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main.py
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main.py
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import cv2
####### From Image #######
def ImgFile():
img = cv2.imread('dog_bike_car.jpg')
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 , 230)
net.setInputScale(1.0 / 127.5)
net.setInputMean((127.5, 127.5, 127.5))
net.setInputSwapRB(True)
classIds, confs, bbox = net.detect(img, confThreshold=0.5)
print(classIds, bbox)
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], (box[0] + 10, box[1] + 20),
cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), thickness=2)
cv2.imshow('Output', img)
cv2.waitKey(0)
######################################
####### From Video or Camera #######
def Camera():
cam = cv2.VideoCapture("street6.mp4")
#cam = cv2.VideoCapture("0")
cam.set(3, 740)
cam.set(4, 580)
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 , 230)
net.setInputScale(1.0 / 127.5)
net.setInputMean((127.5, 127.5, 127.5))
net.setInputSwapRB(True)
while True:
success, img = cam.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), thickness=2)
cv2.putText(img, classNames[classId-1], (box[0] + 10, box[1] + 20),
cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), thickness=2)
cv2.imshow('Output', img)
cv2.waitKey(1)
######################################
## Call ImgFile() Function for Image Or Camera() Function for Video and Camera
#ImgFile()
Camera()