This project uses Template Matching technique for object detecting by detection of template image over base image.

Overview

Object Detection Project Using OpenCV

projectLogo

This project uses Template Matching technique for object detecting by detection the template image over base image.

REQUIREMENTS

  • Python python  

  • OpenCV   

pip install opencv-python
pip install Tkinter

📝 CODE EXPLANATION

Importing Differnt Libraries
import cv2
import tkinter as tk 
from tkinter import filedialog,messagebox
import os
import sys

Taking Image input using Tkinter

Base Image Input Template Image Input
Base Image Input Template Image Input

Taking User Input using TKinter

root = tk.Tk() 
root.withdraw() 
file_path_base = filedialog.askopenfilename(initialdir= os.getcwd(),title="Select Base Image: ")
file_path_temp= filedialog.askopenfilename(initialdir= os.getcwd(),title="Select Template Image: ")

Loading base image and template image using cv2.imread()

Input Image Template Image Result Image
Input Image
Template Image
Result Image
Input Image
Template Image
Result Image
Input Image
Template Image
Result Image
Input Image
Template Image
Result Image
try:
    img = cv2.imread(file_path_base)

cv2.cvtColor()method is used to convert an image from one color space to another. There are more than 150 color-space conversion methods available in OpenCV.

Syntax: cv2.cvtColor(image, code, dst, dstCn)

    img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    template = cv2.imread(file_path_temp,0)

Getting the height and width of the template image using .shape method.

    h ,w = template.shape

Error dialogue box using Tkinter

error

except cv2.error:
   messagebox.showinfo("Warning!","No Image Found!")
   sys.exit(0)

cv2.matchTemplate is used to comapare images. It gives a 2D-array as output.

match = cv2.matchTemplate(img_gray,template,cv2.TM_CCOEFF_NORMED)
threshold = 0.99

cv2.minMaxLoc returns the top-left corner of the template position for the best match.

min_val, max_val, min_location, max_location = cv2.minMaxLoc(match)
location = max_location
font = cv2.FONT_HERSHEY_PLAIN

cv2.rectangle() method is used to draw a rectangle on any image.

Syntax: cv2.rectangle(image, start_point, end_point, color, thickness)

cv2.rectangle(img, location, (location[0] + w, location[1] + h), (0,0,255), 2)

cv2.putText() method is used to draw a text string on any image.

Syntax: cv2.putText(image, text, start_point, font, fontScale, color, thickness, lineType, bottomLeftOrigin)

cv2.putText(img,"Object Spotted.", (location[0]-40,location[1]-5),font , 1, (0,0,0),2)

  • cv2.imwrite() method is used to save an image to any storage device. This will save the image according to the specified format in current working directory.
  • cv2.imshow method is used to display an image in a window. The window automatically fits to the image size.

Syntax: cv2.imwrite(filename, image)

Syntax: cv2.imshow(window_name, image)

cv2.imwrite('images/result.jpg',img)
cv2.imshow('Results.jpg',img)

cv2.waitkey() allows you to wait for a specific time in milliseconds until you press any button on the keyword.

cv2.waitKey(0)

cv2.destroyAllWindows() method destroys all windows whenever any key is pressed.

cv2.destroyAllWindows()

📬 Contact

If you want to contact me, you can reach me through below handles.

@prrthamm   Pratham Bhatnagar

Owner
Pratham Bhatnagar
Computer Science Engineering student at SRM University. || Blockchain || ML Enthusiast || Open Source || Team member @srm-kzilla || Associate @NextTechLab
Pratham Bhatnagar
End-to-end face detection, cropping, norm estimation, and landmark detection in a single onnx model

onnx-facial-lmk-detector End-to-end face detection, cropping, norm estimation, and landmark detection in a single onnx model, model.onnx. Demo You can

atksh 42 Dec 30, 2022
Hardware-accelerated DNN model inference ROS2 packages using NVIDIA Triton/TensorRT for both Jetson and x86_64 with CUDA-capable GPU

Isaac ROS DNN Inference Overview This repository provides two NVIDIA GPU-accelerated ROS2 nodes that perform deep learning inference using custom mode

NVIDIA Isaac ROS 62 Dec 14, 2022
Supplemental Code for "ImpressionNet :A Multi view Approach to Predict Socio Facial Impressions"

Supplemental Code for "ImpressionNet :A Multi view Approach to Predict Socio Facial Impressions" Environment requirement This code is based on Python

Rohan Kumar Gupta 1 Dec 19, 2021
Single Image Super-Resolution (SISR) with SRResNet, EDSR and SRGAN

