In this project, I am using OpenCV and Mobile Net SSD to detect objects. The Object Detection method used is a sweet balance between speed and accuracy. Unlike many other object detection methods, I am able to run this in real time with a good amount of accuracy.
import cv2
thres = 0.45 # Threshold to detect object
cap = cv2.VideoCapture(1)
cap.set(3,1280)
cap.set(4,720)
cap.set(10,70)
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’
weightsPath = ‘frozen_inference_graph.pb’
net = cv2.dnn_DetectionModel(weightsPath,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=thres)
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),thickness=2)
cv2.putText(img,classNames[classId-1].upper(),(box[0]+10,box[1]+30),
cv2.FONT_HERSHEY_COMPLEX,1,(0,255,0),2)
cv2.putText(img,str(round(confidence*100,2)),(box[0]+200,box[1]+30),
cv2.FONT_HERSHEY_COMPLEX,1,(0,255,0),2)
cv2.imshow(“Output”,img)
cv2.waitKey(1)
