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main.py
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main.py
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import cv2
#img = cv2.imread('mine.jpg')
cap = cv2.VideoCapture(1)
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'
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=0.5)
print(classIds, bbox)
if len(classIds) !=0:
for classIds, confidence, box in zip(classIds.flatten(),confs.flatten(), bbox):
cv2.rectangle(img, box, color=(0, 255, 0), thickness=2)
cv2.putText( img, classNames[classIds-1].upper(),(box[0]+10,box[1]+30),
cv2.FONT_HERSHEY_COMPLEX,1,(0,255,0),2)
cv2.imshow("Output", img)
cv2.waitKey(1)