C:\conda --version
conda 4.10.3

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

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

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

notepad requirements.txt

nano  requirements.txt

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

pip install -r requirements.txt

jupyter notebook&

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

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)

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

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))

main()