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small update, commented Grayscale analysis, to avoid needing too RAM, README.md updated
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arnaucode
56
README.md
56
README.md
@@ -2,33 +2,69 @@
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machine learning server, for image classification
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- Reads all the datasets in the folder /dataset
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- Each image is resized to the same size, configured in the config.json
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- Runs a server that allows to upload images to classify
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- Accepts PNG, JPG, JPEG
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- Each image is resized to the same size and converted to PNG type, configured in the config.json
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- For the input images, calculates the euclidean distances
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- Gets the nearest neighbour
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- Show the result, that is the label of the object in the image
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- Applyies KNN (K-Nearest Neighbours algorithm) to classify the images
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- Server returns the classification result, that is the label of the object in the image
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### Instructions
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Put dataset in /dataset directory with subdirectories, where each subdirectory contains images of one element.
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For example:
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```
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dataset/
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leopard/
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img01.png
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img02.png
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img03.png
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...
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laptop/
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img01.png
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img02.png
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...
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camera/
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img01.png
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img02.png
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...
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```
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So, we have each image and to which element category is (the name of subdirectory).
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Then, run the server:
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```
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>./galdric
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```
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Now, just need to perform petitions with new images, to get the response from the server classifying them:
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```bash
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curl -F file=@./testimage.png http://127.0.0.1:3055/image
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```
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And the server will return:
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```
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seems to be a leopard
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```
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Can perform some tests with the test.sh file:
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```
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bash test.sh
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```
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-------------
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#### Useful commands
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send file over ssh:
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```
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scp dataset.tar.gz root@51.255.193.106:/root/galdric
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scp dataset.tar.gz root@SERVERIP:/root/galdric
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```
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on the server, untar file:
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```
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tar -xvzf dataset.tar.gz
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```
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```
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26
knn.go
26
knn.go
@@ -87,14 +87,20 @@ func knn(datasets []Dataset, imgInput image.Image) string {
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k := 6
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var neighbours []Neighbour
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var neighboursED []Neighbour
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var neighboursG []Neighbour
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/*
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var neighboursG []Neighbour
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*/
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imgED := EdgeDetection(imgInput)
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imgG := Grayscale(imgInput)
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/*
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imgG := Grayscale(imgInput)
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*/
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histogram := imageToHistogram(imgInput)
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histogramED := imageToHistogram(imgED)
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histogramG := imageToHistogram(imgG)
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/*
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histogramG := imageToHistogram(imgG)
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*/
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//get a key from map dataset, the key is a label
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label := getMapKey(datasets[0])
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@@ -102,15 +108,21 @@ func knn(datasets []Dataset, imgInput image.Image) string {
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for i := 0; i < k; i++ {
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neighbours = append(neighbours, Neighbour{euclideanDist(datasets[0][label][0], histogram), label})
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neighboursED = append(neighboursED, Neighbour{euclideanDist(datasets[1][label][0], histogramED), label})
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neighboursG = append(neighboursG, Neighbour{euclideanDist(datasets[2][label][0], histogramG), label})
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/*
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neighboursG = append(neighboursG, Neighbour{euclideanDist(datasets[2][label][0], histogramG), label})
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*/
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}
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neighbours = distNeighboursFromDataset(datasets[0], neighbours, histogram)
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neighboursED = distNeighboursFromDataset(datasets[1], neighboursED, histogramED)
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neighboursG = distNeighboursFromDataset(datasets[2], neighboursG, histogramG)
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/*
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neighboursG = distNeighboursFromDataset(datasets[2], neighboursG, histogramG)
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*/
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//neighbours = append(neighbours, neighboursED...)
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neighbours = append(neighbours, neighboursG...)
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neighbours = append(neighbours, neighboursED...)
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/*
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neighbours = append(neighbours, neighboursG...)
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*/
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for i := 0; i < len(neighbours); i++ {
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fmt.Print(neighbours[i].Label + " - ")
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@@ -46,7 +46,9 @@ func readDataset(path string) []Dataset {
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var resultDatasets []Dataset
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dataset := make(Dataset)
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datasetED := make(Dataset)
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datasetG := make(Dataset)
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/*
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datasetG := make(Dataset)
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*/
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folders, _ := ioutil.ReadDir(path)
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for _, folder := range folders {
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@@ -54,7 +56,9 @@ func readDataset(path string) []Dataset {
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var imgDataset ImgDataset
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var imgDatasetED ImgDataset
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var imgDatasetG ImgDataset
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/*
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var imgDatasetG ImgDataset
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*/
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folderFiles, _ := ioutil.ReadDir(path + "/" + folder.Name())
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for _, file := range folderFiles {
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@@ -68,20 +72,26 @@ func readDataset(path string) []Dataset {
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histogramED := imageToHistogram(imageED)
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imgDatasetED = append(imgDatasetED, histogramED)
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//get the image with Grayscale filter
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imageG := Grayscale(image)
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histogramG := imageToHistogram(imageG)
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imgDatasetG = append(imgDatasetG, histogramG)
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/*
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//get the image with Grayscale filter
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imageG := Grayscale(image)
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histogramG := imageToHistogram(imageG)
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imgDatasetG = append(imgDatasetG, histogramG)
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*/
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}
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//add the foldername to the Dataset map
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dataset[folder.Name()] = imgDataset
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datasetED[folder.Name()] = imgDatasetED
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datasetG[folder.Name()] = imgDatasetG
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/*
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datasetG[folder.Name()] = imgDatasetG
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*/
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}
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resultDatasets = append(resultDatasets, dataset)
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resultDatasets = append(resultDatasets, datasetED)
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resultDatasets = append(resultDatasets, datasetG)
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/*
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resultDatasets = append(resultDatasets, datasetG)
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*/
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return resultDatasets
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}
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@@ -74,6 +74,7 @@ func NewImage(w http.ResponseWriter, r *http.Request) {
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_, handler, err := r.FormFile("file")
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check(err)
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fmt.Println(handler.Filename)
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img := readImage(handler.Filename)
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//histogram := imageToHistogram(img)
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result := knn(datasets, img)
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26
test.sh
26
test.sh
@@ -1,24 +1,24 @@
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echo "sending leopard to server"
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echo "server response:"
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curl -F file=@./leop2.jpg http://127.0.0.1:3055/image
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curl -F file=@./testleopard1.png http://127.0.0.1:3055/image
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echo ""
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echo "sending leopard to server"
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echo "server response:"
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curl -F file=@./leopardtest2.jpg http://127.0.0.1:3055/image
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curl -F file=@./testleopard2.png http://127.0.0.1:3055/image
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echo ""
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echo "sending leopard to server"
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echo "server response:"
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curl -F file=@./testleopard3.png http://127.0.0.1:3055/image
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echo ""
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echo "sending leopard to server"
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echo "server response:"
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curl -F file=@./testleopard4.png http://127.0.0.1:3055/image
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echo ""
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echo "sending laptop to server"
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echo "server response:"
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curl -F file=@./laptoptest.jpg http://127.0.0.1:3055/image
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echo ""
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echo "sending leopard to server"
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echo "server response:"
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curl -F file=@./l1.jpg http://127.0.0.1:3055/image
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echo ""
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echo "sending leopard to server"
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echo "server response:"
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curl -F file=@./l2.jpg http://127.0.0.1:3055/image
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curl -F file=@./testlaptop1.png http://127.0.0.1:3055/image
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echo ""
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