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DebugTsangDebugTsang
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‎Object_Detection_Files/Identification_code.txt

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import cv2
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thres = 0.45 # Threshold to detect object
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cap = cv2.VideoCapture(1)
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cap.set(3,1280)
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cap.set(4,720)
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cap.set(10,70)
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classNames= []
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classFile = ‘coco.names’
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with open(classFile,’rt’) as f:
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classNames = f.read().rstrip(‘n’).split(‘n’)
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configPath = ‘ssd_mobilenet_v3_large_coco_2020_01_14.pbtxt’
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weightsPath = ‘frozen_inference_graph.pb’
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net = cv2.dnn_DetectionModel(weightsPath,configPath)
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net.setInputSize(320,320)
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net.setInputScale(1.0/ 127.5)
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net.setInputMean((127.5, 127.5, 127.5))
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net.setInputSwapRB(True)
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while True:
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success,img = cap.read()
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classIds, confs, bbox = net.detect(img,confThreshold=thres)
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print(classIds,bbox)
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if len(classIds) != 0:
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for classId, confidence,box in zip(classIds.flatten(),confs.flatten(),bbox):
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cv2.rectangle(img,box,color=(0,255,0),thickness=2)
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cv2.putText(img,classNames[classId-1].upper(),(box[0]+10,box[1]+30),
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cv2.FONT_HERSHEY_COMPLEX,1,(0,255,0),2)
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cv2.putText(img,str(round(confidence*100,2)),(box[0]+200,box[1]+30),
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cv2.FONT_HERSHEY_COMPLEX,1,(0,255,0),2)
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cv2.imshow(“Output”,img)
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cv2.waitKey(1)

‎Object_Detection_Files/Identification_code_original.txt

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import cv2
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thres = 0.45 # Threshold to detect object
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cap = cv2.VideoCapture(1)
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cap.set(3,1280)
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cap.set(4,720)
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cap.set(10,70)
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classNames= []
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classFile = ‘coco.names’
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with open(classFile,’rt’) as f:
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classNames = f.read().rstrip(‘n’).split(‘n’)
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configPath = ‘ssd_mobilenet_v3_large_coco_2020_01_14.pbtxt’
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weightsPath = ‘frozen_inference_graph.pb’
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net = cv2.dnn_DetectionModel(weightsPath,configPath)
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net.setInputSize(320,320)
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net.setInputScale(1.0/ 127.5)
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net.setInputMean((127.5, 127.5, 127.5))
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net.setInputSwapRB(True)
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while True:
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success,img = cap.read()
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classIds, confs, bbox = net.detect(img,confThreshold=thres)
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print(classIds,bbox)
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if len(classIds) != 0:
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for classId, confidence,box in zip(classIds.flatten(),confs.flatten(),bbox):
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cv2.rectangle(img,box,color=(0,255,0),thickness=2)
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cv2.putText(img,classNames[classId-1].upper(),(box[0]+10,box[1]+30),
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cv2.FONT_HERSHEY_COMPLEX,1,(0,255,0),2)
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cv2.putText(img,str(round(confidence*100,2)),(box[0]+200,box[1]+30),
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cv2.FONT_HERSHEY_COMPLEX,1,(0,255,0),2)
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cv2.imshow(“Output”,img)
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cv2.waitKey(1)

‎Object_Detection_Files/coco.names

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person
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bicycle
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car
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motorcycle
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airplane
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bus
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train
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truck
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boat
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traffic light
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fire hydrant
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street sign
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stop sign
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parking meter
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bench
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bird
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cat
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dog
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horse
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sheep
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cow
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elephant
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bear
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zebra
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giraffe
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hat
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backpack
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umbrella
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shoe
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eye glasses
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handbag
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tie
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suitcase
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frisbee
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skis
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snowboard
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sports ball
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kite
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baseball bat
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baseball glove
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skateboard
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surfboard
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tennis racket
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bottle
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plate
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wine glass
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cup
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fork
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knife
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spoon
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bowl
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banana
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apple
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sandwich
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orange
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broccoli
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carrot
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hot dog
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pizza
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donut
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cake
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chair
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couch
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potted plant
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bed
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mirror
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dining table
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window
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desk
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toilet
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door
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tv
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laptop
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mouse
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remote
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keyboard
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cell phone
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microwave
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oven
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toaster
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sink
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refrigerator
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blender
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book
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clock
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vase
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scissors
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teddy bear
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hair drier
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toothbrush
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hair brush

‎Object_Detection_Files/frozen_inference_graph.pb

12.8 MB
Binary file not shown.

‎Object_Detection_Files/object-ident-2.py

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import cv2
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#thres = 0.45 # Threshold to detect object
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classNames = []
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classFile = "/home/debug.tsang/Documents/Object_Detection_Files/coco.names"
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with open(classFile,"rt") as f:
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classNames = f.read().rstrip("\n").split("\n")
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configPath = "/home/debug.tsang/Documents/Object_Detection_Files/ssd_mobilenet_v3_large_coco_2020_01_14.pbtxt"
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weightsPath = "/home/debug.tsang/Documents/Object_Detection_Files/frozen_inference_graph.pb"
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net = cv2.dnn_DetectionModel(weightsPath,configPath)
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net.setInputSize(320,320)
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net.setInputScale(1.0/ 127.5)
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net.setInputMean((127.5, 127.5, 127.5))
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net.setInputSwapRB(True)
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def getObjects(img, thres, nms, draw=True, objects=[]):
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classIds, confs, bbox = net.detect(img,confThreshold=thres,nmsThreshold=nms)
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#print(classIds,bbox)
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if len(objects) == 0: objects = classNames
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objectInfo =[]
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if len(classIds) != 0:
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for classId, confidence,box in zip(classIds.flatten(),confs.flatten(),bbox):
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className = classNames[classId - 1]
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if className in objects:
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objectInfo.append([box,className])
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if (draw):
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cv2.rectangle(img,box,color=(0,255,0),thickness=2)
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cv2.putText(img,classNames[classId-1].upper(),(box[0]+10,box[1]+30),
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cv2.FONT_HERSHEY_COMPLEX,1,(0,255,0),2)
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cv2.putText(img,str(round(confidence*100,2)),(box[0]+200,box[1]+30),
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cv2.FONT_HERSHEY_COMPLEX,1,(0,255,0),2)
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return img,objectInfo
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if __name__ == "__main__":
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cap = cv2.VideoCapture(0)
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cap.set(3,640)
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cap.set(4,480)
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#cap.set(10,70)
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while True:
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success, img = cap.read()
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result, objectInfo = getObjects(img,0.45,0.2, objects=['person'])
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#print(objectInfo)
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cv2.imshow("Output",img)
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cv2.waitKey(1)

