shaperecog.py

# USAGE
# python detect_color.py --image example_shapes.png

# import the necessary packages
from pyimagesearch.shapedetector import ShapeDetector
from pyimagesearch.colorlabeler import ColorLabeler
import argparse
import imutils
import cv2
import sys
import os


os.chdir(sys.path[0])
# # 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())

cap = cv2.VideoCapture(0)

while True:
	ret, frame = cap.read()
	# load the image and resize it to a smaller factor so that
	# the shapes can be approximated better
	#image = cv2.imread(frame)

	# height, width = frame.shape[:2]

    # # Define ROI Box Dimensions (Note some of these things should be outside the loop)
	# top_left_x = int(width/3)
    
	# top_left_y = int((height / 2) + (height / 4))
	# bottom_right_x = int((width / 3) * 2)
	# bottom_right_y = int((height / 2) - (height / 4))

    # # Draw rectangular window for our region of interest
	# cv2.rectangle(frame, (top_left_x,top_left_y), (bottom_right_x,bottom_right_y), 255, 3)

    # # Crop window of observation we defined above
	# cropped = frame[bottom_right_y:top_left_y , top_left_x:bottom_right_x]

	image = frame

	resized = imutils.resize(image, width=300)
	ratio = image.shape[0] / float(resized.shape[0])

	# blur the resized image slightly, then convert it to both
	# grayscale and the L*a*b* color spaces
	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, 60, 255, cv2.THRESH_BINARY)[1]
	#cv2.imshow("Thresh", thresh)

	# find contours in the thresholded image
	cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
		cv2.CHAIN_APPROX_SIMPLE)
	cnts = imutils.grab_contours(cnts)

	# initialize the shape detector and color labeler
	sd = ShapeDetector()
	cl = ColorLabeler()

	# loop over the contours
	for c in cnts:
		# compute the center of the contour
		M = cv2.moments(c)
		try:
			cX = int((M["m10"] / M["m00"]) * ratio)
			cY = int((M["m01"] / M["m00"]) * ratio)
		except:
			pass

		# 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)

	cv2.imshow('Object Detector using ORB', frame)
	if cv2.waitKey(1) == 13: #13 is the Enter Key
		break

cap.release()
cv2.destroyAllWindows()