Code for How to Detect Shapes in Images in Python using OpenCV Tutorial

shape_detector.py

import numpy as np
import matplotlib.pyplot as plt
import cv2
import sys

# read the image from arguments
image = cv2.imread(sys.argv[1])

# convert to grayscale
grayscale = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# perform edge detection
edges = cv2.Canny(grayscale, 30, 100)

# detect lines in the image using hough lines technique
lines = cv2.HoughLinesP(edges, 1, np.pi/180, 60, np.array([]), 50, 5)
# iterate over the output lines and draw them
for line in lines:
    for x1, y1, x2, y2 in line:
        cv2.line(image, (x1, y1), (x2, y2), color=(20, 220, 20), thickness=3)

# show the image
plt.imshow(image)
plt.show()

live_shape_detector.py

import numpy as np
import matplotlib.pyplot as plt
import cv2

cap = cv2.VideoCapture(0)

while True:
    _, image = cap.read()
    # convert to grayscale
    grayscale = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    # perform edge detection
    edges = cv2.Canny(grayscale, 30, 100)
    # detect lines in the image using hough lines technique
    lines = cv2.HoughLinesP(edges, 1, np.pi/180, 60, np.array([]), 50, 5)
    # iterate over the output lines and draw them
    for line in lines:
        for x1, y1, x2, y2 in line:
            cv2.line(image, (x1, y1), (x2, y2), (255, 0, 0), 3)
            cv2.line(edges, (x1, y1), (x2, y2), (255, 0, 0), 3)
    # show images
    cv2.imshow("image", image)
    cv2.imshow("edges", edges)
    if cv2.waitKey(1) == ord("q"):
        break

cap.release()
cv2.destroyAllWindows()

circle_detector.py

import cv2
import numpy as np
import matplotlib.pyplot as plt
import sys

# load the image
img = cv2.imread(sys.argv[1])
# convert BGR to RGB to be suitable for showing using matplotlib library
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# make a copy of the original image
cimg = img.copy()
# convert image to grayscale
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# apply a blur using the median filter
img = cv2.medianBlur(img, 5)
# finds the circles in the grayscale image using the Hough transform
circles = cv2.HoughCircles(image=img, method=cv2.HOUGH_GRADIENT, dp=0.9, 
                            minDist=80, param1=110, param2=39, maxRadius=70)

for co, i in enumerate(circles[0, :], start=1):
    # draw the outer circle
    cv2.circle(cimg,(i[0],i[1]),i[2],(0,255,0),2)
    # draw the center of the circle
    cv2.circle(cimg,(i[0],i[1]),2,(0,0,255),3)
    
# print the number of circles detected
print("Number of circles detected:", co)
# save the image, convert to BGR to save with proper colors
# cv2.imwrite("coins_circles_detected.png", cimg)
# show the image
plt.imshow(cimg)
plt.show()