Added text detection v1

imageProcessor.js

@@ -0,0 +1,7 @@
+module.exports = function(image){
+    //greyscale
+
+    //blur to remove noise
+    //canny edges
+    //dilate
+}

index.js

@@ -7,7 +7,7 @@ const path = require('path')
 const cliProgress = require('cli-progress');
 const multer = require('multer');
 const express = require('express');
-
+const tesseract = require('node-tesseract');
 const app = express();
 
 const upload = multer({
@@ -19,8 +19,28 @@ app.post('/classify', upload.single("file"), (req, res) => {
     const tempPath = req.file.path;
     console.log(tempPath);
     classifier.predict(tempPath).then(async (result) => {
-        await fs.unlink(tempPath)
+        const readable = ['VIN', 'Data Plates', 'Dash Number', 'Door Tag'];
+        if(readable.includes(result.label)){
+            tesseract.process(path.resolve(__dirname, tempPath),{},  async (err, text) => {
+                result.processedText = text;
+                if(err) {
+                    console.error('Error:', err);
+                }
+
+                await fs.unlink(tempPath, (err) =>{ if(err) console.error('Unlink Error:', err)});
+                res.status(200).json(result);
+            })
+        } else {
+            console.log('Not readable, continuing');
+            await fs.unlink(tempPath, (err) =>{ if(err) console.error('Unlink Error:', err)});
             res.status(200).json(result);
+        }
+
+        // tesseract.process(tempPath, function(err, text) => {
+        //     console.log()
+        // })
+
+
     })
     // res.status(200).json({message: 'Success'})
 })
@@ -56,6 +76,7 @@ async function runAsync() {
         classifier = await library.create();
 
         const matches = await pGlob('training images/**/*');
+        const startTime = new Date().getTime();
         console.log(`Building model with ${matches.length} training items...`)
         const rebuildBar = new cliProgress.SingleBar({}, cliProgress.Presets.shades_grey);
         rebuildBar.start(matches.length);
@@ -73,6 +94,10 @@ async function runAsync() {
         console.log('all examples loaded...');
         console.log('saving dataset...')
         await classifier.save('./export.json');
+        const now = new Date().getTime();
+
+        const elapsed = now - startTime;
+        console.log(`Time to build: ${elapsed / 1000}s`)
     } else {
         classifier = await library.load('./export.json');
     }

package-lock.json

@@ -322,6 +322,11 @@
         "get-intrinsic": "^1.0.2"
       }
     },
+    "canny-edge-detector": {
+      "version": "1.0.0",
+      "resolved": "https://registry.npmjs.org/canny-edge-detector/-/canny-edge-detector-1.0.0.tgz",
+      "integrity": "sha512-SpewmkHDE1PbJ1/AVAcpvZKOufYpUXT0euMvhb5C4Q83Q9XEOmSXC+yR7jl3F4Ae1Ev6OtQKbFgdcPrOdHjzQg=="
+    },
     "chalk": {
       "version": "4.1.2",
       "resolved": "https://registry.npmjs.org/chalk/-/chalk-4.1.2.tgz",
@@ -818,6 +823,11 @@
         "tensorset": "^1.2.6"
       }
     },
+    "image-dilate": {
+      "version": "0.0.114",
+      "resolved": "https://registry.npmjs.org/image-dilate/-/image-dilate-0.0.114.tgz",
+      "integrity": "sha512-tsTcbWBmOWkbAHSDgIMb7CmT34+9OwoBA3rGJ+j+XTcEeZIpqOHU9Lgb2N4d5bskGRo9f5ApNvcVb9uJcTksQQ=="
+    },
     "inflight": {
       "version": "1.0.6",
       "resolved": "https://registry.npmjs.org/inflight/-/inflight-1.0.6.tgz",
@@ -982,6 +992,34 @@
         "tar": "^4.4.2"
       }
     },
+    "node-tesseract": {
+      "version": "0.2.7",
+      "resolved": "https://registry.npmjs.org/node-tesseract/-/node-tesseract-0.2.7.tgz",
+      "integrity": "sha512-RKdVdISi78iSiGSmWWF5UpeC59gjO38vLmR5J5akLrnD4AOnoji+UnaLgRP0qXng8FIEiIkW2zIfl4Lmo0Rv0Q==",
+      "requires": {
+        "glob": "^5.0.10",
+        "node-uuid": "^1.4.1"
+      },
+      "dependencies": {
+        "glob": {
+          "version": "5.0.15",
+          "resolved": "https://registry.npmjs.org/glob/-/glob-5.0.15.tgz",
+          "integrity": "sha512-c9IPMazfRITpmAAKi22dK1VKxGDX9ehhqfABDriL/lzO92xcUKEJPQHrVA/2YHSNFB4iFlykVmWvwo48nr3OxA==",
+          "requires": {
+            "inflight": "^1.0.4",
+            "inherits": "2",
+            "minimatch": "2 || 3",
+            "once": "^1.3.0",
+            "path-is-absolute": "^1.0.0"
+          }
+        }
+      }
+    },
+    "node-uuid": {
+      "version": "1.4.8",
+      "resolved": "https://registry.npmjs.org/node-uuid/-/node-uuid-1.4.8.tgz",
+      "integrity": "sha512-TkCET/3rr9mUuRp+CpO7qfgT++aAxfDRaalQhwPFzI9BY/2rCDn6OfpZOVggi1AXfTPpfkTrg5f5WQx5G1uLxA=="
+    },
     "nopt": {
       "version": "4.0.3",
       "resolved": "https://registry.npmjs.org/nopt/-/nopt-4.0.3.tgz",

