How to detect objects in real time by using Jupyter Notebook and Neural Networks , by using Yolo3

Last update: Sep 28, 2022

Overview

Real Time Object Recognition From your Screen Desktop .

In this post, I will explain how to build a simply program to detect objects from you desktop computer.

We will see how using OpenCV and Python, we can detect objects by applying the most popular YOLO(You Look Only Once) algorithm.

OpenCV is the computer vision library/ framework that we we will be using to support our YOLOv3 algorithm

Darknet Architecture is pre-trained model for classifying 80 different classes. Our goal now is that we will use Darknet(YOLOv3) in OpenCV to classify objects using Python language.

For this project we will consider an standard resolution 1920 x 1080 , in windows 10 in Display Setting , select the resolution 1920 x 1080

Then you need to install Anaconda at this link

After you install it , check that your terminal , recognize conda

C:\conda --version
conda 4.10.3

The environments supported that I will consider is Python 3.7, Keras 2.4.3 and TensorFlow 2.4.0, let us create the environment, go to you command promt terminal and type the following:

conda create -n detector python==3.7.10
conda activate detector

then in your terminal type the following commands:

conda install ipykernel
Proceed ([y]/n)? y

python -m ipykernel install --user --name detector --display-name "Python (Object Detector)"

Then we install the correct versions of the the Tensorflow, and Numpy and Keras

we create a file called requirements.txt

if your are in Windows

notepad requirements.txt

or Linux

nano  requirements.txt

and you paste the following lines

Keras==2.4.3
keras-resnet==0.2.0
numpy==1.19.3
opencv-python==3.4.2.17
tensorflow==2.4.0
tensorflow-estimator==2.4.0
tensorflow-gpu==2.4.0
Pillow==9.0.0

and then we return back to the terminal and install them

pip install -r requirements.txt

then open the Jupyter notebook with the command

jupyter notebook&

then you click create new notebook Python (Object Detector) and then you can test if you can import the the following libraries

import numpy as np
from PIL import ImageGrab
import cv2
import time
import win32gui, win32ui, win32con, win32api

The next step is is define a function that enable record you screen

def grab_screen(region=None):
    hwin = win32gui.GetDesktopWindow()
    if region:
            left,top,x2,y2 = region
            width = x2 - left + 1
            height = y2 - top + 1
    else:
        width = win32api.GetSystemMetrics(win32con.SM_CXVIRTUALSCREEN)
        height = win32api.GetSystemMetrics(win32con.SM_CYVIRTUALSCREEN)
        left = win32api.GetSystemMetrics(win32con.SM_XVIRTUALSCREEN)
        top = win32api.GetSystemMetrics(win32con.SM_YVIRTUALSCREEN)
    hwindc = win32gui.GetWindowDC(hwin)
    srcdc = win32ui.CreateDCFromHandle(hwindc)
    memdc = srcdc.CreateCompatibleDC()
    bmp = win32ui.CreateBitmap()
    bmp.CreateCompatibleBitmap(srcdc, width, height)
    memdc.SelectObject(bmp)
    memdc.BitBlt((0, 0), (width, height), srcdc, (left, top), win32con.SRCCOPY)
    signedIntsArray = bmp.GetBitmapBits(True)
    img = np.fromstring(signedIntsArray, dtype='uint8')
    img.shape = (height,width,4)
    srcdc.DeleteDC()
    memdc.DeleteDC()
    win32gui.ReleaseDC(hwin, hwindc)
    win32gui.DeleteObject(bmp.GetHandle())
    return cv2.cvtColor(img, cv2.COLOR_BGRA2RGB)