‎Object_Detection_Files/object-ident-3.py

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import cv2
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import time
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from gpiozero import AngularServo
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servo =AngularServo(18, initial_angle=0, min_pulse_width=0.0006, max_pulse_width=0.0023)
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#thres = 0.45 # Threshold to detect object
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classNames = []
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classFile = "/home/debug.tsang/Documents/Object_Detection_Files/coco.names"
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with open(classFile,"rt") as f:
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classNames = f.read().rstrip("\n").split("\n")
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configPath = "/home/debug.tsang/Documents/Object_Detection_Files/ssd_mobilenet_v3_large_coco_2020_01_14.pbtxt"
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weightsPath = "/home/debug.tsang/Documents/Object_Detection_Files/frozen_inference_graph.pb"
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net = cv2.dnn_DetectionModel(weightsPath,configPath)
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net.setInputSize(320,320)
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net.setInputScale(1.0/ 127.5)
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net.setInputMean((127.5, 127.5, 127.5))
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net.setInputSwapRB(True)
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def getObjects(img, thres, nms, draw=True, objects=[]):
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classIds, confs, bbox = net.detect(img,confThreshold=thres,nmsThreshold=nms)
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#print(classIds,bbox)
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if len(objects) == 0: objects = classNames
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objectInfo =[]
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if len(classIds) != 0:
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for classId, confidence,box in zip(classIds.flatten(),confs.flatten(),bbox):
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className = classNames[classId - 1]
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if className in objects:
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objectInfo.append([box,className])
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if (draw):
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cv2.rectangle(img,box,color=(0,255,0),thickness=2)
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cv2.putText(img,classNames[classId-1].upper(),(box[0]+10,box[1]+30),
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cv2.FONT_HERSHEY_COMPLEX,1,(0,255,0),2)
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cv2.putText(img,str(round(confidence*100,2)),(box[0]+200,box[1]+30),
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cv2.FONT_HERSHEY_COMPLEX,1,(0,255,0),2)
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servo.angle = -90
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time.sleep = 2
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servo.angle = 90
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return img,objectInfo
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if __name__ == "__main__":
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cap = cv2.VideoCapture(0)
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cap.set(3,640)
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cap.set(4,480)
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#cap.set(10,70)
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while True:
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success, img = cap.read()
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result, objectInfo = getObjects(img,0.45,0.2, objects=['cup','horse'])
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#print(objectInfo)
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cv2.imshow("Output",img)
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cv2.waitKey(1)
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‎Object_Detection_Files/object-ident.py

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import cv2
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thres = 0.6 # Threshold to detect object
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classNames = []
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classFile = "/home/debug.tsang/Documents/Object_Detection_Files/coco.names"
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with open(classFile,"rt") as f:
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classNames = f.read().rstrip("\n").split("\n")
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configPath = "/home/debug.tsang/Documents/Object_Detection_Files/ssd_mobilenet_v3_large_coco_2020_01_14.pbtxt"
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weightsPath = "/home/debug.tsang/Documents/Object_Detection_Files/frozen_inference_graph.pb"
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net = cv2.dnn_DetectionModel(weightsPath,configPath)
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net.setInputSize(320,320)
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net.setInputScale(1.0/ 127.5)
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net.setInputMean((127.5, 127.5, 127.5))
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net.setInputSwapRB(True)
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def getObjects(img, thres, nms, draw=True, objects=[]):
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classIds, confs, bbox = net.detect(img,confThreshold=thres,nmsThreshold=nms)
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#print(classIds,bbox)
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if len(objects) == 0: objects = classNames
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objectInfo =[]
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if len(classIds) != 0:
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for classId, confidence,box in zip(classIds.flatten(),confs.flatten(),bbox):
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className = classNames[classId - 1]
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if className in objects:
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objectInfo.append([box,className])
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if (draw):
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cv2.rectangle(img,box,color=(0,255,0),thickness=2)
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cv2.putText(img,classNames[classId-1].upper(),(box[0]+10,box[1]+30),
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cv2.FONT_HERSHEY_COMPLEX,1,(0,255,0),2)
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cv2.putText(img,str(round(confidence*100,2)),(box[0]+200,box[1]+30),
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cv2.FONT_HERSHEY_COMPLEX,1,(0,255,0),2)
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return img,objectInfo
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if __name__ == "__main__":
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cap = cv2.VideoCapture(0)
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cap.set(3,640)
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cap.set(4,480)
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#cap.set(10,70)
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while True:
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success, img = cap.read()
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result, objectInfo = getObjects(img,thres,0.2)
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for i in objectInfo:
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#print(objectInfo)
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print(i[1])
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cv2.imshow("Output",img)
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cv2.waitKey(1)

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