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Project #171

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9a88b9d
detect the Car Number and fetch the owner details
Abdullah-khan399 Aug 31, 2021
c3a96f9
README.md
Abdullah-khan399 Aug 31, 2021
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234 changes: 234 additions & 0 deletions dev_New_project/LiveCarNumberPlateDetector.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"05NB1786\n",
"KA95NB1786\n",
"KA95N81786\n",
"KA05NB1786\n"
]
}
],
"source": [
"import cv2\n",
"import numpy as np\n",
"from imutils.object_detection import non_max_suppression\n",
"\n",
"## ----------- Load Pre-trained Models ------------\n",
"model = cv2.dnn.readNet('frozen_east_text_detection.pb')\n",
"model1 = cv2.dnn.readNet('crnn.onnx')\n",
"\n",
"def most_likely(scores, char_set):\n",
" text = \"\"\n",
" for i in range(scores.shape[0]):\n",
" c = np.argmax(scores[i][0])\n",
" text += char_set[c]\n",
" return text\n",
"\n",
"def map_rule(text):\n",
" char_list = []\n",
" for i in range(len(text)):\n",
" if i == 0:\n",
" if text[i] != '-':\n",
" char_list.append(text[i])\n",
" else:\n",
" if text[i] != '-' and (not (text[i] == text[i - 1])):\n",
" char_list.append(text[i])\n",
" return ''.join(char_list)\n",
"\n",
"def best_path(scores, char_set):\n",
" text = most_likely(scores, char_set)\n",
" final_text = map_rule(text)\n",
" return final_text\n",
"\n",
"alphabet_set = \"0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ\"\n",
"blank = '-'\n",
"\n",
"char_set = blank + alphabet_set\n",
"\n",
"def mainfn():\n",
" #img = cv2.imread(\"kia-seltos-car-number-plate-designs.jpeg\")\n",
" img = cv2.imread(\"pic.jpg\")\n",
" # use multiple of 32 to set the new img shape\n",
" height, width, _ = img.shape\n",
" new_height = (height//32)*32\n",
" new_width = (width//32)*32\n",
"\n",
" # get the ratio change in width and height\n",
" h_ratio = height/new_height\n",
" w_ratio = width/new_width\n",
"\n",
" blob = cv2.dnn.blobFromImage(img,1.0, (new_width, new_height), (123.68, 116.78, 103.94), True, False)\n",
"\n",
" model.setInput(blob)\n",
" (geometry, scores) = model.forward(model.getUnconnectedOutLayersNames())\n",
"\n",
" rectangles = []\n",
" confidence_score = []\n",
" for i in range(geometry.shape[2]):\n",
" for j in range(0, geometry.shape[3]):\n",
"\n",
" if scores[0][0][i][j] < 0.1:\n",
" continue\n",
"\n",
" bottom_x = int(j*4 + geometry[0][1][i][j])\n",
" bottom_y = int(i*4 + geometry[0][2][i][j])\n",
"\n",
"\n",
" top_x = int(j*4 - geometry[0][3][i][j])\n",
" top_y = int(i*4 - geometry[0][0][i][j])\n",
"\n",
" rectangles.append((top_x, top_y, bottom_x, bottom_y))\n",
" confidence_score.append(float(scores[0][0][i][j]))\n",
"\n",
" # use Non-max suppression to get the required rectangles\n",
" fin_boxes = non_max_suppression(np.array(rectangles), probs=confidence_score, overlapThresh=0.5)\n",
"\n",
" img_copy = img.copy()\n",
" chara = {}\n",
" for (x1, y1, x2, y2) in fin_boxes:\n",
"\n",
" x1 = int(x1 * w_ratio)\n",
" y1 = int(y1 * h_ratio)\n",
" x2 = int(x2 * w_ratio)\n",
" y2 = int(y2 * h_ratio)\n",
" \n",
" segment = img[y1:y2, x1:x2, :]\n",
" #segment = img[y1:y2, x1:x2, :]\n",
" segment_gray = cv2.cvtColor(segment, cv2.COLOR_BGR2GRAY)\n",
" \n",
" blob = cv2.dnn.blobFromImage(segment_gray, scalefactor=1/127.5, size=(100,32), mean=127.5)\n",
