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Color Block Positioning

The main purpose of color block positioning is to achieve color block following as a functional advancement. The principle is to determine the distance and position information of the color block relative to the camera, and to judge by calculating the center point coordinates of the color block in the camera view, thereby achieving color block positioning. In the experimental results, you can see that it will always follow the center point of the color block.

Main Code

Code path: dofbot_ws/src/dofbot_color_follow/Color Block Positioning.ipynb

• Import header files

import cv2 as cv
import threading
import random
from time import sleep
import ipywidgets as widgets
from IPython.display import display
from positioning import color_follow

• Main recognition function, simultaneously obtaining the target center point (color_x, color_y) of the color block

​x
def follow_function(self, img, HSV_config):
        (color_lower, color_upper) = HSV_config
        self.img = cv.resize(img, (640, 480), )
        self.img = cv.GaussianBlur(self.img, (5, 5), 0)
        hsv = cv.cvtColor(self.img, cv.COLOR_BGR2HSV)
        mask = cv.inRange(hsv, color_lower, color_upper)
        mask = cv.erode(mask, None, iterations=2)
        mask = cv.dilate(mask, None, iterations=2)
        mask = cv.GaussianBlur(mask, (5, 5), 0)
        cnts = cv.findContours(mask.copy(), cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)[-2]
​
        if len(cnts) > 0:
            cnt = max(cnts, key=cv.contourArea)
            (color_x, color_y), color_radius = cv.minEnclosingCircle(cnt)
​
            if color_radius > 10:
                # Mark the detected color with the prototype coil
                cv.circle(self.img, (int(color_x), int(color_y)), int(color_radius), (255, 0, 255), 3)
                print(color_x,color_y)
        return self.img

• Create controls

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button_layout = widgets.Layout(width='200px', height='100px', align_self='center')
# Output widget
output = widgets.Output()
# Color tracking
color_follow = widgets.Button(description='color_follow', button_style="success", layout=button_layout)
# Select color
choose_color = widgets.ToggleButtons(options=['red', 'green', 'blue', 'yellow'], button_style="success",
             tooltips=['Description of slow', 'Description of regular', 'Description of fast'])
# Cancel tracking
follow_cancel = widgets.Button(description='follow_cancel', button_style="danger", layout=button_layout)
​
# Exit
exit_button = widgets.Button(description='Exit', button_style="danger", layout=button_layout)
# Image widget
imgbox = widgets.Image(format='jpg', height=480, width=640, layout=widgets.Layout(align_self='auto'))
# Vertical layout
img_box = widgets.VBox([imgbox, choose_color], layout=widgets.Layout(align_self='auto'))
# Vertical layout
Slider_box = widgets.VBox([color_follow,follow_cancel,exit_button],
                          layout=widgets.Layout(align_self='auto'))
# Horizontal layout
controls_box = widgets.HBox([img_box, Slider_box], layout=widgets.Layout(align_self='auto'))
# ['auto', 'flex-start', 'flex-end', 'center', 'baseline', 'stretch', 'inherit', 'initial', 'unset']

Main process:

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def camera():
​
    global HSV_learning,model
    # Open camera
    capture = cv.VideoCapture(0)
    capture.set(3, 640)
    capture.set(4, 480)
    capture.set(5, 30)
    # Be executed in loop when the camera is opened normally
    # Loop when camera is opened normally
    while capture.isOpened():
        try:
            _, img = capture.read()
            img = cv.resize(img, (640, 480))
​
            if model == 'color_follow':<
 PID algorithm basics
Color block tracking