docker rm 91e3611bf6ae
 36 | ```
 37 | 
 38 | We can delete all containers in one go. The `-q` flag means returning only numeric IDs and `-f` 
 39 | flag means filter ouput based on conditions provided.
 40 | ```
 41 | docker ps -a -q -f status=exited
 42 | ```
 43 | 
 44 | The command above will filter docker containers and return numeric IDs of those selected containers,
 45 | now we can go ahead and delete them.
 46 | ```
 47 | docker rm $(docker ps -a -q -f status=exited)
 48 | ```
 49 | 
 50 | Now if we want to run a container and immediately delete it afterward then we can use a flag `--rm`
 51 | ```
 52 | docker run busybox --rm
 53 | ```
 54 | 
 55 | ## Web Applications
 56 | Let's pull an image from Prakhar repository, the guy who wrote the tutorial.
 57 | ```
 58 | docker run prakhar1989/static-site
 59 | ```
 60 | 
 61 | At this point, the container is not exposing any port. We need to run the container is a detached
 62 | mode so we can ask the container to publish ports. The flag `-d` means detached mode. The flag `-P`
 63 | will publish all exposed ports to random ports. Finally, the flag `--name` corresponds to a name we
 64 | want to give to the container.
 65 | ```
 66 | docker run --detach --publish-all --name static-site prakhar1989/static-site
 67 | ```
 68 | 
 69 | Now we ask the docker what are the ports.
 70 | ```
 71 | docker port static-site
 72 | ```
 73 | 
 74 | ## Create Images
 75 | * Base images are images that have no parent image, usually images with an OS like ubuntu, busybox
 76 | or debian.
 77 | * Child images are images that build on base images and add additional functionality.
 78 | * Official images are images that are officially maintained and supported by people at Docker.
 79 | * Usere images are images created and shared by users.
 80 | 
 81 | Let's begin with creating a Dockerfile.
 82 | ```Dockerfile
 83 | FROM golang:1.8 as build
 84 | 
 85 | LABEL authors="Calvin Feng"
 86 | 
 87 | # The public image golang:1.8 specified that all source code must go into /go/src/
 88 | COPY . /go/src/react-ts-go-todo
 89 | WORKDIR /go/src/react-ts-go-todo
 90 | 
 91 | EXPOSE 3000
 92 | 
 93 | RUN go get -u github.com/golang/dep/cmd/dep
 94 | 
 95 | RUN go install
 96 | 
 97 | CMD react-ts-go-todo
 98 | ```
 99 | 
100 | After Dockerfile has been specified, build the image using `build`, and the flag `-t` means tag name.
101 | The actual tag comes after the colon. In this case, the tag is `beta`.
102 | ```
103 | docker build -tag react-ts-go-todo:beta .
104 | ```
105 | 
106 | Now let's run it, using publish flag to forward 3000 to 8000 because we already exposed 3000.
107 | ```
108 | docker run --publish 8000:3000 react-ts-go-todo:beta
109 | ```
110 | 
111 | Or run it in detach mode which is more convenient
112 | ```
113 | docker run --publish 8000:3000 --detach react-ts-go-todo:beta
114 | ```
115 | 
116 | Stop them all
117 | ```
118 | docker stop $(docker ps -q)
119 | ```


--------------------------------------------------------------------------------
/package.json:
--------------------------------------------------------------------------------
 1 | {
 2 |   "name": "tensorgo",
 3 |   "version": "0.1.0",
 4 |   "description": "react typescript image classification",
 5 |   "main": "index.tsx",
 6 |   "scripts": {
 7 |     "build": "webpack",
 8 |     "build:watch": "webpack --watch"
 9 |   },
10 |   "repository": {
11 |     "type": "git",
12 |     "url": "git+https://github.com/calvinfeng/tensorgo.git"
13 |   },
14 |   "keywords": [
15 |     "react",
16 |     "typescript",
17 |     "go",
18 |     "tensorflow"
19 |   ],
20 |   "author": "Calvin Feng",
21 |   "license": "ISC",
22 |   "bugs": {
23 |     "url": "https://github.com/calvinfeng/tensorgo/issues"
24 |   },
25 |   "homepage": "https://github.com/calvinfeng/tensorgo#readme",
26 |   "dependencies": {
27 |     "@material-ui/core": "^1.2.1",
28 |     "@types/axios": "^0.14.0",
29 |     "@types/chart.js": "^2.7.22",
30 |     "@types/react": "^16.3.17",
31 |     "@types/react-dom": "^16.0.6",
32 |     "axios": "^0.18.0",
33 |     "chart.js": "^2.7.2",
34 |     "css-loader": "^0.28.11",
35 |     "node-sass": "^4.9.0",
36 |     "react": "^16.4.0",
37 |     "react-dom": "^16.4.0",
38 |     "sass-loader": "^7.0.3",
39 |     "style-loader": "^0.21.0",
40 |     "ts-loader": "^4.4.1",
41 |     "typescript": "^2.9.1",
42 |     "webpack": "^4.12.0",
43 |     "webpack-cli": "^3.0.3"
44 |   }
45 | }
46 | 