Single Image Super-Resolution (SISR) with SRResNet, EDSR and SRGAN Introduction Image super-resolution (SR) is the process of recovering high-resoluti

8 Apr 15, 2022
Cleaned test data list of DukeMTMC-reID, ICCV2021

Cleaned DukeMTMC-reID Cleaned data list of DukeMTMC-reID released with our paper accepted by ICCV 2021: Learning Instance-level Spatial-Temporal Patte

14 Feb 19, 2022
CoRe: Contrastive Recurrent State-Space Models

CoRe: Contrastive Recurrent State-Space Models This code implements the CoRe model and reproduces experimental results found in Robust Robotic Control

Apple 21 Aug 11, 2022
Public Implementation of ChIRo from "Learning 3D Representations of Molecular Chirality with Invariance to Bond Rotations"

Learning 3D Representations of Molecular Chirality with Invariance to Bond Rotations This directory contains the model architectures and experimental

35 Dec 05, 2022
Learning to Estimate Hidden Motions with Global Motion Aggregation

Learning to Estimate Hidden Motions with Global Motion Aggregation (GMA) This repository contains the source code for our paper: Learning to Estimate

Shihao Jiang (Zac) 221 Dec 18, 2022
harmonic-percussive-residual separation algorithm wrapped as a VST3 plugin (iPlug2)

Harmonic-percussive-residual separation plug-in This work is a study on the plausibility of a sines-transients-noise decomposition inspired algorithm

Derp Learning 9 Sep 01, 2022
People movement type classifier with YOLOv4 detection and SORT tracking.

Movement classification The goal of this project would be movement classification of people, in other words, walking (normal and fast) and running. Yo

4 Sep 21, 2021
Hybrid Neural Fusion for Full-frame Video Stabilization

FuSta: Hybrid Neural Fusion for Full-frame Video Stabilization Project Page | Video | Paper | Google Colab Setup Setup environment for [Yu and Ramamoo

Yu-Lun Liu 430 Jan 04, 2023
Implementation of the HMAX model of vision in PyTorch

PyTorch implementation of HMAX PyTorch implementation of the HMAX model that closely follows that of the MATLAB implementation of The Laboratory for C

Marijn van Vliet 52 Oct 13, 2022
Highly comparative time-series analysis

〰️ hctsa 〰️ : highly comparative time-series analysis hctsa is a software package for running highly comparative time-series analysis using Matlab (fu

Ben Fulcher 569 Dec 21, 2022
OpenCVのGrabCut()を利用したセマンティックセグメンテーション向けアノテーションツール(Annotation tool using GrabCut() of OpenCV. It can be used to create datasets for semantic segmentation.)

[Japanese/English] GrabCut-Annotation-Tool GrabCut-Annotation-Tool.mp4 OpenCVのGrabCut()を利用したアノテーションツールです。 セマンティックセグメンテーション向けのデータセット作成にご使用いただけます。 ※Grab

KazuhitoTakahashi 30 Nov 18, 2022
Faster RCNN pytorch windows

Faster-RCNN-pytorch-windows Faster RCNN implementation with pytorch for windows Open cmd, compile this comands: cd lib python setup.py build develop T

Hwa-Rang Kim 1 Nov 11, 2022
IndoNLI: A Natural Language Inference Dataset for Indonesian

IndoNLI: A Natural Language Inference Dataset for Indonesian This is a repository for data and code accompanying our EMNLP 2021 paper "IndoNLI: A Natu

15 Feb 10, 2022
A Python library for working with arbitrary-dimension hypercomplex numbers following the Cayley-Dickson construction of algebras.

Hypercomplex A Python library for working with quaternions, octonions, sedenions, and beyond following the Cayley-Dickson construction of hypercomplex

7 Nov 04, 2022
MANO hand model porting for the GraspIt simulator

Learning Joint Reconstruction of Hands and Manipulated Objects - ManoGrasp Porting the MANO hand model to GraspIt! simulator Yana Hasson, Gül Varol, D

Lucas Wohlhart 10 Feb 08, 2022
PICARD - Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models

This is the official implementation of the following paper: Torsten Scholak, Nathan Schucher, Dzmitry Bahdanau. PICARD - Parsing Incrementally for Con

ElementAI 217 Jan 01, 2023
Speech Enhancement Generative Adversarial Network Based on Asymmetric AutoEncoder

ASEGAN: Speech Enhancement Generative Adversarial Network Based on Asymmetric AutoEncoder 中文版简介 Readme with English Version 介绍 基于SEGAN模型的改进版本,使用自主设计的非

Nitin 53 Nov 17, 2022