package.json

@@ -9,10 +9,13 @@
   "author": "",
   "license": "ISC",
   "dependencies": {
+    "canny-edge-detector": "^1.0.0",
     "cli-progress": "^3.11.2",
     "express": "^4.18.2",
     "glob": "^8.0.3",
     "image-classifier": "^1.1.0",
-    "multer": "^1.4.5-lts.1"
+    "image-dilate": "0.0.114",
+    "multer": "^1.4.5-lts.1",
+    "node-tesseract": "^0.2.7"
   }
 }

test.py

@@ -0,0 +1,58 @@
+import cv2
+import numpy as np
+import imutils
+import pytesseract
+
+# read image from disk
+image = cv2.imread('test13.jpg')
+# make it gray
+img = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+# blur it to remove noise
+img = cv2.GaussianBlur(img, (7,7), 0)
+
+# perform edge detection, then perform a dilation + erosion to
+# close gaps in between object edges
+edged = cv2.Canny(img, 40, 90)
+dilate = cv2.dilate(edged, None, iterations=2)
+# perform erosion if necessay, it completely depends on the image
+# erode = cv2.erode(dilate, None, iterations=1)
+
+# create an empty masks
+mask = np.ones(img.shape[:2], dtype="uint8") * 255
+
+# find contours
+cnts = cv2.findContours(dilate.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)[0]
+cnts = cnts[0] if imutils.is_cv2() else cnts[1]
+
+orig = img.copy()
+for c in cnts:
+    # if the contour is not sufficiently large, ignore it
+    print('contouring...')
+    if cv2.contourArea(c) < 300:
+        cv2.drawContours(mask, [c], -1, 0, -1)
+    
+    x,y,w,h = cv2.boundingRect(c)
+    
+    # filter more contours if nessesary
+    if(w>h):
+        cv2.drawContours(mask, [c], -1, 0, -1)
+print('contourint done');
+newimage = cv2.bitwise_and(dilate.copy(), dilate.copy(), mask=mask)
+img2 = cv2.dilate(newimage, None, iterations=3)
+ret2,th1 = cv2.threshold(img2 ,0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)
+
+# Tesseract OCR on the image
+temp = pytesseract.image_to_string(th1)
+print(temp)
+# Write results on the image
+cv2.putText(image, temp, (100,100), cv2.FONT_HERSHEY_SIMPLEX, 1.8, (0,255,255), 3)
+
+# show the outputs
+cv2.imshow('Original image', cv2.resize(image,(640,480)))
+cv2.imshow('Dilated', cv2.resize(dilate,(640,480)))
+cv2.imshow('New Image', cv2.resize(newimage,(640,480)))
+cv2.imshow('Inverted Threshold', cv2.resize(th1,(640,480)))
+
+cv2.waitKey(0)
+cv2.destroyAllWindows()
+