--------------------------------------------------------------------------------
/public/index.html:
--------------------------------------------------------------------------------
 1 | <!DOCTYPE html>
 2 | <html>
 3 |   <head>
 4 |     <meta charset="utf-8">
 5 |     <title>Tensorgo</title>
 6 |     <link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Roboto:300,400,500">
 7 |   </head>
 8 |   <body>
 9 |     <div id="root"></div>
10 |     <script src="index.bundle.js"></script>
11 |   </body>
12 | </html>


--------------------------------------------------------------------------------
/requirements.txt:
--------------------------------------------------------------------------------
1 | tensorflow==1.6
2 | keras==2.1.5
3 | matplotlib==2.2.2
4 | h5py==2.7.1


--------------------------------------------------------------------------------
/tensorcv/data_util.go:
--------------------------------------------------------------------------------
 1 | // Package tensorcv provides computer vision handlers for handling image recognition requests.
 2 | package tensorcv
 3 | 
 4 | import (
 5 | 	"encoding/json"
 6 | 	"io/ioutil"
 7 | )
 8 | 
 9 | // LoadLabels takes a JSON file which contains the 1000 classes from ImageNet and returns a map
10 | // from class index to class description, e.g. 1 => "goldfish, Carassiu auratus"
11 | func LoadLabels(jsonPath string) (map[int]string, error) {
12 | 
13 | 	labelMap := make(map[int]string)
14 | 
15 | 	bytes, err := ioutil.ReadFile(jsonPath)
16 | 	if err != nil {
17 | 		return nil, err
18 | 	}
19 | 
20 | 	err = json.Unmarshal(bytes, &labelMap)
21 | 	if err != nil {
22 | 		return nil, err
23 | 	}
24 | 
25 | 	return labelMap, nil
26 | }
27 | 
28 | // ArgMax takes in a list and return the index and value of the max element.
29 | func ArgMax(list []float32, selected map[int]bool) (int, float32) {
30 | 	idx := 0
31 | 	max := list[idx]
32 | 	for i, el := range list {
33 | 		if el > max && !selected[i] {
34 | 			idx = i
35 | 			max = el
36 | 		}
37 | 	}
38 | 
39 | 	selected[idx] = true
40 | 
41 | 	return idx, max
42 | }
43 | 