then you define a new function called main() which will record your screen

def main():
    last_time = time.time()
    while True:
        # 1920 windowed mode
        screen = grab_screen(region=(0,40,1920,1120))
        img = cv2.resize(screen,None,fx=0.4,fy=0.3)
        height,width,channels = img.shape
        #detecting objects
        blob = cv2.dnn.blobFromImage(img,0.00392,(416,416),(0,0,0),True,crop=False)
        net.setInput(blob)
        outs = net.forward(outputlayers)
        #Showing info on screen/ get confidence score of algorithm in detecting an object in blob
        class_ids=[]
        confidences=[]
        boxes=[]
        for out in outs:
            for detection in out:
                scores = detection[5:]
                class_id = np.argmax(scores)
                confidence = scores[class_id]
                if confidence > 0.5:
                    #onject detected
                    center_x= int(detection[0]*width)
                    center_y= int(detection[1]*height)
                    w = int(detection[2]*width)
                    h = int(detection[3]*height)
                    #rectangle co-ordinaters
                    x=int(center_x - w/2)
                    y=int(center_y - h/2)
                    boxes.append([x,y,w,h]) #put all rectangle areas
                    confidences.append(float(confidence)) #how confidence was that object detected and show that percentage
                    class_ids.append(class_id) #name of the object tha was detected
        indexes = cv2.dnn.NMSBoxes(boxes,confidences,0.4,0.6)
        font = cv2.FONT_HERSHEY_PLAIN
        for i in range(len(boxes)):
            if i in indexes:
                x,y,w,h = boxes[i]
                label = str(classes[class_ids[i]])
                color = colors[i]
                cv2.rectangle(img,(x,y),(x+w,y+h),color,2)
                cv2.putText(img,label,(x,y+30),font,1,(255,255,255),2)
        #print('Frame took {} seconds'.format(time.time()-last_time))
        last_time = time.time()
        cv2.imshow('window', img)
        if cv2.waitKey(25) & 0xFF == ord('q'):
            cv2.destroyAllWindows()
            break

and finally we download the following files

yolo.cfg (Download from here) — Configuration file
yolo.weights (Download from here) — pre-trained weights
coco.names (Download from here)- 80 classes names

then you add the following code

net = cv2.dnn.readNetFromDarknet('yolov3.cfg', 'yolov3.weights')
classes = []
with open("coco.names","r") as f:
    classes = [line.strip() for line in f.readlines()]
    
layer_names = net.getLayerNames()
outputlayers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
colors= np.random.uniform(0,255,size=(len(classes),3))

and finally you just run it with the simple code

main()

you can stop with simple press q

for example you want to identiy a Youtube video, of one beautiful girl

or this video https://youtu.be/QW-qWS3StZg?t=170

or the classic traffic recognition https://youtu.be/7HaJArMDKgI

How to detect objects in real time by using Jupyter Notebook and Neural Networks , by using Yolo3

Related tags

Overview

Real Time Object Recognition From your Screen Desktop .

Owner

Ruslan Magana Vsevolodovna

Image augmentation for machine learning experiments.

Official code for "Bridging Video-text Retrieval with Multiple Choice Questions", CVPR 2022 (Oral).

The code for CVPR2022 paper "Likert Scoring with Grade Decoupling for Long-term Action Assessment".

RRD: Rotation-Sensitive Regression for Oriented Scene Text Detection

Introduction to Augmented Reality (AR) with Python 3 and OpenCV 4.2.

A facial recognition device is a device that takes an image or a video of a human face and compares it to another image faces in a database.

Train custom VR face tracking parameters

Let's explore how we can extract text from forms

Python library to extract tabular data from images and scanned PDFs

Papers, Datasets, Algorithms, SOTA for STR. Long-time Maintaining

A version of nrsc5-gui that merges the interface developed by cmnybo with the architecture developed by zefie in order to start a new baseline that is not heavily dependent upon Python processing.

TextField: Learning A Deep Direction Field for Irregular Scene Text Detection (TIP 2019)

Pure Javascript OCR for more than 100 Languages 📖🎉🖥

Python tool that takes the OCR.space JSON output as input and draws a text overlay on top of the image.

Face Detection with DLIB

governance proposal to make fei redeemable for eth

FastOCR is a desktop application for OCR API.

Controlling the computer volume with your hands // OpenCV

A curated list of papers and resources for scene text detection and recognition

A tensorflow implementation of EAST text detector