test.py.save

import numpy as np
import cv2
import pickle
import serial
import time
#############################################


#ser = serial.Serial('/dev/ttyACM1',9600)
ser=serial.Serial('/dev/rfcomm1
',9600)
ser.flush()
time.sleep(4)



frameWidth= 640         # CAMERA RESOLUTION
frameHeight = 480
brightness = 180
threshold = 0.75         # PROBABLITY THRESHOLD
font = cv2.FONT_HERSHEY_SIMPLEX
##############################################

# SETUP THE VIDEO CAMERA
cap = cv2.VideoCapture(0)
cap.set(3, frameWidth)
cap.set(4, frameHeight)
cap.set(10, brightness)
# IMPORT THE TRANNIED MODEL
pickle_in=open("model_trained.p","rb")  ## rb = READ BYTE
model=pickle.load(pickle_in)

def grayscale(img):
    img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
    return img
def equalize(img):
    img =cv2.equalizeHist(img)
    return img
def preprocessing(img):
    img = grayscale(img)
    img = equalize(img)
    img = img/255
    return img
def getCalssName(classNo):
    if   classNo == 0: return 20
    elif classNo == 1: return 30
    elif classNo == 2: return 50
    elif classNo == 3: return 60
    elif classNo == 4: return 70
    elif classNo == 5: return 80
    #elif classNo == 6: return 'End of Speed Limit 80 km/h'
    elif classNo == 7: return 100
    elif classNo == 8: return 120


while True:

    # READ IMAGE
    success, imgOrignal = cap.read()

    # PROCESS IMAGE
    img = np.asarray(imgOrignal)
    img = cv2.resize(img, (32, 32))
    img = preprocessing(img)
    cv2.imshow("Processed Image", img)
    img = img.reshape(1, 32, 32, 1)
    cv2.putText(imgOrignal, "CLASS: " , (20, 35), font, 0.75, (0, 0, 255), 2, cv2.LINE_AA)
    cv2.putText(imgOrignal, "PROBABILITY: ", (20, 75), font, 0.75, (0, 0, 255), 2, cv2.LINE_AA)
    # PREDICT IMAGE
    predictions = model.predict(img)
    classIndex = model.predict_classes(img)
    probabilityValue =np.amax(predictions)
    if probabilityValue > threshold:
        #print(getCalssName(classIndex))
        cv2.putText(imgOrignal,str(classIndex)+" "+ "Speed Limit" + str(getCalssName(classIndex)) +" Km/h", (120, 35), font, 0.75, (0, 0, 255), 2, cv2.LINE_AA)
        cv2.putText(imgOrignal, str(round(probabilityValue*100,2) )+"%", (180, 75), font, 0.75, (0, 0, 255), 2, cv2.LINE_AA)
    cv2.imshow("Result", imgOrignal)

    if(probabilityValue*100 > 80.00):

        k=getCalssName(classIndex)
        ser.write(str(k).encode()+str('\n').encode())
        print(k)
        time.sleep(2)

    if cv2.waitKey(1) & 0xFF == ord('q'):
        break