--------------------------------------------------------------------------------
/tensorcv/handler.go:
--------------------------------------------------------------------------------
  1 | // Package tensorcv provides computer vision handlers for handling image recognition requests.
  2 | package tensorcv
  3 | 
  4 | import (
  5 | 	"bytes"
  6 | 	"encoding/json"
  7 | 	"fmt"
  8 | 	"io"
  9 | 	"net/http"
 10 | 	"strings"
 11 | )
 12 | 
 13 | // ErrorRes defines the structure of a HTTP error JSON response to client.
 14 | type ErrorRes struct {
 15 | 	Status  int    `json:"status"`
 16 | 	Message string `json:"message"`
 17 | }
 18 | 
 19 | // SuccessRes defines the structures of a HTTP success JSON response to client.
 20 | type SuccessRes struct {
 21 | 	Status  int      `json:"status"`
 22 | 	Results []*Class `json:"results"`
 23 | }
 24 | 
 25 | // NewImageRecognitionHandler returns a HTTP handler that will handle a request to perform image
 26 | // recognition.
 27 | func NewImageRecognitionHandler(labels map[int]string, modelPath string) http.HandlerFunc {
 28 | 	return func(w http.ResponseWriter, r *http.Request) {
 29 | 		w.Header().Set("Content-Type", "application/json")
 30 | 
 31 | 		imgFile, header, err := r.FormFile("image")
 32 | 		if err != nil {
 33 | 			renderErrorResponse(w, http.StatusBadRequest, err.Error())
 34 | 			return
 35 | 		}
 36 | 
 37 | 		imgName := strings.Split(header.Filename, ".")
 38 | 
 39 | 		var imgBuffer bytes.Buffer
 40 | 		io.Copy(&imgBuffer, imgFile)
 41 | 		fmt.Printf("Received image %s which as %d bytes\n", imgName, len(imgBuffer.Bytes()))
 42 | 
 43 | 		var imgFormat string
 44 | 		if imgName[1] == "jpeg" || imgName[1] == "jpg" {
 45 | 			imgFormat = "jpeg"
 46 | 		} else {
 47 | 			imgFormat = "png"
 48 | 		}
 49 | 
 50 | 		imgTensor, err := GetTensorFromImageBuffer(imgBuffer, imgFormat, 3)
 51 | 		fmt.Println("Image tensor is loaded:", imgTensor.Shape())
 52 | 
 53 | 		softmaxScore := RunResNetModel(imgTensor, modelPath)
 54 | 		if softmaxScore != nil {
 55 | 			classList := make([]*Class, 0, len(softmaxScore[0]))
 56 | 			for idx, prob := range softmaxScore[0] {
 57 | 				classList = append(classList, &Class{Prob: prob, Index: idx})
 58 | 			}
 59 | 
 60 | 			// Perform sorting
 61 | 			Sort(classList, 0, len(classList)-1)
 62 | 
 63 | 			top5Results := classList[len(classList)-6 : len(classList)-1]
 64 | 
 65 | 			for _, result := range top5Results {
 66 | 				result.Name = labels[result.Index]
 67 | 			}
 68 | 
 69 | 			renderResult(w, top5Results)
 70 | 			return
 71 | 		}
 72 | 
 73 | 		renderErrorResponse(w, http.StatusInternalServerError, "failed to run TF model")
 74 | 	}
 75 | }
 76 | 
 77 | // NewHelloWorldHandler returns a HTTP handler that will return a hello world message from
 78 | // tensorflow to client.
 79 | func NewHelloWorldHandler() http.HandlerFunc {
 80 | 	return func(w http.ResponseWriter, r *http.Request) {
 81 | 		w.Header().Set("Content-Type", "application/json")
 82 | 
 83 | 		msg := HelloWorldFromTF()
 84 | 
 85 | 		w.WriteHeader(http.StatusOK)
 86 | 		w.Write([]byte(msg))
 87 | 	}
 88 | }
 89 | 
 90 | func renderResult(w http.ResponseWriter, results []*Class) {
 91 | 	res := &SuccessRes{
 92 | 		Status:  http.StatusOK,
 93 | 		Results: results,
 94 | 	}
 95 | 
 96 | 	if resBytes, err := json.Marshal(res); err == nil {
 97 | 		w.WriteHeader(http.StatusOK)
 98 | 		w.Write(resBytes)
 99 | 	} else {
100 | 		w.WriteHeader(http.StatusInternalServerError)
101 | 		w.Write([]byte(err.Error()))
102 | 	}
103 | }
104 | 
105 | func renderErrorResponse(w http.ResponseWriter, status int, msg string) {
106 | 	res := &ErrorRes{
107 | 		Status:  status,
108 | 		Message: msg,
109 | 	}
110 | 
111 | 	if resBytes, err := json.Marshal(res); err == nil {
112 | 		w.WriteHeader(status)
113 | 		w.Write(resBytes)
114 | 	} else {
115 | 		w.WriteHeader(http.StatusInternalServerError)
116 | 		w.Write([]byte(err.Error()))
117 | 	}
118 | }
119 | 


--------------------------------------------------------------------------------
/tensorcv/model.go:
--------------------------------------------------------------------------------
 1 | // Package tensorcv provides computer vision handlers for handling image recognition requests.
 2 | package tensorcv
 3 | 
 4 | import (
 5 | 	"fmt"
 6 | 
 7 | 	tf "github.com/tensorflow/tensorflow/tensorflow/go"
 8 | 	"github.com/tensorflow/tensorflow/tensorflow/go/op"
 9 | )
10 | 
11 | // RunResNetModel ...
12 | func RunResNetModel(imgTensor *tf.Tensor, modelPath string) [][]float32 {
13 | 	model, err := tf.LoadSavedModel(modelPath, []string{"serve"}, nil)
14 | 	if err != nil {
15 | 		fmt.Printf("Error loading saved model: %s\n", err.Error())
16 | 		return nil
17 | 	}
18 | 
19 | 	defer model.Session.Close()
20 | 
21 | 	feeds := map[tf.Output]*tf.Tensor{
22 | 		model.Graph.Operation("input_1").Output(0): imgTensor,
23 | 	}
24 | 
25 | 	fmt.Println("Number of available outputs:", model.Graph.Operation("fc1000/Softmax").NumOutputs())
26 | 	fetches := []tf.Output{model.Graph.Operation("fc1000/Softmax").Output(0)}
27 | 
28 | 	result, runErr := model.Session.Run(feeds, fetches, nil)
29 | 	if runErr != nil {
30 | 		fmt.Printf("Error running the session with input, err: %s\n", runErr.Error())
31 | 		return nil
32 | 	}
33 | 
34 | 	return result[0].Value().([][]float32)
35 | }
36 | 
37 | // RunMnistModel ...
38 | func RunMnistModel(imgTensor *tf.Tensor) {
39 | 	model, err := tf.LoadSavedModel("./tf_mnist_py/model", []string{"serve"}, nil)
40 | 	if err != nil {
41 | 		fmt.Printf("Error loading saved model: %s\n", err.Error())
42 | 		return
43 | 	}
44 | 
45 | 	defer model.Session.Close()
46 | 
47 | 	feeds := map[tf.Output]*tf.Tensor{
48 | 		model.Graph.Operation("image_input").Output(0): imgTensor,
49 | 	}
50 | 
51 | 	fetches := []tf.Output{model.Graph.Operation("infer").Output(0)}
52 | 
53 | 	result, runErr := model.Session.Run(feeds, fetches, nil)
54 | 	if runErr != nil {
55 | 		fmt.Printf("Error running the session with input, err: %s\n", runErr.Error())
56 | 		return
57 | 	}
58 | 
59 | 	fmt.Printf("Most likely number in input is %v\n", result[0].Value())
60 | }
61 | 
62 | // HelloWorldFromTF will initialize a session and run it. It will print Hello to the screen.
63 | func HelloWorldFromTF() string {
64 | 	// Construct a graph with an operation that produces a string constant
65 | 	s := op.NewScope()
66 | 	c := op.Const(s, "Hello from TensorFlow version "+tf.Version())
67 | 
68 | 	graph, err := s.Finalize()
69 | 	if err != nil {
70 | 		panic(err)
71 | 	}
72 | 
73 | 	// Execute the graph in a session
74 | 	sess, err := tf.NewSession(graph, nil)
75 | 	if err != nil {
76 | 		panic(err)
77 | 	}
78 | 
79 | 	defer sess.Close()
80 | 
81 | 	output, err := sess.Run(nil, []tf.Output{c}, nil)
82 | 	if err != nil {
83 | 		panic(err)
84 | 	}
85 | 
86 | 	return output[0].Value().(string)
87 | }
88 | 


