Merge branch 'master' of https://github.com/DeriZSY/CV_tools_API

import tool

import serial

# import binascill

import string

# import string

'''

This is a 'hello world' test for pantilt test.

This is a 'hello world' test for pan-tilt test.

Print circle_test.pdf and put the paper close to camera.

'''

def sent(t,x,y):

def sent(transmission, x, y):

    if x > 320:

        yaw_angle = int(3050 - ((x - 320) / 320 * 3050))

    else:

        pitch_angle = int(5000 - ((240 - y) / 240 * 5000))

    else:

        pitch_angle = int(5000 + ((y - 240) / 240 * 1000))

    #t.write(bytes.fromhex(yaw_angle))

    #t.write(bytes.fromhex(pitch_angle))

    mes = bytearray([0,0,0,0,0,0,0,0,0])

    mes[0] = 0xFA

    mes[1] = 0x04

    mes[2] = 0x00

    mes[3] = yaw_angle & 0xFF

    mes[4] = (yaw_angle>>8) & 0xFF

    mes[5] = pitch_angle & 0xFF

    mes[6] = (pitch_angle>>8) & 0xFF

    mes[7] = 0x00

    mes[8] = 0xFB

    print([yaw_angle,pitch_angle])

    t.write(mes)

    # transmission.write(bytes.fromhex(yaw_angle))

    # transmission.write(bytes.fromhex(pitch_angle))

    mes = bytearray([0] * 9)

    mes[0] = 0xFA  # 250

    mes[1] = 0x04  # 004

    mes[2] = 0x00  # 000

    mes[3] = yaw_angle & 0xFF  # Take the last 8 bits

    mes[4] = (yaw_angle >> 8) & 0xFF  # Take the last 9 - 16 bits

    mes[5] = pitch_angle & 0xFF  # Take the last 8 bits

    mes[6] = (pitch_angle >> 8) & 0xFF  # Take the last 9 - 16 bits

    mes[7] = 0x00  # 000

    mes[8] = 0xFB  # 251

    print([yaw_angle, pitch_angle])  # Show yaw_angle and pitch_angle

    transmission.write(mes)

def func_detect_circle(frame,t):

def func_detect_circle(frame, transmission):

    result = [0, 0, 0]

    # frame = cv2.resize(frame, (480, 360))

    b, g, r = cv2.split(frame)

    sp = frame.shape

    # sp = frame.shape

    # 480 640

    r = cv2.pyrDown(r)

    circles1 = cv2.HoughCircles(r,cv.CV_HOUGH_GRADIENT,1.2,1200,param1=250,param2=80,minRadius=30,maxRadius=0)

    if np.any(circles1):

        circles = circles1[0,:,:]

    # Using Gaussian Pyramid to down-sample the red part of the frame we get,

    # thus, the following code could find the circles more efficient and accurate

    '''

    If you want see more about the Image Pyramids, then you might the following website

    https://docs.opencv.org/3.0-beta/doc/tutorials/imgproc/pyramids/pyramids.html?highlight=pyramids

    https://docs.opencv.org/3.0-beta/doc/py_tutorials/py_imgproc/py_pyramids/py_pyramids.html?highlight=pyramids

    '''

    _circles = cv2.HoughCircles(r, cv.CV_HOUGH_GRADIENT, 1.2, 1200,

                                param1=250, param2=80, minRadius=30, maxRadius=0)

    '''

    https://docs.opencv.org/2.4/modules/imgproc/doc/feature_detection.html?highlight=houghcircles#houghcircles

    https://docs.opencv.org/3.0-beta/doc/py_tutorials/py_imgproc/py_houghcircles/py_houghcircles.html?highlight=houghcircles

    '''

    if np.any(_circles):

        circles = _circles[0, :, :]

        circles = np.uint16(np.around(circles))

        for i in circles[:]:

            cv2.circle(frame, (2 * i[0], 2 * i[1]), 2 * i[2], (0, 0, 255), 5)

            #i[0] np.uint16

            x = 0.0+2*i[0]

            y = 0.0+2*i[1]

            sent(t,x,y)

            # Draw the circles we get in the original image

            x = 0.0 + 2 * i[0]  # Get into the float form

            y = 0.0 + 2 * i[1]  # Get into the float form

            # Since we pyramid down the image,

            # we have to recalculate the center of the circle to fit in the original image,

            # that's why you can see 2 * i[0] sort of stuff

            sent(transmission, x, y)

            if i[2] > result[2]:

                result = list(i)

    cv2.imshow('frame', frame)

    cv2.imshow('Frame', frame)

    cv2.waitKey(1)

    # return result if sum(result) else []

    return [2*i[0], 2*i[1]] if sum(result) else []

    return [2 * i[0], 2 * i[1]] if sum(result) else []  # FIXME Could be wrong

t = serial.Serial('/dev/ttyUSB0', 115200)

tool.func_tool_set_quit()

cap = cv2.VideoCapture(0)

while(True):

while 1:

    ret, frame = cap.read()

    func_detect_circle(frame, t)

    #print (func_detect_circle(frame, t))

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