detectshapes.py

#Detect shape of objects

# 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
# the shapes can be approximated better
image = cv2.imread(args["image"])
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 result for all the shapes in the image
cv2.imshow("Image", image)
cv2.waitKey(0)