--------------------------------------------------------------------------------
/tensorcv/route.go:
--------------------------------------------------------------------------------
 1 | // Package tensorcv provides computer vision handlers for handling image recognition requests.
 2 | package tensorcv
 3 | 
 4 | import (
 5 | 	"net/http"
 6 | 
 7 | 	"github.com/gorilla/mux"
 8 | )
 9 | 
10 | // LoadRoutes returns a http.Handler as a multiplexer to various routes.
11 | func LoadRoutes(labels map[int]string, modelPath string) http.Handler {
12 | 	r := mux.NewRouter()
13 | 
14 | 	api := r.PathPrefix("/api").Subrouter()
15 | 	api.Handle("/tf/recognition/", NewImageRecognitionHandler(labels, modelPath)).Methods("POST")
16 | 	api.Handle("/tf/hello/", NewHelloWorldHandler()).Methods("GET")
17 | 
18 | 	r.PathPrefix("/").Handler(http.FileServer(http.Dir("./public")))
19 | 
20 | 	return r
21 | }
22 | 


--------------------------------------------------------------------------------
/tensorcv/tensor_util.go:
--------------------------------------------------------------------------------
  1 | // Package tensorcv provides computer vision handlers for handling image recognition requests.
  2 | package tensorcv
  3 | 
  4 | import (
  5 | 	"bytes"
  6 | 	"io"
  7 | 	"os"
  8 | 
  9 | 	tf "github.com/tensorflow/tensorflow/tensorflow/go"
 10 | 	"github.com/tensorflow/tensorflow/tensorflow/go/op"
 11 | )
 12 | 
 13 | // Constants for scaling images.
 14 | const (
 15 | 	ImageHeight = 224
 16 | 	ImageWidth  = 224
 17 | 	Mean        = float32(0)
 18 | 	Scale       = float32(1)
 19 | )
 20 | 
 21 | // GetTensorFromImagePath creates a tensor by taking an image path.
 22 | func GetTensorFromImagePath(imgPath, imgFormat string, numChan int64) (*tf.Tensor, error) {
 23 | 	var err error
 24 | 
 25 | 	imgFile, err := os.Open(imgPath)
 26 | 	if err != nil {
 27 | 		return nil, err
 28 | 	}
 29 | 
 30 | 	defer imgFile.Close()
 31 | 
 32 | 	var imgBuffer bytes.Buffer
 33 | 	io.Copy(&imgBuffer, imgFile)
 34 | 
 35 | 	return GetTensorFromImageBuffer(imgBuffer, imgFormat, numChan)
 36 | }
 37 | 
 38 | // GetTensorFromImageBuffer creates a tensor by taking a byte buffer.
 39 | func GetTensorFromImageBuffer(imgBuffer bytes.Buffer, imgFormat string,
 40 | 	numChan int64) (*tf.Tensor, error) {
 41 | 
 42 | 	imgTensor, err := tf.NewTensor(imgBuffer.String())
 43 | 	if err != nil {
 44 | 		return nil, err
 45 | 	}
 46 | 
 47 | 	graph, input, output, err := createImageTransformGraph(imgFormat, numChan)
 48 | 	if err != nil {
 49 | 		return nil, err
 50 | 	}
 51 | 
 52 | 	sess, err := tf.NewSession(graph, nil)
 53 | 	if err != nil {
 54 | 		return nil, err
 55 | 	}
 56 | 
 57 | 	defer sess.Close()
 58 | 
 59 | 	feeds := map[tf.Output]*tf.Tensor{
 60 | 		input: imgTensor,
 61 | 	}
 62 | 
 63 | 	fetches := []tf.Output{output}
 64 | 	normalized, err := sess.Run(feeds, fetches, nil)
 65 | 	if err == nil {
 66 | 		return normalized[0], err
 67 | 	}
 68 | 
 69 | 	return nil, err
 70 | }
 71 | 
 72 | // createImageTransformGraph creates a TensorFlow graph that will perform normalization and image
 73 | // resizing on a given image.
 74 | func createImageTransformGraph(imgFormat string, numChan int64) (*tf.Graph, tf.Output, tf.Output, error) {
 75 | 	s := op.NewScope()
 76 | 	input := op.Placeholder(s, tf.String)
 77 | 
 78 | 	// Decode either PNG or JPEG
 79 | 	var decode tf.Output
 80 | 	if imgFormat == "png" {
 81 | 		decode = op.DecodePng(s, input, op.DecodePngChannels(numChan))
 82 | 	} else {
 83 | 		decode = op.DecodeJpeg(s, input, op.DecodeJpegChannels(numChan))
 84 | 	}
 85 | 
 86 | 	// Build the graph step by step
 87 | 	expand := op.ExpandDims(s, op.Cast(s, decode, tf.Float), op.Const(s.SubScope("make_batch"), int32(0)))
 88 | 	resize := op.ResizeBilinear(s, expand, op.Const(s.SubScope("size"), []int32{ImageHeight, ImageWidth}))
 89 | 	subtractMean := op.Sub(s, resize, op.Const(s.SubScope("mean"), Mean))
