detect_meter.py

import cv2
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
from matplotlib.pyplot import imshow


def detect():
    #Load camera
    cap = cv2.VideoCapture(1,cv2.CAP_DSHOW)
    angle = []
    i = 0
    if not cap.isOpened():
        print("Error opening Video File.")
    while(1):
        # get a frame
        ret, frame = cap.read()
        i += 1
        # show a frame
        cv2.imshow("capture", frame)
        #detect a frame
        angle.append(meter_reading(frame))
        if i == 20:
            break
        if cv2.waitKey(5) & 0xff == ord('q'):
            break
            # if frame is read correctly, ret is True
        if not ret:
           print("Can't retrieve frame - stream may have ended. Exiting..")
           break
    print('final_angle:', stats.mode(angle)[0][0])    
    #release the camera
    cap.release()
    cv2.destroyAllWindows()
def meter_reading(cimg):
    #cimg = cv2.imread('C:/Users/Sara/Desktop/oven_panel/meter/imageset_2/14.JPG')
    img = cv2.cvtColor(cimg,cv2.COLOR_RGB2GRAY)
    img_w = img.shape[1]
    img_h = img.shape[0]
    imshow(img,cmap='gray')

    #Find circle opencv
    #https://docs.opencv.org/2.4/modules/imgproc/doc/feature_detection.html#houghcircles
    """
    #original oven panel 
    circles = cv2.HoughCircles(img,cv2.HOUGH_GRADIENT,dp=3,minDist=20,
                                param1=50,param2=40,minRadius=100,maxRadius=250)
    """
    #new oven panel
    circles = cv2.HoughCircles(img,cv2.HOUGH_GRADIENT,dp=2,minDist=15,
                            param1=56,param2=30,minRadius=15,maxRadius=150)
    draw_img = np.copy(cimg)
    circles = np.uint16(np.around(circles))
    x,y,r = circles[0,0,:]
    cv2.circle(draw_img,(x,y),r,(0,255,0),2)
    cv2.circle(draw_img,(x,y),2,(255,0,0),3)

    imshow(draw_img)
    #Find arrow

    #Warp forward
    cwarped = cv2.linearPolar(cimg,(x,y),r,cv2.WARP_FILL_OUTLIERS)
    cwarped = cv2.rotate(cwarped, cv2.ROTATE_90_COUNTERCLOCKWISE);

    #plt.plot()
    #plt.imshow(cwarped)
   # plt.show()

    #Calculate column sum and minimum
    warped = cv2.cvtColor(cwarped,cv2.COLOR_RGB2GRAY)
    col_sum = np.sum(warped, axis=0)
    indx = np.argmin(col_sum)

    cols = warped.shape[1]
    angle_degrees = 360.0-(indx/cols)*360.0

    #Plot
    #plt.plot(col_sum)
    #plt.ylabel('sum')
    #plt.axvline(x=indx,c='r')
    #plt.show()

    #Draw and warp back
    cwarped[:,indx-3:indx+3] = (255,255,0)
    cwarped = cv2.rotate(cwarped, cv2.ROTATE_90_CLOCKWISE);
    inv_warped = cv2.linearPolar(cwarped,(x,y),r,cv2.WARP_INVERSE_MAP)
    # plt.imshow(inv_warped)
    # plt.show()

    #Combine results
    mask = np.zeros(img.shape,np.uint8)
    cv2.circle(mask, (x,y), r, 255, -1)
    inv_mask = cv2.bitwise_not(mask)
    # plt.imshow(mask)
    # plt.show()

    res = cv2.bitwise_and(inv_warped,inv_warped,mask=mask) + cv2.bitwise_and(cimg,cimg,mask=inv_mask)
    cv2.circle(res,(x,y),r,(0,255,0),2)
    cv2.circle(res,(x,y),2,(255,0,0),3)
    #plt.imshow(res)
    #plt.show()

    print('angle:', angle_degrees)
    return(angle_degrees)