import cv2

# img = cv2.imread('person.jpg')
cap = cv2.VideoCapture(0)
cap.set(3,640)
cap.set(4,480)

classNames = []
classFile = 'coco.names'
with open(classFile,'rt') as f:
    classNames= f.read().rstrip('\n').split('\n')

configPath = 'ssd_mobilenet_v3_large_coco_2020_01_14.pbtxt'
weightPath = 'frozen_inference_graph.pb'

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

while True:
    success , img = cap.read()
    classIds, confs , bbox = net.detect(img,confThreshold=0.5)
    print(classIds,bbox )

    if len(classIds) !=0:
        for classId , confidence, box in zip(classIds.flatten(),confs.flatten(),bbox):
            # cv2.rectangle(img,box,color=(0,255,0))
            print(box)
            cv2.rectangle(img,box,color=(0,255,0),thickness=2)
            cv2.putText(img,classNames[classId-1].upper(),(box[0]+10,box[1]+30),cv2.FONT_HERSHEY_COMPLEX,1,(255,0,0),2)
            cv2.putText(img, str(round(confidence*100,1)), (box[0]+200,box[1]+30), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 0, 0), 2)
            
    cv2.imshow("Output",img)
    cv2.waitKey(1)
    
    if(cv2.waitKey(1) & 0xff == ord('q')):
        break

cap.release()
cv2.destroyAllWindows()