 90 | 	output := op.Div(s, subtractMean, op.Const(s.SubScope("scale"), Scale))
 91 | 
 92 | 	// Graph is finalized!
 93 | 	graph, err := s.Finalize()
 94 | 
 95 | 	return graph, input, output, err
 96 | }
 97 | 
 98 | // Class refers to the classification of an image
 99 | type Class struct {
100 | 	Prob  float32 `json:"prob"`
101 | 	Index int     `json:"index"`
102 | 	Name  string  `json:"name"`
103 | }
104 | 
105 | // Sort will perform quick sort in place on a list of Class elements.
106 | func Sort(list []*Class, low, high int) {
107 | 	if low < high {
108 | 		partIdx := Partition(list, low, high)
109 | 		Sort(list, low, partIdx-1)
110 | 		Sort(list, partIdx+1, high)
111 | 	}
112 | }
113 | 
114 | // Partition will perform quick sort partitioning in place.
115 | func Partition(list []*Class, low, high int) int {
116 | 	pivot := list[high]
117 | 
118 | 	i := low - 1 // Index of the smaller element
119 | 	for j := low; j < high; j++ {
120 | 		if list[j].Prob <= pivot.Prob {
121 | 			i++
122 | 			temp := list[i]
123 | 			list[i] = list[j]
124 | 			list[j] = temp
125 | 		}
126 | 	}
127 | 
128 | 	temp := list[i+1]
129 | 	list[i+1] = list[high]
130 | 	list[high] = temp
131 | 
132 | 	return i + 1
133 | }
134 | 


--------------------------------------------------------------------------------
/tf_models/create_mnist_model.py:
--------------------------------------------------------------------------------
 1 | from tensorflow.examples.tutorials.mnist import input_data
 2 | from matplotlib import pyplot as plt
 3 | 
 4 | import tensorflow as tf
 5 | 
 6 | 
 7 | DATA_DIR = 'datasets/'
 8 | def main():
 9 |     """
10 |     Visit https://www.tensorflow.org/versions/r1.2/get_started/mnist/beginners and download the
11 |     MNIST data
12 |     """
13 |     mnist = input_data.read_data_sets(DATA_DIR, one_hot=True)
14 | 
15 |     # Assuming each image is 28x28 with 1 channel
16 |     x = tf.placeholder(tf.float32, [None, 28, 28, 1], name='image_input')
17 |     W = tf.Variable(tf.zeros([784, 10]))
18 |     b = tf.Variable(tf.zeros([10]))
19 |     y = tf.add(tf.matmul(tf.reshape(x, [-1, 784]), W), b)
20 |     labels = tf.placeholder(tf.float32, [None, 10])
21 |     cross_entropy_loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=labels, 
22 |                                                                                 logits=y))
23 |     train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy_loss)
24 | 
25 |     with tf.Session() as sess:
26 |         with tf.device("/cpu:0"):
27 |             sess.run(tf.global_variables_initializer())
28 |             for i in range(1000):
29 |                 # The data come in as (N, 784), thus we must reshape it.
30 |                 batch_x, batch_label = mnist.train.next_batch(100)
31 |                 batch_x = batch_x.reshape((-1, 28, 28, 1))
32 | 
33 |                 loss, _ = sess.run([cross_entropy_loss, train_step], 
34 |                                    feed_dict={x: batch_x, labels: batch_label})
35 |                 print 'Iteration %d: %f' % (i + 1, loss)
36 | 
37 |             infer = tf.argmax(y, axis=1, name='infer')
38 |             truth = tf.argmax(labels, axis=1)
39 |             correct_prediction = tf.equal(infer, truth)
40 |             accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
41 | 
42 |             test_x = mnist.test.images.reshape((-1, 28, 28, 1))
43 |             test_y = mnist.test.labels
44 |             print 'Accuracy on test data:', sess.run(accuracy, 
45 |                                                      feed_dict={x: test_x, labels: test_y})
46 | 
47 |             print 'Time to save the graph!'
48 |             builder = tf.saved_model.builder.SavedModelBuilder('mnist_model')
49 |             builder.add_meta_graph_and_variables(sess, ['serve'])
50 |             builder.save()
51 | 
52 | 
53 | if __name__ == '__main__':
54 |     main()


