Python OpenCV | cv2.circle () method

Syntax: cv2.circle(image, center_coordinates, radius, color, thickness) Parameters: image: It is the image on which circle is to be drawn. center_coordinates: It is the center coordinates of circle. The coordinates are represented as tuples of two values i.e. (X coordinate value, Y coordinate value). radius: It is the radius of circle. color: It is the color of border line of circle to be drawn. For BGR, we pass a tuple. eg: (255, 0, 0) for blue color. thickness: It is the thickness of the circle border line in px. Thickness of -1 px will fill the circle shape by the specified color. Return Value: It returns an image.

How to draw a circle over image OpenCV?

StackOverflow question

import cv2
import numpy
import sys

if __name__ == ’__main__’:


    #get current frame from webcam
    cam = cv2.VideoCapture(0)
    img = cam.read()

    #how draw a circle????

    cv2.imshow(’WebCam’, img)

    cv2.waitKey()

Answer

cv2.circle(img, center, radius, color, thickness=1, lineType=8, shift=0) ‚Üí None
Draws a circle.

Parameters: 
img (CvArr) – Image where the circle is drawn
center (CvPoint) – Center of the circle
radius (int) – Radius of the circle
color (CvScalar) – Circle color
thickness (int) – Thickness of the circle outline if positive, otherwise this indicates that a filled circle is to be drawn
lineType (int) – Type of the circle boundary, see Line description
shift (int) – Number of fractional bits in the center coordinates and radius value

Example #1

def blob(x):
    """Given an Nx3 matrix of blob positions and size, 
    create N img_size x img_size images, each with a blob drawn on 
    them given by the value in each row of x
    
    One row of x = [x,y,radius]."""
    y = np.zeros((x.shape[0], img_size, img_size))
    for i, particle in enumerate(x):
        rr, cc = skimage.draw.circle(
            particle[0], particle[1], max(particle[2], 1), shape=(img_size, img_size)
        )
        y[i, rr, cc] = 1
    return y


#%%

# names (this is just for reference for the moment!) 

Example #2

def update(self, radarData):
        self.img = np.zeros((self.height, self.width, self.channels), np.uint8)
        cv2.line(self.img, (10, 0), (self.width/2 - 5, self.height), (100, 255, 255))
        cv2.line(self.img, (self.width - 10, 0), (self.width/2 + 5, self.height), (100, 255, 255))

        for track_number in range(1, 65):
            if str(track_number)+’_track_range’ in radarData:
                track_range = radarData[str(track_number)+’_track_range’]
                track_angle = (float(radarData[str(track_number)+’_track_angle’])+90.0)*math.pi/180

                x_pos = math.cos(track_angle)*track_range*4
                y_pos = math.sin(track_angle)*track_range*4

                cv2.circle(self.img, (self.width/2 + int(x_pos), self.height - int(y_pos) - 10), 5, (255, 255, 255))
                #cv2.putText(self.img, str(track_number), 
                #    (self.width/2 + int(x_pos)-2, self.height - int(y_pos) - 10), self.font, 1, (255,255,255), 2)

        cv2.imshow("Radar", self.img)
        cv2.waitKey(2) 

Example #3

def ProcessFrame(self, frame):
        # segment arm region
        segment = self.SegmentArm(frame)

        # make a copy of the segmented image to draw on
        draw = cv2.cvtColor(segment, cv2.COLOR_GRAY2RGB)

        # draw some helpers for correctly placing hand
        cv2.circle(draw,(self.imgWidth/2,self.imgHeight/2),3,[255,102,0],2)       
        cv2.rectangle(draw, (self.imgWidth/3,self.imgHeight/3), (self.imgWidth*2/3, self.imgHeight*2/3), [255,102,0],2)

        # find the hull of the segmented area, and based on that find the
        # convexity defects
        [contours,defects] = self.FindHullDefects(segment)

        # detect the number of fingers depending on the contours and convexity defects
        # draw defects that belong to fingers green, others red
        [nofingers,draw] = self.DetectNumberFingers(contours, defects, draw)

