Dmitry Maslov
Published © MIT

Deep Learning Sumo Robot

Hajimete! Let DL pushing madness begin!

IntermediateProtip3 hours250
Deep Learning Sumo Robot

Things used in this project

Hardware components

Seeed M.A.R.K kit
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Software apps and online services

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Story

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Code

MARK micropython sumo code

MicroPython
import sensor,image,lcd, os, time
from maix_motor import Maix_motor
import KPU as kpu

lcd.init()
sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QVGA)
sensor.set_windowing((224, 224))
sensor.set_vflip(1)
sensor.run(1)
DEBUG = 0
classes = ["mark"]
task = kpu.load(0x200000)
a = kpu.set_outputs(task, 0, 7,7,30)
anchor = (0.57273, 0.677385, 1.87446, 2.06253, 3.33843, 5.47434, 7.88282, 3.52778, 9.77052, 9.16828)
a = kpu.init_yolo2(task, 0.6, 0.3, 5, anchor)
while(True):
    img = sensor.snapshot().rotation_corr(z_rotation=90.0)
    a = img.pix_to_ai()
    code = kpu.run_yolo2(task, img)
    if code:
        for i in code:
            a=img.draw_rectangle(i.rect(),color = (0, 255, 0))
            a = img.draw_string(i.x(),i.y(), classes[i.classid()], color=(255,0,0), scale=3)
            x_center = i.x()+i.w()/2
            print(x_center)
            if not DEBUG:
                if x_center >= 100 and x_center <= 124:
                    while 1:
                        Maix_motor.motor_motion(3, 1, 0)
                        time.sleep(1.5)
                        Maix_motor.motor_motion(3, 3, 0)
                        time.sleep(0.1)
                        Maix_motor.motor_motion(3, 4, 0)
                        time.sleep(0.1)
                if x_center < 100 and x_center > 0: Maix_motor.motor_motion(1, 4, 0)
                if x_center > 124: Maix_motor.motor_motion(1, 3, 0)
        a = lcd.display(img)
    else:
        a = lcd.display(img)
        if not DEBUG: Maix_motor.motor_motion(1, 3, 0)
a = kpu.deinit(task)

OpenCV blob detection for annotations

Python
# Standard imports
import cv2
import numpy as np;
import os
from pascal_voc_writer import Writer

def create_ann(filename, boundRect):
    writer = Writer(os.path.join('Mark',filename), 3000, 4000)
    writer.addObject('mark', boundRect[0], boundRect[1], boundRect[0]+boundRect[2], boundRect[1]+boundRect[3])
    name = filename.split('.')
    writer.save('ann/'+name[0]+'.xml')

for file in os.listdir("Mark"):
    print(file)
    img = cv2.imread(os.path.join("Mark",file))
    R,G,B = cv2.split(img)

    #Rfilter = cv2.bilateralFilter(G,25,25,10)

    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    ret, thresh = cv2.threshold(gray,50,255,cv2.THRESH_BINARY_INV)

    contours, hierarchy = cv2.findContours(thresh,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)

    finalImage = cv2.drawContours(img, contours, -1,(0,0,255),3)

    maxContour = 0
    for contour in contours:
        contourSize = cv2.contourArea(contour)
        if contourSize > maxContour:
            maxContour = contourSize
            maxContourData = contour

    contours_poly = cv2.approxPolyDP(maxContourData, 3, True)
    boundRect = cv2.boundingRect(contours_poly)
    print(boundRect)

    # Create a mask from the largest contour
    mask = np.zeros_like(thresh)
    cv2.fillPoly(mask,[maxContourData],1)

    # Use mask to crop data from original image
    finalImage = np.zeros_like(img)
    finalImage[:,:,0] = np.multiply(R,mask)
    finalImage[:,:,1] = np.multiply(G,mask)
    finalImage[:,:,2] = np.multiply(B,mask)

    print(finalImage.shape)
    cv2.rectangle(finalImage, (int(boundRect[0]), int(boundRect[1])), (int(boundRect[0]+boundRect[2]), int(boundRect[1]+boundRect[3])), (0,255,0),3)

    finalImage = cv2.resize(finalImage,(640,480))

    cv2.imshow('final',finalImage)
    cv2.waitKey(50)
    create_ann(file, boundRect)
cv2.destroyAllWindows()

Credits

Dmitry Maslov

Dmitry Maslov

7 projects • 13 followers

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