detect_shapes.py

# USAGE
# python detect_shapes.py --image shapes_and_colors.png

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

# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True,
	help="path to the input image")
args = vars(ap.parse_args())

# 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

blurred = cv2.GaussianBlur(resized, (5, 5), 0)
gray = cv2.cvtColor(blurred, cv2.COLOR_BGR2GRAY)
lab = cv2.cvtColor(blurred, cv2.COLOR_BGR2LAB)
thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)[1]


# gray = cv2.cvtColor(resized, cv2.COLOR_BGR2GRAY)
# blurred = cv2.GaussianBlur(gray, (5, 5), 0)
# thresh = cv2.threshold(blurred, 127, 255, cv2.THRESH_BINARY)[1]

cv2.imshow("asdasd", thresh)

output = open('out.txt', 'w+')
output.write(thresh)
# 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()
cl = ColorLabeler()

# loop over the contours
for c in cnts:
	# compute the center of the contour
	M = cv2.moments(c)
	if M["m00"] == 0:
		continue

	cX = int((M["m10"] / M["m00"]) * ratio)
	cY = int((M["m01"] / M["m00"]) * ratio)

	# detect the shape of the contour and label the color
	shape = sd.detect(c)
	color = cl.label(lab, c)

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

	# show the output image
cv2.imshow("Image", image)
cv2.waitKey(0)