        # print number of fingers on image
        cv2.putText(draw, str(nofingers), (30,30), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255))
        return draw 

Example #4

def mark_hand_center(frame_in,cont):    
    max_d=0
    pt=(0,0)
    x,y,w,h = cv2.boundingRect(cont)
    for ind_y in xrange(int(y+0.3*h),int(y+0.8*h)): #around 0.25 to 0.6 region of height (Faster calculation with ok results)
        for ind_x in xrange(int(x+0.3*w),int(x+0.6*w)): #around 0.3 to 0.6 region of width (Faster calculation with ok results)
            dist= cv2.pointPolygonTest(cont,(ind_x,ind_y),True)
            if(dist>max_d):
                max_d=dist
                pt=(ind_x,ind_y)
    if(max_d>radius_thresh*frame_in.shape[1]):
        thresh_score=True
        cv2.circle(frame_in,pt,int(max_d),(255,0,0),2)
    else:
        thresh_score=False
    return frame_in,pt,max_d,thresh_score

# 6. Find and display gesture 

Example #5

def update(self, radarData):
        self.img = np.zeros((self.height, self.width, self.channels), np.uint8)
        cv2.line(self.img, (10, 0), (self.width/2 - 5, self.height), (100, 255, 255))
        cv2.line(self.img, (self.width - 10, 0), (self.width/2 + 5, self.height), (100, 255, 255))

        for track_number in range(1, 65):
            if str(track_number)+’_track_range’ in radarData:
                track_range = radarData[str(track_number)+’_track_range’]
                track_angle = (float(radarData[str(track_number)+’_track_angle’])+90.0)*math.pi/180

                x_pos = math.cos(track_angle)*track_range*4
                y_pos = math.sin(track_angle)*track_range*4

                cv2.circle(self.img, (self.width/2 + int(x_pos), self.height - int(y_pos) - 10), 5, (255, 255, 255))
                #cv2.putText(self.img, str(track_number), 
                #    (self.width/2 + int(x_pos)-2, self.height - int(y_pos) - 10), self.font, 1, (255,255,255), 2)

        cv2.imshow("Radar", self.img)
        cv2.waitKey(2) 

Archived version

Syntax: cv2.circle (image, center_coordinates, radius, color , thickness) Parameters: image: It is the image on which circle is to be drawn. center_coordinates: It is the center coordinates of circle. The coordinates are represented as tuples of two values ​​ie ( X coordinate value, Y coordinate value). radius: It is the radius of circle. color: It is the color of border line of circle to be drawn. For BGR , we pass a tuple. eg: (255, 0, 0) for blue color. thickness: It is the thickness of the circle border line in px . Thickness of -1 px will fill the rectangle shape by the specified color. Return Value: It returns an image. The image is used for all examples below: Example # 1:

# Python program to explain the cv2.circle () method ¬† # cv2 import import cv2¬† ¬† # path path = r ’C: UsersRajnishDesktoppythonengineeringgeeks.png’ ¬†¬† # Read image in default mode image = cv2.imread (path) ¬† # Name of the window in which the image is displayed window_name = ’Image’ ¬†¬† # Center coordinates center_coordinates = ( 120 , 50 ) ¬† # Circle radius radius = 20 ¬†¬† # Blue color in BGR color = ( 255 , 0 , 0 ) ¬† # Line width 2 px thickness = 2 ¬† # Using the cv2.circle () method # Draw a circle with a 2 px blue border image = cv2.circle (image, center_coordinates, radius, color, thickness) ¬† # Display image cv2.imshow (window_name, image)¬†

Output: Example # 2: Using -1 px thickness to fill the rectangle with red.

