Detector.py

import cv2

import numpy as np 
import time

np.random.seed(20)    #if this is used then single color generate
class Detector:
    def __init__(self, videoPath, configPath, modelPath, classesPath):
        self.videoPath = videoPath
        self.configPath = configPath
        self.modelPath = modelPath
        self.classesPath = classesPath

        self.net = cv2.dnn_DetectionModel(self.modelPath, self.configPath)
        self.net.setInputSize(320, 320)
        self.net.setInputScale(1.0 / 127.5)
        self.net.setInputMean((127.5, 127.5, 127.5))
        self.net.setInputSwapRB(True)

        self.readClasses()

    def readClasses(self):
        with open(self.classesPath, 'r') as f:
            self.classesList = f.read().splitlines()

        self.classesList.insert(0, '__Background__')

        self.colorList=np.random.uniform(low=0,high=255,size=(len(self.classesList), 3))

        # print(self.classesList)
    
    def onVideo(self):
        cap=cv2.VideoCapture(self.videoPath)

        if (cap.isOpened()==False):
            print("Error opening file..")
            return
        (success, image)=cap.read()

        startTime=0

        while success:
            currentTime=time.time()
            fps=1/(currentTime-startTime)
            startTime=currentTime
            classsLabelIDs,confidences,bboxs =self.net.detect(image,confThreshold=0.4)

            bboxs=list(bboxs)
            confidences=list(np.array(confidences).reshape(1,-1)[0])
            confidences=list(map(float,confidences))

            bboxIdx=cv2.dnn.NMSBoxes(bboxs, confidences, score_threshold=0.5, nms_threshold=0.2)

            if len(bboxIdx) != 0:
                for i in range(0,len(bboxIdx)):

                    bbox=bboxs[np.squeeze(bboxIdx[i])]
                    classConfidences=confidences[np.squeeze(bboxIdx[i])]
                    classsLabelID=np.squeeze(classsLabelIDs[np.squeeze(bboxIdx[i])])
                    classLabel=self.classesList[classsLabelID]
                    classColor=[int(c) for c in self.colorList[classsLabelID]]

                    displayText="{}:{:.2f}".format(classLabel,classConfidences)

                    x,y,w,h=bbox

                    cv2.rectangle(image,(x,y),(x+w, y+h), color=classColor, thickness=1)
                    cv2.putText(image,displayText,(x,y-10),cv2.FONT_HERSHEY_PLAIN,1,classColor,2)


                    ######################### use for line in corner
                    lineWidth=min(int(w*0.3),int(h*0.3))
                    cv2.line(image,(x,y),(x+lineWidth, y), classColor, thickness=5)
                    cv2.line(image,(x,y),(x, y+lineWidth), classColor, thickness=5)

                    cv2.line(image,(x+w,y),(x+w-lineWidth, y), classColor, thickness=5)
                    cv2.line(image,(x+w,y),(x+w, y+lineWidth), classColor, thickness=5)

                    ##########################

                    cv2.line(image,(x,y+h),(x+lineWidth, y+h), classColor, thickness=5)
                    cv2.line(image,(x,y+h),(x, y+h-lineWidth), classColor, thickness=5)

                    cv2.line(image,(x+w,y+h),(x+w-lineWidth, y+h), classColor, thickness=5)
                    cv2.line(image,(x+w,y+h),(x+w, y+h-lineWidth), classColor, thickness=5)

            cv2.putText(image,"FPS: "+str(int(fps)),(20,70),cv2.FONT_HERSHEY_PLAIN,2,(0,255,0),2)
            cv2.imshow("Result",image)

            key=cv2.waitKey(1) & 0xFF
            if key==ord("q"):
                break

            (success,image)=cap.read()
        cv2.destroyAllWindows()