--------------------------------------------------------------------------------
/tf_models/create_resnet_model.py:
--------------------------------------------------------------------------------
 1 | from tensorflow.python.tools import inspect_checkpoint as ckpt
 2 | from keras.applications.resnet50 import ResNet50
 3 | from keras import backend as K
 4 | 
 5 | import tensorflow as tf
 6 | import time
 7 | 
 8 | 
 9 | def export_from_keras():
10 |     """
11 |     Loads ResNet50 model from Keras and then uses Keras backend session to find the graph and save 
12 |     it to a protobuf file. However, in order to access variables and operations of the graph, one 
13 |     must know the correct naming scheme for every operations. In this case, ResNet50 has a variable 
14 |     named "input_1" for image tensor input and a "fc1000/softmax" as the final layer of the network.
15 |     """
16 |     model = ResNet50(weights='imagenet')
17 |     print 'Model is loaded!'
18 |     sess = K.get_session()
19 | 
20 |     operation_names = []
21 |     for op in sess.graph.get_operations():
22 |         operation_names.append(str(op.name))
23 | 
24 |     print 'List of first few operations', operation_names[0:10]
25 | 
26 |     builder = tf.saved_model.builder.SavedModelBuilder('resnet_50_model')
27 |     builder.add_meta_graph_and_variables(K.get_session(), ['serve'])
28 |     builder.save()
29 | 
30 |     print 'Model is saved!'
31 | 
32 | 
33 | def export_from_checkpoint():
34 |     dir = './resnet50/resnet_v2_50.ckpt'
35 |     ckpt.print_tensors_in_checkpoint_file(dir, tensor_name='', 
36 |                                                all_tensors=False, 
37 |                                                all_tensor_names=False)
38 |     tf.reset_default_graph()
39 |     saver = tf.train.Saver()
40 |     with tf.Session() as sess:
41 |         saver.restore(sess, './resnet50/resnet_v2_50.ckpt')
42 |         print 'Model is restored!'
43 | 
44 |         builder = tf.saved_model.builder.SavedModelBuilder('resnet_50_model')
45 |         builder.add_meta_graph_and_variables(sess, ['serve'])
46 |         builder.save()
47 | 
48 | 
49 | def main():
50 |     export_from_keras()
51 | 
52 | 
53 | if __name__ == '__main__':
54 |     main()


--------------------------------------------------------------------------------
/tsconfig.json:
--------------------------------------------------------------------------------
 1 | {
 2 |     "compilerOptions": {
 3 |         "target": "es6",
 4 |         "jsx": "react",
 5 |         "module": "commonjs",
 6 |         "sourceMap": true
 7 |     },
 8 |     "exclude": [
 9 |         "node_modules"
10 |     ]
11 | }


--------------------------------------------------------------------------------
/ui/components/bar_chart.tsx:
--------------------------------------------------------------------------------
 1 | // External imports
 2 | import * as React from 'react';
 3 | import * as Chart from 'chart.js';
 4 | 
 5 | // Internal imports
 6 | import { Colors } from './enums';
 7 | 
 8 | 
 9 | interface BarChartProps {
10 |     title: string;
11 |     label: string;
12 |     y: number[];
13 |     x: string[];
14 | }
15 | 
16 | 
17 | class BarChart extends React.Component<BarChartProps, any> {
18 |     private chart: Chart;
19 | 
20 |     constructor(props) {
21 |         super(props);
22 |     }
23 | 
24 |     chartData(props): Chart.ChartData {
25 |         const dataSet = {
26 |             label: props.label,
27 |             backgroundColor: Colors.FETCH_BLUE,
28 |             borderColor: Colors.FETCH_BLUE,
29 |             borderWidth: 1,
30 |             data: props.y
31 |         }
32 | 
33 |         const chartData: Chart.ChartData = {
34 |             labels: props.x,
35 |             datasets: [dataSet]
36 |         }
37 | 
38 |         return chartData;
39 |     }
40 | 
41 |     componentWillReceiveProps(nextProps) {
42 |         this.chart.data = this.chartData(nextProps);
43 |         this.chart.update();
44 |     }
45 | 
46 |     componentDidMount() {
47 |         const canvas:any = document.getElementById("bar-chart");
48 |         
49 |         const options: Chart.ChartOptions = {
50 |             responsive: true,
51 |             legend: { position: 'top' },
52 |             title: { display: true, text: this.props.title }
53 |         };
54 | 
55 |         const config: Chart.ChartConfiguration = {
56 |             type: 'bar',
57 |             data: this.chartData(this.props),
58 |             options
59 |         };
60 | 
61 |         this.chart = new Chart(canvas.getContext('2d'), config);
62 |     }
63 | 
64 |     render() {
65 |         return <canvas id="bar-chart"></canvas>
66 |     }
67 | }
68 | 
69 | export default BarChart;