" model1.setInput(blob)\n",
" scores = model1.forward()\n",
" text = best_path(scores, char_set)\n",
" chara[x1] = text\n",
" #print(text)\n",
" \n",
" cv2.rectangle(img_copy, (x1, y1), (x2, y2), (0, 255, 0), 2)\n",
" cv2.putText(img_copy, text.strip(), (x1,y1-2), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0,0,255),2)\n",
"\n",
" final=[]\n",
" for j in sorted(chara):\n",
" final.append(chara[j])\n",
" #print(final)\n",
" s = \"\"\n",
" s = s.join(final)\n",
" return s\n",
"\n",
"## ---------- Performing OCR in live video ----------\n",
"\n",
"cap = cv2.VideoCapture(0)\n",
"count=0\n",
"while cap.isOpened():\n",
" \n",
" count=count+1\n",
" ret, img = cap.read()\n",
" if count == 10:\n",
" cv2.imwrite(\"pic.jpg\", img)\n",
" #print(\"pic clicked\")\n",
" output_val = mainfn()\n",
" print(output_val) \n",
" #break\n",
" count=0\n",
" # use multiple of 32 to set the new img shape\n",
" height, width, _ = img.shape\n",
" new_height = (height//32)*32\n",
" new_width = (width//32)*32\n",
"\n",
" # get the ratio change in width and height\n",
" h_ratio = height/new_height\n",
" w_ratio = width/new_width\n",
"\n",
" blob = cv2.dnn.blobFromImage(img,1.0, (new_width, new_height), (123.68, 116.78, 103.94), True, False)\n",
"\n",
" model.setInput(blob)\n",
" (geometry, scores) = model.forward(model.getUnconnectedOutLayersNames())\n",
"\n",
" rectangles = []\n",
" confidence_score = []\n",
" for i in range(geometry.shape[2]):\n",
" for j in range(0, geometry.shape[3]):\n",
"\n",
" if scores[0][0][i][j] < 0.1:\n",
" continue\n",
"\n",
" bottom_x = int(j*4 + geometry[0][1][i][j])\n",
" bottom_y = int(i*4 + geometry[0][2][i][j])\n",
"\n",
"\n",
" top_x = int(j*4 - geometry[0][3][i][j])\n",
" top_y = int(i*4 - geometry[0][0][i][j])\n",
"\n",
" rectangles.append((top_x, top_y, bottom_x, bottom_y))\n",
" confidence_score.append(float(scores[0][0][i][j]))\n",
"\n",
" # use Non-max suppression to get the required rectangles\n",
" fin_boxes = non_max_suppression(np.array(rectangles), probs=confidence_score, overlapThresh=0.5)\n",
"\n",
" img_copy = img.copy()\n",
" for (x1, y1, x2, y2) in fin_boxes:\n",
"\n",
" x1 = int(x1 * w_ratio)\n",
" y1 = int(y1 * h_ratio)\n",
" x2 = int(x2 * w_ratio)\n",
" y2 = int(y2 * h_ratio)\n",
" \n",
" #segment = img[y1:y2+4, x1:x2+2, :]\n",
" segment = img[y1:y2, x1:x2, :]\n",
" segment_gray = cv2.cvtColor(segment, cv2.COLOR_BGR2GRAY)\n",
" \n",
" blob = cv2.dnn.blobFromImage(segment_gray, scalefactor=1/127.5, size=(100,32), mean=127.5)\n",
" model1.setInput(blob)\n",
" scores = model1.forward()\n",
" text = best_path(scores, char_set)\n",
" #print(text)\n",
"\n",
" cv2.rectangle(img_copy, (x1, y1), (x2, y2), (0, 255, 0), 2)\n",
" cv2.putText(img_copy, text.strip(), (x1,y1-2), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0,0,255),2)\n",
"\n",
" cv2.imshow(\"Text Detection\", img_copy)\n",
" if cv2.waitKey(1) == 113:\n",
" break\n",
"\n",
"cap.release()\n",
"cv2.destroyAllWindows() \n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
1 change: 1 addition & 0 deletions dev_New_project/README.md
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All about project
27 changes: 27 additions & 0 deletions dev_New_project/cgi-bin/api/main.py
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import requests
import json

