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main.py
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main.py
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import cv2
thres = 0.45 # Threshold to detect object
cap = cv2.VideoCapture(0)
cap.set(3, 1280)
cap.set(4, 720)
cap.set(10, 70)
classNames = []
classFile = 'coco.names'
with open(classFile,'rt') as f:
classNames = [line.rstrip() for line in f]
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)
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)
object = classNames[classId - 1].upper()
if object == "PERSON":
print(object)
else:
print("NO PERSON DETECTED")