--------------------------------------------------------------------------------
/ui/components/enums.ts:
--------------------------------------------------------------------------------
1 | export enum Colors {
2 |     FETCH_BLUE = "#0081c2",
3 |     FETCH_FADED_BLUE = "#81a9ce"
4 | }
5 | 


--------------------------------------------------------------------------------
/ui/index.scss:
--------------------------------------------------------------------------------
 1 | * {
 2 |     font-family: 'Roboto';
 3 | }
 4 | 
 5 | 
 6 | body {
 7 |     width: 100%;
 8 |     height: 100%;
 9 | }
10 | 
11 | #root {
12 |     display: flex;
13 |     align-items: center;
14 |     flex-direction: column;
15 |     width: 100%;
16 |     height: 100%;
17 | 
18 |     .index-container {
19 |         display: inherit;
20 |         flex-direction: column;
21 |         max-width: 1024px;
22 |     
23 |         .image-classifier {
24 |             display: flex;
25 |             flex-direction: column;
26 |             width: 100%;
27 |         
28 |             .image-loader {
29 |                 display: inherit;
30 |                 flex-direction: inherit;
31 |                 width: 100%;
32 | 
33 |                 .image-container {
34 |                     display: inherit; 
35 |                     flex-direction: row;
36 |                     width: 100%;
37 |                     justify-content: center;
38 |                     margin: 1rem;
39 |                 }
40 | 
41 |                 .image-upload-form {
42 |                     display: inherit;
43 |                     flex-direction: row;
44 |                     max-height: 36px;
45 |                     width: 100%;
46 |                     justify-content: flex-end;
47 | 
48 |                     label {
49 |                         display: inherit;
50 |                         flex-direction: inherit;
51 |                     }
52 |                 }
53 |             }
54 |         
55 |             .progress {
56 |                 margin: 1rem;
57 |                 display: inherit;
58 |                 flex-direction: row;
59 | 
60 |                 p {
61 |                     margin-left: 1rem;
62 |                 }
63 |             }
64 |         }
65 | 
66 |         #bar-chart{
67 |             width: 800px;
68 |         }
69 |     }
70 | }
71 | 
72 | 
73 | 
74 | #hidden-image-file-input {
75 |     display: none;
76 | }


