USAGE

python detect_shapes.py --image shapes_and_colors.png

import the necessary packages

from pyimagesearch.shapedetector import ShapeDetector import argparse import imutils import cv2

load the image and resize it to a smaller factor so that

image = cv2.imread("RecogPark90.png") resized = imutils.resize(image, width=300) ratio = image.shape[0] / float(resized.shape[0])

convert the resized image to grayscale, blur it slightly,

and threshold it

gray = cv2.cvtColor(resized, cv2.COLOR_BGR2GRAY) blurred = cv2.GaussianBlur(gray, (5, 5), 0) thresh = cv2.threshold(blurred, 60, 255, cv2.THRESH_BINARY)[1]

find contours in the thresholded image and initialize the

shape detector

cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) cnts = cnts[0] if imutils.is_cv2() else cnts[1] sd = ShapeDetector()

loop over the contours

for c in cnts: # compute the center of the contour, then detect the name of the # shape using only the contour M = cv2.moments(c) cX = int((M["m10"] / M["m00"]) * ratio) cY = int((M["m01"] / M["m00"]) * ratio) shape = sd.detect(c)

# multiply the contour (x, y)-coordinates by the resize ratio,
# then draw the contours and the name of the shape on the image
c = c.astype("float")
c *= ratio
c = c.astype("int")
cv2.drawContours(image, [c], -1, (0, 255, 0), 2)
cv2.putText(image, shape, (cX, cY), cv2.FONT_HERSHEY_SIMPLEX,
    0.5, (255, 255, 255), 2)
# show the output image

cv2.imshow("Image", image) cv2.waitKey(0)