# Python program to explain the cv2.circle () method ¬† # cv2 import import cv2¬† ¬† # path path = r ’C: UsersRajnishDesktoppythonengineeringgeeks.png’ ¬† # Read image in default mode image = cv2.imread (path) ¬† # Name of the window in which the image is displayed window_name = ’Image’ ¬†¬† # Center coordinates center_coordinates = ( 120 , 100 ) ¬†¬† # Circle radius radius = 30 ¬†¬† # Red in BGR color = ( 0 , 0 , 255 ) ¬† # Line thickness -1 px thickness = - 1 ¬† # Using the cv2.circle () method # Draw a -1 px red circle image = cv2.circle (image, center_coordinates, radius, color, thickness) <¬†code class = "undefined spaces">¬†¬† # Displaying an image cv2.imshow (window_name, image )¬† ¬†

Output:

Python OpenCV | cv2.circle () method around: Questions

  import matplotlib.image as mpimg
  import matplotlib.pyplot as plt

  fig = plt.figure(1)
  img = mpimg.imread(path)
  plt.imshow(img)
  ax=fig.add_subplot(1,1,1)

  extent = ax.get_window_extent().transformed(fig.dpi_scale_trans.inverted())
  plt.savefig("1.png", bbox_inches=extent)

  plt.axis("off") 
  plt.show()

import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import networkx as nx

G = nx.Graph()
G.add_node(1)
G.add_node(2)
G.add_node(3)
G.add_edge(1,3)
G.add_edge(1,2)
pos = {1:[100,120], 2:[200,300], 3:[50,75]}

fig = plt.figure(1)
img = mpimg.imread("image.jpg")
plt.imshow(img)
ax=fig.add_subplot(1,1,1)

nx.draw(G, pos=pos)

extent = ax.get_window_extent().transformed(fig.dpi_scale_trans.inverted())
plt.savefig("1.png", bbox_inches = extent)

plt.axis("off") 
plt.show()

plt.savefig("test.png",bbox_inches="tight")

plt.gca().set_axis_off()
plt.subplots_adjust(top = 1, bottom = 0, right = 1, left = 0, 
            hspace = 0, wspace = 0)
plt.margins(0,0)
plt.gca().xaxis.set_major_locator(plt.NullLocator())
plt.gca().yaxis.set_major_locator(plt.NullLocator())
plt.savefig("filename.pdf", bbox_inches = "tight",
    pad_inches = 0)

Python OpenCV | cv2.circle () method circle: Questions

Optional kwargs control the Collection properties; in particular:

    edgecolors:
        The string ‘none’ to plot faces with no outlines
    facecolors:
        The string ‘none’ to plot unfilled outlines

import matplotlib.pyplot as plt 
import numpy as np 

x = np.random.randn(60) 
y = np.random.randn(60)

plt.scatter(x, y, s=80, facecolors="none", edgecolors="r")
plt.show()

import matplotlib.pyplot as plt
circle=plt.Circle((0,0),2)
# here must be something like circle.plot() or not?
plt.show()

import matplotlib.pyplot as plt

circle1 = plt.Circle((0, 0), 0.2, color="r")
circle2 = plt.Circle((0.5, 0.5), 0.2, color="blue")
circle3 = plt.Circle((1, 1), 0.2, color="g", clip_on=False)

fig, ax = plt.subplots() # note we must use plt.subplots, not plt.subplot
# (or if you have an existing figure)
# fig = plt.gcf()
# ax = fig.gca()

ax.add_patch(circle1)
ax.add_patch(circle2)
ax.add_patch(circle3)

fig.savefig("plotcircles.png")

circle1 = plt.Circle((0, 0), 2, color="r")
# now make a circle with no fill, which is good for hi-lighting key results
circle2 = plt.Circle((5, 5), 0.5, color="b", fill=False)
circle3 = plt.Circle((10, 10), 2, color="g", clip_on=False)
    
ax = plt.gca()
ax.cla() # clear things for fresh plot

# change default range so that new circles will work
ax.set_xlim((0, 10))
ax.set_ylim((0, 10))
# some data
ax.plot(range(11), "o", color="black")
# key data point that we are encircling
ax.plot((5), (5), "o", color="y")
    
ax.add_patch(circle1)
ax.add_patch(circle2)
ax.add_patch(circle3)
fig.savefig("plotcircles2.png")