BASE = 'http://127.0.0.1:5000/'

data = [{'Registration': 'MH01AV8866',
'Owner': 'M//S PANDURONGA TIMBLO INDUSTRIES',
'Maker': 'JAGUAR LAND ROVER INDIA LIMITED/ JAGUAR(XF 5.0LV8',
'Vehicle': 'MOTOR CAR (L)',
'Fuel_Type': 'PETROL',
'Chassis': 'SAJAAC07P8BLRXXXXX',
'Engine_Number': '10052323430XXXXX',
'Registration_Date': '22-FEB-2011',
'Insurance_Valid_Upto': '17-FEB-2012'
}

]

for i in range(len(data)):
response = requests.put(BASE + "vehicle/" + data[i]['Registration'], data[i])
print(response.json())

input()

response = requests.get(BASE + "vehicle/"+ data[0]['Registration'])

print(json.dumps(response.json(), indent=1))
93 changes: 93 additions & 0 deletions dev_New_project/cgi-bin/api/rto-api.py
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'''
Install all these packages with pip
Flask==1.1.2
Flask-RESTful==0.3.8
Flask-SQLAlchemy==2.4.3
Jinja2==2.11.2
SQLAlchemy==1.3.18
'''

from flask import Flask
from flask_restful import Api, Resource, reqparse, abort, fields, marshal_with
from flask_sqlalchemy import SQLAlchemy


app = Flask(__name__)
#Restful
api = Api(app)
# Creating/loading sqlite data base. Can be treated as fake database fo RTO
app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///rto.db'
db = SQLAlchemy(app)

# Adding the columns to the model.
class RtoModel(db.Model):
Registration = db.Column(db.String(64), primary_key=True)
Owner = db.Column(db.String(64), nullable=False)
Maker = db.Column(db.String(64), nullable=False)
Vehicle = db.Column(db.String(64), nullable=False)
Fuel_Type = db.Column(db.String(64), nullable=False)
Chassis = db.Column(db.String(64), nullable=False)
Engine_Number = db.Column(db.String(64), nullable=False)
Registration_Date = db.Column(db.String(64), nullable=False)
Insurance_Valid_Upto = db.Column(db.String(64), nullable=False)

def __repr__(self):
return f"RTO( Registration={Registration}, Owner={Owner}, Maker={Maker}, Vehicle={Vehicle}, Fuel_Type={Fuel_Type}, Chassis={Chassis}, Engine_Number={Engine_Number}, Registration_Date={Registration_Date}, Insurance_Valid_Upto={Insurance_Valid_Upto})"


# do the below thing only once for creating the table.
#db.create_all()

video_put_args = reqparse.RequestParser()
video_put_args.add_argument('Registration', type=str, help='Registration number is required', required=True)
video_put_args.add_argument('Owner', type=str, help="Owner's Name is required", required=True)
video_put_args.add_argument('Maker', type=str, help='Maker details are required', required=True)
video_put_args.add_argument('Vehicle', type=str, help='Vehicle type is required', required=True)
video_put_args.add_argument('Fuel_Type', type=str, help='Type of Fuel is required', required=True)
video_put_args.add_argument('Chassis', type=str, help='Chassis Number is required', required=True)
video_put_args.add_argument('Engine_Number', type=str, help='Engine Number is required', required=True)
video_put_args.add_argument('Registration_Date', type=str, help='Registration Date is required', required=True)
video_put_args.add_argument('Insurance_Valid_Upto', type=str, help='Insurance Due Date is required', required=True)


Resource_fields = {
'Registration' : fields.String,
'Owner': fields.String,
'Maker': fields.String,
'Vehicle': fields.String,
'Fuel_Type': fields.String,
'Chassis': fields.String,
'Engine_Number': fields.String,
'Registration_Date': fields.String,
'Insurance_Valid_Upto': fields.String
}


class RTO(Resource):
@marshal_with(Resource_fields)
def get(self, vehicle_number):
result = RtoModel.query.filter_by(Registration=vehicle_number).first()
if not result:
abort(404, message="Vahicle details Not found...")
return result

@marshal_with(Resource_fields)
def put(self, vehicle_number):
args = video_put_args.parse_args()
result = RtoModel.query.filter_by(Registration=vehicle_number).first()
if result:
abort(409, message="Vehicle already exists...")
vehicle = RtoModel(Registration=vehicle_number, Owner=args['Owner'], Maker=args['Maker'], Vehicle=args['Vehicle'], Fuel_Type=args['Fuel_Type'], Chassis=args['Chassis'], Engine_Number=args['Engine_Number'], Registration_Date=args['Registration_Date'], Insurance_Valid_Upto=args['Insurance_Valid_Upto'])
db.session.add(vehicle)
db.session.commit()
return vehicle , 201

def delete(self, video_id):
return "", 204

api.add_resource(RTO, "/vehicle/<string:vehicle_number>")



if __name__ == '__main__':
app.run(debug=True)
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19 changes: 19 additions & 0 deletions dev_New_project/cgi-bin/get-info.py
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#!/usr/bin/python3

print("content-type: text/html")
print()

import cgi
import requests
import json

BASE = 'http://127.0.0.1:5000/'

field = cgi.FieldStorage()
vehicle_number = field.getvalue("vehicle_number")


response = requests.get(BASE + "vehicle/" + vehicle_number)
responseJson = json.dumps(response.json(), indent=1)

print(responseJson.replace("{", "").replace("}", ""))
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