--------------------------------------------------------------------------------
/ui/index.tsx:
--------------------------------------------------------------------------------
  1 | /**
  2 |  * @author Calvin Feng
  3 |  */
  4 | 
  5 | // Libraries
  6 | import * as React from 'react';
  7 | import * as ReactDOM from 'react-dom';
  8 | import axios from 'axios';
  9 | import Button from '@material-ui/core/Button';
 10 | import CircularProgress from '@material-ui/core/CircularProgress';
 11 | 
 12 | // Components
 13 | import BarChart from './components/bar_chart';
 14 | 
 15 | // Helpers
 16 | import { classifyImageFile } from './util';
 17 | 
 18 | // Style
 19 | import './index.scss';
 20 | 
 21 | 
 22 | interface IndexState {
 23 |     currentImage: HTMLImageElement;
 24 |     errorMessage: string | undefined;
 25 |     x: string[];
 26 |     y: number[];
 27 |     isLoading: boolean;
 28 | }
 29 | 
 30 | type ClassResult = {
 31 |     prob: number;
 32 |     index: number;
 33 |     name: string;
 34 | }
 35 | 
 36 | class Index extends React.Component<any, IndexState> {
 37 | 
 38 |     private fileReader: FileReader
 39 | 
 40 |     constructor(props) {
 41 |         super(props);
 42 | 
 43 |         this.state = {
 44 |             isLoading: false,
 45 |             currentImage: undefined,
 46 |             errorMessage: undefined,
 47 |             x: [],
 48 |             y: []
 49 |         };
 50 | 
 51 |         this.fileReader = new FileReader();
 52 |         this.fileReader.onload = this.handleFileOnLoad;
 53 |     }
 54 | 
 55 |     handleFileOnLoad = (e: ProgressEvent): void => {
 56 |         const img = new Image();
 57 |         img.src = this.fileReader.result;
 58 | 
 59 |         this.setState({ currentImage: img });
 60 |     }
 61 | 
 62 |     handleImageFileSelected = (e: React.FormEvent<HTMLInputElement>): void => {
 63 |         const imageType = /image.*/;
 64 |         const file: File = e.currentTarget.files[0];
 65 |         if (file.type.match(imageType)) {
 66 |             this.fileReader.readAsDataURL(file);
 67 | 
 68 |             classifyImageFile(file).then((results: ClassResult[]) => {
 69 |                 this.setState({
 70 |                     x: results.map((result) => result.name.split(',')[0]),
 71 |                     y: results.map((result) => result.prob),
 72 |                     isLoading: false
 73 |                 });
 74 |             }).catch((err) => {
 75 |                 this.setState({
 76 |                     errorMessage: err.message
 77 |                 });
 78 |             });
 79 |             
 80 |             this.setState({ isLoading: true });
 81 |         } else {
 82 |             this.setState({ errorMessage: "File not supported!" });
 83 |         }
 84 |     }
 85 | 
 86 |     get imageLoader(): JSX.Element {
 87 |         let image: JSX.Element;
 88 |         if (this.state.currentImage) {
 89 |             image = <img src={this.state.currentImage.src} />;
 90 |         } else {
 91 |             image = <div />;
 92 |         }
 93 | 
 94 |         return (
 95 |             <section className="image-loader">
 96 |                 <form className="image-upload-form">
 97 |                     <input className="image-input"
 98 |                             accept="image/*"
 99 |                             id="hidden-image-file-input" 
100 |                             type="file" 
101 |                             name="imageLoader" 
102 |                             onChange={this.handleImageFileSelected} />
103 |                     <label htmlFor="hidden-image-file-input">
104 |                         <Button variant="contained" component="span">Upload</Button>
105 |                     </label>
106 |                 </form>
107 |                 <div className="image-container">
108 |                     {image}
109 |                 </div>
110 |             </section>
111 |         );
112 |     }
113 | 
114 |     get progress(): JSX.Element {
115 |         if (this.state.isLoading) {
116 |             return (
117 |                 <div className="progress">
118 |                     <CircularProgress />
119 |                     <p>TensorFlow is computing...</p>
120 |                 </div>
121 |             );
122 |         }
123 | 
124 |         return <div className="progress"></div>;
125 |     }
126 | 
127 |     get error(): JSX.Element {
128 |         if (this.state.errorMessage) {
129 |             return <p>{this.state.errorMessage}</p>;
130 |         }
131 | 
132 |         return <div></div>;
133 |     }
134 | 
135 |     render() {
136 |         return (
137 |             <section className="index-container">
138 |                 <h1>Image Classification</h1>
139 |                 <p>Classify an image using residual neural network with 50 layers</p>
140 |                 <section className="image-classifier">
141 |                     {this.imageLoader}
142 |                     {this.error}
143 |                     {this.progress}
144 |                 </section>
145 |                 <BarChart 
146 |                     title={"Results from ResNet-50"}
147 |                     label={"Softmax Probability"}
148 |                     x={this.state.x}
149 |                     y={this.state.y}  />
150 |             </section>
151 |         )
152 |     }
153 | }
154 | 
155 | 
156 | ReactDOM.render(<Index />, document.getElementById('root'));


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/ui/util.ts:
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 1 | /**
 2 |  * @author Calvin Feng
 3 |  */
 4 | 
 5 |  import Axios from "axios";
 6 | 
 7 | 
 8 |  export async function classifyImageFile(file: File): Promise<any> {
 9 |     const formData: FormData = new FormData();
10 |     formData.append("image", file);
11 | 
12 |     try {
13 |         const response = await Axios.post("api/tf/recognition/", formData, {
14 |             headers: { 'Content-Type': 'multipart/form-data' }
15 |         });
16 |         return response.data.results;
17 |     } catch(e) {
18 |         throw e.response.data;
19 |     }
20 |  }


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/webpack.config.js:
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 1 | module.exports = {
 2 |     mode: "development",
 3 |     devtool: "inline-source-map",
 4 |     entry: "./ui/index.tsx",
 5 |     output: {
 6 |         path: __dirname + "/public",
 7 |         filename: "index.bundle.js"
 8 |     },
 9 |     resolve: {
10 |         extensions: [".ts", ".tsx", ".js", ".jsx"]
11 |     },
12 |     module: {
13 |         rules: [
14 |             { test: /\.tsx?$/, loader: "ts-loader" },
15 |             { test: /\.scss$/, use: ["style-loader", "css-loader", "sass-loader"] }
16 |         ]
17 |     }
18 | };


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